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Why do brains oscillate within specific frequency ranges?

Why do brains oscillate within specific frequency ranges?



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Why do brains oscillate within specific frequencies?

I have two specific questions:

  1. What are the physiological mechanisms which generate the oscillations within these very frequencies?
  2. What would go wrong if the frequencies were magically changed? I very strongly assume that this would go badly rather quickly, but I'd like to have a little more detailed information.

What are brain oscillations?

I think it is first important to recognize what brain oscillations refer to: they are small, somewhat localized fluctuations in voltage that are often measured by EEG (electroencephalogram), though they can also be measured inside the skull or inside the brain.

Most of these oscillations are also seen in the membrane potentials of individual neurons. Fast oscillations, like gamma oscillations, can also be seen in membrane potentials but are best associated with spiking activity. The cause of the skull-measured oscillations is the coordinated activity of many many neurons acting in concert.

Why certain frequencies?

Many brain oscillations are created by coupling of excitation and inhibition. The gamma oscillation, for example, is most associated with interactions between excitatory neurons and particular inhibitory neurons called fast-spiking and/or parvalbumin-positive cells. The frequency of the gamma oscillation is due to the time constants and conduction times between the reciprocal circuitry between excitatory cells and fast-spiking cells. If you were to maintain the same activity, but change these time constants, the gamma frequency would change.

Other frequencies may be slower because they involve circuits over a longer distance, such as interactions between thalamus and cortex.

Some of the slowest oscillations, called delta oscillations, come about due to long periods of high and low activity, sometimes called "UP" and "DOWN" states or "ON" and "OFF" periods. Oscillations are often nested within each other, referred to as cross-frequency coupling, or more specifically often phase-amplitude coupling. During the "DOWN" phase of a slow delta oscillation, amplitude in higher frequencies is low because there is little spiking activity; during the "UP" phase the amplitude in higher frequencies increases.

What is the purpose of brain oscillations?

We don't know. There are many theories that certain oscillations are important for certain functions, but most of the evidence for these theories is either theoretical or correlational. In very general terms, slower oscillations tend to be associated with rest whereas higher frequency oscillations are associated with active processing.

For example, gamma oscillations are possibly involved in 'binding' different types of information across different cortical areas: synchronous gamma activity could be the way a brain region primarily processing sounds can associate that information with a brain region primarily processing the visual object producing that sound.

It is also possible that many oscillations are simply epiphenomena: they result from particular types of activity, rather than being the cause of certain functions. Gamma, for example, seems to increase simply whenever overall neuronal activity increases.

The problem is that there is really no way to study an oscillation independent of the rest of the brain activity: anything that would impact an oscillation is necessarily going to be influencing neural activity in other ways.

Probably the best evidence for an actual functional role of brain oscillations is in phase coding in the theta band (and associated gamma oscillations), best-studied in the navigational networks of the hippocampus in rodents. The timing of spikes relative to the phase of an ongoing oscillation can carry more information than the spikes in solitude: the oscillation provides a reference signal.

What happens if brain oscillations were to magically change?

It sort of depends on what the mechanism of the magic is: you could certainly change oscillations by doing extreme things like stopping all neuronal activity: that would go quite badly. Epileptic seizures are a more naturally-occurring but similarly damaging example of oscillations gone wrong. Absence epilepsy is of particular interest since the mechanisms are a bit more complex than grand mal seizures.

However, you can also "magically" change oscillations as simply as closing your eyes: increases in alpha power are well-known to occur when you just briefly close your eyes. Changes in brain oscillations are a normal part of brain function: oscillations are very different during sleep and wake, for example. The amplitude and frequency of certain oscillations can be modulated by how alert or attentive you are. Depending on the specific type, anesthetic agents tend to produce brain oscillations that are at least qualitatively similar to sleep.

There are also correlations between different oscillation patterns and psychological diseases or disorders. People with schizophrenia show different patterns of gamma activity than non-schizophrenics, for example. However, it is unclear if it makes sense to think about oscillation differences as causal versus as a symptom. Autism, for example, is associated with reduced synchrony over long distances in the brain. This reduced synchrony is thought to be due to differences in connectivity. Depending on your perspective, you could highlight the change in synchrony as an important feature, or you could treat it as a symptom of the underlying difference in connectivity.

Summary, Conclusions, and Caution

Brain oscillations are a key window to understanding nervous function, especially in humans where they can be recorded more easily than any other type of neuronal activity. However, we need to be careful about thinking about oscillations as specific entities.

Oscillations in one frequency band could come about by completely different mechanisms in different situations. Changes in oscillations measured outside the skull can reflect changes in synchrony over long distances rather than changes in local oscillatory activity in individual columns. As an analogy, consider ripples in a pond: the amplitude of the ripples might tell you a bit about the size of an object that disrupted the water's surface, but if that's all you have you can't know whether the object came initially from above or below the surface, whether it was a bird, fish, or rock, etc.

Many papers are published that tie oscillations to certain states, conditions, or diseases, and these can be pathways to understanding but they are unlikely to directly identify underlying mechanisms.

For the references below, I've tried to use review articles where possible that are fairly digestible without too much additional information. In particular, I bolded a couple that I think are great starting points.


References:

Bazhenov, M., Timofeev, I., Steriade, M., & Sejnowski, T. J. (2002). Model of thalamocortical slow-wave sleep oscillations and transitions to activated states. Journal of neuroscience, 22(19), 8691-8704.

Buzsáki, G., & Wang, X. J. (2012). Mechanisms of gamma oscillations. Annual review of neuroscience, 35, 203-225.

Cohen, M. X., Elger, C. E., & Fell, J. (2008). Oscillatory activity and phase-amplitude coupling in the human medial frontal cortex during decision making. Journal of cognitive neuroscience, 21(2), 390-402.

Goldman, R. I., Stern, J. M., Engel Jr, J., & Cohen, M. S. (2002). Simultaneous EEG and fMRI of the alpha rhythm. Neuroreport, 13(18), 2487.

Harris, K. D., Henze, D. A., Hirase, H., Leinekugel, X., Dragoi, G., Czurkó, A., & Buzsáki, G. (2002). Spike train dynamics predicts theta-related phase precession in hippocampal pyramidal cells. Nature, 417(6890), 738.

Hasselmo, M. E., Bodelón, C., & Wyble, B. P. (2002). A proposed function for hippocampal theta rhythm: separate phases of encoding and retrieval enhance reversal of prior learning. Neural computation, 14(4), 793-817.

Kwon, J. S., O'donnell, B. F., Wallenstein, G. V., Greene, R. W., Hirayasu, Y., Nestor, P. G.,… & McCarley, R. W. (1999). Gamma frequency-range abnormalities to auditory stimulation in schizophrenia. Archives of general psychiatry, 56(11), 1001-1005.

Lisman, J. E., & Jensen, O. (2013). The theta-gamma neural code. Neuron, 77(6), 1002-1016.

Steriade, M., McCormick, D. A., & Sejnowski, T. J. (1993). Thalamocortical oscillations in the sleeping and aroused brain. Science, 262(5134), 679-685.

Tallon-Baudry, C., & Bertrand, O. (1999). Oscillatory gamma activity in humans and its role in object representation. Trends in cognitive sciences, 3(4), 151-162.

Uhlhaas, P. J., & Singer, W. (2006). Neural synchrony in brain disorders: relevance for cognitive dysfunctions and pathophysiology. Neuron, 52(1), 155-168.


I'm interested in this matter too. I'm not an expert on this subject but, as far as I understand, brain oscillations seem to be essential in the functioning together of a large number of neurons: to not to cancel out each other information and to unite their information into one coherent information. And there seem to be oscillation generators for various rhythms in the brain. That's as far as I can answer you here. There is a lot of literature on brain oscillations, you can search for them at NCBI. A few articles that may be interesting to you are the following:

… It is known that individual neurons are not capable of executing complex cognitive operations in isolation, and instead must form functional networks with other neurons, and so synchronous neuronal activity is thought to be relevant to cognition… . Rhythms of the hippocampal network

Rhythms are a prominent signature of brain activity. Their expression is correlated with numerous examples of healthy information processing and their fluctuations are a marker of disease states. Yet, their causal or epiphenomenal role in brain function is still highly debated… When brain rhythms aren't “rhythmic”: implication for their mechanisms and meaning

The hippocampus, a structure required for many types of memory, connects to the medial prefrontal cortex, an area that helps direct neuronal information streams during intentional behaviors. Increasing evidence suggests that oscillations regulate communication between these two regions. Theta rhythms may facilitate hippocampal inputs to the medial prefrontal cortex during mnemonic tasks and may also integrate series of functionally relevant gamma-mediated cell assemblies in the medial prefrontal cortex… . Oscillations and hippocampal-prefrontal synchrony


Multitasking by Brain Wave

Although our bodies stay stubbornly stuck in real time, our minds can flit between the past and future and jump large stretches of time in just a moment. Such feats rely on the brain&rsquos ability to continuously store information as it happens while also retrieving dramatically condensed versions of past events. Until now, scientists weren't sure how the brain simultaneously handles these competing tasks.

Researchers from The University of Texas at Austin found evidence that in the brain&rsquos spatial system this balancing act is accomplished via dueling electrical frequencies. Results from their study in rats suggest the hippocampus, an area crucial for memory formation, rapidly switches between the two frequencies to concurrently process the current surroundings and serve up orientation clues encoded in prior experiences. &ldquoThe hippocampus has to have a way for keeping what&rsquos actually happening and being encoded into new memory storage from interfering with recall or retrieval of previously stored memories,&rdquo explains U.T. Austin neuroscientist Laura Colgin, the study&rsquos senior author. Her findings may have implications for the treatment of schizophrenia, and they also offer clues to another mental mystery&mdashhow the brain manages to replay a daylong memory in mere seconds.

Dueling brain waves
In the new study, published last week in the journal Neuron, Colgin&rsquos team recorded electrical activity in a type of hippocampal cells called &ldquoplace cells.&rdquo Place-cell activation corresponds to specific locations in space. As a rat navigates a maze, researchers can tell by which place cells are firing where the rat is in the maze&mdashor what part of the maze the rat is thinking of.

Like all of the brain&rsquos neurons, place cells produce electrical signals that oscillate in waves. In particular, past research suggests that when place cells encode and compress spatial memories they produce theta waves, which operate on a relatively slow, long-wave frequency. But these theta oscillations do not work alone. They also contain shorter and more frequent gamma rhythms nested within them like folded accordion bellows.

The gamma oscillations contribute to memory compression, explains Brandeis University neuroscientist John Lisman, an expert on the theta&ndashgamma code who was not involved in the current study. As each wave of electrical activity pops up at the gamma frequency, it conveys new information nuggets to the interacting theta wave. One overarching theta wave sees several gamma&ndashencoded memory cues, which effectively form a compressed highlights reel relative to the longer theta wave.

In a study published in Nature in 2009 Colgin and her colleagues described an additional level of complexity in these theta&ndashgamma interactions in the rat hippocampus, demonstrating that the gamma waves oscillate at different frequencies depending on the task at hand. When the hippocampus communicated with a brain area that relays as-it-happens sensory information from the outside world, for example, the team saw theta signals supported by so-called &ldquofast&rdquo gamma rhythms oscillating at 60 to 100 hertz frequencies. A second, previously unappreciated set of &ldquoslow&rdquo gamma rhythms&mdashelectrical waves in the 25 to 55 hertz range&mdashseemed to be interacting with theta waves when the hippocampus swapped messages with another part of the brain that replays memories and plans movements through space and time, Colgin explains.

Those results hinted that fast gamma rhythms might be transmitting immediate information about the environment whereas slow gamma rhythms may shuttle information related to memory retrieval.

Clues from place cells
In their current analysis, Colgin and her colleagues found new, more robust evidence that fast gamma rhythms are indeed responsible for coding new information based on an animal&rsquos current experiences. After recording electrical signals from hippocampal place cells in seven rats as they negotiated a short linear track over three 10-minute sessions each day, the team looked at how theta and gamma waves coincided with each rat&rsquos actual position on the track.

When the place-cell activity matched a rat&rsquos current location on the track, the researchers found that theta sequences interacted with the shorter wave, fast gamma signals already suspected of dealing with in-the-moment spatial information. But slow gamma waves replaced fast ones when place-cell activity represented locations ahead of the rat&rsquos current position&mdashperhaps reflecting the animal&rsquos memory of the upcoming route and anticipation of the track ahead. &ldquoThe idea is that the animal is actually retrieving the representation of that location before they get there,&rdquo Colgin explains.

The new results are powerful evidence that the different frequency brain waves keep incoming information and memory retrieval separate&mdashwhich has implications for human conditions. If the slow gamma frequency really does keep real or imagined remembrances from interfering with new information coding and vice versa, it is conceivable that the two brain frequencies may get mixed up in conditions such as schizophrenia, Colgin says. Indeed, researchers have detected diminished slow gamma synchrony between the hippocampus and other brain regions in an animal model of the disease, boosting that theory. Future therapies could try to help increase gamma synchrony and keep thoughts separate from new sensory information&mdashalthough how such a feat could be accomplished remains unknown.

How memories are compressed
In the new study the researchers also made a second discovery, which may be a clue about how the brain compresses memories. Using place-cell patterns unraveled from the theta sequences, the researchers saw a jump in the amount of track being represented per millisecond when rats were using slow gamma rhythm, even though the such rhythm produces fewer new waves of electricity in any given stretch of time than the higher frequency fast gamma rhythm.

Based on how quickly the rats seemed to anticipate upcoming sections of track, the researchers speculate that a single slow gamma wave must contain more than one piece of information, implying another level of compression within an already compressed theta&ndashgamma code. This additional degree of compression could explain how we are able to replay memories of minutes&rsquo or hours&rsquo worth of activity in mere seconds.

Lisman is unconvinced of the additional-compression interpretation, although he praised Colgin and her team for uncovering functional roles for the slow gamma frequency in the hippocampus. To accomplish the ultrafast coding necessary for each gamma wave to contain more than one piece of information, he explains, neurons would have to differentiate between bits of information appearing just a few milliseconds apart&mdashfaster than current biophysical estimates say is possible.

Loren Frank, a neuroscience researcher with the University of California, San Francisco, who studies spatial coding in the hippocampus but was not involved in the study, was less skeptical of the authors' interpretation, saying it &ldquomakes a great deal of sense.&rdquo

&ldquoIt says the things associated with memory may be going on very, very quickly,&rdquo he says, noting that the electrical signals making up each slow gamma signal could represent multiple levels of cellular organization capable of seriously speedy coding. &ldquoI was surprised to see the results,&rdquo Frank concedes, &ldquobut I don't think there's any reason to think the brain can't do things like that.&rdquo


Rhythmic Sampling of a Single Stimulus: Discrete vs. Continuous Perception

Suppose that a new stimulus suddenly appears in your visual field, say a red light at the traffic intersection. For such a transient onset, a sequence of visual processing mechanisms from your retina to your high-level visual cortex will automatically come into play, allowing you after a more or less fixed latency to “perceive” this stimulus, i.e., experience it as part of the world in front of you. Hopefully you should then stop at the intersection. What happens next? For as long as the stimulus remains in the visual field, you will continue to experience it. But how do you know it is still there? You might argue that if it were gone, the same process as previously would now signal the transient offset (together with the onset of the green light), and you would then recognize that the red light is gone. But in-between those two moments, you did experience the red light as present – did you only fill in the mental contents of this intervening period after the green light appeared? This sounds unlikely, at least if your traffic lights last as long as they do around here. Maybe the different stages of your visual system were constantly processing their (unchanged) inputs and feeding their (unchanged) outputs to the next stage, just in case the stimulus might happen to change right then – a costly but plausible strategy. An intermediate alternative would consist in sampling the external world periodically to verify, and potentially update, its contents the period could be chosen to minimize metabolic effort, while maximizing the chances of detecting any changes within an ecologically useful delay (e.g., to avoid honking from impatient drivers behind you when you take too long to notice the green light). These last two strategies are respectively known as continuous and discrete perception.

The specific logic of the above example may have urged you to favor discrete perception, but the scientific community traditionally sides with the continuous idea. It has not always been so, however. In particular, the first observations of EEG oscillations in the early twentieth century (Berger, 1929), together with the simultaneous popularization of the cinema, prompted many post-war scientists to propose that the role of brain oscillations could be to chunk sensory information into unitary events or “snapshots,” similar to what happens in the movies (Pitts and McCulloch, 1947 Stroud, 1956 Harter, 1967). Much experimental research ensued, which we have already reviewed elsewhere (VanRullen and Koch, 2003). The question was never fully decided, however, and the community’s interest eventually faded. The experimental efforts that we describe in this section all result from an attempt to follow up on this past work and revive the scientific appeal of the discrete perception theory.

Periodicities in Reaction Time Distributions

Some authors have reasoned that if the visual system samples the external world discretely, the time it would take an observer to react after the light turns green would depend on the precise moment at which this event occurred, relative to the ongoing samples: if the stimulus is not detected within one given sample then the response will be delayed at least until the next sampling period. This relation may be visible in histograms of reaction time (RT). Indeed, multiple peaks separated by a more or less constant period are often apparent in RT histograms: these multimodal distributions have been reported with a period of approximately 100 ms for verbal choice responses (Venables, 1960), 10� ms for auditory and visual discrimination responses (Dehaene, 1993), 10� ms for saccadic responses (Latour, 1967), 30 ms for smooth pursuit eye movement initiation responses (Poppel and Logothetis, 1986). It must be emphasized, however, that an oscillation can only be found in a histogram of post-stimulus RTs if each stimulus either evokes a novel oscillation, or resets an existing one. Otherwise (and assuming that the experiment is properly designed, i.e., with unpredictable stimulus onsets), the moment of periodic sampling will always occur at a random time with respect to the stimulus onset thus, the peaks of response probability corresponding to the recurring sampling moments will average out, when the histogram is computed over many trials. In other words, even though these periodicities in RT distributions are intriguing, they do not unambiguously demonstrate that perception samples the world periodically – for example, it could just be that each stimulus onset triggers an oscillation in the motor system that will subsequently constrain the response generation process. In the following sections, we present other psychophysical methods that can reveal perceptual periodicities within ongoing brain activity, i.e., without assuming a post-stimulus phase reset.

Double-Detection Functions

As illustrated in the previous section, there is an inherent difficulty in studying the perceptual consequences of ongoing oscillations: even if the pre-stimulus oscillatory phase modulates the sensory processing of the stimulus, this pre-stimulus phase will be different on successive repetitions of the experimental trial, and the average performance over many trials will show no signs of the modulation. Obviously, this problem can be overcome if the phase on each trial can be precisely estimated, for example using EEG recordings (VanRullen et al., 2011). With purely psychophysical methods, however, the problem is a real challenge.

An elegant way to get around this challenge has been proposed by Latour (1967). With this method, he showed preliminary evidence that visual detection thresholds could fluctuate along with ongoing oscillations in the gamma range (30� Hz). The idea is to present two stimuli on each trial, with a variable delay between them, and measure the observer’s performance for detecting (or discriminating, recognizing, etc.) both stimuli: even if each stimulus’s absolute relation to an ongoing oscillatory phase cannot be estimated, the probability of double-detection should oscillate as a function of the inter-stimulus delay (Figure 1). In plain English, the logic is that when the inter-stimulus delay is a multiple of the oscillatory period, the observer will be very likely to detect both stimuli (if they both fall at the optimal phase of the oscillation) or to miss both stimuli altogether (if they both fall at the opposite phase) on the other hand, if the delay is chosen in-between two multiples of the oscillatory period, then the observer will be very likely to detect only one of the two stimuli (if the first stimulus occurs at the optimal phase, the other will fall at the opposite, and vice-versa).

Figure 1. Double-detection functions can reveal periodicities even when the phase varies across trials. (A) Protocol. Let us assume that the probability of detecting a stimulus (i.e., the system’s sensitivity) fluctuates periodically along with the phase of an ongoing oscillatory process. By definition, this process bears no relation with the timing of each trial, and thus the phase will differ on each trial. On successive trials, not one but two stimuli are presented, with a variable delay between them. (B) Expected results. Because the phase of the oscillatory process at the moment of stimulus presentation is fully unpredictable, the average probability of detecting each stimulus as a function of time (using an absolute reference, such as the trial onset) will be constant (left). The probability of detecting the second stimulus will also be independent of the time elapsed since the first one (middle). However, the probability of detecting both stimuli (albeit smaller) will oscillate as a function of the delay between them, and the period of this oscillation will be equal to the period of the original ongoing oscillatory process (adapted from Latour, 1967).

More formally, let us assume that the probability of measuring our psychological variable ψ (e.g., target detection, discrimination, recognition, etc.) depends periodically (with period 2π/ω) on the time of presentation of the stimulation s to a first approximation this can be noted:

where p0 is the average expected measurement probability, and a is the amplitude of the periodic modulation. Since the time t of stimulation (with respect to the ongoing oscillation) may change for different repetitions of the measurement, only p0 can be measured with classical trial averaging methods (i.e., the “sine” term will average out to a mean value of zero). However, if two identical stimulations are presented, separated by an interval δt, the conditional probability of measuring our psychological variable twice can be shown to be (there is no room here, unfortunately, for the corresponding mathematical demonstration):

The resulting probability only depends on the interval δt (chosen by the experimenter), and thus does not require knowledge of the exact oscillatory phase on every trial. This means that, using double stimulations and double-detection functions, one can derive psychophysically the rate ω of the periodic process, and its modulation amplitude a (Figure 1).

In practice, unfortunately, this method is not as easy to apply as it sounds. One important caveat was already mentioned by Latour: the inter-stimulus delay must be chosen to be long enough to avoid direct interactions between the two stimuli (e.g., masking, apparent motion, etc.). This is because the mathematical derivation of Eq. 2 assumes independence between the detection probabilities for the two stimuli. To ensure that this condition is satisfied, the stimuli should be separated by a few 100 ms (corresponding to the integration period for masking or apparent motion) on the other hand, this implies that several oscillatory cycles will occur between the two stimuli, and many external factors (e.g., phase slip, reset) can thus interfere and decrease the measured oscillation. This in turn suggests that the method may be more appropriate for revealing low-frequency oscillations than high-frequency ones (e.g., gamma). Another important limitation is that the magnitude of the measured oscillation in the double-detection function (2) is squared, compared to the magnitude of the original perceptual oscillation. Although this is not a problem if the perceptual oscillation is strong (i.e., the square of a number close to 1 is also close to 1), it can become troublesome when the original perceptual oscillation is already subtle (e.g., for a 20% modulation of the visual threshold, one can only expect a 4% modulation in the double-detection function). Altogether, these limitations may explain why Latour’s results have, so far, not been replicated or extended.

Temporal Aliasing: The Wagon Wheel Illusion

Engineers know that any signal sampled by a discrete or periodic system is subject to potential 𠇊liasing” artifacts (Figure 2): when the sampling resolution is lower than a critical limit (the “Nyquist rate”) the signal can be interpreted erroneously. This is true, for instance, when a signal is sampled in the temporal domain (Figure 2A). When this signal is a periodic visual pattern in motion, aliasing produces a phenomenon called the “wagon wheel illusion” (Figure 2B): the pattern appears to move in the wrong direction. This is often observed in movies or on television, due to the discrete sampling of video cameras (generally around 24 frames per second). Interestingly, a similar perceptual effect has also been reported under continuous conditions of illumination, e.g., daylight (Schouten, 1967 Purves et al., 1996 VanRullen et al., 2005b). In this case, however, because no artificial device is imposing a periodic sampling of the stimulus, the logical conclusion is that the illusion must be caused by aliasing within the visual system itself. Thus, this 𠇌ontinuous version of the wagon wheel illusion” (or 𠇌-WWI”) has been interpreted as evidence that the visual system samples motion information periodically (Purves et al., 1996 Andrews et al., 2005 Simpson et al., 2005 VanRullen et al., 2005b).

Figure 2. Temporal aliasing. (A) Concept. Sampling a temporal signal using too low a sampling rate leads to systematic errors about the signal, known as 𠇊liasing errors.” Here, the original signal is periodic, but its frequency is too high compared with the system’s sampling rate (i.e., it is above the system’s “Nyquist” frequency, defined as half of its sampling rate). As a result, the successive samples skip ahead by almost one full period of the original oscillation: instead of normally going through the angular phases of zero, π/2, π, 3π/2, and back to zero, the successive samples describe the opposite pattern, i.e., zero, 3π/2, π, π/2, and so on. The aliasing is particularly clear in the complex domain (right), where the representations of the original and estimated signals describe circles in opposite directions. (B) The wagon wheel illusion. When the original signal is a periodically moving stimulus, temporal aliasing transpires as a reversal of the perceived direction. This wagon wheel illusion is typically observed in movies due to the discrete sampling of video cameras. The continuous version of this wagon wheel illusion (c-WWI) differs in that it occurs when directly observing the moving pattern in continuous illumination in this case, it has been proposed that reversed motion indicates a form of discrete sampling occurring in the visual system itself.

There are many arguments in favor of this 𠇍iscrete” interpretation of the c-WWI. First, the illusion occurs in a very specific range of stimulus temporal frequencies, compatible with a discrete sampling rate of approximately 13 Hz (Purves et al., 1996 Simpson et al., 2005 VanRullen et al., 2005b). As expected according to the discrete sampling idea, this critical frequency remains unchanged when manipulating the spatial frequency of the stimulus (Simpson et al., 2005 VanRullen et al., 2005b) or the type of motion employed, i.e., rotation vs. translation motion, or first-order vs. second-order motion (VanRullen et al., 2005b). EEG correlates of the perceived illusion confirm these psychophysical findings and point to an oscillation in the same frequency range around 13 Hz (VanRullen et al., 2006 Piantoni et al., 2010). Altogether, these data suggest that (at least part of) the motion perception system proceeds by sampling its inputs periodically, at a rate of 13 samples per second.

There are, of course, alternative accounts of the phenomenon. First, it is noteworthy that the illusion is not instantaneous, and does not last indefinitely, but it is instead a bistable phenomenon, which comes and goes with stochastic dynamics such a process implies the existence of a competition between neural mechanisms supporting the veridical and the erroneous motion directions (Blake and Logothetis, 2002). Within this context, the debate centers around the source of the erroneous signals: some authors have argued that they arise not from periodic sampling and aliasing, but from spurious activation in low-level motion detectors (Kline et al., 2004 Holcombe et al., 2005) or from motion adaptation processes that would momentarily prevail over the steady input (Holcombe and Seizova-Cajic, 2008 Kline and Eagleman, 2008). We find these accounts unsatisfactory, because they do not seem compatible with the following experimental observations: (i) the illusion is always maximal around the same temporal frequency, whereas the temporal frequency tuning of low-level motion detectors differs widely between first and second-order motion (Hutchinson and Ledgeway, 2006) (ii) not only the magnitude of the illusion, but also its spatial extent and its optimal temporal frequency – which we take as a reflection of the system’s periodic sampling rate – are all affected by attentional manipulations (VanRullen et al., 2005b VanRullen, 2006 Macdonald et al., under review) in contrast, the amount of motion adaptation could be assumed to vary with attentional load (Chaudhuri, 1990 Rezec et al., 2004), but probably not the frequency tuning of low-level motion detectors (iii) motion adaptation itself can be dissociated from the wagon wheel illusion using appropriate stimulus manipulations for example, varying stimulus contrast or eccentricity can make the motion aftereffects (both static and dynamic versions) decrease while the c-WWI magnitude increases, and vice-versa (VanRullen, 2007) (iv) finally, the brain regions responsible for the c-WWI effect, repeatedly identified in the right parietal lobe (VanRullen et al., 2006, 2008 Reddy et al., 2011), point to a higher-level cause than the mere adaptation of low-level motion detectors.

Disentangling the neural mechanisms of the continuous wagon wheel illusion could be (and actually, is) the topic of an entirely separate review (VanRullen et al., 2010). To summarize, our current view is that the reversed motion signals most likely originate as a form of aliasing due to periodic temporal sampling by attention-based motion perception systems, at a rate of � Hz the bistability of the illusion is due to the simultaneous encoding of the veridical motion direction by other (low-level, or 𠇏irst-order”) motion perception systems. The debate, however, is as yet far from settled. At any rate, this phenomenon illustrates the potential value of temporal aliasing as a paradigm to probe the discrete nature of sensory perception.

Other Forms of Temporal Aliasing

The sampling frequency evidenced with the c-WWI paradigm may be specific to attention-based motion perception mechanisms. It is only natural to try and extend the temporal aliasing methodology to perception of other types of motion, to perception of visual features other than motion or to perception in sensory modalities other than vision. If evidence for temporal aliasing could be found in these cases, the corresponding sampling frequencies may then be compared to one another and further inform our understanding of discrete perception. Is there a single rhythm, a central (attentional) clock that samples all sensory inputs? Or is information from any single channel of sensory information read out periodically at its own rate, independently from other channels? While the first proposition reflects the understanding that most have of the theory of discrete perception (Kline and Eagleman, 2008), the latter may be a much more faithful description of reality additionally, the sampling rate for a given channel may vary depending on task demands and attentional state, further blurring intrinsic periodicities.

The simple generic paradigm which we advocate to probe the brain for temporal aliasing is as follows. Human observers are presented with a periodic time-varying input which physically evolves in an unambiguously defined direction they are asked to make a two-alternative forced choice judgment on the direction of evolution of this input, whose frequency is systematically varied by the experimenter across trials. A consistent report of the wrong direction at a given input frequency may be taken as a behavioral correlate of temporal aliasing, and the frequencies at which this occurs inform the experimenter about the underlying sampling frequency of the brain for this input.

Two main hurdles may be encountered in applying this paradigm. The first one lies in what should be considered a 𠇌onsistent” report of the wrong direction. Clearly, for an engineered sampling system, one can find input frequencies at which the system will always output the wrong direction. For a human observer, however, several factors could be expected to lower the tendency to report the wrong direction, even at frequencies that are subject to aliasing: measurement noise, the potential variability of the hypothetical sampling frequency over the duration of the experiment, and most importantly, the potential presence of alternate sources of information (as in the c-WWI example, where competition occurs between low-level and attention-based motion systems). In the end, even if aliasing occurs, it may not manifest as a clear and reliable percept of the erroneous direction, but rather as a subtle increase of the probability of reporting the wrong direction at certain frequencies. Recently, we proposed a method to evaluate the presence of aliasing in psychometric functions, based on model fitting (Dubois and VanRullen, 2009). (A write-up of this method and associated findings can be accessed at http://www.cerco.ups-tlse.fr/∼rufin/assc09/). Results of a 2-AFC motion discrimination experiment were well explained by considering two motion sensing systems, one that functions continuously and one that takes periodic samples of position to infer motion. These two systems each give rise to predictable psychometric functions with few parameters, whose respective contributions to performance can be inferred by model fitting. Evidence for a significant contribution of a discrete process sampling at 13 Hz was found – thus confirming our previous conclusions from the c-WWI phenomenon. Furthermore, the discrete process contributed more strongly to the perceptual outcome when motion was presented inter-ocularly, than binocularly this is compatible with our postulate that discrete sampling in the c-WWI is a high-level effect, since inter-ocular motion perception depends on higher-level motion perception systems (Lu and Sperling, 2001).

The second pitfall is that the temporal resolution for discriminating the direction of the time-varying input under consideration should be at least as good as the hypothesized sampling frequency. If the psychometric function is already at chance at the frequency where aliasing is expected to take place, this aliasing will simply not be observed – whether the perceptual process relies on periodic sampling or not. Our lab learned this the hard way: many of the features that we experimented with so far, besides luminance and contrast-defined motion, can only be discriminated at low-temporal frequencies – they belong to Holcombe’s “seeing slow” category (Holcombe, 2009). For example, we hypothesized that motion stimuli designed to be invisible to the first-order motion perception system, such as stereo-defined motion (Tseng et al., 2006), would yield maximal aliasing as there is no other motion perception system offering competing information. Unfortunately, these stimuli do not yield a clear percept at temporal frequencies beyond 3𠄴 Hz, meaning that any aliasing occurring at higher frequencies would have escaped our notice. The “motion standstill” phenomenon reported by Lu and colleagues (Lu et al., 1999 Tseng et al. 2006) with similar stimuli at frequencies around 5 Hz remains a potential manifestation of temporal aliasing, although we have not satisfactorily replicated it in our lab yet. We also hypothesized that binding of spatially distinct feature conjunctions, such as color and motion, could rely on sequential attentional sampling of the two features (Moutoussis and Zeki, 1997), and should thus be subject to aliasing. Again, we were disappointed to find that performance was at chance level at presentation rates higher than 3𠄴 Hz (Holcombe, 2009), precluding further analysis. We also attempted to adapt the wagon wheel phenomenon to the auditory modality. Here, perception of sound source motion (e.g., a sound rotating around the listener) also appeared limited to about 3 Hz (Feron et al., 2010). We then reasoned that frequency, rather than spatial position, was the primary feature for auditory perception, and designed periodic stimuli that moved in particular directions in the frequency domain – so-called Shepard or Risset sequences (Shepard, 1964). Again, we found that the direction of these periodic frequency sweeps could not be identified when the temporal frequency of presentation was increased beyond 3𠄴 Hz.

In sum, although temporal aliasing is, in principle, a choice paradigm to probe the rhythms of perception, our attempts so far at applying this technique to other perceptual domains than motion (the c-WWI) have been foiled by the strict temporal limits of the corresponding sensory systems. What we can safely conclude is that, if discrete sampling exists in any of these other perceptual domains, it will be at a sampling rate above 3𠄴 Hz. We have not exhausted all possible stimuli and encourage others to conduct their own experiments. There are two faces to the challenge: finding stimuli that the brain “sees fast” enough, and using an appropriate model to infer the contribution of periodic sampling to the psychometric performance (in case other sources of information and sizeable variability across trials should blur the influence of discrete processes).


Unfortunately the term "wave" is ambiguous in neuroscience. What you are referring to, alpha waves, only means that neuronal activity tends to oscillate at about 10Hz. Neural oscillations are a widespread phenomena occurring in all brain areas. But it doesn't necessarily mean these waves travels. There are examples of traveling waves, whose speed can vary a lot (from $10^<-1>$ to $10^<-5> m.s^<-1>$ depending on the studies). Oscillations across brain areas tends to be synchronized during a task (so one could argue, infinite speed), and within area activity tends to hold a specific phase relationship which varies spatially (as if a wave was "frozen", in which case its speed would be 0). Traveling waves and neural synchrony/coherence might be different phenomena implementing different functions. It is still unclear what brain oscillations are for, or even if they have any purpose at all. Below are some reviews on these topics.

Ermentrout, G.B. and Kleinfeld, D. (2001) Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role. Neuron 29, 33–44

Sato, T. K., Nauhaus, I., & Carandini, M. (2012). Traveling waves in visual cortex. Neuron, 75(2), 218-229.

Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top–down processing. Nature Reviews Neuroscience, 2(10), 704-716.

Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences, 9(10), 474-480.


Advantages of Head MRI

  • MRI does not use ionizing radiation, and is thus preferred over CT in children and patients requiring multiple imaging examinations
  • MRI has a much greater range of available soft tissue contrast, depicts anatomy in greater detail, and is more sensitive and specific for abnormalities within the brain itself
  • MRI scanning can be performed in any imaging plane without having to physically move the patient
  • MRI contrast agents have a considerably smaller risk of causing potentially lethal allergic reaction
  • MRI allows the evaluation of structures that may be obscured by artifacts from bone in CT images

The complexity of the organ that determines how a person thinks, moves, feels, and remembers is overshadowed only by its unique vulnerability. The brain is hidden from direct view by the skull, which not only shields it from injury but also hinders the study of its function in both health and disease. The cells in the arteries that supply the brain are so tightly bound that even most normal cells in the bloodstream are prevented from crossing the so-called “blood-brain barrier,” thereby rendering the normal chemistry of the brain invisible to the routine laboratory blood tests that are often used to evaluate the heart, liver or kidneys.

Computed tomography (CT) and magnetic resonance imaging (MRI) have revolutionized the study of the brain by allowing doctors and researchers to look at the brain noninvasively. These diagnostic imaging techniques have allowed for the first time the noninvasive evaluation of brain structure, allowing doctors to infer causes of abnormal function due to different diseases.


Communication between Brain Areas Based on Nested Oscillations

Unraveling how brain regions communicate is crucial for understanding how the brain processes external and internal information. Neuronal oscillations within and across brain regions have been proposed to play a crucial role in this process. Two main hypotheses have been suggested for routing of information based on oscillations, namely communication through coherence and gating by inhibition. Here, we propose a framework unifying these two hypotheses that is based on recent empirical findings. We discuss a theory in which communication between two regions is established by phase synchronization of oscillations at lower frequencies (<25 Hz), which serve as temporal reference frame for information carried by high-frequency activity (>40 Hz). Our framework, consistent with numerous recent empirical findings, posits that cross-frequency interactions are essential for understanding how large-scale cognitive and perceptual networks operate.


Brain Mechanisms and Reading Remediation: More Questions Than Answers

Dyslexia is generally diagnosed in childhood and is characterised by poor literacy skills with associated phonological and perceptual problems. Compensated dyslexic readers are adult readers who have a documented history of childhood dyslexia but as adults can read and comprehend written text well. Uncompensated dyslexic readers are adults who similarly have a documented history of reading impairment but remain functionally reading-impaired all their lives. There is little understanding of the neurophysiological basis for how or why some children become compensated, while others do not, and there is little knowledge about neurophysiological changes that occur with remedial programs for reading disability. This paper will review research looking at reading remediation, particularly in the context of the underlying neurophysiology.

1. Brain Mechanisms and Reading Remediation: More Questions Than Answers

Approximately 10% of children suffer a specific reading difficulty such as dyslexia [1]. Despite some residual deficits in core skills, (e.g., phonological processing), some of these individuals will ultimately learn good reading skills as adults (become compensated), while others will remain functionally reading-impaired all their lives (uncompensated) [2, 3]. On the last page of her seminal book on dyslexia, Snowling [2] concludes “The research agenda for the next decade must certainly be directed to the treatment resisters,… those poor readers who do not respond well to current intervention programs.” Yet despite the huge personal and social costs of dyslexia, virtually nothing is known about how or why some young dyslexic readers ultimately learn to read, while others remain functionally dyslexic their whole lives. The aim of the current review is to consider some of the research on reading remediation, particularly within the context of underlying brain mechanisms.

A number of reviews have been conducted regarding the functional organisation of the normal reading network in the brain (refer to [4] for a recent review) and there is some research that has looked at compensatory brain mechanisms that develop as poor readers develop good reading skills [5]. However, a full understanding of how cortical networks develop in response to acquiring reading skills requires not only an understanding of what those networks look like but also an understanding of how those networks are functionally connected. Functional connectivity in language is well documented (e.g., [6]), and it is common for researchers to draw on this literature to also describe reading networks however, cortical networks in reading are quite different from cortical networks in language. Our ability to acquire and use language is entirely different from our ability to learn to read and very different from the processes involved in reading fluently. The primary difference is that language acquisition and use are likely to be innate, whereas reading skills are learned. This is an important difference because the former assumes the existence of a naturally occurring underlying biological substrate, whereas the latter does not. When reading the brain has had to learn to recruit resources from quite disparate parts of the brain that have likely evolved to perform quite unique functions. Therefore, reading is a learned process that is quite different from the acquisition of language which is more like fine tuning preexisting neural circuitry.

It is assumed that lexical representations are encapsulated as specific neural traces, and the various components of the reading process, such as pattern recognition, and memory, phonological decoding, are similarly represented as neural responses in the brain. When reading, the brain has had to learn to recruit resources from quite disparate parts of the brain that have likely evolved to perform quite unique functions. Thus, with all learned skills, there are some individuals who are good at the skill, many who are average, and a number who are very poor. In reading, the very poor category constitutes the group of children that we consider to be dyslexic.

The following review is designed to consider theoretical models for ways in which the brain might compensate for dyslexia and to evaluate some of the scientific knowledge in this area. The aim is to broaden and clarify our understanding of the dynamics and cortical plasticity involved in acquiring reading skills. Then at the practical level, if we can understand how compensated dyslexics read, we can make predictions about how they overcame their disability and provide a strong scientific foundation from which to later formulate strategic intervention programs to facilitate successful reading skills in all dyslexic readers. Finally, an understanding of these concepts has broader implications for neuroscience, by contributing to our understanding of neural plasticity in large, distributed cognitive networks.

2. Dyslexia Sometimes Persists into Adulthood

Dyslexia is not just a childhood problem. Many adults who have suffered from developmental dyslexia as children never develop good reading skills [7]. Those who do compensate for their reading difficulty and become good readers invariably suffer from residual difficulties such as poor spelling and poor phonological coding [8]. Uncompensated dyslexic readers never develop functional reading skills despite normal exposure to reading instruction, and in some cases many hours of reading remediation. These readers are the treatment resisters [2].

Compensated dyslexic readers—also sometimes referred to as resilient readers [9]—are adult readers who have a documented history of childhood dyslexia but as adults can comprehend written text well, in spite of residual difficulties in low-level decoding skills [3, 10–12]. These are adult readers who are functionally sound readers and are quite often high achievers, despite having a history of childhood reading difficulties. Little is known about how dyslexic children go on to develop reasonable reading skills much of the literature suggests that such children develop idiosyncratic behavioural coping strategies as a consequence of high levels of motivation and educational opportunities (e.g., [13]). Nevertheless, the question of interest here is, neurophysiologically, how is it that a dyslexic child has learned to read? Has the brain adapted to the reading difficulty and slowly developed a normal reading network? Or has the brain developed a new, compensatory network to bypass functional impairment?

Conversely, there are adult dyslexic readers who similarly have a documented history of childhood reading impairment, but unlike compensated dyslexics, they remain functionally reading impaired all their lives. Such adults will still score well below average on standard adult reading tests, despite normal adult IQ and—again—no comorbid difficulties. These are referred to as uncompensated dyslexic adults (or sometimes adults with persistent dyslexia), as their poor reading and spelling skills have persisted into adulthood.

Neurophysiologically, compensated and uncompensated dyslexic adults are likely to be quite distinct: in one case the adult brain has adapted to a childhood, developmental impairment to learn the skill of reading, whereas in the other, the brain has not developed a functional reading network at all or has developed a reading network that remains dysfunctional, supporting only rudimentary reading skills. Few studies have explored the neurophysiological distinction between the two groups. More importantly, there is little consistent convention in the scientific literature of the use of either group, such that many—if not most—studies using adult dyslexics fail to report whether the participants are compensated or not. Clearly, this can be a significant problem given that the neurophysiological profiles of each of the groups are likely to be quite different, making it difficult to make unambiguous statements about cortical functionality in adult dyslexics.

As an aside, it is worth noting that both compensated and uncompensated dyslexia in adults are different from acquired dyslexia which is also usually associated with adults. In the former, poor reading skills have been a life-long affliction. However, in the latter, the adult has had a history of normal reading as a child, but reading difficulties occur as a consequence of specific brain injury such as stroke or an accident. Here then, neurophysiologically, reading difficulties are reasonably straight forward and easily identified as a consequence of specific damage to a specific part of the cortex. Referred to as Alexia, it seems to be associated with left occipital lesions with right homonymous hemenopia (e.g., [14–17]), although pure alexia has also been associated with thalamic impairment [18], without homonymous hemenopia (e.g., [19, 20]), and closer to the inferior temporal areas [21]. The most common understanding of the disorder is as a disconnection syndrome such that bilateral visual stimuli fail to reach the angular gyrus—as described by Déjerine back in 1891 [22].

3. The Neurophysiology of Component Behavioural Mechanisms in Compensated Dyslexia

In order to understand the cortical maps involved in reading and dyslexia, it is important to become familiar with the component neurocognitive mechanisms of the network. There are a number of behavioural mechanisms that have been implicated in dyslexia.

3.1. Sensory Processing

There is a large collection of evidence implicating early sensory coding difficulties in dyslexia in the visual domain [23–33] and auditory processing [34–37]. However, the degree to which sensory coding persists into adulthood and can characterise compensated and uncompensated adult readers remains unclear. Birch and Chase [38] failed to find differences on measures of visual processing such as contrast sensitivity and sine wave detection, although they used static stimuli. Hämäläinen et al. [39] measured rise times in amplitude modulation in auditory processing. Again there were no systematic differences between compensated and uncompensated groups, but there was a correlation between phonological ability and sensitivity to rise times, which may explain the lack of difference between the groups, as poor phonological sensitivity in dyslexic readers frequently persists into adulthood irrespective of reading skill [12, 40, 41].

3.2. Phonological and Orthographic Processing

Despite the acquisition of normal functional reading skills, compensated dyslexic readers frequently maintain residual problems in phonemic awareness [3, 10, 12]. Given that regular words can be read by either conversion of graphemes to phonemes, or by the recognition of the orthographic form of a word, it has thus been suggested that normal reading skills in compensated readers are acquired as a result of dependence on whole-word orthographic skills [42]. Neurophysiologically, compensated dyslexic readers engage in different cognitive networks when processing tasks that require phonological manipulations, such as less activation for compensated readers in the insula, left premotor, and Wernicke’s regions [43]. A study directly comparing normal, compensated, and uncompensated readers on a nonword rhyming task found that both dyslexic groups demonstrated less activation in superior-temporal and occipitotemporal regions with overactivation in right inferior frontal areas. Compensated readers differed from both groups by activating right superior frontal and mid-temporal regions [44]. Ingvar et al. [45] investigated differences between normal and compensated dyslexic readers in a single word reading task, demonstrating that compensated readers showed an increased activation in right temporal regions.

3.3. Semantic Encoding

There is substantial evidence for a dissociation between word decoding and semantic processing or comprehension [46]. Some evidence suggests that both adult dyslexic readers [47] and children with dyslexia [48, 49] may rely more heavily on the influence of semantic context when reading, with research implicating stronger activation in the inferior frontal regions when processing semantic and contextual information [50]. Similarly, underengagement of the dorsal and ventral aspects of the left posterior cortex has been demonstrated in poor readers, but with a disproportionately higher activation in the inferior frontal gyrus (IFG), suggested to be important in the integration of semantic associations [47, 51, 52]. Moreover, distributed models of reading comprehension have suggested that poor low-level decoding skills may be compensated by more sophisticated higher-level skills such as semantic context [53]. Again therefore, while some studies have suggested that compensated readers may recruit from different areas of the network to enable greater reliance on semantic encoding, few studies have investigated this in the context of adult reading skills ranging from compensated to uncompensated. Thus, we do not know if this is a successful adaptation acquired by better readers or a general adaptation arising from reading failure.

4. How Do Poor Readers Become Compensated?

Neurophysiologically, there are a number of ways in which a child with dyslexia could develop functionally adequate reading skills. There is little evidence supporting any position, and the following are logical derivations from our knowledge of existing reading networks and how complex cortical connectivity occurs. Thus the following are hypotheses, exploring these should be the focus of the next generation of research.

One possibility is that dyslexic readers ultimately learn good reading skills (become compensated readers) by eventually developing the cortical connections required for normal reading networks rather than developing uniquely different cortical connections. The assumption here then is that normal reading is associated with the development of consistent cortical networks that are observable over most—if not all—readers. That there is a normal reading network that is consistent over most readers has been demonstrated elsewhere. For example, we demonstrated [54] that unique areas of the brain synchronised at 8–12 Hz (alpha range) in response to different reading requirements. In this study, participants were presented with continuous text presented at rates that made comprehension easy, effortful, very difficult (only the general gist of the story was apparent), or impossible (random text). Left hemisphere cortical activations consistent with a reading network were activated at 8–12 Hz in a dynamic way that reflected the cognitive requirements of the reading task and were consistent over participants.

The scenario that adult dyslexics simply develop the normal networks eventually points to the possibility that dyslexia occurs as a consequence of slow or delayed development of dedicated reading networks. If this is the case, then a logical biological substrate would be that this occurs as a result of poor or slower neuronal maturation. This is consistent with Wright and Zecker [55] who found age-dependent differences in auditory functioning for dyslexic children, and they suggested that slower neurological development may be further arrested with the onset of puberty. Certainly, it is well accepted by neurophysiologists that neuronal plasticity decreases in older animals that have reached sexual maturity [56]. Moreover, McArthur and Bishop [57–60] have put forth the Maturational Hypothesis, where dyslexia may be partially caused by delays in the development of cortical connectivity rather than a specific deficit in the network itself. Thus, compensation in this neurophysiological scenario occurs because a normal, predictable neural network eventually develops as the neural connections strengthen.

Presumably then, persistent adult dyslexia is a consequence of the normal network failing to reach full maturity. This proposal points to clear empirical predictions: using neuroimaging techniques, it should be possible to demonstrate that all adult readers, both normal and dyslexic, should show activation in the same basic regions in the same order and demonstrate the same connectivity. Poorer adult readers would show weaker activation and/or temporally delayed (slower) signals [61]. This would show up as a positive correlation for reading ability with activation and a negative correlation between latency and reading ability.

Another possibility is that dyslexic readers become compensated readers because they develop new, alternative cortical connections to support reading that are unique to the individual. Here then, there is a normal reading network observable in most readers, but for some reason in dyslexic readers this network fails to develop, but with constant exposure to reading, the individual develops a bypass model to support reading skills. Because learning the skill of reading ultimately requires the development of new cortical networks for all readers, there is no specific reason that the brain must solve the same problem (learning to read) in exactly the same way for all people. It is not unreasonable to expect that if the cortical connections that develop to support reading are unsuitable for whatever reason, then through remediation, the brain could develop entirely different connections to support the same behavioural outcome (reading). There is some support for this proposition as well, with the evidence that some dyslexic readers activate unique areas when reading compared to normal readers. For example, there is a tendency for poor readers to demonstrate abnormal activations in the left temporoparietal regions of the brain during language processing and reading tasks [51, 62]. Pugh et al. [47] have suggested that when reading, poor readers engage frontal sites, such as the IFG and prefrontal dorsolateral sites [52, 63, 64], more so than normal readers. Similarly, Salmelin et al. [61] and Brunswick et al. [51] showed greater activation of inferior precentral gyrus (Broca’s area) in poor readers when processing visually presented words with post-200msec responses. Thus, for some reason (e.g., poor sensory input), the normal reading network cannot be used and neuronal patterns of activation attempt to bypass the deficient mechanisms to meet reading requirements. Indeed, there is also evidence of white-matter connectivity differences between dyslexic and normal readers [65]. Furthermore, Horwitz et al. [66] demonstrated that compared to control adult readers, compensated adult readers failed to activate the same brain areas during reading tasks. In this study they measured the functional correlation of activity across the brain with the left angular gyrus to reading irregular and nonwords. They demonstrated that normal readers generated an activity pattern that included visual association areas and Wernicke’s area however, such a pattern of activity was absent in the compensated dyslexics. See also Wimmer et al. [67] who demonstrated different patterns of activity in compensated dyslexic adults compared to normal readers, particularly in visual, occipital areas.

If a new, unique reading network develops, then logically there are a number of ways in which this could occur: once the normal reading network is unattainable, there is another common network that is accessed to meet reading requirements, irrespective of the type of damage existing in the normal system. Compensated dyslexic readers simply get better at using this network than uncompensated readers. This would predict that the network dynamics for dyslexic readers would be different from good readers, but consistent within a dyslexic group. Another possibility is that different spatiotemporal maps of activity develop to bypass particular damage. For example, an increase in activity in the IFG may be associated with poor phonological skills as found by Pugh et al. In this scenario, similar neural connections that develop in response to reading would map onto similar behavioural results in component reading skills. Common components of the network (e.g., increased activation in the IFG) in different reading situations would be associated behaviourally with component skills (e.g., poor phonological sensitivity). Compensatory mechanisms may develop in this case with the need for fewer bypass mechanisms or the more efficient use of the mechanisms. In another possibility, dyslexic readers in general develop unique and idiosyncratic neurophysiological mechanisms to read. Here then, compensatory behaviour occurs as a result of the development of more successful connections that is unique to the individual and predicated on other factors such as motivation and individual strategy development. In this scenario, there would be little systematic mapping to behavioural outcomes. Some examples are described in Figure 1.


Individual dyslexic readers (brains A, B, C) may develop their own unique cortical networks, but common deficits map back onto common behavioural deficits. Brain D here is from Kujala et al. [54] representing normal network connectivity when reading.

Thus, there are a number of logically derived possibilities to explain how compensatory mechanisms might develop, and the strength of these possibilities is that they generate clear and testable hypotheses which should be the focus of the next wave of research into dyslexia and reading remediation.

5. Changes in Cortical Connectivity When Dyslexic Readers Learn to Read

Some research has been conducted looking at neurophysiological changes in children after remedial programs. It may be possible to extrapolate from these studies to support one or more of the proposals suggested previously.

Reading improvement in children with dyslexia has been demonstrated to be associated with activity in the left inferior frontal gyrus [68] such that white matter integrity was positively correlated with reading gain. This indicates that those dyslexic children who are most likely to acquire good reading skills are those children who have more extensive connectivity in the left inferior frontal cortex. Thus, connectivity with the right inferior frontal cortex may be an important component in the development of compensatory brain networks in reading. This is consistent with other studies looking at the development of compensatory mechanisms (e.g., [69–72]).

Structural changes in the brain have been well documented as a consequence of reading intervention and typically involve phonological interventions. For example, Eden et al. [70] tested adult dyslexic and nondyslexic readers before, and then after eight weeks of a multisensory, phonological-based reading intervention. They demonstrated an increase in response in the left angular gyrus, and the fusiform/parahippocampal gyrus anterior to the Visual Word Form Area, in response to phonological tasks. Changes in white and grey matter have also been demonstrated: Keller and Just [73] investigated white matter organisation in dyslexic children after 100 hours of phonological reading and spelling instruction. After training, the children performed better at phonological tasks, and this was correlated with an increase in white matter—specifically in left anterior tracts. Indeed, the location of increased white matter was the same area that showed decreased activation in dyslexic readers compared to good readers before intervention. Grey matter changes have also been demonstrated with intervention. Krafnick et al. [74] demonstrated increases in grey matter volume in the left fusiform and precuneus and right hippocampus and cerebellum. Interestingly, the intervention used here was less phonological and was based more heavily on imagery and multisensory processing. Given the multitude of reading intervention possibilities, it remains to be seen whether the type of reading intervention is important for neurophysiological changes in response to reading intervention.

That behavioural change reflects changes in cortical connections is highlighted in a recent paper by Koyama et al. [5]. These authors employed a technique called intrinsic functional MRI (I-fMRI). This technique is unique in that it represents a way of measuring functional connectivity between different brain regions when at rest [75]. By identifying a specific brain region, researchers are able to identify other parts of the brain that demonstrate correlated fluctuations in activity over time. This provides an excellent mechanism for understanding and mapping large-scale cortical interactions that are particularly characteristic of cognitive functioning (refer to [76] for a recent review) because it turns out that many of the neural networks that are activated when an individual engages in a cognitive task, are also active when the brain is at rest. This is a particularly useful technique for investigating cognitive disorders such as dyslexia, because the participant does not need to engage in any reading behaviour, thus avoiding many of the other confounds that plague dyslexia research such as performance motivation, stress, and anxiety. Koyama et al. investigated I-fMRI in dyslexic and control children where the dyslexic children were either dyslexic with no intervention, dyslexic but had experienced reading intervention, or dyslexic and had experienced reading and spelling intervention. The interventions in this case varied from child to child but were predominately language based, and all the remediated children had no demonstrable reading problem by the time of the study. Of the many seeding locations initially identified in the brain, the authors demonstrated that intrinsic functional connectivity between the left intraparietal sulcus and left middle frontal gyrus (BA9) was significantly correlated with reading intervention, with normal readers showing the strongest connectivity, followed by the two intervention groups, with the no-intervention dyslexic group showing little or no connectivity. Similarly, the left fusiform gyrus demonstrated differential functional connectivity between the groups. For example, connectivity strength with frontal areas was higher for the two intervention groups but virtually nonexistent for the control and no-intervention groups. This is a particularly exciting study for demonstrating the efficacy of training techniques. Of particular interest is that reading intervention is not only effective in developing normal connectivity, which supports the Neuronal Maturation hypothesis proposed above, but also in developing compensatory connections in the brain. This has enormous implications for the development of reading interventions.

However, the vital piece of the puzzle that is missing here thus far in how dyslexic readers become compensated readers is the link with cortical frequency dynamics. It is not just where cortical connections are formed in the brain that is important, but how the different parts of the brain communicate that is vital for a full understanding of brain activity in dyslexic remediation. This is particularly important when evaluating different models of compensation because timing between activations at cortical sites may be just as important as the spatial distribution (e.g. [30, 31, 61, 77]).

6. Frequency Dynamics and Cortical Connectivity

Both EEG and MEG measure the synchronous firing of large populations of cells in the cortex. Cells within a given population are said to be firing synchronously when their firing rhythm coincides at a particular frequency, and different firing frequencies are believed to reflect different functional states.

Changes in oscillatory power refer to an increase or decrease in the amplitude of power within specific frequency bands. It is believed to reflect changes in oscillatory dynamics at the local level [78], potentially within a particular cortical area or structure. Event-related synchronisation (ERS) and event-related desynchronisation (ERD) reflect increases or decreases in power, respectively. When a functionally specific subset or population of neurons process incoming information, they will disengage from the larger neuronal set to synchronise and oscillate at a different frequency.

Oscillatory coherence is believed to reflect the transient synchronisation at specific frequencies of disparate areas of the cortex [78], further than 1 cm or so away from each other [79]. This is the situation where large-scale neuronal assemblies across the cortex start talking to each other. While ERS and ERDs are likely to reflect local functionality in specific cortical sites, oscillatory coherence is more likely to reflect complex cognition, where many different parts of the brain need to talk to each other quickly and fluidly such as in reading.

Different cortical areas exhibit rhythmical activity to internal or external input, with characteristic frequency ranges, such as 8–12 Hz (alpha oscillations), 13–24 Hz (beta oscillations), 25–50 Hz (gamma oscillations), and >50 Hz (high gamma oscillations), and spatially distributed components of cerebral networks are assumed to talk via such synchronised neural firing [80]. Cognitive functions are thought to depend on this type of connectivity between large-scale neural networks [81]. Therefore, the frequencies at which populations of neural cells oscillate are believed to be a mechanism by which different regions of the brain communicate, with different sensory and cognitive experiences inducing unique oscillatory signatures. The functional significance of synchronous oscillations has been demonstrated as mediating other cognitive processes, such as memory [82–85], attention and attentional processes (e.g., [86]), face perception (e.g., [87]), and object detection (e.g., [88–90]).

Consistent with other cognitive processes, it is not unreasonable to predict that reading and word recognition rely heavily on the functional states of cortical networks. However, little research has been conducted to look at connectivity in terms of frequency dynamics between different cortical areas in response to intervention. This is a vital piece of the puzzle when it comes to reading intervention and remediating poor readers, as changes in reading skill may be closely associated with changes in the way in which populations of neurons communicate, rather than just changes in spatial maps of activity. Nazari et al. [91], used neurofeedback to train children to decrease delta (1–4 Hz) and theta (4–8 Hz) brain oscillations and to increase beta (15–18) oscillations over eight training sessions. They demonstrated an increase in reading speed and accuracy with neurofeedback training. However, they did not explicitly manipulate reading intervention, and the dependent variable was reading outcome, although they do also report an increase in coherence for theta rhythms. So the logic of this study was that explicitly decreasing delta and theta rhythms and increasing beta rhythms resulted in improved reading outcomes. However, it is unclear what the behavioural changes are in response to and whether the results could have been due to test repetition.

Although little or no research has looked at cortical frequency dynamics in dyslexia remediation, it has recently been investigated in the context of language impairment, which has been reported to have a 50% comorbidity with dyslexia [92]. In this study [93], children with language impairment were given language remediation, some of which is phonologically based. The neurophysiological responses were measured in response to passive listening to tone pairs. They specifically focused on gamma activity in this study and demonstrated an increase in gamma activity and gamma phase locking for the children who had experienced the intervention. Thus, this recent project provides in-principal evidence for the importance of cortical coherence in reading remediation.

7. Conclusion

Understanding the processes by which some dyslexic children come to learn to read as adults provides an enormous resource for understanding the plasticity of complex cognitive networks such as reading, how neurobiological processes can adapt to meet specific cognitive requirements, and how we might better design remedial reading treatment in children to exploit such processes. For some young dyslexic readers it might be better to foster reading skills that already exist rather than attempt to develop functionality in brain mechanisms which are less responsive. Demonstrating successful cortical plasticity in compensated dyslexic adults addresses questions regarding whether treatment intervention should target the relative strengths of the dyslexic reader rather than persisting in standard intervention practices. Such research also has important consequences for how we teach reading identifying intrinsic functional components of the cortical reading network would provide vital information to inform current debates regarding the relative importance of whole-word versus phonological decoding in reading instruction. By identifying in compensated adults, those components of the neural reading network that are intrinsic to the reading process, such research would provide a basis for strategies for the early detection of dyslexia, such that deficits in component skills could be identified before children learn to read.

Thus, how dyslexic readers develop neural connectivity to ultimately acquire good reading skills remains speculative however, research such as that of Koyama et al. [94] suggests that the full story may ultimately be a hybrid of the possibilities posited above, with dyslexic readers developing both new and idiosyncratic connections, as well as finally developing normal reading networks. Nevertheless, a major limitation of this recent study is its cross-sectional design. In this it is difficult to make assumptions about the antecedent behaviors that might have occurred before testing. and a lack of ability to control the nature of the intervention. Subsequent studies are now well placed to develop this concept further by using longitudinal designs in which the intervention is targeted and controlled with a larger sample of children.

Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

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Copyright © 2014 Kristen Pammer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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Hunt, A. M. (2015). Boundaries and potentials of traditional and alternative neuroscience research methods in music therapy research. Front. Hum. Neurosci. 9:342. doi: 10.3389/fnhum.2015.00342

Keller, P. E., Novembre, G., and Hove, M. J. (2014). Rhythm in joint action: psychological and neurophysiological mechanisms for real-time interpersonal coordination. Philos. Trans. R. Soc. B Biol. Sci. 369:20130394. doi: 10.1098/rstb.2013.0394

Leicht, E., and Newman, M. (2008). Community structure in directed networks. Phys. Rev. Lett. 100:118703. doi: 10.1103/PhysRevLett.100.118703

Lindenberger, U., Li, S.-C., Gruber, W., and Müller, V. (2009). Brains swinging in concert: cortical phase synchronization while playing guitar. BMC Neurosci. 10:22. doi: 10.1186/1471-2202-10-22

Müller, V., Delius, J. A. M., and Lindenberger, U. (2018a). Complex networks emerging during choir singing. Ann. N. Y. Acad. Sci. 1431, 85�. doi: 10.1111/nyas.13940

Müller, V., Delius, J. A. M., and Lindenberger, U. (2019). Hyper-frequency network topology changes during choral singing. Front. Physiol. 10:207. doi: 10.3389/fphys.2019.00207

Müller, V., and Lindenberger, U. (2011). Cardiac and respiratory patterns synchronize between persons during choir singing. PLoS ONE 6:e24893. doi: 10.1371/journal.pone.0024893

Müller, V., Sänger, J., and Lindenberger, U. (2013). Intra- and inter-brain synchronization during musical improvisation on the guitar. PLoS ONE 8:e73852. doi: 10.1371/journal.pone.0073852

Müller, V., Sänger, J., and Lindenberger, U. (2018b). Hyperbrain network properties of guitarists playing in quartet. Ann. N. Y. Acad. Sci. 1423, 198�. doi: 10.1111/nyas.13656

Pecenka, N., and Keller, P. E. (2011). The role of temporal prediction abilities in interpersonal sensorimotor synchronization. Exp. Brain Res. 211, 505�. doi: 10.1007/s00221-011-2616-0

Rubinov, M., and Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059�. doi: 10.1016/j.neuroimage.2009.10.003

Sänger, J., Lindenberger, U., and Müller, V. (2011). Interactive brains, social minds. Commun. Integr. Biol. 4, 655�. doi: 10.4161/cib.17934

Sänger, J., Müller, V., and Lindenberger, U. (2012). Intra- and interbrain synchronization and network properties when playing guitar in duets. Front. Hum. Neurosci. 6:312. doi: 10.3389/fnhum.2012.00312

Sänger, J., Müller, V., and Lindenberger, U. (2013). Directionality in hyperbrain networks discriminates between leaders and followers in guitar duets. Front. Hum. Neurosci. 7:234. doi: 10.3389/fnhum.2013.00234

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Zamm, A., Debener, S., Bauer, A. R., Bleichner, M. G., Demos, A. P., and Palmer, C. (2018). Amplitude envelope correlations measure synchronous cortical oscillations in performing musicians. Ann. N. Y. Acad. Sci. 1423, 251�. doi: 10.1111/nyas.13738

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Keywords: intra- and inter-brain coupling, brain-instrument coupling, graph-theoretical approach, EEG hyperscanning, phase synchronization, extended hyper-brain networks, social interaction

Citation: Müller V and Lindenberger U (2019) Dynamic Orchestration of Brains and Instruments During Free Guitar Improvisation. Front. Integr. Neurosci. 13:50. doi: 10.3389/fnint.2019.00050

Received: 15 May 2019 Accepted: 20 August 2019
Published: 04 September 2019.

Assal Habibi, University of Southern California, United States

Donald Glowinski, Université de Genève, Switzerland
Shoji Tanaka, Sophia University, Japan

Copyright © 2019 Müller and Lindenberger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Alpha Brain Waves: What do they do?

Most people associate alpha brain waves with calmness and relaxing with our eyes closed. Others believe that they are a natural cure for anxiety and stress. Some researchers even think that alpha activity at 10 Hz may be linked to states of relaxed “peak performance.” In any regard, below are some effects of dominant alpha activity.

  • Balanced mood: Individuals that are depressed as a result of being “over-stressed” could feel an antidepressant effect when their alpha activity increases. Alpha activity – specifically in the right hemisphere of the brain has been shown to boost mood. Some people also experience a more “balanced” mood when they increase their alpha waves.
  • Calmness: Most people associate the alpha range with feelings of calmness. People that are stressed have a diminished ability to produce these waves. When we are at our calmest with our eyes closed and are idly relaxing, this is when we experience alpha as a dominant brain wave state.
  • Creativity: It has been hypothesized that alpha activity is linked to increases in creativity. Since it is produced predominantly by the right hemisphere, it is thought that it may enhance artistic ability as well as creative problem solving skills. Sometimes when we relax, we experience “aha” moments – this is a result of the alpha wave. It is creative, relaxed, problem solving which gives us a different perspective.
  • Daydreaming: If you close your eyes or daydream a lot, this may be evidence of alpha increases. Most adults tend to be very focused and task-oriented. Alpha is more common in younger children that tend to daydream a lot and have difficulties focusing. In some cases, it is even linked to ADHD if the increased alpha is accompanied by other slow wave activity (e.g. theta).
  • Decreased focus: During the alpha brain wave state, many individuals have poorer focus and concentration. It has been shown that the greater the amount of alpha activity, the more likely someone is to make a mistake. Studies have shown that when someone makes a mistake, alpha activity increases an average of 25%. In other words, the person is on “auto pilot” or too “idle” to perform a certain task. When alpha decreases, attention and focus increase. Therefore, alpha is not ideal for critical thinking and/or detail-oriented, technical work.
  • Flow state of mind: It has been thought that the 10 Hz alpha wave is key in a “flow” state of consciousness. A state of flow is characterized by being calm and focused at the same time. In other words, the saying “mind like water” holds true. It is able to go with the flow without getting overly aroused or being “idle.” This is considered a happy medium between the fast paced beta waves and the drowsy theta waves.
  • Immune system: Some have hypothesized that our immune system benefits from increasing alpha waves. This in part has to do with the fact that relaxation can help our bodies recover from the toxic effect of stress. It is known that stress can cause all sorts of health problems, so it is no wonder that relaxing (in the alpha range) helps boost the immune system.
  • Peak performance: Despite the fact that the 40 Hz gamma wave has been linked to peak performance, so has the 10 Hz alpha wave. It is hypothesized that a synchronized 10 Hz rhythm across both hemispheres may play a role in helping people achieve a relaxed state of peak performance. There have been studies conducted involving basketball players and golfers and when they missed a shot (basketball) and/or hit a bad shot (golf), they experienced spikes of beta activity. When they sank a free throw or hit a good shot, they maintained alpha activity.
  • Positive thinking: This isn’t the high-energy, excitement-type, positive thinking of the beta range. However, when people experience alpha increases it is linked to having a more optimistic outlook on life. People tend to be calm and think fairly positive with dominant alpha. Think of this as the opposite of rapid-negative stressful or angry thoughts.
  • Problem solving: For thinking outside the box, some would argue that the alpha range is what helps. Individuals sometimes get so stressed out that all they do is keep thinking and ruminating about possible solutions, but can never solve their problem. Sometimes it helps to simply slow the mind down and then the solution appears easily and naturally in the alpha state.
  • Relaxation: Anytime you feel deeply relaxed, you are experiencing alpha brain waves. Think of times right before you go to bed and transition into sleep. Your eyes are closed, you are relaxing and you experience a sense of calmness. If you like to lay out in the sun and tan and experience a sense of relaxation while lying with your eyes closed, this also provides an alpha boost.
  • Serotonin: When we relax, our body is able to naturally produce more serotonin. It is hypothesized that alpha may release more serotonin and thus increase our ability to relax, stay calm, and ward off stress.
  • Slower visual acuity: Since alpha activity decreases when people are fully awake with eyes open, it is linked with slower visual acuity. In other words, visual processing speed while you are awake is negatively affected by increases in alpha.
  • Super learning: Some have argued that increases in alpha contribute to a state of “super learning.” Meaning more of our brain is able to absorb information and thus we learn better. I tend to disagree with this hypothesis based on the fact that alpha activity tends to decrease focus. There may be some degree of truth to this for certain types of learning if accompanied by appropriate beta.
  • Visualization: If you like to close your eyes and visualize, this is the brain wave that you will experience. Anytime you close your eyes, are relaxed, and visualizing internally (e.g. mind’s eye), you will come to learn what the alpha range feels like.

Note: Alcohol and drug abuse can significantly reduce alpha frequency and amplitude. Thus decreasing the benefits to be had from this particular range. In part this may be why individuals that abuse drugs and alcohol have a difficult time relaxing once the “high” wears off.

Alpha Brain Waves Research

Discovery: German neurologist Hans Berger was the first individual to discover alpha waves. He did this by measuring electrical activity in the brain’s of hospital patients with skull damage. He documented these waves along with beta activity. He found that when alpha waves decrease and beta activity becomes dominant, we are fully awake. Since he discovered this wave, they have been referred to as “Berger’s Wave.”

Biofeedback: This is a technique that involves helping people naturally train their brains to produce certain brain waves. The idea behind it is that you receive “feedback” when your brain increases activity of a certain wave. The goal is to eventually learn how to consciously produce this type of brain wave activity without feedback after multiple training sessions.

Seizure resistance: Some hypothesize that increasing the amplitude and dominance of alpha brain waves can help individuals that are seizure-prone. Research in cats indicates that if alpha activity is trained, they have a greater resistance to seizures. Whether this holds true for humans is somewhat controversial.

Stress relief: Research has shown that individuals that are stressed out tend to produce an overabundance of beta waves. Most of these individuals may experience what is called “alpha blocking” or blocked alpha activity. Meaning the alpha activity is so low, that it doesn’t allow the individual to lower their level of arousal. In order to decrease stress, it has been found that training alpha via biofeedback has had some positive results.

Related Posts:

Alpha-heavy states don’t necessarily diminish focus. It sounds like you’re speaking from your own experience. I do technical, detailed oriented work — often over a hundred hours a week — and rarely lack concentration.

I use a technique called “open-eyed alpha” the entire time I work, and it has been a blessing for me. It is discussed in the book The High-Performance Mind by Anna Wise. I think your article is misleading people away from using this beautiful state of mind for work that requires high levels of concentration and detail.

That’s what I was thinking… more than that: less stress = more focus.

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Additional Effects of MDMA on the Brain

There are studies of long-term MDMA users that have indicated several other effects of heavy use of the drug:

  • Global form processing: One study found that recreational MDMA use affected the users' ability to process certain types of visual information, such as the ability to integrate local orientation information into a global form percept.  
  • Impaired impulse control: Other researchers believe that, like many other drugs, MDMA affects the region of the brain involved with impulse control and therefore can contribute to the development of substance abuse disorders.  
  • Impaired sexual arousal: Because MDMA affects serotonin levels more than dopamine levels in some users, some researchers believe long-time use may impact sexual arousal.  

Additional Effects of MDMA on the Brain

There are studies of long-term MDMA users that have indicated several other effects of heavy use of the drug:

  • Global form processing: One study found that recreational MDMA use affected the users' ability to process certain types of visual information, such as the ability to integrate local orientation information into a global form percept.  
  • Impaired impulse control: Other researchers believe that, like many other drugs, MDMA affects the region of the brain involved with impulse control and therefore can contribute to the development of substance abuse disorders.  
  • Impaired sexual arousal: Because MDMA affects serotonin levels more than dopamine levels in some users, some researchers believe long-time use may impact sexual arousal.  

Advantages of Head MRI

  • MRI does not use ionizing radiation, and is thus preferred over CT in children and patients requiring multiple imaging examinations
  • MRI has a much greater range of available soft tissue contrast, depicts anatomy in greater detail, and is more sensitive and specific for abnormalities within the brain itself
  • MRI scanning can be performed in any imaging plane without having to physically move the patient
  • MRI contrast agents have a considerably smaller risk of causing potentially lethal allergic reaction
  • MRI allows the evaluation of structures that may be obscured by artifacts from bone in CT images

The complexity of the organ that determines how a person thinks, moves, feels, and remembers is overshadowed only by its unique vulnerability. The brain is hidden from direct view by the skull, which not only shields it from injury but also hinders the study of its function in both health and disease. The cells in the arteries that supply the brain are so tightly bound that even most normal cells in the bloodstream are prevented from crossing the so-called “blood-brain barrier,” thereby rendering the normal chemistry of the brain invisible to the routine laboratory blood tests that are often used to evaluate the heart, liver or kidneys.

Computed tomography (CT) and magnetic resonance imaging (MRI) have revolutionized the study of the brain by allowing doctors and researchers to look at the brain noninvasively. These diagnostic imaging techniques have allowed for the first time the noninvasive evaluation of brain structure, allowing doctors to infer causes of abnormal function due to different diseases.


Multitasking by Brain Wave

Although our bodies stay stubbornly stuck in real time, our minds can flit between the past and future and jump large stretches of time in just a moment. Such feats rely on the brain&rsquos ability to continuously store information as it happens while also retrieving dramatically condensed versions of past events. Until now, scientists weren't sure how the brain simultaneously handles these competing tasks.

Researchers from The University of Texas at Austin found evidence that in the brain&rsquos spatial system this balancing act is accomplished via dueling electrical frequencies. Results from their study in rats suggest the hippocampus, an area crucial for memory formation, rapidly switches between the two frequencies to concurrently process the current surroundings and serve up orientation clues encoded in prior experiences. &ldquoThe hippocampus has to have a way for keeping what&rsquos actually happening and being encoded into new memory storage from interfering with recall or retrieval of previously stored memories,&rdquo explains U.T. Austin neuroscientist Laura Colgin, the study&rsquos senior author. Her findings may have implications for the treatment of schizophrenia, and they also offer clues to another mental mystery&mdashhow the brain manages to replay a daylong memory in mere seconds.

Dueling brain waves
In the new study, published last week in the journal Neuron, Colgin&rsquos team recorded electrical activity in a type of hippocampal cells called &ldquoplace cells.&rdquo Place-cell activation corresponds to specific locations in space. As a rat navigates a maze, researchers can tell by which place cells are firing where the rat is in the maze&mdashor what part of the maze the rat is thinking of.

Like all of the brain&rsquos neurons, place cells produce electrical signals that oscillate in waves. In particular, past research suggests that when place cells encode and compress spatial memories they produce theta waves, which operate on a relatively slow, long-wave frequency. But these theta oscillations do not work alone. They also contain shorter and more frequent gamma rhythms nested within them like folded accordion bellows.

The gamma oscillations contribute to memory compression, explains Brandeis University neuroscientist John Lisman, an expert on the theta&ndashgamma code who was not involved in the current study. As each wave of electrical activity pops up at the gamma frequency, it conveys new information nuggets to the interacting theta wave. One overarching theta wave sees several gamma&ndashencoded memory cues, which effectively form a compressed highlights reel relative to the longer theta wave.

In a study published in Nature in 2009 Colgin and her colleagues described an additional level of complexity in these theta&ndashgamma interactions in the rat hippocampus, demonstrating that the gamma waves oscillate at different frequencies depending on the task at hand. When the hippocampus communicated with a brain area that relays as-it-happens sensory information from the outside world, for example, the team saw theta signals supported by so-called &ldquofast&rdquo gamma rhythms oscillating at 60 to 100 hertz frequencies. A second, previously unappreciated set of &ldquoslow&rdquo gamma rhythms&mdashelectrical waves in the 25 to 55 hertz range&mdashseemed to be interacting with theta waves when the hippocampus swapped messages with another part of the brain that replays memories and plans movements through space and time, Colgin explains.

Those results hinted that fast gamma rhythms might be transmitting immediate information about the environment whereas slow gamma rhythms may shuttle information related to memory retrieval.

Clues from place cells
In their current analysis, Colgin and her colleagues found new, more robust evidence that fast gamma rhythms are indeed responsible for coding new information based on an animal&rsquos current experiences. After recording electrical signals from hippocampal place cells in seven rats as they negotiated a short linear track over three 10-minute sessions each day, the team looked at how theta and gamma waves coincided with each rat&rsquos actual position on the track.

When the place-cell activity matched a rat&rsquos current location on the track, the researchers found that theta sequences interacted with the shorter wave, fast gamma signals already suspected of dealing with in-the-moment spatial information. But slow gamma waves replaced fast ones when place-cell activity represented locations ahead of the rat&rsquos current position&mdashperhaps reflecting the animal&rsquos memory of the upcoming route and anticipation of the track ahead. &ldquoThe idea is that the animal is actually retrieving the representation of that location before they get there,&rdquo Colgin explains.

The new results are powerful evidence that the different frequency brain waves keep incoming information and memory retrieval separate&mdashwhich has implications for human conditions. If the slow gamma frequency really does keep real or imagined remembrances from interfering with new information coding and vice versa, it is conceivable that the two brain frequencies may get mixed up in conditions such as schizophrenia, Colgin says. Indeed, researchers have detected diminished slow gamma synchrony between the hippocampus and other brain regions in an animal model of the disease, boosting that theory. Future therapies could try to help increase gamma synchrony and keep thoughts separate from new sensory information&mdashalthough how such a feat could be accomplished remains unknown.

How memories are compressed
In the new study the researchers also made a second discovery, which may be a clue about how the brain compresses memories. Using place-cell patterns unraveled from the theta sequences, the researchers saw a jump in the amount of track being represented per millisecond when rats were using slow gamma rhythm, even though the such rhythm produces fewer new waves of electricity in any given stretch of time than the higher frequency fast gamma rhythm.

Based on how quickly the rats seemed to anticipate upcoming sections of track, the researchers speculate that a single slow gamma wave must contain more than one piece of information, implying another level of compression within an already compressed theta&ndashgamma code. This additional degree of compression could explain how we are able to replay memories of minutes&rsquo or hours&rsquo worth of activity in mere seconds.

Lisman is unconvinced of the additional-compression interpretation, although he praised Colgin and her team for uncovering functional roles for the slow gamma frequency in the hippocampus. To accomplish the ultrafast coding necessary for each gamma wave to contain more than one piece of information, he explains, neurons would have to differentiate between bits of information appearing just a few milliseconds apart&mdashfaster than current biophysical estimates say is possible.

Loren Frank, a neuroscience researcher with the University of California, San Francisco, who studies spatial coding in the hippocampus but was not involved in the study, was less skeptical of the authors' interpretation, saying it &ldquomakes a great deal of sense.&rdquo

&ldquoIt says the things associated with memory may be going on very, very quickly,&rdquo he says, noting that the electrical signals making up each slow gamma signal could represent multiple levels of cellular organization capable of seriously speedy coding. &ldquoI was surprised to see the results,&rdquo Frank concedes, &ldquobut I don't think there's any reason to think the brain can't do things like that.&rdquo


Rhythmic Sampling of a Single Stimulus: Discrete vs. Continuous Perception

Suppose that a new stimulus suddenly appears in your visual field, say a red light at the traffic intersection. For such a transient onset, a sequence of visual processing mechanisms from your retina to your high-level visual cortex will automatically come into play, allowing you after a more or less fixed latency to “perceive” this stimulus, i.e., experience it as part of the world in front of you. Hopefully you should then stop at the intersection. What happens next? For as long as the stimulus remains in the visual field, you will continue to experience it. But how do you know it is still there? You might argue that if it were gone, the same process as previously would now signal the transient offset (together with the onset of the green light), and you would then recognize that the red light is gone. But in-between those two moments, you did experience the red light as present – did you only fill in the mental contents of this intervening period after the green light appeared? This sounds unlikely, at least if your traffic lights last as long as they do around here. Maybe the different stages of your visual system were constantly processing their (unchanged) inputs and feeding their (unchanged) outputs to the next stage, just in case the stimulus might happen to change right then – a costly but plausible strategy. An intermediate alternative would consist in sampling the external world periodically to verify, and potentially update, its contents the period could be chosen to minimize metabolic effort, while maximizing the chances of detecting any changes within an ecologically useful delay (e.g., to avoid honking from impatient drivers behind you when you take too long to notice the green light). These last two strategies are respectively known as continuous and discrete perception.

The specific logic of the above example may have urged you to favor discrete perception, but the scientific community traditionally sides with the continuous idea. It has not always been so, however. In particular, the first observations of EEG oscillations in the early twentieth century (Berger, 1929), together with the simultaneous popularization of the cinema, prompted many post-war scientists to propose that the role of brain oscillations could be to chunk sensory information into unitary events or “snapshots,” similar to what happens in the movies (Pitts and McCulloch, 1947 Stroud, 1956 Harter, 1967). Much experimental research ensued, which we have already reviewed elsewhere (VanRullen and Koch, 2003). The question was never fully decided, however, and the community’s interest eventually faded. The experimental efforts that we describe in this section all result from an attempt to follow up on this past work and revive the scientific appeal of the discrete perception theory.

Periodicities in Reaction Time Distributions

Some authors have reasoned that if the visual system samples the external world discretely, the time it would take an observer to react after the light turns green would depend on the precise moment at which this event occurred, relative to the ongoing samples: if the stimulus is not detected within one given sample then the response will be delayed at least until the next sampling period. This relation may be visible in histograms of reaction time (RT). Indeed, multiple peaks separated by a more or less constant period are often apparent in RT histograms: these multimodal distributions have been reported with a period of approximately 100 ms for verbal choice responses (Venables, 1960), 10� ms for auditory and visual discrimination responses (Dehaene, 1993), 10� ms for saccadic responses (Latour, 1967), 30 ms for smooth pursuit eye movement initiation responses (Poppel and Logothetis, 1986). It must be emphasized, however, that an oscillation can only be found in a histogram of post-stimulus RTs if each stimulus either evokes a novel oscillation, or resets an existing one. Otherwise (and assuming that the experiment is properly designed, i.e., with unpredictable stimulus onsets), the moment of periodic sampling will always occur at a random time with respect to the stimulus onset thus, the peaks of response probability corresponding to the recurring sampling moments will average out, when the histogram is computed over many trials. In other words, even though these periodicities in RT distributions are intriguing, they do not unambiguously demonstrate that perception samples the world periodically – for example, it could just be that each stimulus onset triggers an oscillation in the motor system that will subsequently constrain the response generation process. In the following sections, we present other psychophysical methods that can reveal perceptual periodicities within ongoing brain activity, i.e., without assuming a post-stimulus phase reset.

Double-Detection Functions

As illustrated in the previous section, there is an inherent difficulty in studying the perceptual consequences of ongoing oscillations: even if the pre-stimulus oscillatory phase modulates the sensory processing of the stimulus, this pre-stimulus phase will be different on successive repetitions of the experimental trial, and the average performance over many trials will show no signs of the modulation. Obviously, this problem can be overcome if the phase on each trial can be precisely estimated, for example using EEG recordings (VanRullen et al., 2011). With purely psychophysical methods, however, the problem is a real challenge.

An elegant way to get around this challenge has been proposed by Latour (1967). With this method, he showed preliminary evidence that visual detection thresholds could fluctuate along with ongoing oscillations in the gamma range (30� Hz). The idea is to present two stimuli on each trial, with a variable delay between them, and measure the observer’s performance for detecting (or discriminating, recognizing, etc.) both stimuli: even if each stimulus’s absolute relation to an ongoing oscillatory phase cannot be estimated, the probability of double-detection should oscillate as a function of the inter-stimulus delay (Figure 1). In plain English, the logic is that when the inter-stimulus delay is a multiple of the oscillatory period, the observer will be very likely to detect both stimuli (if they both fall at the optimal phase of the oscillation) or to miss both stimuli altogether (if they both fall at the opposite phase) on the other hand, if the delay is chosen in-between two multiples of the oscillatory period, then the observer will be very likely to detect only one of the two stimuli (if the first stimulus occurs at the optimal phase, the other will fall at the opposite, and vice-versa).

Figure 1. Double-detection functions can reveal periodicities even when the phase varies across trials. (A) Protocol. Let us assume that the probability of detecting a stimulus (i.e., the system’s sensitivity) fluctuates periodically along with the phase of an ongoing oscillatory process. By definition, this process bears no relation with the timing of each trial, and thus the phase will differ on each trial. On successive trials, not one but two stimuli are presented, with a variable delay between them. (B) Expected results. Because the phase of the oscillatory process at the moment of stimulus presentation is fully unpredictable, the average probability of detecting each stimulus as a function of time (using an absolute reference, such as the trial onset) will be constant (left). The probability of detecting the second stimulus will also be independent of the time elapsed since the first one (middle). However, the probability of detecting both stimuli (albeit smaller) will oscillate as a function of the delay between them, and the period of this oscillation will be equal to the period of the original ongoing oscillatory process (adapted from Latour, 1967).

More formally, let us assume that the probability of measuring our psychological variable ψ (e.g., target detection, discrimination, recognition, etc.) depends periodically (with period 2π/ω) on the time of presentation of the stimulation s to a first approximation this can be noted:

where p0 is the average expected measurement probability, and a is the amplitude of the periodic modulation. Since the time t of stimulation (with respect to the ongoing oscillation) may change for different repetitions of the measurement, only p0 can be measured with classical trial averaging methods (i.e., the “sine” term will average out to a mean value of zero). However, if two identical stimulations are presented, separated by an interval δt, the conditional probability of measuring our psychological variable twice can be shown to be (there is no room here, unfortunately, for the corresponding mathematical demonstration):

The resulting probability only depends on the interval δt (chosen by the experimenter), and thus does not require knowledge of the exact oscillatory phase on every trial. This means that, using double stimulations and double-detection functions, one can derive psychophysically the rate ω of the periodic process, and its modulation amplitude a (Figure 1).

In practice, unfortunately, this method is not as easy to apply as it sounds. One important caveat was already mentioned by Latour: the inter-stimulus delay must be chosen to be long enough to avoid direct interactions between the two stimuli (e.g., masking, apparent motion, etc.). This is because the mathematical derivation of Eq. 2 assumes independence between the detection probabilities for the two stimuli. To ensure that this condition is satisfied, the stimuli should be separated by a few 100 ms (corresponding to the integration period for masking or apparent motion) on the other hand, this implies that several oscillatory cycles will occur between the two stimuli, and many external factors (e.g., phase slip, reset) can thus interfere and decrease the measured oscillation. This in turn suggests that the method may be more appropriate for revealing low-frequency oscillations than high-frequency ones (e.g., gamma). Another important limitation is that the magnitude of the measured oscillation in the double-detection function (2) is squared, compared to the magnitude of the original perceptual oscillation. Although this is not a problem if the perceptual oscillation is strong (i.e., the square of a number close to 1 is also close to 1), it can become troublesome when the original perceptual oscillation is already subtle (e.g., for a 20% modulation of the visual threshold, one can only expect a 4% modulation in the double-detection function). Altogether, these limitations may explain why Latour’s results have, so far, not been replicated or extended.

Temporal Aliasing: The Wagon Wheel Illusion

Engineers know that any signal sampled by a discrete or periodic system is subject to potential 𠇊liasing” artifacts (Figure 2): when the sampling resolution is lower than a critical limit (the “Nyquist rate”) the signal can be interpreted erroneously. This is true, for instance, when a signal is sampled in the temporal domain (Figure 2A). When this signal is a periodic visual pattern in motion, aliasing produces a phenomenon called the “wagon wheel illusion” (Figure 2B): the pattern appears to move in the wrong direction. This is often observed in movies or on television, due to the discrete sampling of video cameras (generally around 24 frames per second). Interestingly, a similar perceptual effect has also been reported under continuous conditions of illumination, e.g., daylight (Schouten, 1967 Purves et al., 1996 VanRullen et al., 2005b). In this case, however, because no artificial device is imposing a periodic sampling of the stimulus, the logical conclusion is that the illusion must be caused by aliasing within the visual system itself. Thus, this 𠇌ontinuous version of the wagon wheel illusion” (or 𠇌-WWI”) has been interpreted as evidence that the visual system samples motion information periodically (Purves et al., 1996 Andrews et al., 2005 Simpson et al., 2005 VanRullen et al., 2005b).

Figure 2. Temporal aliasing. (A) Concept. Sampling a temporal signal using too low a sampling rate leads to systematic errors about the signal, known as 𠇊liasing errors.” Here, the original signal is periodic, but its frequency is too high compared with the system’s sampling rate (i.e., it is above the system’s “Nyquist” frequency, defined as half of its sampling rate). As a result, the successive samples skip ahead by almost one full period of the original oscillation: instead of normally going through the angular phases of zero, π/2, π, 3π/2, and back to zero, the successive samples describe the opposite pattern, i.e., zero, 3π/2, π, π/2, and so on. The aliasing is particularly clear in the complex domain (right), where the representations of the original and estimated signals describe circles in opposite directions. (B) The wagon wheel illusion. When the original signal is a periodically moving stimulus, temporal aliasing transpires as a reversal of the perceived direction. This wagon wheel illusion is typically observed in movies due to the discrete sampling of video cameras. The continuous version of this wagon wheel illusion (c-WWI) differs in that it occurs when directly observing the moving pattern in continuous illumination in this case, it has been proposed that reversed motion indicates a form of discrete sampling occurring in the visual system itself.

There are many arguments in favor of this 𠇍iscrete” interpretation of the c-WWI. First, the illusion occurs in a very specific range of stimulus temporal frequencies, compatible with a discrete sampling rate of approximately 13 Hz (Purves et al., 1996 Simpson et al., 2005 VanRullen et al., 2005b). As expected according to the discrete sampling idea, this critical frequency remains unchanged when manipulating the spatial frequency of the stimulus (Simpson et al., 2005 VanRullen et al., 2005b) or the type of motion employed, i.e., rotation vs. translation motion, or first-order vs. second-order motion (VanRullen et al., 2005b). EEG correlates of the perceived illusion confirm these psychophysical findings and point to an oscillation in the same frequency range around 13 Hz (VanRullen et al., 2006 Piantoni et al., 2010). Altogether, these data suggest that (at least part of) the motion perception system proceeds by sampling its inputs periodically, at a rate of 13 samples per second.

There are, of course, alternative accounts of the phenomenon. First, it is noteworthy that the illusion is not instantaneous, and does not last indefinitely, but it is instead a bistable phenomenon, which comes and goes with stochastic dynamics such a process implies the existence of a competition between neural mechanisms supporting the veridical and the erroneous motion directions (Blake and Logothetis, 2002). Within this context, the debate centers around the source of the erroneous signals: some authors have argued that they arise not from periodic sampling and aliasing, but from spurious activation in low-level motion detectors (Kline et al., 2004 Holcombe et al., 2005) or from motion adaptation processes that would momentarily prevail over the steady input (Holcombe and Seizova-Cajic, 2008 Kline and Eagleman, 2008). We find these accounts unsatisfactory, because they do not seem compatible with the following experimental observations: (i) the illusion is always maximal around the same temporal frequency, whereas the temporal frequency tuning of low-level motion detectors differs widely between first and second-order motion (Hutchinson and Ledgeway, 2006) (ii) not only the magnitude of the illusion, but also its spatial extent and its optimal temporal frequency – which we take as a reflection of the system’s periodic sampling rate – are all affected by attentional manipulations (VanRullen et al., 2005b VanRullen, 2006 Macdonald et al., under review) in contrast, the amount of motion adaptation could be assumed to vary with attentional load (Chaudhuri, 1990 Rezec et al., 2004), but probably not the frequency tuning of low-level motion detectors (iii) motion adaptation itself can be dissociated from the wagon wheel illusion using appropriate stimulus manipulations for example, varying stimulus contrast or eccentricity can make the motion aftereffects (both static and dynamic versions) decrease while the c-WWI magnitude increases, and vice-versa (VanRullen, 2007) (iv) finally, the brain regions responsible for the c-WWI effect, repeatedly identified in the right parietal lobe (VanRullen et al., 2006, 2008 Reddy et al., 2011), point to a higher-level cause than the mere adaptation of low-level motion detectors.

Disentangling the neural mechanisms of the continuous wagon wheel illusion could be (and actually, is) the topic of an entirely separate review (VanRullen et al., 2010). To summarize, our current view is that the reversed motion signals most likely originate as a form of aliasing due to periodic temporal sampling by attention-based motion perception systems, at a rate of � Hz the bistability of the illusion is due to the simultaneous encoding of the veridical motion direction by other (low-level, or 𠇏irst-order”) motion perception systems. The debate, however, is as yet far from settled. At any rate, this phenomenon illustrates the potential value of temporal aliasing as a paradigm to probe the discrete nature of sensory perception.

Other Forms of Temporal Aliasing

The sampling frequency evidenced with the c-WWI paradigm may be specific to attention-based motion perception mechanisms. It is only natural to try and extend the temporal aliasing methodology to perception of other types of motion, to perception of visual features other than motion or to perception in sensory modalities other than vision. If evidence for temporal aliasing could be found in these cases, the corresponding sampling frequencies may then be compared to one another and further inform our understanding of discrete perception. Is there a single rhythm, a central (attentional) clock that samples all sensory inputs? Or is information from any single channel of sensory information read out periodically at its own rate, independently from other channels? While the first proposition reflects the understanding that most have of the theory of discrete perception (Kline and Eagleman, 2008), the latter may be a much more faithful description of reality additionally, the sampling rate for a given channel may vary depending on task demands and attentional state, further blurring intrinsic periodicities.

The simple generic paradigm which we advocate to probe the brain for temporal aliasing is as follows. Human observers are presented with a periodic time-varying input which physically evolves in an unambiguously defined direction they are asked to make a two-alternative forced choice judgment on the direction of evolution of this input, whose frequency is systematically varied by the experimenter across trials. A consistent report of the wrong direction at a given input frequency may be taken as a behavioral correlate of temporal aliasing, and the frequencies at which this occurs inform the experimenter about the underlying sampling frequency of the brain for this input.

Two main hurdles may be encountered in applying this paradigm. The first one lies in what should be considered a 𠇌onsistent” report of the wrong direction. Clearly, for an engineered sampling system, one can find input frequencies at which the system will always output the wrong direction. For a human observer, however, several factors could be expected to lower the tendency to report the wrong direction, even at frequencies that are subject to aliasing: measurement noise, the potential variability of the hypothetical sampling frequency over the duration of the experiment, and most importantly, the potential presence of alternate sources of information (as in the c-WWI example, where competition occurs between low-level and attention-based motion systems). In the end, even if aliasing occurs, it may not manifest as a clear and reliable percept of the erroneous direction, but rather as a subtle increase of the probability of reporting the wrong direction at certain frequencies. Recently, we proposed a method to evaluate the presence of aliasing in psychometric functions, based on model fitting (Dubois and VanRullen, 2009). (A write-up of this method and associated findings can be accessed at http://www.cerco.ups-tlse.fr/∼rufin/assc09/). Results of a 2-AFC motion discrimination experiment were well explained by considering two motion sensing systems, one that functions continuously and one that takes periodic samples of position to infer motion. These two systems each give rise to predictable psychometric functions with few parameters, whose respective contributions to performance can be inferred by model fitting. Evidence for a significant contribution of a discrete process sampling at 13 Hz was found – thus confirming our previous conclusions from the c-WWI phenomenon. Furthermore, the discrete process contributed more strongly to the perceptual outcome when motion was presented inter-ocularly, than binocularly this is compatible with our postulate that discrete sampling in the c-WWI is a high-level effect, since inter-ocular motion perception depends on higher-level motion perception systems (Lu and Sperling, 2001).

The second pitfall is that the temporal resolution for discriminating the direction of the time-varying input under consideration should be at least as good as the hypothesized sampling frequency. If the psychometric function is already at chance at the frequency where aliasing is expected to take place, this aliasing will simply not be observed – whether the perceptual process relies on periodic sampling or not. Our lab learned this the hard way: many of the features that we experimented with so far, besides luminance and contrast-defined motion, can only be discriminated at low-temporal frequencies – they belong to Holcombe’s “seeing slow” category (Holcombe, 2009). For example, we hypothesized that motion stimuli designed to be invisible to the first-order motion perception system, such as stereo-defined motion (Tseng et al., 2006), would yield maximal aliasing as there is no other motion perception system offering competing information. Unfortunately, these stimuli do not yield a clear percept at temporal frequencies beyond 3𠄴 Hz, meaning that any aliasing occurring at higher frequencies would have escaped our notice. The “motion standstill” phenomenon reported by Lu and colleagues (Lu et al., 1999 Tseng et al. 2006) with similar stimuli at frequencies around 5 Hz remains a potential manifestation of temporal aliasing, although we have not satisfactorily replicated it in our lab yet. We also hypothesized that binding of spatially distinct feature conjunctions, such as color and motion, could rely on sequential attentional sampling of the two features (Moutoussis and Zeki, 1997), and should thus be subject to aliasing. Again, we were disappointed to find that performance was at chance level at presentation rates higher than 3𠄴 Hz (Holcombe, 2009), precluding further analysis. We also attempted to adapt the wagon wheel phenomenon to the auditory modality. Here, perception of sound source motion (e.g., a sound rotating around the listener) also appeared limited to about 3 Hz (Feron et al., 2010). We then reasoned that frequency, rather than spatial position, was the primary feature for auditory perception, and designed periodic stimuli that moved in particular directions in the frequency domain – so-called Shepard or Risset sequences (Shepard, 1964). Again, we found that the direction of these periodic frequency sweeps could not be identified when the temporal frequency of presentation was increased beyond 3𠄴 Hz.

In sum, although temporal aliasing is, in principle, a choice paradigm to probe the rhythms of perception, our attempts so far at applying this technique to other perceptual domains than motion (the c-WWI) have been foiled by the strict temporal limits of the corresponding sensory systems. What we can safely conclude is that, if discrete sampling exists in any of these other perceptual domains, it will be at a sampling rate above 3𠄴 Hz. We have not exhausted all possible stimuli and encourage others to conduct their own experiments. There are two faces to the challenge: finding stimuli that the brain “sees fast” enough, and using an appropriate model to infer the contribution of periodic sampling to the psychometric performance (in case other sources of information and sizeable variability across trials should blur the influence of discrete processes).


Alpha Brain Waves: What do they do?

Most people associate alpha brain waves with calmness and relaxing with our eyes closed. Others believe that they are a natural cure for anxiety and stress. Some researchers even think that alpha activity at 10 Hz may be linked to states of relaxed “peak performance.” In any regard, below are some effects of dominant alpha activity.

  • Balanced mood: Individuals that are depressed as a result of being “over-stressed” could feel an antidepressant effect when their alpha activity increases. Alpha activity – specifically in the right hemisphere of the brain has been shown to boost mood. Some people also experience a more “balanced” mood when they increase their alpha waves.
  • Calmness: Most people associate the alpha range with feelings of calmness. People that are stressed have a diminished ability to produce these waves. When we are at our calmest with our eyes closed and are idly relaxing, this is when we experience alpha as a dominant brain wave state.
  • Creativity: It has been hypothesized that alpha activity is linked to increases in creativity. Since it is produced predominantly by the right hemisphere, it is thought that it may enhance artistic ability as well as creative problem solving skills. Sometimes when we relax, we experience “aha” moments – this is a result of the alpha wave. It is creative, relaxed, problem solving which gives us a different perspective.
  • Daydreaming: If you close your eyes or daydream a lot, this may be evidence of alpha increases. Most adults tend to be very focused and task-oriented. Alpha is more common in younger children that tend to daydream a lot and have difficulties focusing. In some cases, it is even linked to ADHD if the increased alpha is accompanied by other slow wave activity (e.g. theta).
  • Decreased focus: During the alpha brain wave state, many individuals have poorer focus and concentration. It has been shown that the greater the amount of alpha activity, the more likely someone is to make a mistake. Studies have shown that when someone makes a mistake, alpha activity increases an average of 25%. In other words, the person is on “auto pilot” or too “idle” to perform a certain task. When alpha decreases, attention and focus increase. Therefore, alpha is not ideal for critical thinking and/or detail-oriented, technical work.
  • Flow state of mind: It has been thought that the 10 Hz alpha wave is key in a “flow” state of consciousness. A state of flow is characterized by being calm and focused at the same time. In other words, the saying “mind like water” holds true. It is able to go with the flow without getting overly aroused or being “idle.” This is considered a happy medium between the fast paced beta waves and the drowsy theta waves.
  • Immune system: Some have hypothesized that our immune system benefits from increasing alpha waves. This in part has to do with the fact that relaxation can help our bodies recover from the toxic effect of stress. It is known that stress can cause all sorts of health problems, so it is no wonder that relaxing (in the alpha range) helps boost the immune system.
  • Peak performance: Despite the fact that the 40 Hz gamma wave has been linked to peak performance, so has the 10 Hz alpha wave. It is hypothesized that a synchronized 10 Hz rhythm across both hemispheres may play a role in helping people achieve a relaxed state of peak performance. There have been studies conducted involving basketball players and golfers and when they missed a shot (basketball) and/or hit a bad shot (golf), they experienced spikes of beta activity. When they sank a free throw or hit a good shot, they maintained alpha activity.
  • Positive thinking: This isn’t the high-energy, excitement-type, positive thinking of the beta range. However, when people experience alpha increases it is linked to having a more optimistic outlook on life. People tend to be calm and think fairly positive with dominant alpha. Think of this as the opposite of rapid-negative stressful or angry thoughts.
  • Problem solving: For thinking outside the box, some would argue that the alpha range is what helps. Individuals sometimes get so stressed out that all they do is keep thinking and ruminating about possible solutions, but can never solve their problem. Sometimes it helps to simply slow the mind down and then the solution appears easily and naturally in the alpha state.
  • Relaxation: Anytime you feel deeply relaxed, you are experiencing alpha brain waves. Think of times right before you go to bed and transition into sleep. Your eyes are closed, you are relaxing and you experience a sense of calmness. If you like to lay out in the sun and tan and experience a sense of relaxation while lying with your eyes closed, this also provides an alpha boost.
  • Serotonin: When we relax, our body is able to naturally produce more serotonin. It is hypothesized that alpha may release more serotonin and thus increase our ability to relax, stay calm, and ward off stress.
  • Slower visual acuity: Since alpha activity decreases when people are fully awake with eyes open, it is linked with slower visual acuity. In other words, visual processing speed while you are awake is negatively affected by increases in alpha.
  • Super learning: Some have argued that increases in alpha contribute to a state of “super learning.” Meaning more of our brain is able to absorb information and thus we learn better. I tend to disagree with this hypothesis based on the fact that alpha activity tends to decrease focus. There may be some degree of truth to this for certain types of learning if accompanied by appropriate beta.
  • Visualization: If you like to close your eyes and visualize, this is the brain wave that you will experience. Anytime you close your eyes, are relaxed, and visualizing internally (e.g. mind’s eye), you will come to learn what the alpha range feels like.

Note: Alcohol and drug abuse can significantly reduce alpha frequency and amplitude. Thus decreasing the benefits to be had from this particular range. In part this may be why individuals that abuse drugs and alcohol have a difficult time relaxing once the “high” wears off.

Alpha Brain Waves Research

Discovery: German neurologist Hans Berger was the first individual to discover alpha waves. He did this by measuring electrical activity in the brain’s of hospital patients with skull damage. He documented these waves along with beta activity. He found that when alpha waves decrease and beta activity becomes dominant, we are fully awake. Since he discovered this wave, they have been referred to as “Berger’s Wave.”

Biofeedback: This is a technique that involves helping people naturally train their brains to produce certain brain waves. The idea behind it is that you receive “feedback” when your brain increases activity of a certain wave. The goal is to eventually learn how to consciously produce this type of brain wave activity without feedback after multiple training sessions.

Seizure resistance: Some hypothesize that increasing the amplitude and dominance of alpha brain waves can help individuals that are seizure-prone. Research in cats indicates that if alpha activity is trained, they have a greater resistance to seizures. Whether this holds true for humans is somewhat controversial.

Stress relief: Research has shown that individuals that are stressed out tend to produce an overabundance of beta waves. Most of these individuals may experience what is called “alpha blocking” or blocked alpha activity. Meaning the alpha activity is so low, that it doesn’t allow the individual to lower their level of arousal. In order to decrease stress, it has been found that training alpha via biofeedback has had some positive results.

Related Posts:

Alpha-heavy states don’t necessarily diminish focus. It sounds like you’re speaking from your own experience. I do technical, detailed oriented work — often over a hundred hours a week — and rarely lack concentration.

I use a technique called “open-eyed alpha” the entire time I work, and it has been a blessing for me. It is discussed in the book The High-Performance Mind by Anna Wise. I think your article is misleading people away from using this beautiful state of mind for work that requires high levels of concentration and detail.

That’s what I was thinking… more than that: less stress = more focus.

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Unfortunately the term "wave" is ambiguous in neuroscience. What you are referring to, alpha waves, only means that neuronal activity tends to oscillate at about 10Hz. Neural oscillations are a widespread phenomena occurring in all brain areas. But it doesn't necessarily mean these waves travels. There are examples of traveling waves, whose speed can vary a lot (from $10^<-1>$ to $10^<-5> m.s^<-1>$ depending on the studies). Oscillations across brain areas tends to be synchronized during a task (so one could argue, infinite speed), and within area activity tends to hold a specific phase relationship which varies spatially (as if a wave was "frozen", in which case its speed would be 0). Traveling waves and neural synchrony/coherence might be different phenomena implementing different functions. It is still unclear what brain oscillations are for, or even if they have any purpose at all. Below are some reviews on these topics.

Ermentrout, G.B. and Kleinfeld, D. (2001) Traveling electrical waves in cortex: insights from phase dynamics and speculation on a computational role. Neuron 29, 33–44

Sato, T. K., Nauhaus, I., & Carandini, M. (2012). Traveling waves in visual cortex. Neuron, 75(2), 218-229.

Engel, A. K., Fries, P., & Singer, W. (2001). Dynamic predictions: oscillations and synchrony in top–down processing. Nature Reviews Neuroscience, 2(10), 704-716.

Fries, P. (2005). A mechanism for cognitive dynamics: neuronal communication through neuronal coherence. Trends in cognitive sciences, 9(10), 474-480.


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Keywords: intra- and inter-brain coupling, brain-instrument coupling, graph-theoretical approach, EEG hyperscanning, phase synchronization, extended hyper-brain networks, social interaction

Citation: Müller V and Lindenberger U (2019) Dynamic Orchestration of Brains and Instruments During Free Guitar Improvisation. Front. Integr. Neurosci. 13:50. doi: 10.3389/fnint.2019.00050

Received: 15 May 2019 Accepted: 20 August 2019
Published: 04 September 2019.

Assal Habibi, University of Southern California, United States

Donald Glowinski, Université de Genève, Switzerland
Shoji Tanaka, Sophia University, Japan

Copyright © 2019 Müller and Lindenberger. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


Brain Mechanisms and Reading Remediation: More Questions Than Answers

Dyslexia is generally diagnosed in childhood and is characterised by poor literacy skills with associated phonological and perceptual problems. Compensated dyslexic readers are adult readers who have a documented history of childhood dyslexia but as adults can read and comprehend written text well. Uncompensated dyslexic readers are adults who similarly have a documented history of reading impairment but remain functionally reading-impaired all their lives. There is little understanding of the neurophysiological basis for how or why some children become compensated, while others do not, and there is little knowledge about neurophysiological changes that occur with remedial programs for reading disability. This paper will review research looking at reading remediation, particularly in the context of the underlying neurophysiology.

1. Brain Mechanisms and Reading Remediation: More Questions Than Answers

Approximately 10% of children suffer a specific reading difficulty such as dyslexia [1]. Despite some residual deficits in core skills, (e.g., phonological processing), some of these individuals will ultimately learn good reading skills as adults (become compensated), while others will remain functionally reading-impaired all their lives (uncompensated) [2, 3]. On the last page of her seminal book on dyslexia, Snowling [2] concludes “The research agenda for the next decade must certainly be directed to the treatment resisters,… those poor readers who do not respond well to current intervention programs.” Yet despite the huge personal and social costs of dyslexia, virtually nothing is known about how or why some young dyslexic readers ultimately learn to read, while others remain functionally dyslexic their whole lives. The aim of the current review is to consider some of the research on reading remediation, particularly within the context of underlying brain mechanisms.

A number of reviews have been conducted regarding the functional organisation of the normal reading network in the brain (refer to [4] for a recent review) and there is some research that has looked at compensatory brain mechanisms that develop as poor readers develop good reading skills [5]. However, a full understanding of how cortical networks develop in response to acquiring reading skills requires not only an understanding of what those networks look like but also an understanding of how those networks are functionally connected. Functional connectivity in language is well documented (e.g., [6]), and it is common for researchers to draw on this literature to also describe reading networks however, cortical networks in reading are quite different from cortical networks in language. Our ability to acquire and use language is entirely different from our ability to learn to read and very different from the processes involved in reading fluently. The primary difference is that language acquisition and use are likely to be innate, whereas reading skills are learned. This is an important difference because the former assumes the existence of a naturally occurring underlying biological substrate, whereas the latter does not. When reading the brain has had to learn to recruit resources from quite disparate parts of the brain that have likely evolved to perform quite unique functions. Therefore, reading is a learned process that is quite different from the acquisition of language which is more like fine tuning preexisting neural circuitry.

It is assumed that lexical representations are encapsulated as specific neural traces, and the various components of the reading process, such as pattern recognition, and memory, phonological decoding, are similarly represented as neural responses in the brain. When reading, the brain has had to learn to recruit resources from quite disparate parts of the brain that have likely evolved to perform quite unique functions. Thus, with all learned skills, there are some individuals who are good at the skill, many who are average, and a number who are very poor. In reading, the very poor category constitutes the group of children that we consider to be dyslexic.

The following review is designed to consider theoretical models for ways in which the brain might compensate for dyslexia and to evaluate some of the scientific knowledge in this area. The aim is to broaden and clarify our understanding of the dynamics and cortical plasticity involved in acquiring reading skills. Then at the practical level, if we can understand how compensated dyslexics read, we can make predictions about how they overcame their disability and provide a strong scientific foundation from which to later formulate strategic intervention programs to facilitate successful reading skills in all dyslexic readers. Finally, an understanding of these concepts has broader implications for neuroscience, by contributing to our understanding of neural plasticity in large, distributed cognitive networks.

2. Dyslexia Sometimes Persists into Adulthood

Dyslexia is not just a childhood problem. Many adults who have suffered from developmental dyslexia as children never develop good reading skills [7]. Those who do compensate for their reading difficulty and become good readers invariably suffer from residual difficulties such as poor spelling and poor phonological coding [8]. Uncompensated dyslexic readers never develop functional reading skills despite normal exposure to reading instruction, and in some cases many hours of reading remediation. These readers are the treatment resisters [2].

Compensated dyslexic readers—also sometimes referred to as resilient readers [9]—are adult readers who have a documented history of childhood dyslexia but as adults can comprehend written text well, in spite of residual difficulties in low-level decoding skills [3, 10–12]. These are adult readers who are functionally sound readers and are quite often high achievers, despite having a history of childhood reading difficulties. Little is known about how dyslexic children go on to develop reasonable reading skills much of the literature suggests that such children develop idiosyncratic behavioural coping strategies as a consequence of high levels of motivation and educational opportunities (e.g., [13]). Nevertheless, the question of interest here is, neurophysiologically, how is it that a dyslexic child has learned to read? Has the brain adapted to the reading difficulty and slowly developed a normal reading network? Or has the brain developed a new, compensatory network to bypass functional impairment?

Conversely, there are adult dyslexic readers who similarly have a documented history of childhood reading impairment, but unlike compensated dyslexics, they remain functionally reading impaired all their lives. Such adults will still score well below average on standard adult reading tests, despite normal adult IQ and—again—no comorbid difficulties. These are referred to as uncompensated dyslexic adults (or sometimes adults with persistent dyslexia), as their poor reading and spelling skills have persisted into adulthood.

Neurophysiologically, compensated and uncompensated dyslexic adults are likely to be quite distinct: in one case the adult brain has adapted to a childhood, developmental impairment to learn the skill of reading, whereas in the other, the brain has not developed a functional reading network at all or has developed a reading network that remains dysfunctional, supporting only rudimentary reading skills. Few studies have explored the neurophysiological distinction between the two groups. More importantly, there is little consistent convention in the scientific literature of the use of either group, such that many—if not most—studies using adult dyslexics fail to report whether the participants are compensated or not. Clearly, this can be a significant problem given that the neurophysiological profiles of each of the groups are likely to be quite different, making it difficult to make unambiguous statements about cortical functionality in adult dyslexics.

As an aside, it is worth noting that both compensated and uncompensated dyslexia in adults are different from acquired dyslexia which is also usually associated with adults. In the former, poor reading skills have been a life-long affliction. However, in the latter, the adult has had a history of normal reading as a child, but reading difficulties occur as a consequence of specific brain injury such as stroke or an accident. Here then, neurophysiologically, reading difficulties are reasonably straight forward and easily identified as a consequence of specific damage to a specific part of the cortex. Referred to as Alexia, it seems to be associated with left occipital lesions with right homonymous hemenopia (e.g., [14–17]), although pure alexia has also been associated with thalamic impairment [18], without homonymous hemenopia (e.g., [19, 20]), and closer to the inferior temporal areas [21]. The most common understanding of the disorder is as a disconnection syndrome such that bilateral visual stimuli fail to reach the angular gyrus—as described by Déjerine back in 1891 [22].

3. The Neurophysiology of Component Behavioural Mechanisms in Compensated Dyslexia

In order to understand the cortical maps involved in reading and dyslexia, it is important to become familiar with the component neurocognitive mechanisms of the network. There are a number of behavioural mechanisms that have been implicated in dyslexia.

3.1. Sensory Processing

There is a large collection of evidence implicating early sensory coding difficulties in dyslexia in the visual domain [23–33] and auditory processing [34–37]. However, the degree to which sensory coding persists into adulthood and can characterise compensated and uncompensated adult readers remains unclear. Birch and Chase [38] failed to find differences on measures of visual processing such as contrast sensitivity and sine wave detection, although they used static stimuli. Hämäläinen et al. [39] measured rise times in amplitude modulation in auditory processing. Again there were no systematic differences between compensated and uncompensated groups, but there was a correlation between phonological ability and sensitivity to rise times, which may explain the lack of difference between the groups, as poor phonological sensitivity in dyslexic readers frequently persists into adulthood irrespective of reading skill [12, 40, 41].

3.2. Phonological and Orthographic Processing

Despite the acquisition of normal functional reading skills, compensated dyslexic readers frequently maintain residual problems in phonemic awareness [3, 10, 12]. Given that regular words can be read by either conversion of graphemes to phonemes, or by the recognition of the orthographic form of a word, it has thus been suggested that normal reading skills in compensated readers are acquired as a result of dependence on whole-word orthographic skills [42]. Neurophysiologically, compensated dyslexic readers engage in different cognitive networks when processing tasks that require phonological manipulations, such as less activation for compensated readers in the insula, left premotor, and Wernicke’s regions [43]. A study directly comparing normal, compensated, and uncompensated readers on a nonword rhyming task found that both dyslexic groups demonstrated less activation in superior-temporal and occipitotemporal regions with overactivation in right inferior frontal areas. Compensated readers differed from both groups by activating right superior frontal and mid-temporal regions [44]. Ingvar et al. [45] investigated differences between normal and compensated dyslexic readers in a single word reading task, demonstrating that compensated readers showed an increased activation in right temporal regions.

3.3. Semantic Encoding

There is substantial evidence for a dissociation between word decoding and semantic processing or comprehension [46]. Some evidence suggests that both adult dyslexic readers [47] and children with dyslexia [48, 49] may rely more heavily on the influence of semantic context when reading, with research implicating stronger activation in the inferior frontal regions when processing semantic and contextual information [50]. Similarly, underengagement of the dorsal and ventral aspects of the left posterior cortex has been demonstrated in poor readers, but with a disproportionately higher activation in the inferior frontal gyrus (IFG), suggested to be important in the integration of semantic associations [47, 51, 52]. Moreover, distributed models of reading comprehension have suggested that poor low-level decoding skills may be compensated by more sophisticated higher-level skills such as semantic context [53]. Again therefore, while some studies have suggested that compensated readers may recruit from different areas of the network to enable greater reliance on semantic encoding, few studies have investigated this in the context of adult reading skills ranging from compensated to uncompensated. Thus, we do not know if this is a successful adaptation acquired by better readers or a general adaptation arising from reading failure.

4. How Do Poor Readers Become Compensated?

Neurophysiologically, there are a number of ways in which a child with dyslexia could develop functionally adequate reading skills. There is little evidence supporting any position, and the following are logical derivations from our knowledge of existing reading networks and how complex cortical connectivity occurs. Thus the following are hypotheses, exploring these should be the focus of the next generation of research.

One possibility is that dyslexic readers ultimately learn good reading skills (become compensated readers) by eventually developing the cortical connections required for normal reading networks rather than developing uniquely different cortical connections. The assumption here then is that normal reading is associated with the development of consistent cortical networks that are observable over most—if not all—readers. That there is a normal reading network that is consistent over most readers has been demonstrated elsewhere. For example, we demonstrated [54] that unique areas of the brain synchronised at 8–12 Hz (alpha range) in response to different reading requirements. In this study, participants were presented with continuous text presented at rates that made comprehension easy, effortful, very difficult (only the general gist of the story was apparent), or impossible (random text). Left hemisphere cortical activations consistent with a reading network were activated at 8–12 Hz in a dynamic way that reflected the cognitive requirements of the reading task and were consistent over participants.

The scenario that adult dyslexics simply develop the normal networks eventually points to the possibility that dyslexia occurs as a consequence of slow or delayed development of dedicated reading networks. If this is the case, then a logical biological substrate would be that this occurs as a result of poor or slower neuronal maturation. This is consistent with Wright and Zecker [55] who found age-dependent differences in auditory functioning for dyslexic children, and they suggested that slower neurological development may be further arrested with the onset of puberty. Certainly, it is well accepted by neurophysiologists that neuronal plasticity decreases in older animals that have reached sexual maturity [56]. Moreover, McArthur and Bishop [57–60] have put forth the Maturational Hypothesis, where dyslexia may be partially caused by delays in the development of cortical connectivity rather than a specific deficit in the network itself. Thus, compensation in this neurophysiological scenario occurs because a normal, predictable neural network eventually develops as the neural connections strengthen.

Presumably then, persistent adult dyslexia is a consequence of the normal network failing to reach full maturity. This proposal points to clear empirical predictions: using neuroimaging techniques, it should be possible to demonstrate that all adult readers, both normal and dyslexic, should show activation in the same basic regions in the same order and demonstrate the same connectivity. Poorer adult readers would show weaker activation and/or temporally delayed (slower) signals [61]. This would show up as a positive correlation for reading ability with activation and a negative correlation between latency and reading ability.

Another possibility is that dyslexic readers become compensated readers because they develop new, alternative cortical connections to support reading that are unique to the individual. Here then, there is a normal reading network observable in most readers, but for some reason in dyslexic readers this network fails to develop, but with constant exposure to reading, the individual develops a bypass model to support reading skills. Because learning the skill of reading ultimately requires the development of new cortical networks for all readers, there is no specific reason that the brain must solve the same problem (learning to read) in exactly the same way for all people. It is not unreasonable to expect that if the cortical connections that develop to support reading are unsuitable for whatever reason, then through remediation, the brain could develop entirely different connections to support the same behavioural outcome (reading). There is some support for this proposition as well, with the evidence that some dyslexic readers activate unique areas when reading compared to normal readers. For example, there is a tendency for poor readers to demonstrate abnormal activations in the left temporoparietal regions of the brain during language processing and reading tasks [51, 62]. Pugh et al. [47] have suggested that when reading, poor readers engage frontal sites, such as the IFG and prefrontal dorsolateral sites [52, 63, 64], more so than normal readers. Similarly, Salmelin et al. [61] and Brunswick et al. [51] showed greater activation of inferior precentral gyrus (Broca’s area) in poor readers when processing visually presented words with post-200msec responses. Thus, for some reason (e.g., poor sensory input), the normal reading network cannot be used and neuronal patterns of activation attempt to bypass the deficient mechanisms to meet reading requirements. Indeed, there is also evidence of white-matter connectivity differences between dyslexic and normal readers [65]. Furthermore, Horwitz et al. [66] demonstrated that compared to control adult readers, compensated adult readers failed to activate the same brain areas during reading tasks. In this study they measured the functional correlation of activity across the brain with the left angular gyrus to reading irregular and nonwords. They demonstrated that normal readers generated an activity pattern that included visual association areas and Wernicke’s area however, such a pattern of activity was absent in the compensated dyslexics. See also Wimmer et al. [67] who demonstrated different patterns of activity in compensated dyslexic adults compared to normal readers, particularly in visual, occipital areas.

If a new, unique reading network develops, then logically there are a number of ways in which this could occur: once the normal reading network is unattainable, there is another common network that is accessed to meet reading requirements, irrespective of the type of damage existing in the normal system. Compensated dyslexic readers simply get better at using this network than uncompensated readers. This would predict that the network dynamics for dyslexic readers would be different from good readers, but consistent within a dyslexic group. Another possibility is that different spatiotemporal maps of activity develop to bypass particular damage. For example, an increase in activity in the IFG may be associated with poor phonological skills as found by Pugh et al. In this scenario, similar neural connections that develop in response to reading would map onto similar behavioural results in component reading skills. Common components of the network (e.g., increased activation in the IFG) in different reading situations would be associated behaviourally with component skills (e.g., poor phonological sensitivity). Compensatory mechanisms may develop in this case with the need for fewer bypass mechanisms or the more efficient use of the mechanisms. In another possibility, dyslexic readers in general develop unique and idiosyncratic neurophysiological mechanisms to read. Here then, compensatory behaviour occurs as a result of the development of more successful connections that is unique to the individual and predicated on other factors such as motivation and individual strategy development. In this scenario, there would be little systematic mapping to behavioural outcomes. Some examples are described in Figure 1.


Individual dyslexic readers (brains A, B, C) may develop their own unique cortical networks, but common deficits map back onto common behavioural deficits. Brain D here is from Kujala et al. [54] representing normal network connectivity when reading.

Thus, there are a number of logically derived possibilities to explain how compensatory mechanisms might develop, and the strength of these possibilities is that they generate clear and testable hypotheses which should be the focus of the next wave of research into dyslexia and reading remediation.

5. Changes in Cortical Connectivity When Dyslexic Readers Learn to Read

Some research has been conducted looking at neurophysiological changes in children after remedial programs. It may be possible to extrapolate from these studies to support one or more of the proposals suggested previously.

Reading improvement in children with dyslexia has been demonstrated to be associated with activity in the left inferior frontal gyrus [68] such that white matter integrity was positively correlated with reading gain. This indicates that those dyslexic children who are most likely to acquire good reading skills are those children who have more extensive connectivity in the left inferior frontal cortex. Thus, connectivity with the right inferior frontal cortex may be an important component in the development of compensatory brain networks in reading. This is consistent with other studies looking at the development of compensatory mechanisms (e.g., [69–72]).

Structural changes in the brain have been well documented as a consequence of reading intervention and typically involve phonological interventions. For example, Eden et al. [70] tested adult dyslexic and nondyslexic readers before, and then after eight weeks of a multisensory, phonological-based reading intervention. They demonstrated an increase in response in the left angular gyrus, and the fusiform/parahippocampal gyrus anterior to the Visual Word Form Area, in response to phonological tasks. Changes in white and grey matter have also been demonstrated: Keller and Just [73] investigated white matter organisation in dyslexic children after 100 hours of phonological reading and spelling instruction. After training, the children performed better at phonological tasks, and this was correlated with an increase in white matter—specifically in left anterior tracts. Indeed, the location of increased white matter was the same area that showed decreased activation in dyslexic readers compared to good readers before intervention. Grey matter changes have also been demonstrated with intervention. Krafnick et al. [74] demonstrated increases in grey matter volume in the left fusiform and precuneus and right hippocampus and cerebellum. Interestingly, the intervention used here was less phonological and was based more heavily on imagery and multisensory processing. Given the multitude of reading intervention possibilities, it remains to be seen whether the type of reading intervention is important for neurophysiological changes in response to reading intervention.

That behavioural change reflects changes in cortical connections is highlighted in a recent paper by Koyama et al. [5]. These authors employed a technique called intrinsic functional MRI (I-fMRI). This technique is unique in that it represents a way of measuring functional connectivity between different brain regions when at rest [75]. By identifying a specific brain region, researchers are able to identify other parts of the brain that demonstrate correlated fluctuations in activity over time. This provides an excellent mechanism for understanding and mapping large-scale cortical interactions that are particularly characteristic of cognitive functioning (refer to [76] for a recent review) because it turns out that many of the neural networks that are activated when an individual engages in a cognitive task, are also active when the brain is at rest. This is a particularly useful technique for investigating cognitive disorders such as dyslexia, because the participant does not need to engage in any reading behaviour, thus avoiding many of the other confounds that plague dyslexia research such as performance motivation, stress, and anxiety. Koyama et al. investigated I-fMRI in dyslexic and control children where the dyslexic children were either dyslexic with no intervention, dyslexic but had experienced reading intervention, or dyslexic and had experienced reading and spelling intervention. The interventions in this case varied from child to child but were predominately language based, and all the remediated children had no demonstrable reading problem by the time of the study. Of the many seeding locations initially identified in the brain, the authors demonstrated that intrinsic functional connectivity between the left intraparietal sulcus and left middle frontal gyrus (BA9) was significantly correlated with reading intervention, with normal readers showing the strongest connectivity, followed by the two intervention groups, with the no-intervention dyslexic group showing little or no connectivity. Similarly, the left fusiform gyrus demonstrated differential functional connectivity between the groups. For example, connectivity strength with frontal areas was higher for the two intervention groups but virtually nonexistent for the control and no-intervention groups. This is a particularly exciting study for demonstrating the efficacy of training techniques. Of particular interest is that reading intervention is not only effective in developing normal connectivity, which supports the Neuronal Maturation hypothesis proposed above, but also in developing compensatory connections in the brain. This has enormous implications for the development of reading interventions.

However, the vital piece of the puzzle that is missing here thus far in how dyslexic readers become compensated readers is the link with cortical frequency dynamics. It is not just where cortical connections are formed in the brain that is important, but how the different parts of the brain communicate that is vital for a full understanding of brain activity in dyslexic remediation. This is particularly important when evaluating different models of compensation because timing between activations at cortical sites may be just as important as the spatial distribution (e.g. [30, 31, 61, 77]).

6. Frequency Dynamics and Cortical Connectivity

Both EEG and MEG measure the synchronous firing of large populations of cells in the cortex. Cells within a given population are said to be firing synchronously when their firing rhythm coincides at a particular frequency, and different firing frequencies are believed to reflect different functional states.

Changes in oscillatory power refer to an increase or decrease in the amplitude of power within specific frequency bands. It is believed to reflect changes in oscillatory dynamics at the local level [78], potentially within a particular cortical area or structure. Event-related synchronisation (ERS) and event-related desynchronisation (ERD) reflect increases or decreases in power, respectively. When a functionally specific subset or population of neurons process incoming information, they will disengage from the larger neuronal set to synchronise and oscillate at a different frequency.

Oscillatory coherence is believed to reflect the transient synchronisation at specific frequencies of disparate areas of the cortex [78], further than 1 cm or so away from each other [79]. This is the situation where large-scale neuronal assemblies across the cortex start talking to each other. While ERS and ERDs are likely to reflect local functionality in specific cortical sites, oscillatory coherence is more likely to reflect complex cognition, where many different parts of the brain need to talk to each other quickly and fluidly such as in reading.

Different cortical areas exhibit rhythmical activity to internal or external input, with characteristic frequency ranges, such as 8–12 Hz (alpha oscillations), 13–24 Hz (beta oscillations), 25–50 Hz (gamma oscillations), and >50 Hz (high gamma oscillations), and spatially distributed components of cerebral networks are assumed to talk via such synchronised neural firing [80]. Cognitive functions are thought to depend on this type of connectivity between large-scale neural networks [81]. Therefore, the frequencies at which populations of neural cells oscillate are believed to be a mechanism by which different regions of the brain communicate, with different sensory and cognitive experiences inducing unique oscillatory signatures. The functional significance of synchronous oscillations has been demonstrated as mediating other cognitive processes, such as memory [82–85], attention and attentional processes (e.g., [86]), face perception (e.g., [87]), and object detection (e.g., [88–90]).

Consistent with other cognitive processes, it is not unreasonable to predict that reading and word recognition rely heavily on the functional states of cortical networks. However, little research has been conducted to look at connectivity in terms of frequency dynamics between different cortical areas in response to intervention. This is a vital piece of the puzzle when it comes to reading intervention and remediating poor readers, as changes in reading skill may be closely associated with changes in the way in which populations of neurons communicate, rather than just changes in spatial maps of activity. Nazari et al. [91], used neurofeedback to train children to decrease delta (1–4 Hz) and theta (4–8 Hz) brain oscillations and to increase beta (15–18) oscillations over eight training sessions. They demonstrated an increase in reading speed and accuracy with neurofeedback training. However, they did not explicitly manipulate reading intervention, and the dependent variable was reading outcome, although they do also report an increase in coherence for theta rhythms. So the logic of this study was that explicitly decreasing delta and theta rhythms and increasing beta rhythms resulted in improved reading outcomes. However, it is unclear what the behavioural changes are in response to and whether the results could have been due to test repetition.

Although little or no research has looked at cortical frequency dynamics in dyslexia remediation, it has recently been investigated in the context of language impairment, which has been reported to have a 50% comorbidity with dyslexia [92]. In this study [93], children with language impairment were given language remediation, some of which is phonologically based. The neurophysiological responses were measured in response to passive listening to tone pairs. They specifically focused on gamma activity in this study and demonstrated an increase in gamma activity and gamma phase locking for the children who had experienced the intervention. Thus, this recent project provides in-principal evidence for the importance of cortical coherence in reading remediation.

7. Conclusion

Understanding the processes by which some dyslexic children come to learn to read as adults provides an enormous resource for understanding the plasticity of complex cognitive networks such as reading, how neurobiological processes can adapt to meet specific cognitive requirements, and how we might better design remedial reading treatment in children to exploit such processes. For some young dyslexic readers it might be better to foster reading skills that already exist rather than attempt to develop functionality in brain mechanisms which are less responsive. Demonstrating successful cortical plasticity in compensated dyslexic adults addresses questions regarding whether treatment intervention should target the relative strengths of the dyslexic reader rather than persisting in standard intervention practices. Such research also has important consequences for how we teach reading identifying intrinsic functional components of the cortical reading network would provide vital information to inform current debates regarding the relative importance of whole-word versus phonological decoding in reading instruction. By identifying in compensated adults, those components of the neural reading network that are intrinsic to the reading process, such research would provide a basis for strategies for the early detection of dyslexia, such that deficits in component skills could be identified before children learn to read.

Thus, how dyslexic readers develop neural connectivity to ultimately acquire good reading skills remains speculative however, research such as that of Koyama et al. [94] suggests that the full story may ultimately be a hybrid of the possibilities posited above, with dyslexic readers developing both new and idiosyncratic connections, as well as finally developing normal reading networks. Nevertheless, a major limitation of this recent study is its cross-sectional design. In this it is difficult to make assumptions about the antecedent behaviors that might have occurred before testing. and a lack of ability to control the nature of the intervention. Subsequent studies are now well placed to develop this concept further by using longitudinal designs in which the intervention is targeted and controlled with a larger sample of children.

Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

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Copyright

Copyright © 2014 Kristen Pammer. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Communication between Brain Areas Based on Nested Oscillations

Unraveling how brain regions communicate is crucial for understanding how the brain processes external and internal information. Neuronal oscillations within and across brain regions have been proposed to play a crucial role in this process. Two main hypotheses have been suggested for routing of information based on oscillations, namely communication through coherence and gating by inhibition. Here, we propose a framework unifying these two hypotheses that is based on recent empirical findings. We discuss a theory in which communication between two regions is established by phase synchronization of oscillations at lower frequencies (<25 Hz), which serve as temporal reference frame for information carried by high-frequency activity (>40 Hz). Our framework, consistent with numerous recent empirical findings, posits that cross-frequency interactions are essential for understanding how large-scale cognitive and perceptual networks operate.