- Pompeu Fabra University, Center for Brain and Cognition, Graduate StudentUniversity of Oxford, Psychiatry, Post-Docadd
- Biomedical Engineer, PhD in Computational Neuroscienceedit
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We investigate differences between the dynamic exploration of resting-state functional connectivity (FC) states using fMRI data from 71 schizophrenia (SZ) patients and 74 healthy controls (HCs) by employing the Leading Eigenvector... more
We investigate differences between the dynamic exploration of resting-state functional connectivity (FC) states using fMRI data from 71 schizophrenia (SZ) patients and 74 healthy controls (HCs) by employing the Leading Eigenvector Dynamics Analysis (LEiDA) method to provide potential biomarkers of this disorder. We found a reduced ability of SZ patients to access and remain in a state of global BOLD phase coherence. Functionally meaningful states presented increased occurrence, limiting probability and altered dynamic transitions in SZ patients. These findings expose pronounced differences between SZ patients and HCs - supporting and developing current knowledge regarding disrupted brain dynamics in schizophrenia.
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Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding of sounds forming a complex... more
Information encoding has received a wide neuroscientific attention, but the underlying rapid spatiotemporal brain dynamics remain largely unknown. Here, we investigated the rapid brain mechanisms for encoding of sounds forming a complex temporal sequence. Specifically, we used magnetoencephalography (MEG) to record the brain activity of 68 participants while they listened to a highly structured musical prelude. Functional connectivity analyses performed using phase synchronisation and graph theoretical measures showed a large network of brain areas recruited during encoding of sounds, comprising primary and secondary auditory cortices, frontal operculum, insula, hippocampus and basal ganglia. Moreover, our results highlighted the rapid transition of brain activity from primary auditory cortex to higher order association areas including insula and superior temporal pole within a whole-brain network, occurring during the first 220 ms of the encoding process. Further, we discovered that individual differences along cognitive abilities and musicianship modulated the degree centrality of the brain areas implicated in the encoding process. Indeed, participants with higher musical expertise presented a stronger centrality of superior temporal gyrus and insula, while individuals with high working memory abilities showed a stronger centrality of frontal operculum. In conclusion, our study revealed the rapid unfolding of brain network dynamics responsible for the encoding of sounds and their relationship with individual differences, showing a complex picture which extends beyond the well-known involvement of auditory areas. Indeed, our results expanded our understanding of the general mechanisms underlying auditory pattern encoding in the human brain.
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Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in... more
Spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations, and in particular the link with locally-detected ultra-slow oscillations, are not fully understood. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from female rats across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with t...
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Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more... more
Disorders of consciousness are complex conditions characterised by persistent loss of responsiveness due to brain injury. They present diagnostic challenges and limited options for treatment, and highlight the urgent need for a more thorough understanding of how human consciousness arises from coordinated neural activity. The increasing availability of multimodal neuroimaging data has given rise to a wide range of clinically- and scientifically-motivated modelling efforts, seeking to improve data-driven stratification of patients, to identify causal mechanisms for patient pathophysiology and loss of consciousness more broadly, and to develop simulations as a means of testing in silico potential treatment avenues to restore consciousness. As a dedicated Working Group of clinicians and neuroscientists of the international Curing Coma Campaign, here we provide our framework and vision to understand the diverse statistical and generative computational modelling approaches that are being...
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IntroductionRecurrence in major depression disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most... more
IntroductionRecurrence in major depression disorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain’s dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode.MethodsSixty drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying leading eigenvector dynamics analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation data...
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Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using... more
Schizophrenia is a chronic psychotic disorder characterized by the disruption of thought processes, perception, cognition, and behaviors, for which there is still a lack of objective and quantitative biomarkers in brain activity. Using functional magnetic resonance imaging (fMRI) data from an open-source database, this study investigated differences between the dynamic exploration of resting-state networks in 71 schizophrenia patients and 74 healthy controls. Focusing on recurrent states of phase coherence in fMRI signals, brain activity was examined for intergroup differences through the lens of dynamical systems theory. Results showed reduced fractional occupancy and dwell time of a globally synchronized state in schizophrenia. Conversely, patients exhibited increased fractional occupancy, dwell time and limiting probability of being in states during which canonical functional networks—i.e., Limbic, Dorsal Attention and Somatomotor—synchronized in anti-phase with respect to the re...
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Spontaneous fluctuations in functional Magnetic Resonance Imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations - and... more
Spontaneous fluctuations in functional Magnetic Resonance Imaging (fMRI) signals correlate across distant brain areas, shaping functionally relevant intrinsic networks. However, the generative mechanism of fMRI signal correlations - and in particular their link with locally-detected ultra-slow oscillations - remain unclear. To investigate this link, we record ultrafast ultrahigh field fMRI signals (9.4 Tesla, temporal resolution = 38 milliseconds) from rat brains across three anesthesia conditions. Power at frequencies extending up to 0.3 Hz is detected consistently across rat brains, and is modulated by anesthesia level. Principal component analysis reveals a repertoire of modes, in which transient oscillations organize with fixed phase relationships across distinct cortical and subcortical structures. Oscillatory modes are found to vary between conditions, resonating at faster frequencies under medetomidine sedation and reducing both in number, frequency, and duration with the add...
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Brain activity during rest has been demonstrated to evolve through a repertoire of functional connectivity (FC) patterns, whose alterations may provide biomarkers of schizophrenia - a psychotic disorder characterized by dysfunctional... more
Brain activity during rest has been demonstrated to evolve through a repertoire of functional connectivity (FC) patterns, whose alterations may provide biomarkers of schizophrenia - a psychotic disorder characterized by dysfunctional brain connectivity. In this study, differences between the dynamic exploration of resting-state networks using functional magnetic resonance imaging (fMRI) data from 71 schizophrenia patients and 74 healthy controls were investigated using a method focusing on the dominant fMRI signal phase coherence pattern at each time point. Through the lens of dynamical systems theory, brain activity in the form of temporal FC state trajectories was examined for intergroup differences by calculating the fractional occupancy, dwell time, limiting probability of each state and the transition probabilities between states. Results showed reduced fractional occupancy of a globally synchronized state in schizophrenia. Conversely, FC states overlapping with canonical funct...
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Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test... more
Computational models lie at the intersection of basic neuroscience and healthcare applications because they allow researchers to test hypotheses in silico and predict the outcome of experiments and interactions that are very hard to test in reality. Yet, what is meant by “computational model” is understood in many different ways by researchers in different fields of neuroscience and psychology, hindering communication and collaboration. In this review, we point out the state of the art of computational modeling in Electroencephalography (EEG) and outline how these models can be used to integrate findings from electrophysiology, network-level models, and behavior. On the one hand, computational models serve to investigate the mechanisms that generate brain activity, for example measured with EEG, such as the transient emergence of oscillations at different frequency bands and/or with different spatial topographies. On the other hand, computational models serve to design experiments a...
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Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in... more
Decades of neurobiological research have disclosed the diverse manners in which the response properties of neurons are dynamically modulated to support adaptive cognitive functions. This neuromodulation is achieved through alterations in the biophysical properties of the neuron. However, changes in cognitive function do not arise directly from the modulation of individual neurons, but are mediated by population dynamics in mesoscopic neural ensembles. Understanding this multiscale mapping is an important but nontrivial issue. Here, we bridge these different levels of description by showing how computational models parametrically map classic neuromodulatory processes onto systems-level models of neural activity. The ensuing critical balance of systems-level activity supports perception and action, although our knowledge of this mapping remains incomplete. In this way, quantitative models that link microscale neuronal neuromodulation to systems-level brain function highlight gaps in knowledge and suggest new directions for integrating theoretical and experimental work.
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The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m... more
The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. Here, we demonstrate that the CF-PLM successfully retrieves the (different) frequencies of the original broad-band signals involved in the connectivity proces...
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Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson’s disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on... more
Beyond the established effects of subthalamic nucleus deep brain stimulation (STN-DBS) in reducing motor symptoms in Parkinson’s disease, recent evidence has highlighted the effect on non-motor symptoms. However, the impact of STN-DBS on disseminated networks remains unclear. This study aimed to perform a quantitative evaluation of network-specific modulation induced by STN-DBS using Leading Eigenvector Dynamics Analysis (LEiDA). We calculated the occupancy of resting-state networks (RSNs) in functional MRI data from 10 patients with Parkinson’s disease implanted with STN-DBS and statistically compared between ON and OFF conditions. STN-DBS was found to specifically modulate the occupancy of networks overlapping with limbic RSNs. STN-DBS significantly increased the occupancy of an orbitofrontal limbic subsystem with respect to both DBS OFF (p = 0.0057) and 49 age-matched healthy controls (p = 0.0033). Occupancy of a diffuse limbic RSN was increased with STN-DBS OFF when compared wit...
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Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here we leveraged the differential outcome in responders and non-responders to psilocybin (10mg and 25mg, 7 days... more
Psilocybin therapy for depression has started to show promise, yet the underlying causal mechanisms are not currently known. Here we leveraged the differential outcome in responders and non-responders to psilocybin (10mg and 25mg, 7 days apart) therapy for depression - to gain new insights into regions and networks implicated in the restoration of healthy brain dynamics. We used whole-brain modelling to fit the spatiotemporal brain dynamics at rest in both responders and non-responders before treatment. Dynamic sensitivity analysis of systematic perturbation of these models enabled us to identify specific brain regions implicated in a transition from a depressive brain state to a heathy one. Binarizing the sample into treatment responders (>50% reduction in depressive symptoms) versus non-responders enabled us to identify a subset of regions implicated in this change. Interestingly, these regions correlate with in vivo density maps of serotonin receptors 5-HT2A and 5-HT1A, which ...
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The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond... more
The modern understanding of sleep is based on the classification of sleep into stages defined by their electroencephalography (EEG) signatures, but the underlying brain dynamics remain unclear. Here we aimed to move significantly beyond the current state-of-the-art description of sleep, and in particular to characterise the spatiotemporal complexity of whole-brain networks and state transitions during sleep. In order to obtain the most unbiased estimate of how whole-brain network states evolve through the human sleep cycle, we used a Markovian data-driven analysis of continuous neuroimaging data from 57 healthy participants falling asleep during simultaneous functional magnetic resonance imaging (fMRI) and EEG. This Hidden Markov Model (HMM) facilitated discovery of the dynamic choreography between different whole-brain networks across the wake-non-REM sleep cycle. Notably, our results reveal key trajectories to switch within and between EEG-based sleep stages, while highlighting th...
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Autism has been characterised by different behavioural and cognitive profiles compared to typically developing (TD) individuals, and increasingly these differences have been associated with differences in structural and functional brain... more
Autism has been characterised by different behavioural and cognitive profiles compared to typically developing (TD) individuals, and increasingly these differences have been associated with differences in structural and functional brain connectivity. It is currently unknown as to whether autistic and TD listeners process music in the same way: emotionally, mnemonically, and perceptually. The present study explores the brain’s dynamical landscape linked to music familiarity in an fMRI dataset from autistic and TD individuals. Group analysis using leading eigenvector dynamics analysis (LEiDA) revealed significantly higher probability of occurrence of a brain network in TD compared to autistic individuals during listening to familiar music. This network includes limbic and paralimbic areas (amygdala, hippocampus, parahippocampal gyrus, and temporal pole). No significant differences were found between autistic and TD individuals while listening to a scrambled, i.e. unfamiliar and more u...
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modelling the impact of structural connectivity changes on the dynamics of spontaneous functional networks
A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain under debate.... more
A rich repertoire of oscillatory signals is detected from human brains with electro- and magnetoencephalography (EEG/MEG). However, the principles underwriting coherent oscillations and their link with neural activity remain under debate. Here, we revisit the mechanistic hypothesis that transient brain rhythms are a signature of metastable synchronization, occurring at reduced collective frequencies due to delays between brain areas. We consider a system of damped oscillators – approximating the short-lived gamma-frequency oscillations generated within neuronal circuits – coupled according to the diffusion weighted tractography between brain areas. Varying only the global coupling strength and conduction speed, we identify a critical regime where spatially and spectrally resolved metastable oscillatory modes (MOMs) emerge at sub-gamma frequencies, approximating the MEG power spectra from 89 healthy individuals at rest. Further, we demonstrate that the frequency, duration, and scale ...
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ABSTRACTMusic is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal... more
ABSTRACTMusic is a non-verbal human language, built on logical structures and articulated in balanced hierarchies between sounds, offering excellent opportunities to explore how the brain creates meaning for complex spatiotemporal auditory patterns. Using the high temporal resolution of magnetoencephalography in 70 participants, we investigated their unfolding brain dynamics during the recognition of previously memorized J.S. Bach’s musical patterns from prelude in C minor BWV 847 compared to novel patterns matched in terms of entropy and information content. Remarkably, the recognition of the memorized music ignited a widespread brain network comprising primary auditory cortex, superior temporal gyrus, insula, frontal operculum, cingulate gyrus, orbitofrontal cortex, basal ganglia, thalamus and hippocampus. Furthermore, measures of both brain activity and functional connectivity presented an overall increase over time, following the evolution and unfolding of the memorized musical ...
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Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the... more
Remarkable progress has come from whole-brain models linking anatomy and function. Paradoxically, it is not clear how a neuronal dynamical system running in the fixed human anatomical connectome can give rise to the rich changes in the functional repertoire associated with human brain function, which is impossible to explain through long-term plasticity. Neuromodulation evolved to allow for such flexibility by dynamically updating the effectivity of the fixed anatomical connectivity. Here, we introduce a theoretical framework modeling the dynamical mutual coupling between the neuronal and neurotransmitter systems. We demonstrate that this framework is crucial to advance our understanding of whole-brain dynamics by bidirectional coupling of the two systems through combining multimodal neuroimaging data (diffusion magnetic resonance imaging [dMRI], functional magnetic resonance imaging [fMRI], and positron electron tomography [PET]) to explain the functional effects of specific seroto...
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The neuroscience of jazz improvisation has shown promising results for understanding domain-specific and domain-general processes of creativity. Here, we used fMRI to measure for the first time the dynamic neural substrates of musical... more
The neuroscience of jazz improvisation has shown promising results for understanding domain-specific and domain-general processes of creativity. Here, we used fMRI to measure for the first time the dynamic neural substrates of musical creativity in 16 skilled jazz pianists while they played by memory, improvised freely (iFreely) and by melody (iMelody), and during resting-state. We used the Leading Eigenvector Dynamics Analysis (LEiDA) to examine how different modes of improvisation (musical creativity) evolve over time, and which cognitive mechanisms are responsible for different stages of musical creation. Our results reveal that a substate comprising auditory, sensorimotor and posterior salience networks had a significantly higher probability of occurrence (POc) in both modes of improvisation than in resting-state and play by memory. Another substate comprising the default mode (DMN), executive control (ECN) and language networks had significantly lower POc in iFreely than in res...
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A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a... more
A fundamental problem in systems neuroscience is how to force a transition from one brain state to another by external driven stimulation in, for example, wakefulness, sleep, coma, or neuropsychiatric diseases. This requires a quantitative and robust definition of a brain state, which has so far proven elusive. Here, we provide such a definition, which, together with whole-brain modeling, permits the systematic study in silico of how simulated brain stimulation can force transitions between different brain states in humans. Specifically, we use a unique neuroimaging dataset of human sleep to systematically investigate where to stimulate the brain to force an awakening of the human sleeping brain and vice versa. We show where this is possible using a definition of a brain state as an ensemble of “metastable substates,” each with a probabilistic stability and occurrence frequency fitted by a generative whole-brain model, fine-tuned on the basis of the effective connectivity. Given the...
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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... more
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 brain’s dynamical landscape might be changed in altered states of consciousness, such as the psychedelic state. The present study investigated changes in the brain’s 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....
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Human social behaviour is complex, and the biological and neural mechanisms underpinning it remain debated. A particularly interesting social phenomenon is our ability and tendency to fall into synchrony with other humans. Our ability to... more
Human social behaviour is complex, and the biological and neural mechanisms underpinning it remain debated. A particularly interesting social phenomenon is our ability and tendency to fall into synchrony with other humans. Our ability to coordinate actions and goals relies on the ability to distinguish between and integrate self and other, which when impaired can lead to devastating consequences. Interpersonal synchronization has been a widely used framework for studying action coordination and self-other integration, showing that in simple interactions, such as joint finger tapping, complex interpersonal dynamics emerge. Here we propose a computational model of self-other integration via within- and between-person action-perception links, implemented as a simple Kuramoto model with four oscillators. The model abstracts each member of a dyad as a unit consisting of two connected oscillators, representing intrinsic processes of perception and action. By fitting this model to data fro...
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Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation... more
Human neuroimaging research has revealed that wakefulness and sleep involve very different activity patterns. Yet, it is not clear why brain states differ in their dynamical complexity, e.g. in the level of integration and segregation across brain networks over time. Here, we investigate the mechanisms underlying the dynamical stability of brain states using a novel off-line in silico perturbation protocol. We first adjust a whole-brain computational model to the basal dynamics of wakefulness and deep sleep recorded with fMRI in two independent human fMRI datasets. Then, the models of sleep and awake brain states are perturbed using two distinct multifocal protocols either promoting or disrupting synchronization in randomly selected brain areas. Once perturbation is halted, we use a novel measure, the Perturbative Integration Latency Index (PILI), to evaluate the recovery back to baseline. We find a clear distinction between models, consistently showing larger PILI in wakefulness th...
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Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have... more
Over the last decade, we have observed a revolution in brain structural and functional Connectomics. On one hand, we have an ever-more detailed characterization of the brain's white matter structural connectome. On the other, we have a repertoire of consistent functional networks that form and dissipate over time during rest. Despite the evident spatial similarities between structural and functional connectivity, understanding how different time-evolving functional networks spontaneously emerge from a single structural network requires analyzing the problem from the perspective of complex network dynamics and dynamical system's theory. In that direction, bottom-up computational models are useful tools to test theoretical scenarios and depict the mechanisms at the genesis of resting-state activity. Here, we provide an overview of the different mechanistic scenarios proposed over the last decade via computational models. Importantly, we highlight the need of incorporating addi...