Impact of brain parcellation on prediction performance in models of cognition and demographics.

Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.

A mean-field model of glutamate and GABA synaptic dynamics for functional MRS.

Advances in functional magnetic resonance spectroscopy (fMRS) have enabled the quantification of activity-dependent changes in neurotransmitter concentrations in vivo. However, the physiological basis of the large changes in GABA and glutamate observed by fMRS (>10%) over short time scales of less than a minute remain unclear as such changes cannot be accounted for by known synthesis or degradation metabolic pathways. Instead, it has been hypothesized that fMRS detects shifts in neurotransmitter concentrations as they cycle from presynaptic vesicles, where they are largely invisible, to extracellular and cytosolic pools, where they are detectable. The present paper uses a computational modelling approach to demonstrate the viability of this hypothesis. A new mean-field model of the neural mechanisms generating the fMRS signal in a cortical voxel is derived. The proposed macroscopic mean-field model is based on a microscopic description of the neurotransmitter dynamics at the level of the synapse. Specifically, GABA and glutamate are assumed to cycle between three metabolic pools: packaged in the vesicles; active in the synaptic cleft; and undergoing recycling and repackaging in the astrocytic or neuronal cytosol. Computational simulations from the model are used to generate predicted changes in GABA and glutamate concentrations in response to different types of stimuli including pain, vision, and electric current stimulation. The predicted changes in the extracellular and cytosolic pools corresponded to those reported in empirical fMRS data. Furthermore, the model predicts a selective control mechanism of the GABA/glutamate relationship, whereby inhibitory stimulation reduces both neurotransmitters, whereas excitatory stimulation increases glutamate and decreases GABA. The proposed model bridges between neural dynamics and fMRS and provides a mechanistic account for the activity-dependent changes in the glutamate and GABA fMRS signals. Lastly, these results indicate that echo-time may be an important timing parameter that can be leveraged to maximise fMRS experimental outcomes.

Using graph theory as a common language to combine neural structure and function in models of healthy cognitive performance.

Graph theory has been used in cognitive neuroscience to understand how organisational properties of structural and functional brain networks relate to cognitive function. Graph theory may bridge the gap in integration of structural and functional connectivity by introducing common measures of network characteristics. However, the explanatory and predictive value of combined structural and functional graph theory have not been investigated in modelling of cognitive performance of healthy adults. In this work, a Principal Component Regression approach with embedded Step-Wise Regression was used to fit multiple regression models of Executive Function, Self-regulation, Language, Encoding and Sequence Processing with a collection of 20 different graph theoretic measures of structural and functional network organisation used as regressors. The predictive ability of graph theory-based models was compared to that of connectivity-based models. The present work shows that using combinations of graph theory metrics to predict cognition in healthy populations does not produce a consistent benefit relative to making predictions based on structural and functional connectivity values directly.

Combination of structural and functional connectivity explains unique variation in specific domains of cognitive function.

The relationship between structural and functional brain networks has been characterised as complex: the two networks mirror each other and show mutual influence but they also diverge in their organisation. This work explored whether a combination of structural and functional connectivity can improve the fit of regression models of cognitive performance. Principal Component Analysis (PCA) was first applied to cognitive data from the Human Connectome Project to identify latent cognitive components: Executive Function, Self-regulation, Language, Encoding and Sequence Processing. A Principal Component Regression approach with embedded Step-Wise Regression (SWR-PCR) was then used to fit regression models of each cognitive domain based on structural (SC), functional (FC) or combined structural-functional (CC) connectivity. Executive Function was best explained by the CC model. Self-regulation was equally well explained by SC and FC. Language was equally well explained by CC and FC models. Encoding and Sequence Processing were best explained by SC. Evaluation of out-of-sample models' skill via cross-validation showed that SC, FC and CC produced generalisable models of Language performance. SC models performed most effectively at predicting Language performance in unseen sample. Executive Function was most effectively predicted by SC models, followed only by CC models. Self-regulation was only effectively predicted by CC models and Sequence Processing was only effectively predicted by FC models. The present study demonstrates that integrating structural and functional connectivity can help explaining cognitive performance, but that the added explanatory value (in-sample) may be domain-specific and can come at the expense of reduced generalisation performance (out-of-sample).

A tutorial and tool for exploring feature similarity gradients with MRI data.

There has been an increasing interest in examining organisational principles of the cerebral cortex (and subcortical regions) using different MRI features such as structural or functional connectivity. Despite the widespread interest, introductory tutorials on the underlying technique targeted for the novice neuroimager are sparse in the literature. Articles that investigate various "neural gradients" (for example based on region studied "cortical gradients," "cerebellar gradients," "hippocampal gradients" etc … or feature of interest "functional gradients," "cytoarchitectural gradients," "myeloarchitectural gradients" etc …) have increased in popularity. Thus, we believe that it is opportune to discuss what is generally meant by "gradient analysis". We introduce basics concepts in graph theory, such as graphs themselves, the degree matrix, and the adjacency matrix. We discuss how one can think about gradients of feature similarity (the similarity between timeseries in fMRI, or streamline in tractography) using graph theory and we extend this to explore such gradients across the whole MRI scale; from the voxel level to the whole brain level. We proceed to introduce a measure for quantifying the level of similarity in regions of interest. We propose the term "the Vogt-Bailey index" for such quantification to pay homage to our history as a brain mapping community. We run through the techniques on sample datasets including a brain MRI as an example of the application of the techniques on real data and we provide several appendices that expand upon details. To maximise intuition, the appendices contain a didactic example describing how one could use these techniques to solve a particularly pernicious problem that one may encounter at a wedding. Accompanying the article is a tool, available in both MATLAB and Python, that enables readers to perform the analysis described in this article on their own data. We refer readers to the graphical abstract as an overview of the analysis pipeline presented in this work.

Evidence for frequency-dependent cortical plasticity in the human brain.

Frequency-dependent plasticity (FDP) describes adaptation at the synapse in response to stimulation at different frequencies. Its consequence on the structure and function of cortical networks is unknown. We tested whether cortical "resonance," favorable stimulation frequencies at which the sensory cortices respond maximally, influenced the impact of FDP on perception, functional topography, and connectivity of the primary somatosensory cortex using psychophysics and functional imaging (fMRI). We costimulated two digits on the hand synchronously at, above, or below the resonance frequency of the somatosensory cortex, and tested subjects' accuracy and speed on tactile localization before and after costimulation. More errors and slower response times followed costimulation at above- or below-resonance, respectively. Response times were faster after at-resonance costimulation. In the fMRI, the cortical representations of the two digits costimulated above-resonance shifted closer, potentially accounting for the poorer performance. Costimulation at-resonance did not shift the digit regions, but increased the functional coupling between them, potentially accounting for the improved response time. To relate these results to synaptic plasticity, we simulated a network of oscillators incorporating Hebbian learning. Two neighboring patches embedded in a cortical sheet, mimicking the two digit regions, were costimulated at different frequencies. Network activation outside the stimulated patches was greatest at above-resonance frequencies, reproducing the spread of digit representations seen with fMRI. Connection strengths within the patches increased following at-resonance costimulation, reproducing the increased fMRI connectivity. We show that FDP extends to the cortical level and is influenced by cortical resonance.

Cortical Resonance Frequencies Emerge from Network Size and Connectivity.

Neural oscillations occur within a wide frequency range with different brain regions exhibiting resonance-like characteristics at specific points in the spectrum. At the microscopic scale, single neurons possess intrinsic oscillatory properties, such that is not yet known whether cortical resonance is consequential to neural oscillations or an emergent property of the networks that interconnect them. Using a network model of loosely-coupled Wilson-Cowan oscillators to simulate a patch of cortical sheet, we demonstrate that the size of the activated network is inversely related to its resonance frequency. Further analysis of the parameter space indicated that the number of excitatory and inhibitory connections, as well as the average transmission delay between units, determined the resonance frequency. The model predicted that if an activated network within the visual cortex increased in size, the resonance frequency of the network would decrease. We tested this prediction experimentally using the steady-state visual evoked potential where we stimulated the visual cortex with different size stimuli at a range of driving frequencies. We demonstrate that the frequency corresponding to peak steady-state response inversely correlated with the size of the network. We conclude that although individual neurons possess resonance properties, oscillatory activity at the macroscopic level is strongly influenced by network interactions, and that the steady-state response can be used to investigate functional networks.

Hearing silences: human auditory processing relies on preactivation of sound-specific brain activity patterns.

The remarkable capabilities displayed by humans in making sense of an overwhelming amount of sensory information cannot be explained easily if perception is viewed as a passive process. Current theoretical and computational models assume that to achieve meaningful and coherent perception, the human brain must anticipate upcoming stimulation. But how are upcoming stimuli predicted in the brain? We unmasked the neural representation of a prediction by omitting the predicted sensory input. Electrophysiological brain signals showed that when a clear prediction can be formulated, the brain activates a template of its response to the predicted stimulus before it arrives to our senses.

Temporal regularity facilitates higher-order sensory predictions in fast auditory sequences.

Does temporal regularity facilitate prediction in audition? To test this, we recorded human event-related potentials to frequent standard tones and infrequent pitch deviant tones, pre-attentively delivered within isochronous and anisochronous (20% onset jitter) rapid sequences. Deviant tones were repeated, either with high or low probability. Standard tone repetition sets a first-order prediction, which is violated by deviant tone onset, leading to a first-order prediction error response (Mismatch Negativity). The response to highly probable deviant repetitions is, however, attenuated relative to less probable repetitions, reflecting the formation of higher-order sensory predictions. Results show that temporal regularity is required for higher-order predictions, but does not modulate first-order prediction error responses. Inverse solution analyses (Variable Resolution Electrical Tomography; VARETA) localized the error response attenuation to posterior regions of the left superior temporal gyrus. In a control experiment with a slower stimulus rate, we found no evidence for higher-order predictions, and again no effect of temporal information on first-order prediction error. We conclude that: (i) temporal regularity facilitates the establishing of higher-order sensory predictions, i.e. 'knowing what next', in fast auditory sequences; (ii) first-order prediction error relies predominantly on stimulus feature mismatch, reflecting the adaptive fit of fast deviance detection processes.

Test-retest reliability of Bayesian estimations of the effects of stimulation, prior information and individual traits on pain perception.

BACKGROUND: There is inter-individual variability in the influence of different components (e.g. nociception and expectations) on pain perception. Identifying the individual effect of these components could serve for patient stratification, but only if these influences are stable in time. METHODS: In this study, 30 healthy participants underwent a cognitive pain paradigm in which they rated pain after viewing a probabilistic cue informing of forthcoming pain intensity and then receiving electrical stimulation. The trial information was then used in a Bayesian probability model to compute the relative weight each participant put on stimulation, cue, cue uncertainty and trait-like bias. The same procedure was repeated 2 weeks later. Relative and absolute test-retest reliability of all measures was assessed. RESULTS: Intraclass correlation results showed good reliability for the effect of the stimulation (0.83), the effect of the cue (0.75) and the trait-like bias (0.75 and 0.75), and a moderate reliability for the effect of the cue uncertainty (0.55). Absolute reliability measures also supported the temporal stability of the results and indicated that a change in parameters corresponding to a difference in pain ratings ranging between 0.47 and 1.45 (depending on the parameters) would be needed to consider differences in outcomes significant. The comparison of these measures with the closest clinical data we possess supports the reliability of our results. CONCLUSIONS: These findings support the hypothesis that inter-individual differences in the weight placed on different pain factors are stable in time and could therefore be a possible target for patient stratification. SIGNIFICANCE: Our results demonstrate the temporal stability of the weight healthy individuals place on the different factors leading to the pain response. These findings give validity to the idea of using Bayesian estimations of the influence of different factors on pain as a way to stratify patients for treatment personalization.

A switching multi-scale dynamical network model of EEG/MEG.

We introduce a new generative model of the Encephalography (EEG/MEG) data, the inversion of which allows for inferring the locations and temporal evolution of the underlying sources as well as their dynamical interactions. The proposed Switching Mesostate Space Model (SMSM) builds on the multi-scale generative model for EEG/MEG by Daunizeau and Friston (2007). SMSM inherits the assumptions that (1) bioelectromagnetic activity is generated by a set of distributed sources, (2) the dynamics of these sources can be modelled as random fluctuations about a small number of mesostates, and (3) the number of mesostates engaged by a cognitive task is small. Additionally, four generalising assumptions are now included: (4) the mesostates interact according to a full Dynamical Causal Network (DCN) that can be estimated; (5) the dynamics of the mesostates can switch between multiple approximately linear operating regimes; (6) each operating regime remains stable over finite periods of time (temporal clusters); and (7) the total number of times the mesostates' dynamics can switch is small. The proposed model adds, therefore, a level of flexibility by accommodating complex brain processes that cannot be characterised by purely linear and stationary Gaussian dynamics. Importantly, the SMSM furnishes a new interpretation of the EEG/MEG data in which the source activity may have multiple discrete modes of behaviour, each with approximately linear dynamics. This is modelled by assuming that the connection strengths of the underlying mesoscopic DCN are time-dependent but piecewise constant, i.e. they can undergo discrete changes over time. A Variational Bayes inversion scheme is derived to estimate all the parameters of the model by maximising a (Negative Free Energy) lower bound on the model evidence. This bound is used to select among different model choices that are defined by the number of mesostates as well as by the number of stationary linear regimes. The full model is compared to a simplified version that uses no dynamical assumptions as well as to a standard EEG inversion technique. The comparison is carried out using an extensive set of simulations, and the application of SMSM to a real data set is also demonstrated. Our results show that for experimental situations in which we have some a priori belief that there are multiple approximately linear dynamical regimes, the proposed SMSM provides a natural modelling tool.

Successful memory encoding is associated with increased cross-frequency coupling between frontal theta and posterior gamma oscillations in human scalp-recorded EEG.

Although previous studies have established that successful memory encoding is associated with increased synchronization of theta-band and gamma-band oscillations, it is unclear if there is a functional relationship between oscillations in these frequency bands. Using scalp-recorded EEG in healthy human participants, we demonstrate that cross-frequency coupling between frontal theta phase and posterior gamma power is enhanced during the encoding of visual stimuli which participants later on remember versus items which participants subsequently forget ("subsequent memory effect," SME). Conventional wavelet analyses and source localizations revealed SMEs in spectral power of theta-, alpha-, and gamma-band. Successful compared to unsuccessful encoding was reflected in increased theta-band activity in right frontal cortex as well as increased gamma-band activity in parietal-occipital regions. Moreover, decreased alpha-band activity in prefrontal and occipital cortex was also related to successful encoding. Overall, these findings support the idea that during the formation of new memories frontal cortex regions interact with cortical representations in posterior areas.

Repetition priming effects dissociate between miniature eye movements and induced gamma-band responses in the human electroencephalogram.

The role of induced gamma-band responses (iGBRs) in the human electroencephalogram (EEG) is a controversial topic. On the one hand, iGBRs have been associated with neuronal activity reflecting the (re-)activation of cortical object representations. On the other hand, it was shown that miniature saccades (MSs) lead to high-frequency artifacts in the EEG that can mimic cortical iGBRs. We recorded EEG and eye movements simultaneously while participants were engaged in a combined repetition priming and object recognition experiment. MS rates were mainly modulated by object familiarity in a time window from 100 to 300 ms after stimulus onset. In contrast, artifact-corrected iGBRs were sensitive to object repetition and object familiarity in a prolonged time window. EEG source analyses revealed that stimulus repetitions modulated iGBRs in temporal and occipital cortex regions while familiarity was associated with activity in parieto-occipital regions. These results are in line with neuroimaging studies employing functional magnetic resonance imaging or magnetoencephalography. We conclude that MSs reflect early mechanisms of visual perception while iGBRs mirror the activation of cortical networks representing a perceived object.

Relating Cognition to both Brain Structure and Function: A Systematic Review of Methods.

Introduction: Cognitive neuroscience explores the mechanisms of cognition by studying its structural and functional brain correlates. Many studies have combined structural and functional neuroimaging techniques to uncover the complex relationship between them. In this study, we report the first systematic review that assesses how information from structural and functional neuroimaging methods can be integrated to investigate the brain substrates of cognition. Procedure: Web of Science and Scopus databases were searched for studies of healthy young adult populations that collected cognitive data and structural and functional neuroimaging data. Results: Five percent of screened studies met all inclusion criteria. Next, 50% of included studies related cognitive performance to brain structure and function without quantitative analysis of the relationship. Finally, 31% of studies formally integrated structural and functional brain data. Overall, many studies consider either structural or functional neural correlates of cognition, and of those that consider both, they have rarely been integrated. We identified four emergent approaches to the characterization of the relationship between brain structure, function, and cognition; comparative, predictive, fusion, and complementary. Discussion: We discuss the insights provided in each approach about the relationship between brain structure and function and how it impacts cognitive performance. In addition, we discuss how authors can select approaches to suit their research questions. Impact statement The relationship between structural and functional brain networks and their relationship to cognition is a matter of current investigations. This work surveys how researchers have studied the relationship between brain structure and function and its impact on cognitive function in healthy adult populations. We review four emergent approaches of quantitative analysis of this multivariate problem; comparative, predictive, fusion, and complementary. We explain the characteristics of each approach, discuss the insights provided in each approach, and how authors can combine approaches to suit their research questions.

Perceiving the tree in the woods: segregating brain responses to stimuli constituting natural scenes.

Conventional neuroscientific methods are inadequate for separating the brain responses related to the simultaneous processing of different parts of a natural scene. In the present human electroencephalogram (EEG) study, we overcame this limitation by tagging concurrently presented backgrounds and objects with different presentation frequencies. As a result, background and object elicited different steady-state visual evoked potentials (SSVEPs), which were separately quantified in the frequency domain. We analyzed the effects of semantic consistency and inconsistency between background and object on SSVEP amplitudes, topography, and tomography [variable resolution electromagnetic tomography (VARETA)]. The results revealed that SSVEPs related to background processing showed higher amplitudes in the consistent as opposed to the inconsistent condition, whereas object-related SSVEPs showed the reversed pattern of effects. Given the SSVEPs' sensitivity to visual attention, the results indicate that semantic inconsistency leads to greater attention focused on the object. If all image parts are semantically related, attention is rather directed to the background. The attentional advantage to inconsistent objects in a scene is likely the result of a mismatch between background-based expectations and semantic object information. A clear lateralization of the consistency effect in the anterior temporal lobes indicates functional hemispheric asymmetries in processing background- and object-related semantic information. In summary, the present study is the first to demonstrate the feasibility of SSVEPs to unravel the respective contributions of concurrent neuronal processes involved in the perception of background and object.

Frontal alpha asymmetry: A potential biomarker of approach-withdrawal motivation towards pain.

Pain-related catastrophising is a maladaptive coping strategy known to have a strong influence on clinical pain outcomes and treatment efficacy. Notwithstanding, little is known about its neurophysiological correlates. There is evidence to suggest catastrophising is associated with resting-state EEG frontal alpha asymmetry (FAA) patterns reflective of greater relative right frontal activity, which is known to be linked to withdrawal motivation and avoidance of aversive stimuli. The present study aims to investigate whether such a relationship occurs in the situational context of experimental pain. A placebo intervention was also included to evaluate effects of a potential pain-relieving intervention on FAA. 35 participants, including both chronic pain patients and healthy subjects, completed the Pain Catastrophising Scale (PCS) questionnaire followed by EEG recordings during cold pressor test (CPT)-induced tonic pain with or without prior application of placebo cream. There was a negative correlation between FAA and PCS-subscale helplessness scores, but not rumination or magnification, during the pre-placebo CPT condition. Moreover, FAA scores were shown to increase significantly in response to pain, indicative of greater relative left frontal activity that relates to approach-oriented behaviours. Placebo treatment elicited a decrease in FAA in low helplessness scorers, but no significant effects in individuals scoring above the mean on PCS-helplessness. These findings suggest that, during painful events, FAA may reflect the motivational drive to obtain reward of pain relief, which may be diminished in individuals who are prone to feel helpless about their pain. This study provides valuable insights into biomarkers of pain-related catastrophising and prospects of identifying promising targets of brain-based therapies for chronic pain management.

Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients: a proof-of-concept controlled study.

One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through noninvasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential noninvasive and nonpharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications. However, evidence supporting its use in patients with chronic pain is lacking. This study explores whether (a) alpha entrainment increase alpha power in patients and (b) whether this increase in alpha correlates with analgesia. In total, 28 patients with chronic pain sat in a comfortable position and underwent 4-min visual stimulation using customised goggles at 10 Hz (alpha) and 7 Hz (control) frequency blocks in a randomised cross-over design. 64-channel electroencephalography and 11-point numeric rating scale pain intensity and pain unpleasantness scores were recorded before and after stimulation. Electroencephalography analysis revealed frontal alpha power was significantly higher when stimulating at 10 Hz when compared to 7 Hz. There was a significant positive correlation between increased frontal alpha and reduction in pain intensity (r = 0.33; P < 0.05) and pain unpleasantness (r = 0.40; P < 0.05) in the 10 Hz block. This study provides the first proof of concept that changes in alpha power resulting from entrainment correlate with an analgesic response in patients with chronic pain. Further studies are warranted to investigate dose-response parameters and equivalence to analgesia provided by medications.

Entraining Alpha Activity Using Visual Stimulation in Patients With Chronic Musculoskeletal Pain: A Feasibility Study.

Entraining alpha activity with rhythmic visual, auditory, and electrical stimulation can reduce experimentally induced pain. However, evidence for alpha entrainment and pain reduction in patients with chronic pain is limited. This feasibility study investigated whether visual alpha stimulation can increase alpha power in patients with chronic musculoskeletal pain and, secondarily, if chronic pain was reduced following stimulation. In a within-subject design, 20 patients underwent 4-min periods of stimulation at 10 Hz (alpha), 7 Hz (high-theta, control), and 1 Hz (control) in a pseudo-randomized order. Patients underwent stimulation both sitting and standing and verbally rated their pain before and after each stimulation block on a 0-10 numerical rating scale. Global alpha power was significantly higher during 10 Hz compared to 1 Hz stimulation when patients were standing (t = -6.08, p < 0.001). On a more regional level, a significant increase of alpha power was found for 10 Hz stimulation in the right-middle and left-posterior region when patients were sitting. With respect to our secondary aim, no significant reduction of pain intensity and unpleasantness was found. However, only the alpha stimulation resulted in a minimal clinically important difference in at least 50% of participants for pain intensity (50%) and unpleasantness ratings (65%) in the sitting condition. This study provides initial evidence for the potential of visual stimulation as a means to enhance alpha activity in patients with chronic musculoskeletal pain. The brief period of stimulation was insufficient to reduce chronic pain significantly. This study is the first to provide evidence that a brief period of visual stimulation at alpha frequency can significantly increase alpha power in patients with chronic musculoskeletal pain. A further larger study is warranted to investigate optimal dose and individual stimulation parameters to achieve pain relief in these patients.

Using EEG Alpha States to Understand Learning During Alpha Neurofeedback Training for Chronic Pain.

OBJECTIVE: Alpha-neurofeedback (α-NFB) is a novel therapy which trains individuals to volitionally increase their alpha power to improve pain. Learning during NFB is commonly measured using static parameters such as mean alpha power. Considering the biphasic nature of alpha rhythm (high and low alpha), dynamic parameters describing the time spent by individuals in high alpha state and the pattern of transitioning between states might be more useful. Here, we quantify the changes during α-NFB for chronic pain in terms of dynamic changes in alpha states. METHODS: Four chronic pain and four healthy participants received five NFB sessions designed to increase frontal alpha power. Changes in pain resilience were measured using visual analogue scale (VAS) during repeated cold-pressor tests (CPT). Changes in alpha state static and dynamic parameters such as fractional occupancy (time in high alpha state), dwell time (length of high alpha state) and transition probability (probability of moving from low to high alpha state) were analyzed using Friedman's Test and correlated with changes in pain scores using Pearson's correlation. RESULTS: There was no significant change in mean frontal alpha power during NFB. There was a trend of an increase in fractional occupancy, mean dwell duration and transition probability of high alpha state over the five sessions in chronic pain patients only. Significant correlations were observed between change in pain scores and fractional occupancy (r = -0.45, p = 0.03), mean dwell time (r = -0.48, p = 0.04) and transition probability from a low to high state (r = -0.47, p = 0.03) in chronic pain patients but not in healthy participants. CONCLUSION: There is a differential effect between patients and healthy participants in terms of correlation between change in pain scores and alpha state parameters. Parameters providing a more precise description of the alpha power dynamics than the mean may help understand the therapeutic effect of neurofeedback on chronic pain.

GABA Modulates Frequency-Dependent Plasticity in Humans.

Frequency-dependent reorganization of the primary somatosensory cortex, together with perceptual changes, arises following repetitive sensory stimulation. Here, we investigate the role of GABA in this process. We co-stimulated two finger tips and measured GABA and Glx using magnetic resonance (MR) spectroscopy at the beginning and end of the stimulation. Participants performed a perceptual learning task before and after stimulation. There were 2 sessions with stimulation frequency either at or above the resonance frequency of the primary somatosensory cortex (23 and 39 Hz, respectively). Perceptual learning occurred following above resonance stimulation only, while GABA reduced during this condition. Lower levels of early GABA were associated with greater perceptual learning. One possible mechanism underlying this finding is that cortical disinhibition "unmasks" lateral connections within the cortex to permit adaptation to the sensory environment. These results provide evidence in humans for a frequency-dependent inhibitory mechanism underlying learning and suggest a mechanism-based approach for optimizing neurostimulation frequency.