Biology subjects

Deco, G.

Publications and source records attributed to Deco, G..

At least 19 recordsLinked to original sources

bioRxiv2018

Analysis of brain network dynamics estimated from fMRI data: A new framework based on communicability and flow

Neuroimaging techniques such as MRI have been widely used to explore the associations between brain areas. Structural connectivity (SC) captures the anatomical pathways across the brain and functional connectivity (FC) measures the correlation between the activity of brain regions. These connectivity measures have been much studied using network theory in order to uncover the distributed organization of brain structures, in particular FC for task-specific brain communication. However, the application of network theory to study FC matrices is often "static" despite the dynamic nature of time series obtained from fMRI. The present study aims to overcome this limitation by introducing a network-oriented analysis applied to whole-brain effective connectivity (EC) useful to interpret the brain dynamics. Technically, we tune a multivariate Ornstein-Uhlenbeck (MOU) process to reproduce the statistics of the whole-brain resting-state fMRI signals, which provides estimates for MOU-EC as well as input properties (similar to local excitabilities). The network analysis is then based on the Green function (or network impulse response) that describes the interactions between nodes across time for the estimated dynamics. This model-based approach provides time-dependent graph-like descriptor, named communicability, that characterize the roles that either nodes or connections play in the propagation of activity within the network. They can be used at both global and local levels, and also enables the comparison of estimates from real data with surrogates (e.g. random network or ring lattice). In contrast to classical graph approaches to study SC or FC, our framework stresses the importance of taking the temporal aspect of fMRI signals into account. Our results show a merging of functional communities over time (in which input properties play a role), moving from segregated to global integration of the network activity. Our formalism sets a solid ground for the analysis and interpretation of fMRI data, including task-evoked activity.

neuroscience↗

bioRxiv2018

Multiscale dynamic mean field (MDMF) model to relate resting state brain dynamics with local cortical excitatory-inhibitory neurotransmitter homeostasis in health and disease.

Previous neuro-computational studies have established the connection of spontaneous resting-state brain activity with "large-scale" neuronal ensembles using dynamic mean field approach and showed the impact of local excitatory-inhibitory (E-I) balance in sculpting dynamical patterns. Here, we argue that whole brain models that link multiple scales of physiological organization namely brain metabolism that governs synaptic concentrations of gamma-aminobutyric acid (GABA) and glutamate on one hand and neural field dynamics that operate on the macroscopic scale. The multiscale dynamic mean field (MDMF) model captures the synaptic gating dynamics over a cortical macrocolumn as a function of neurotransmitter kinetics. Multiple MDMF units were placed in brain locations guided by an anatomical parcellation and connected by tractography data from diffusion tensor imaging. The resulting whole-brain model generates the resting-state functional connectivity and also reveal that optimal configurations of glutamate and GABA captures the dynamic working point of the brain, that is the state of maximum metsatability as observed in BOLD signals. To demonstrate test-retest reliability we validate the observation that healthy resting brain dynamics is governed by optimal glutamate-GABA configurations using two different brain parcellations for model set-up. Furthermore, graph theoretical measures of segregation (modularity and clustering coefficient) and integration (global efficiency and characteristic path length) on the functional connectivity generated from healthy and pathological brain network studies could be explained by the MDMF model. In conclusion, the MDMF model could relate the various scales of observations from neurotransmitter concentrations to dynamics of synaptic gating to whole-brain resting-state network topology in health and disease.

neuroscience↗

bioRxiv2018

Altered trajectories in the dynamical repertoire of functional network states under psilocybin

Brain activity can be understood as the exploration of a dynamical landscape of activity configurations over both space and time. This dynamical landscape may be defined in terms of spontaneous transitions within a repertoire of discrete metastable states of functional connectivity (FC), which underlie different mental processes. However, it remains unclear how the brains dynamical landscape might be changed in altered states of consciousness, such as the psychedelic state. The present study investigated changes in the brains dynamical repertoire in an fMRI dataset of healthy participants intravenously injected with the psychedelic compound psilocybin, which is found in \"magic mushrooms\". We employed a data-driven approach to study brain dynamics in the psychedelic state, which focuses on the dominant FC pattern captured by the leading eigenvector of dynamic FC matrices, and enables the identification of recurrent FC patterns (\"FC-states\"), and their transition profiles over time. We found that a FC state closely corresponding to the fronto-parietal control system was strongly destabilized in the psychedelic state, while transitions toward a globally synchronized FC state were enhanced. These differences between brain state trajectories in normal waking consciousness and the psychedelic state suggest that the latter biases a global mode of functional integration at the expense of locally segregated activity in specific networks. These results provide a mechanistic perspective on subjective quality of the psychedelic experience, and further raise the possibility that mapping the brains dynamical landscape may help guide pharmacological interventions in neuropsychiatric disorders.

neuroscience↗

bioRxiv2018

Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics

Over the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.

neuroscience↗

bioRxiv2018

Single-neuron interactions between the somatosensory thalamo-cortical circuits during perception

Sensory thalamo-cortical interactions are key components of the neuronal chains associated with stimulus perception, but surprisingly, they are poorly understood. We addressed this problem by evaluating a directional measure between simultaneously recorded neurons from somatosensory thalamus (VPL) and somatosensory cortex (S1) sharing the same cutaneous receptive field, while monkeys judged the presence or absence of a tactile stimulus. During the stimulus-presence, feedforward (VPL[->]S1) interactions increased, while pure feedback (S1[->]VPL) interactions were unaffected. Remarkably, bidirectional interactions (VPL{leftrightarrow}S1) emerged with high stimulus amplitude, establishing a functional thalamo-cortical loop. Furthermore, feedforward interactions were modulated by task context and error trials. Additionally, significant stimulus modulations were found on intra-cortical (S1[->]S1) interactions, but not on intra-thalamic (VPL[->]VPL) interactions. Thus, these results show the directionality of the information flow between the thalamo-cortical circuits during tactile perception. We suggest that these interactions may contribute to stimulus perception during the detection task used here.

neuroscience↗

bioRxiv2018

Distinct Functional Connectivity Mode during Viewing Natural Scenes Revealed by Principal Component Analysis

A fundamental question in systems neuroscience is how spontaneous activity at rest is reorganized during task performance. Recent studies suggest a strong relationship between resting and task FC. Furthermore, the relationship between resting and task FC has been shown to reflect individual differences. Particularly, various studies have demonstrated that the FC has higher reliability and provides enhanced detection of individual differences while viewing natural scenes. Although the large-scale organization of FC during rest and movie-viewing conditions have been well studied in relation to individual variations, the re-organization of FC during viewing natural scenes have not been studied in depth. In this study, we used principal component analysis on FC during rest and movie-viewing condition to characterize the dimensionality of FC patterns across conditions and subjects. We found that the variations in FC patterns related to viewing natural scenes can be explained by a single component, which enables identification of the task over subjects with 100% accuracy. We showed that the FC mode associated to viewing natural scenes better reflects individual variations. Furthermore, we investigated the signatures of movie-viewing-specific functional modes in dynamic FC based on phase-locking values between brain regions. We found that the movie-specific functional mode is persistent across time; suggesting the emergence of a stable processing mode. To explain the reorganization of whole-brain FC through the changes in local dynamics, we appeal to a large-scale computational model. This modelling suggested that the reorganization of whole-brain FC is associated to the interaction between frontal-parietal and frontal-temporal activation patterns.

neuroscience↗

bioRxiv2017

Disrupted structural connectivity in Pediatric Bipolar Disorder

Bipolar disorder (BD) has been linked to disrupted structural and functional connectivity between prefrontal networks and limbic brain regions. Studies of patients with pediatric bipolar disorder (PBD) can help elucidate the developmental origins of altered structural connectivity underlying BD and provide novel insights into the aetiology of BD. Here we compare the network properties of whole-brain structural connectomes of PBD patients with psychosis and euthymic matched healthy controls. Our results show widespread changes in the structural connectivity of PBD patients in both cortical and subcortical networks, notably affecting the orbitofrontal cortex, frontal gyrus, amygdala, hippocampus and basal ganglia. Graph theoretical analysis revealed that PBD connectomes have fewer hubs, weaker rich club organization, different modular fingerprint and inter-modular communication, compared to healthy participants. The relationship between network features and neurocognitive and psychotic scores was also assessed. Patients IQ and psychotic symptoms significantly correlated with the local efficiency of the orbitofrontal cortex. Our findings reveal that PBD is associated with significant widespread changes in structural network topology, thus strengthening the hypothesis of a reduced capacity for integrative processing of information across brain regions. Localised network changes involve core regions for emotional processing and regulation, as well as memory and executive function, some of which correlate with neurocognitive faculties and symptoms. Together, our findings provide the first comprehensive characterisation of the alterations in local and global structural brain connectivity and network topology, which may contribute to the deficits in cognition and emotion processing and regulation found in PBD.

neuroscience↗

bioRxiv2017

Reconstruction of resting-state networks from macaqueelectrocorticographic data

The discovery of haemodynamic (BOLD-fMRI) resting-state networks (RSNs) has brought about a fundamental shift in our thinking about the role of intrinsic brain activity. The electrophysiological underpinnings of RSNs remain largely elusive and it has been shown only recently that electrophysiological cortical rhythms are organized into RSNs. Most electrophysiological studies into RSNs use magnetoencephalography (MEG) or electroencephalography (EEG), which limits the spatial scale on which RSNs can be investigated. Due to their close proximity to the cortical surface, electroencephalographic (ECoG) recordings can potentially provide a more detailed picture of the functional organization of resting-state cortical rhythms. In this study we propose using source-space independent component analysis for identifying generators of resting-state cortical rhythms as recorded with ECoG and reconstructing their network structure. Their network structure is characterized by two kinds of connectivity: instantaneous correlations between band-limited amplitude envelopes and oscillatory phase-locking. Using simulated data, we find that the reconstruction of oscillatory phase-locking is more challenging than that of amplitude correlations, particularly for low signal-to-noise levels. Specifically, phase-lags can both be over- and underestimated as a consequence of first-order and higher-order volume-conduction effects, which troubles the interpretation of interaction measures based on imaginary phase-locking or coherence. The methodology is applied to resting-state beta (15-30 Hz) rhythms within the motor system of a macaque monkey and leads to the identification of a functional network of seven cortical generators that are distributed across the sensorimotor system. The spatial extent of the identified generators, together with consistent phase-lags, suggests that these rhythms can be viewed as being spatially continuous with complex dynamics including traveling waves. Our findings illustrate the level of spatial detail attainable with source-projected ECoG and motivates wider use of the methodology for studying resting-state as well as event-related cortical dynamics in macaque and human.

neuroscience↗

bioRxiv2017

Subject- and behavior-specific signatures extracted from fMRI data using whole-brain effective connectivity

The study of brain communication based on fMRI data is often limited because such measurements are a mixture of session-to-session variability with subject- and condition-related information. Disentangling these contributions is crucial for real-life applications, in particular when only a few recording sessions are available. The present study aims to define a reliable standard for the extraction of multiple signatures from fMRI data, while verifying that they do not mix information about the different modalities. In particular, condition-specific signature should not be contaminated by subject-related information. Practically, signatures correspond to subnetworks of directed interactions between brain regions (typically 100 covering the whole brain) supporting the subject and condition identification for single fMRI sessions. The key for robust prediction is using effective connectivity instead of functional connectivity. Our method demonstrates excellent generalization capabilities for subject identification in two datasets, using only a few sessions per subject as reference. Using another dataset with resting state and movie viewing, we show that the two signatures related to subjects and tasks correspond to distinct subnetworks, which are thus topologically orthogonal. Our results set solid foundations for applications tailored to individual subjects, such as clinical diagnostic.

neuroscience↗

bioRxiv2017

Propagation of BOLD activity reveals directed interactions across human visual cortex

It has recently been shown that large-scale propagation of blood-oxygen level dependent (BOLD) activity is constrained by anatomical connections and reflects transitions between behavioral states. It remains to be seen, however, if the propagation of BOLD activity can also relate to the brain anatomical structure at a more local scale. Here, we hypothesized that BOLD propagation reflects structured neuronal activity across early visual field maps. To explore this hypothesis, we characterize the propagation of BOLD activity across V1, V2 and V3 using a modeling approach that aims to disentangle the contributions of local activity and directed interactions in shaping BOLD propagation. It does so by estimating the effective connectivity (EC) and the excitability of a noise-diffusion network to reproduce the spatiotemporal covariance structure of the data. We apply our approach to 7T fMRI recordings acquired during resting state (RS) and visual field mapping (VFM). Our results reveal different EC interactions and changes in cortical excitability in RS and VFM, and point to a reconfiguration of feedforward and feedback interactions across the visual system. We conclude that the propagation of BOLD activity has functional relevance, as it reveals directed interactions and changes in cortical excitability in a task-dependent manner.

neuroscience↗

bioRxiv2017

Task-related effective connectivity reveals that the cortical rich club gates cortex-wide communication

Higher cognition may require the globally coordinated integration of specialized brain regions into functional networks. A collection of structural cortical hubs - referred to as the rich club - has been hypothesized to support task-specific functional integration. In the present paper, we use a whole-cortex model to estimate directed interactions between 68 cortical regions from fMRI activity for four different tasks (reflecting different cognitive domains) and resting state. We analyze the state-dependent input and output effective connectivity of the structural rich club and relate these to whole-cortex dynamics and network reconfigurations. We find that the cortical rich club exhibits an increase in outgoing effective connectivity during task performance as compared to rest while incoming connectivity remains constant. Increased outgoing connectivity targets a sparse set of peripheral regions with specific regions strongly overlapping between tasks. At the same time, community detection analyses reveal massive reorganizations of interactions among peripheral regions, including those serving as target of increased rich club output. This suggests that while peripheral regions may play a role in several tasks, their concrete interplay might nonetheless be task-specific. Furthermore, we observe that whole-cortex dynamics are faster during task as compared to rest. The decoupling effects usually accompanying faster dynamics appear to be counteracted by the increased rich club outgoing effective connectivity. Together our findings speak to a gating mechanism of the rich club that supports fast-paced information exchange among relevant peripheral regions in a task-specific and goal-directed fashion, while constantly listening to the whole network.

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bioRxiv2017

Harmonic brain modes: a unifying framework for linking space and time in brain dynamics

A fundamental characteristic of spontaneous brain activity is coherent oscillations covering a wide range of frequencies. Interestingly, these temporal oscillations are highly correlated among spatially distributed cortical areas forming structured correlation patterns known as the resting state networks, although the brain is never truly at rest. Here, we introduce the concept of \"harmonic brain modes\" - fundamental building blocks of complex spatiotemporal patterns of neural activity. We define these elementary harmonic brain modes as harmonic modes of structural connectivity; i.e. connectome harmonics, yielding fully synchronous neural activity patterns with different frequency oscillations emerging on and constrained by the particular structure of the brain. Hence, this particular definition implicitly links the hitherto poorly understood dimensions of space and time in brain dynamics and its underlying anatomy. Further we show how harmonic brain modes can explain the relationship between neurophysiological, temporal and network-level changes in the brain across different mental states; (wakefulness, sleep, anaesthesia, psychedelic). Notably, when decoded as activation of connectome harmonics, spatial and temporal characteristics of neural activity naturally emerge from the interplay between excitation and inhibition and this critical relation fits the spatial, temporal and neurophysiological changes associated with different mental states. Thus, the introduced framework of harmonic brain modes not only establishes a relation between the spatial structure of correlation patterns and temporal oscillations (linking space and time in brain dynamics), but also enables a new dimension of tools for understanding fundamental principles underlying brain dynamics in different states of consciousness.

neuroscience↗

bioRxiv2017

Connectome-harmonic decomposition of human brain activity reveals dynamical repertoire re-organisation under LSD

Recent studies have started to elucidate the effects of lysergic acid diethylamide (LSD) on the human brain but the underlying dynamics are not yet fully understood. Here we used connectome-harmonic decomposition, a novel method to investigate the dynamical changes in brain states. We found that LSD alters the energy and the power of individual harmonic brain states in a frequency-selective manner. Remarkably, this leads to an expansion of the repertoire of active brain states, suggestive of a general re-organization of brain dynamics given the non-random increase in co-activation across frequencies. Interestingly, the frequency distribution of the active repertoire of brain states under LSD closely follows power-laws indicating a re-organization of the dynamics at the edge of criticality. Beyond the present findings, these methods open up for a better understanding of the complex brain dynamics in health and disease.\n\nHighlightsO_LIIntroducing a novel connectome-harmonic decomposition of human brain activity, which is used to study the dynamics after administration of lysergic acid diethylamide (LSD)\nC_LIO_LILSD alters the total energy and power of individual harmonics in a frequency-selective manner and increases the size of the dynamical repertoire of these brain states\nC_LIO_LIThe repertoire expansion occurs in a non-random fashion suggesting a re-organization of brain dynamics at the edge of criticality\nC_LI

neuroscience↗

bioRxiv2017

Inferring multi-scale neural mechanisms with brain network modelling

The neurophysiological processes underlying non-invasive brain activity measurements are not well understood. Here, we developed a novel connectome-based brain network model that integrates individual structural and functional data with neural population dynamics to support multi-scale neurophysiological inference. Simulated populations were linked by structural connectivity and, as a novelty, driven by electroencephalography (EEG) source activity. Simulations not only predicted subjects individual resting-state functional magnetic resonance imaging (fMRI) time series and spatial network topologies over 20 minutes of activity, but more importantly, they also revealed precise neurophysiological mechanisms that underlie and link six empirical observations from different scales and modalities: (1) slow resting-state fMRI oscillations, (2) spatial topologies of functional connectivity networks, (3) excitation-inhibition balance, (4, 5) pulsed inhibition on short and long time scales, and (6) fMRI power-law scaling. These findings underscore the potential of this new modelling framework for general inference and integration of neurophysiological knowledge to complement empirical studies.

bioinformatics↗

bioRxiv2017

Human Resting-State Electrophysiological Networks In The Alpha Frequency Band: Evidence From Magnetoencephalographic Source Imaging

In the resting-state, extended regions of the human cortex engage in electrical oscillations within the alpha-frequency band (7-14 Hz) that can be measured outside the head by magnetoencephalography (MEG). Given the accumulating evidence that alpha oscillations play a fundamental role in attentional processing and working memory, it becomes increasingly important to characterize their cortical organization. Event-related studies have demonstrated that attentional allocation can modulate alpha power selectively within the visual, auditory, and somatosensory cortices, as well as in higher-level regions. Such studies demonstrate the existence of multiple generators by exploiting experimental contrasts and trial-averaging. The identification of alpha generators from resting-state data alone has proven much harder and, consequently, relatively little is known about their organization: Apart from the classical visual, somatosensory, and auditory rhythms, it is unclear how many more generators can be observed with MEG and how they are organized into functional networks. Such knowledge, however, possibly enables to delineate separate cognitive, perceptual, and motor processes that co-occur in the resting-state and is therefore important. In this study we use the resting-state MEG data-set provided by the Human Connectome Project to identify cortical alpha generators and to characterize their organization into functional networks. The large number of subjects (N = 94), multiple scans per subject, and state-of-the-art surface-based cortical registration enable a detailed characterization of alpha in human cortex. By applying non-negative matrix factorization to source-projected power fluctuations, we identify 16 reliable cortical generators in each hemisphere. These include the classical sensory alpha rhythms as well as several additional ones in the lateral occipital and temporal lobes and in inferior parietal cortex. We show that the generators are coordinated across hemispheres and hence form resting-state networks (RSNs), two of which are the default mode network (DMN) and the ventral attention network (VAN). Our study hence provides a further subdivision of RSNs within the alpha frequency band and shows that these RSNs are supported by alpha generators. As such, it links the classical literature on human alpha with more recent research into electrophysiological RNSs.

neuroscience↗

bioRxiv2017

Stereotypical Modulations In Dynamic Functional Connectivity Explained By Changes In BOLD Variance

Spontaneous activity measured in human subject under the absence of any task exhibits complex patterns of correlation that largely correspond to large-scale functional topographies obtained with a wide variety of cognitive and perceptual tasks. These \"resting state networks\" (RSNs) fluctuate over time, forming and dissolving on the scale of seconds to minutes. While these fluctuations, most prominently those of the default mode network, have been linked to cognitive function, it remains unclear whether they result from random noise or whether they index a non-stationary process which could be described as state switching.\n\nIn this study, we use a sliding windows-approach to relate temporal dynamics of RSNs to global modulations in correlation and BOLD variance. We compare empirical data, phase-randomized surrogate data, and data simulated with a stationary model. We find that RSN time courses exhibit a large amount of coactivation in all three cases, and that the modulations in their activity are closely linked to global dynamics of the underlying BOLD signal.\n\nWe find that many properties of the observed fluctuations in FC and BOLD, including their ranges and their correlations amongst each other, are explained by fluctuations around the average FC structure. However, we also encounter interesting characteristics that are not explained in this way. In particular, we find that the brain spends more time in the troughs of modulations than can be expected from stationary dynamics.

neuroscience↗

bioRxiv2017

Effective connectivity inferred from fMRI transition dynamics during movie viewing points to a balanced reconfiguration of cortical interactions

Our behavior entails a flexible and context-sensitive interplay between brain areas to integrate information according to goal-directed requirements. How-ever, the neural mechanisms governing the entrainment of functionally specialized brain areas remain poorly understood. In particular, the question arises whether observed changes in the regional activity for different cognitive conditions are explained by modifications of the inputs to the brain or its connectivity? We observe that transitions of fMRI activity between areas convey information about the tasks performed by 19 subjects, watching a movie versus a black screen (rest). We use a model-based framework that explains this spatiotemporal functional connectivity pattern by the local variability for 66 cortical regions and the network effective connectivity between them. We find that, among the estimated model parameters, movie viewing affects to a larger extent the local activity, which we interpret as extrinsic changes related to the increased stimulus load. However, detailed changes in the effective connectivity preserve a balance in the propagating activity and select specific pathways such that high-level brain regions integrate visual and auditory information, in particular boosting the communication between the two brain hemispheres. These findings speak to a dynamic coordination underlying the functional integration in the brain.

neuroscience↗

bioRxiv2017

Consistent local dynamics in the brain across sessions are revealed by whole brain modeling of resting state activity

Resting state fMRI has been the primary tool for studying the functional organization of the human brain. However, even at so-called \"rest\", ongoing brain activity and its underlying physiological organization is highly dynamic and yet most of the information generated so far comes from group analysis. Here we developed an imaging-based technique capable of portraying information of local dynamics at a single-subject level reliably by using a whole-brain model that estimates a local bifurcation parameter, which reflects if a brain region presents stable, asynchronous or transitory oscillations. Using 50 longitudinal resting state sessions of one single subject and single resting state sessions from a group of 50 participants we demonstrated that individual global and local brain dynamics can be estimated consistently with respect to a reference group using only a scanning time of 15 to 20 minutes. We also showed that brain hubs are closer to a transition point between synchronous and asynchronous oscillatory dynamics and that dynamics in frontal areas have larger variations compared to other regions. Finally, we analyzed the variability and error of these dynamics and found high symmetry between hemispheres, which interestingly was reduced by adding more sessions. The framework presented here can be used to study functional brain dynamics on an individual level, opening new avenues for possible clinical applications.\n\nBullet pointsLocal brain dynamics are consistent across scans.\n\nFour scans of five minutes each are enough to get highly reliable and consistent results.\n\nHub areas are in a transition point between a synchronous and asynchronous regime.\n\nVariability and error of local dynamics presented high symmetry between hemispheres.

neuroscience↗