Event-related potentials of social comparisons in depression and social anxiety.

Social comparison is central in human life and can be especially challenging in depression and social anxiety. We assessed event-related potentials and emotions using a social comparison task in which participants received feedback on both their own and a co-player's performance, in participants with depression and/or social anxiety (n = 63) and healthy controls (n = 72). Participants reported more negative emotions for downward (being better than the co-player [participant correct, co-player wrong]) and upward (being worse than the co-player [participant wrong, co-player correct]) comparisons versus even outcomes, with these effects being stronger in depression and social anxiety. At the Medial Frontal Negativity, both controls and depressed participants showed a more negative amplitude for upward comparison versus both the participant and co-player performing wrong. Socially anxious subjects showed the opposite effect, possibly due to greater expectations about being worse than others. The P300 decreased for downward and upward comparisons compared to even outcomes, which may relate to the higher levels of conflict of social inequality. Depressed and socially anxious subjects showed a blunted P300 increase over time in response to the task outcomes, suggesting deficits in allocating resources for the attention of incoming social information. The LPP showed increased amplitude for downward and upward comparison versus the even outcomes and no group effect. Emotional findings suggest that social comparisons are more difficult for depressed and socially anxious individuals. Event-related potentials findings may shed light on the neural substrates of these difficulties.

Functional Connectivity and Quantitative EEG in Women with Alcohol Use Disorders: A Resting-State Study.

This study was aimed at exploring the electroencephalographic features associated with alcohol use disorders (AUD) during a resting-state condition, by using quantitative EEG and Functional Connectivity analyses. In addition, we explored whether EEG functional connectivity is associated with trait impulsivity. Absolute and relative powers and Synchronization Likelihood (SL) as a measure of functional connectivity were analyzed in 15 AUD women and fifteen controls matched in age, gender and education. Correlation analysis between self-report impulsivity as measured by the Barratt impulsiveness Scale (BIS-11) and SL values of AUD patients were performed. Our results showed increased absolute and relative beta power in AUD patients compared to matched controls, and reduced functional connectivity in AUD patients predominantly in the beta and alpha bands. Impaired connectivity was distributed at fronto-central and occipito-parietal regions in the alpha band, and over the entire scalp in the beta band. We also found that impaired functional connectivity particularly in alpha band at fronto-central areas was negative correlated with non-planning dimension of impulsivity. These findings suggest that functional brain abnormalities are present in AUD patients and a disruption of resting-state EEG functional connectivity is associated with psychopathological traits of addictive behavior.

CiftiStorm pipeline: facilitating reproducible EEG/MEG source connectomics.

We present CiftiStorm, an electrophysiological source imaging (ESI) pipeline incorporating recently developed methods to improve forward and inverse solutions. The CiftiStorm pipeline produces Human Connectome Project (HCP) and megconnectome-compliant outputs from dataset inputs with varying degrees of spatial resolution. The input data can range from low-sensor-density electroencephalogram (EEG) or magnetoencephalogram (MEG) recordings without structural magnetic resonance imaging (sMRI) to high-density EEG/MEG recordings with an HCP multimodal sMRI compliant protocol. CiftiStorm introduces a numerical quality control of the lead field and geometrical corrections to the head and source models for forward modeling. For the inverse modeling, we present a Bayesian estimation of the cross-spectrum of sources based on multiple priors. We facilitate ESI in the T1w/FSAverage32k high-resolution space obtained from individual sMRI. We validate this feature by comparing CiftiStorm outputs for EEG and MRI data from the Cuban Human Brain Mapping Project (CHBMP) acquired with technologies a decade before the HCP MEG and MRI standardized dataset.

More to explore in music reading as a cross-modal process: a comment on Lee and Lei (2012).

Lee and Lei (2012) used a pitch task and a duration task in different blocks of trials and measured event-related potentials in 12 musicians and 24 non-musicians as they read musical scores. The authors claimed to disentangle pitch and duration processing. From the perspectives of cognitive neuropsychology there is great interest in studying the processes involved in reading musical scores. However, we argue that the design used by Lee and Lei (2012) does not allow disentangling pitch and duration processing because both are expressed within the musical score. Moreover, we emphasize the importance of longitudinal studies over cross-sectional studies to pinpoint the specific influence of musical expertise on score reading.

Event-related potentials during the ultimatum game in people with symptoms of depression and/or social anxiety.

Depression and social anxiety are common disorders that have a profound impact on social functioning. The need for studying the neural substrates of social interactions in mental disorders using interactive tasks has been emphasized. The field of neuroeconomics, which combines neuroscience techniques and behavioral economics multiplayer tasks such as the Ultimatum Game (UG), can contribute in this direction. We assessed emotions, behavior, and Event-Related Potentials in participants with depression and/or social anxiety symptoms (MD/SA, n = 63, 57 females) and healthy controls (n = 72, 67 females), while they played the UG. In this task, participants received fair, mid-value, and unfair offers from other players. Mixed linear models were implemented to assess trial level changes in neural activity. The MD/SA group reported higher levels of sadness in response to mid-value and unfair offers compared to controls. In controls, the Medial Frontal Negativity associated with fair offers increased over time, while this dynamic was not observed in the MD/SA group. The MD/SA group showed a decreased P3/LPP in all offers, compared to controls. These results indicate an enhanced negative emotional response to unfairness in the MD/SA group. Neural results reveal a blunted response over time to positive social stimuli in the MD/SA group. Moreover, between-group differences in P3/LPP may relate to a reduced saliency of offers and/or to a reduced availability of resources for processing incoming stimuli in the MD/SA group. Findings may shed light into the neural substrates of social difficulties in these disorders.

DIVA Meets EEG: Model Validation Using Formant-Shift Reflex.

The neurocomputational model 'Directions into Velocities of Articulators' (DIVA) was developed to account for various aspects of normal and disordered speech production and acquisition. The neural substrates of DIVA were established through functional magnetic resonance imaging (fMRI), providing physiological validation of the model. This study introduces DIVA_EEG an extension of DIVA that utilizes electroencephalography (EEG) to leverage the high temporal resolution and broad availability of EEG over fMRI. For the development of DIVA_EEG, EEG-like signals were derived from original equations describing the activity of the different DIVA maps. Synthetic EEG associated with the utterance of syllables was generated when both unperturbed and perturbed auditory feedback (first formant perturbations) were simulated. The cortical activation maps derived from synthetic EEG closely resembled those of the original DIVA model. To validate DIVA_EEG, the EEG of individuals with typical voices (N = 30) was acquired during an altered auditory feedback paradigm. The resulting empirical brain activity maps significantly overlapped with those predicted by DIVA_EEG. In conjunction with other recent model extensions, DIVA_EEG lays the foundations for constructing a complete neurocomputational framework to tackle vocal and speech disorders, which can guide model-driven personalized interventions.

Identifying oscillatory brain networks with hidden Gaussian graphical spectral models of MEEG.

Identifying the functional networks underpinning indirectly observed processes poses an inverse problem for neurosciences or other fields. A solution of such inverse problems estimates as a first step the activity emerging within functional networks from EEG or MEG data. These EEG or MEG estimates are a direct reflection of functional brain network activity with a temporal resolution that no other in vivo neuroimage may provide. A second step estimating functional connectivity from such activity pseudodata unveil the oscillatory brain networks that strongly correlate with all cognition and behavior. Simulations of such MEG or EEG inverse problem also reveal estimation errors of the functional connectivity determined by any of the state-of-the-art inverse solutions. We disclose a significant cause of estimation errors originating from misspecification of the functional network model incorporated into either inverse solution steps. We introduce the Bayesian identification of a Hidden Gaussian Graphical Spectral (HIGGS) model specifying such oscillatory brain networks model. In human EEG alpha rhythm simulations, the estimation errors measured as ROC performance do not surpass 2% in our HIGGS inverse solution and reach 20% in state-of-the-art methods. Macaque simultaneous EEG/ECoG recordings provide experimental confirmation for our results with 1/3 times larger congruence according to Riemannian distances than state-of-the-art methods.

Functional brain connectivity after corrective cardiac surgery for critical congenital heart disease: a preliminary near-infrared spectroscopy (NIRS) report.

Patients with congenital heart disease (CHD) requiring cardiac surgery in infancy are at high risk for neurodevelopmental impairments. Neonatal imaging studies have reported disruptions of brain functional organization before surgery. Yet, the extent to which functional network alterations are present after cardiac repair remains unexplored. This preliminary study aimed at investigating cortical functional connectivity in 4-month-old infants with repaired CHD, using resting-state functional near-infrared spectroscopy (fNIRS). After fNIRS signal frequency decomposition, we compared values of magnitude-squared coherence as a measure of connectivity strength, between 21 infants with corrected CHD and 31 healthy controls. We identified a subset of connections with differences between groups at an uncorrected statistical level of p < .05 while controlling for sex and maternal socioeconomic status, with most of these connections showing reduced connectivity in infants with CHD. Although none of these differences reach statistical significance after FDR correction, likely due to the small sample size, moderate to large effect sizes were found for group-differences. If replicated, these results would therefore suggest preliminary evidence that alterations of brain functional connectivity are present in the months after cardiac surgery. Additional studies involving larger sample size are needed to replicate our data, and comparisons between pre- and postoperative findings would allow to further delineate alterations of functional brain connectivity in this population.

Minimizing the distortions in electrophysiological source imaging of cortical oscillatory activity via Spectral Structured Sparse Bayesian Learning.

Oscillatory processes at all spatial scales and on all frequencies underpin brain function. Electrophysiological Source Imaging (ESI) is the data-driven brain imaging modality that provides the inverse solutions to the source processes of the EEG, MEG, or ECoG data. This study aimed to carry out an ESI of the source cross-spectrum while controlling common distortions of the estimates. As with all ESI-related problems under realistic settings, the main obstacle we faced is a severely ill-conditioned and high-dimensional inverse problem. Therefore, we opted for Bayesian inverse solutions that posited a priori probabilities on the source process. Indeed, rigorously specifying both the likelihoods and a priori probabilities of the problem leads to the proper Bayesian inverse problem of cross-spectral matrices. These inverse solutions are our formal definition for cross-spectral ESI (cESI), which requires a priori of the source cross-spectrum to counter the severe ill-condition and high-dimensionality of matrices. However, inverse solutions for this problem were NP-hard to tackle or approximated within iterations with bad-conditioned matrices in the standard ESI setup. We introduce cESI with a joint a priori probability upon the source cross-spectrum to avoid these problems. cESI inverse solutions are low-dimensional ones for the set of random vector instances and not random matrices. We achieved cESI inverse solutions through the variational approximations via our Spectral Structured Sparse Bayesian Learning (ssSBL) algorithm https://github.com/CCC-members/Spectral-Structured-Sparse-Bayesian-Learning. We compared low-density EEG (10-20 system) ssSBL inverse solutions with reference cESIs for two experiments: (a) high-density MEG that were used to simulate EEG and (b) high-density macaque ECoG that were recorded simultaneously with EEG. The ssSBL resulted in two orders of magnitude with less distortion than the state-of-the-art ESI methods. Our cESI toolbox, including the ssSBL method, is available at https://github.com/CCC-members/BC-VARETA_Toolbox.

Resting state basal ganglia network in idiopathic generalized epilepsy.

The basal ganglia, a brain structure related to motor control, is implicated in the modulation of epileptic discharges generalization in patients with idiopathic generalized epilepsy (IGE). Using group independent component analysis (ICA) on resting-state fMRI data, this study identified a resting state functional network that predominantly consisted of the basal ganglia in both healthy controls and patients with IGE. In order to gain a better understanding of the basal ganglia network(BGN) in IGE patients, we compared the BGN functional connectivity of controls with that of epilepsy patients, either with interictal epileptic discharges (with-discharge period, WDP) or without epileptic discharge (nondischarge period, NDP) while scanning. Compared with controls, functional connectivity of BGN in IGE patients demonstrated significantly more integration within BGN except cerebellum and supplementary motor area (SMA) during both periods. Compared with the NDP group, the increased functional connectivity was found in bilateral caudate nucleus and the putamen, and decreases were observed in the bilateral cerebellum and SMA in WDP group. In accord with the proposal that the basal ganglia modulates epileptic discharge activity, the results showed that the modulation enhanced the integration in BGN of patients, and modulation during WDP was stronger than that during NDP. Furthermore, reduction of functional connectivity in cerebellum and SMA, the abnormality might be further aggravated during WDP, was consistent with the behavioral manifestations with disturbed motor function in IGE. These resting-state fMRI findings in the current study provided evidence confirming the role of the BGN as an important modulator in IGE.

Mother and stranger: an electrophysiological study of voice processing in newborns.

In the mature adult brain, there are voice selective regions that are especially tuned to familiar voices. Yet, little is known about how the infant's brain treats such information. Here, we investigated, using electrophysiology and source analyses, how newborns process their mother's voice compared with that of a stranger. Results suggest that, shortly after birth, newborns distinctly process their mother's voice at an early preattentional level and at a later presumably cognitive level. Activation sources revealed that exposure to the maternal voice elicited early language-relevant processing, whereas the stranger's voice elicited more voice-specific responses. A central probably motor response was also observed at a later time, which may reflect an innate auditory-articulatory loop. The singularity of left-dominant brain activation pattern together with its ensuing sustained greater central activation in response to the mother's voice may provide the first neurophysiologic index of the preferential mother's role in language acquisition.

Maturational time course of the envelope following response to amplitude-modulated acoustic signals in rats.

OBJECTIVE: The maturation pattern of the envelope following response (EFR) was described using rats as an experimental model. DESIGN: EFRs were recorded in animals at different postnatal ages (15, 20, 25, 35, and 70 postnatal days) in response to broadband noise (BBN) and tones of 8000 and 4000 Hz modulated in amplitude using a continuous sweep of modulation frequencies. Responses were analysed in the 90-190 Hz modulation frequency (MF) range. STUDY SAMPLE: Forty individuals (eight individuals for each age bracket) were included in the present study. RESULTS: During maturation, the MF at which the maximum amplitude was obtained (BMF, best modulation frequency) shifted to higher values when animals were stimulated with tones. At the same time, the amplitude of the response at the BMF increased. For every group of animals, the amplitude of the response continuously decreased for MFs higher than the BMF. However, less steep decreases of amplitude were obtained as animals became adults. CONCLUSIONS: These results provide normative data regarding the maturation of the EFR in rats. They provide information for the development of predictor models to estimate the temporal resolution of the auditory system during maturation.

Parallel factor analysis for multidimensional decomposition of functional near-infrared spectroscopy data.

Significance: Current techniques for data analysis in functional near-infrared spectroscopy (fNIRS), such as artifact correction, do not allow to integrate the information originating from both wavelengths, considering only temporal and spatial dimensions of the signal's structure. Parallel factor analysis (PARAFAC) has previously been validated as a multidimensional decomposition technique in other neuroimaging fields. Aim: We aimed to introduce and validate the use of PARAFAC for the analysis of fNIRS data, which is inherently multidimensional (time, space, and wavelength). Approach: We used data acquired in 17 healthy adults during a verbal fluency task to compare the efficacy of PARAFAC for motion artifact correction to traditional two-dimensional decomposition techniques, i.e., target principal (tPCA) and independent component analysis (ICA). Correction performance was further evaluated under controlled conditions with simulated artifacts and hemodynamic response functions. Results: PARAFAC achieved significantly higher improvement in data quality as compared to tPCA and ICA. Correction in several simulated signals further validated its use and promoted it as a robust method independent of the artifact's characteristics. Conclusions: This study describes the first implementation of PARAFAC in fNIRS and provides validation for its use to correct artifacts. PARAFAC is a promising data-driven alternative for multidimensional data analyses in fNIRS and this study paves the way for further applications.

LIONirs: flexible Matlab toolbox for fNIRS data analysis.

BACKGROUND: Functional near-infrared spectroscopy (fNIRS) is a suitable tool for recording brain function in pediatric or challenging populations. As with other neuroimaging techniques, the scientific community is engaged in an evolving debate regarding the most adequate methods for performing fNIRS data analyses. NEW METHOD: We introduce LIONirs, a neuroinformatics toolbox for fNIRS data analysis, designed to follow two main goals: (1) flexibility, to explore several methods in parallel and verify results using 3D visualization; (2) simplicity, to apply a defined processing pipeline to a large dataset of subjects by using the MATLAB Batch System and available on GitHub. RESULTS: Within the graphical user interfaces (DisplayGUI), the user can reject noisy intervals and correct artifacts, while visualizing the topographical projection of the data onto the 3D head representation. Data decomposition methods are available for the identification of relevant signatures, such as brain responses or artifacts. Multimodal data recorded simultaneously to fNIRS, such as physiology, electroencephalography or audio-video, can be visualized using the DisplayGUI. The toolbox includes several functions that allow one to read, preprocess, and analyze fNIRS data, including task-based and functional connectivity measures. COMPARISON WITH EXISTING METHODS: Several good neuroinformatics tools for fNIRS data analysis are currently available. None of them emphasize multimodal visualization of the data throughout the preprocessing steps and multidimensional decomposition, which are essential for understanding challenging data. Furthermore, LIONirs provides compatibility and complementarity with other existing tools by supporting common data format. CONCLUSIONS: LIONirs offers a flexible platform for basic and advanced fNIRS data analysis, shown through real experimental examples.

A comparison of methods for assessing alpha phase resetting in electrophysiology, with application to intracerebral EEG in visual areas.

There are two competing views on the mechanisms underlying the generation of visual evoked potentials/fields in EEG/MEG. The classical hypothesis assumes an additive wave on top of background noise. Another hypothesis states that the evoked activity can totally or partially arise from a phase resetting of the ongoing alpha rhythm. There is no consensus however, on the best tools for distinguishing between these two hypotheses. In this study, we have tested different measures on a large series of simulations under a variety of scenarios, involving in particular trial-to-trial variability and different dynamics of ongoing alpha rhythm. No single measure or set of measures was found to be necessary or sufficient for defining phase resetting in the context of our simulations. Still, simulations permitted to define criteria that were the most reliable in practice for distinguishing additive and phase resetting hypotheses. We have then applied these criteria on intracerebral EEG data recordings in the visual areas during a visual discrimination task. We investigated the intracerebral channels that presented both ERP and ongoing alpha oscillations (n=37). Within these channels, a total of 30% fulfilled phase resetting criteria during the generation of the visual evoked potential, based on criteria derived from simulations. Moreover, 19% of the 37 channels presented dependence of the ERP on the level of pre-stimulus alpha. Only 5% of channels fulfilled both the simulation-related criteria and dependence on baseline alpha level. Our simulation study points out to the difficulty of clearly assessing phase resetting based on observed macroscopic electrophysiological signals. Still, some channels presented an indication of phase resetting in the context of our simulations. This needs to be confirmed by further work, in particular at a smaller recording scale.

Neural processing of iterated prisoner's dilemma outcomes indicates next-round choice and speed to reciprocate cooperation.

The iterated prisoner's dilemma (iPD) game is a well-established model for testing how people cooperate, and the neural processes that unfold after its distinct outcomes have been partly described. Recent theoretical models suggest evolution favors intuitive cooperation, which raises questions on the behavioral but also neural timelines involved. We studied the outcome/feedback stage of iPD rounds with electroencephalography (EEG) methods. Results showed that neural signals associated with this stage also relate to future choice, in an outcome-dependent manner: (i) after zero-gain "sucker's payoffs" (unreciprocated cooperation), a participant's decision thereafter relates to changes to the feedback-related negativity (FRN); (ii) after one-sided non-cooperation (participant wins at co-player's expense), by the P3; (iii) after mutual cooperation, by late frontal delta-band modulations. Critically, faster reciprocation behavior towards a co-player's choice to cooperate was predicted, on a single-trial basis, by players' P3 and frontal delta modulations at the immediately preceding trial. Delta-band signaling is discussed in relation to homeostatic regulation processing in the literature. The findings relate the early outcome/feedback stage to subsequent decisional processes in the iPD, providing a first neural account of the brief timelines implied in heuristic modes of cooperation.

White matter architecture rather than cortical surface area correlates with the EEG alpha rhythm.

There are few studies on the neuroanatomical determinants of EEG spectral properties that would explain its substantial inter-individual variability in spite of decades of biophysical modeling that predicts this type of relationship. An exception is the negative relation between head size and the spectral position of the alpha peak (P(alpha)) reported in Nunez et al. (1978)-proposed as evidence of the influence of global boundary conditions on slightly damped neocortical waves. Here, we attempt to reexamine this finding by computing the correlations of occipital P(alpha) with various measures of head size and cortical surface area, for 222 subjects from the EEG/MRI database of the Cuban Human Brain Mapping Project. No relation is found (p>0.05). On the other hand, biophysical models also predict that white matter architecture, determining time delays and connectivities, could have an important influence on P(alpha). This led us to explore relations between P(alpha) and DTI fractional anisotropy by means of a multivariate penalized regression. Clusters of voxels with highly significant relations were found. These were positive within the Posterior and Superior Corona Radiata for both hemispheres, supporting biophysical theories predicting that the period of cortico-thalamocortical cycles might be modulating the alpha frequency. Posterior commissural fibers of the Corpus Callosum present the strongest relationships, negative in the inferior part (Splenium), connecting the inferior occipital lobes and positive in the superior part (Isthmus and Tapetum), connecting the superior occipital cortices. We found that white matter architecture rather than neocortical area determines the dynamics of the alpha rhythm.

False discovery rate and permutation test: an evaluation in ERP data analysis.

Current analysis of event-related potentials (ERP) data is usually based on the a priori selection of channels and time windows of interest for studying the differences between experimental conditions in the spatio-temporal domain. In this work we put forward a new strategy designed for situations when there is not a priori information about 'when' and 'where' these differences appear in the spatio-temporal domain, simultaneously testing numerous hypotheses, which increase the risk of false positives. This issue is known as the problem of multiple comparisons and has been managed with methods that control the false discovery rate (FDR), such as permutation test and FDR methods. Although the former has been previously applied, to our knowledge, the FDR methods have not been introduced in the ERP data analysis. Here we compare the performance (on simulated and real data) of permutation test and two FDR methods (Benjamini and Hochberg (BH) and local-fdr, by Efron). All these methods have been shown to be valid for dealing with the problem of multiple comparisons in the ERP analysis, avoiding the ad hoc selection of channels and/or time windows. FDR methods are a good alternative to the common and computationally more expensive permutation test. The BH method for independent tests gave the best overall performance regarding the balance between type I and type II errors. The local-fdr method is preferable for high dimensional (multichannel) problems where most of the tests conform to the empirical null hypothesis. Differences among the methods according to assumptions, null distributions and dimensionality of the problem are also discussed.

Chirp Analyzer for Estimating Amplitude and Latency of Steady-State Auditory Envelope Following Responses.

OBJECTIVE: The envelope following response (EFR) is a clinically relevant evoked potential, reflecting the synchronization of the auditory pathway to the temporal envelope of sounds. Since there is no standard analysis of this potential, we here aim at contrasting the relative accuracy of known time-frequency methods and new strategies for the reliable estimation of the EFR amplitude and latency. METHODS: The EFR was estimated using explicit time-frequency methods: the Short-Term Fourier Transform (STFT) and the Morlet Continuous Wavelet Transform (CWT). Furthermore, the Chirp Analyzer (CA) was introduced as a new tool for the reliable estimation of the EFR. The applicability of the methods was tested in animal and human recordings. RESULTS: Using simulated data for comparing the estimation performance by each method, we found that the CA is able to correctly estimate EFR amplitudes, without the typical bias observed in the STFT estimates. The CA is more robust to noise than the CWT method, although with higher sensitivity to the latency of the response. Thus, the estimation of the EFR amplitude with any of the methods, but especially with CA, should be corrected by using the estimated delay. Analysis of real data confirmed these results and showed that all methods offer estimated EFRs similar to those found in previous studies using the classical Fourier Analyzer. CONCLUSION AND SIGNIFICANCE: The CA is a potential valuable tool for the analysis of the EFR, which could be extended for the estimation of oscillatory evoked potentials of other sensory modalities.

Modeling functional resting-state brain networks through neural message passing on the human connectome.

In this work, we propose a natural model for information flow in the brain through a neural message-passing dynamics on a structural network of macroscopic regions, such as the human connectome (HC). In our model, each brain region is assumed to have a binary behavior (active or not), the strengths of interactions among them are encoded in the anatomical connectivity matrix defined by the HC, and the dynamics of the system is defined by the Belief Propagation (BP) algorithm, working near the critical point of the network. We show that in the absence of direct external stimuli the BP algorithm converges to a spatial map of activations that is similar to the Default Mode Network (DMN) of the brain, which has been defined from the analysis of functional MRI data. Moreover, we use Susceptibility Propagation (SP) to compute the matrix of long-range correlations between the different regions and show that the modules defined by a clustering of this matrix resemble several Resting State Networks (RSN) determined experimentally. Both results suggest that the functional DMN and RSNs can be seen as simple consequences of the anatomical structure of the brain and a neural message-passing dynamics between macroscopic regions. With the new model, we explore predictions on how functional maps change when the anatomical brain network suffers structural alterations, like in Alzheimer's disease and in lesions of the Corpus Callosum. The implications and novel interpretations suggested by the model, as well as the role of criticality, are discussed.