ORCID Profile
0000-0002-8251-8860
Current Organisation
Aix-Marseille Université
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Publisher: Elsevier BV
Date: 02-2013
DOI: 10.1016/J.NEUROIMAGE.2012.10.039
Abstract: Neural fields are spatially continuous state variables described by integro-differential equations, which are well suited to describe the spatiotemporal evolution of cortical activations on multiple scales. Here we develop a multi-resolution approximation (MRA) framework for the integro-difference equation (IDE) neural field model based on semi-orthogonal cardinal B-spline wavelets. In this way, a flexible framework is created, whereby both macroscopic and microscopic behavior of the system can be represented simultaneously. State and parameter estimation is performed using the expectation maximization (EM) algorithm. A synthetic ex le is provided to demonstrate the framework.
Publisher: Cold Spring Harbor Laboratory
Date: 22-02-2023
DOI: 10.1101/2023.02.19.23285768
Abstract: Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of inter-regional correlations, whose structured flexibility relates to cognitive performance. Here we analyze resting state dynamic Functional Connectivity (dFC) in a cohort of older adults, including amnesic Mild Cognitive Impairment (aMCI, N = 34) and Alzheimer’s Disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively “super-normal” (SN, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher-order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections. Brain functions emerge from the coordinated dynamics of many brain regions. Dynamic Functional Connectivity (dFC) analyses are a key tool to describe such dynamic complexity and have been shown to be good predictors of cognitive performance. This is particularly true in the case of Alzheimer’s Disease (AD) in which an impoverished dFC could indicate compromised functional reserve due to the detrimental effects of neurodegeneration. Here we observe that in healthy ageing dFC is indeed spatiotemporally organized, as reflected by high-order correlations between multiple regions. However, in people with aMCI or AD, dFC becomes less “entangled”, more random-like, and intermittently bursty. We speculate that this degraded spatiotemporal coordination may reflect dysfunctional information processing, thus ultimately leading to worsening of cognitive deficits.
Publisher: MIT Press
Date: 17-10-2023
DOI: 10.1162/NETN_A_00332
Abstract: Spontaneous activity during the resting state, tracked by BOLD fMRI imaging, or shortly rsfMRI, gives rise to brain-wide dynamic patterns of inter-regional correlations, whose structured flexibility relates to cognitive performance. Here we analyze resting state dynamic Functional Connectivity (dFC) in a cohort of older adults, including amnesic Mild Cognitive Impairment (aMCI, N = 34) and Alzheimer’s Disease (AD, N = 13) patients, as well as normal control (NC, N = 16) and cognitively “super-normal” (SN, N = 10) subjects. Using complementary state-based and state-free approaches, we find that resting state fluctuations of different functional links are not independent but are constrained by high-order correlations between triplets or quadruplets of functionally connected regions. When contrasting patients with healthy subjects, we find that dFC between cingulate and other limbic regions is increasingly bursty and intermittent when ranking the four groups from SNC to NC, aMCI and AD. Furthermore, regions affected at early stages of AD pathology are less involved in higher-order interactions in patient than in control groups, while pairwise interactions are not significantly reduced. Our analyses thus suggest that the spatiotemporal complexity of dFC organization is precociously degraded in AD and provides a richer window into the underlying neurobiology than time-averaged FC connections.
Publisher: Cold Spring Harbor Laboratory
Date: 09-02-2020
DOI: 10.1101/2020.02.08.940072
Abstract: Seizures are a disruption of normal brain activity present across a vast range of species, diseases, and conditions. Here we introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory, and applied it to the analysis of EEG data. The “dynamotype” of a seizure is the part of its dynamic composition that defines its observable characteristics, including how it starts, evolves and terminates. Analyzing over 2000 focal-onset seizures recorded from 7 epilepsy centers on five continents, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain dynamotypes are more common), non-bijectivity (a patient may display multiple dynamotypes) and pairing preference (multiple dynamotypes may occur during one seizure). TSD not only provides a way to stratify patients in complement to present practical classifications but also guides biophysically based mechanistic approaches and provides a language to describe the most critical features of seizure dynamics. Taxonomy of Seizure Dynamics (TSD) provides a rigorous method for classifying and quantifying seizures and a principled framework for understanding seizure initiation and propagation.
Publisher: Wiley
Date: 20-05-2017
DOI: 10.1111/EPI.13791
Abstract: Epileptogenic networks are defined by the brain regions involved in the production and propagation of epileptic activities. In this review we describe the historical, methodologic, and conceptual bases of this model in the analysis of electrophysiologic intracerebral recordings. In the context of epilepsy surgery, the determination of cerebral regions producing seizures (i.e., the "epileptogenic zone") is a crucial objective. In contrast with a traditional focal vision of focal drug-resistant epilepsies, the concept of epileptogenic networks has been progressively introduced as a model better able to describe the complexity of seizure dynamics and realistically describe the distribution of epileptogenic anomalies in the brain. The concept of epileptogenic networks is historically linked to the development of the stereoelectroencephalography (SEEG) method and subsequent introduction of means of quantifying the recorded signals. Seizures, and preictal and interictal discharges produce clear patterns on SEEG. These patterns can be analyzed utilizing signal analysis methods that quantify high-frequency oscillations or changes in functional connectivity. Dramatic changes in SEEG brain connectivity can be described during seizure genesis and propagation within cortical and subcortical regions, associated with the production of different patterns of seizure semiology. The interictal state is characterized by networks generating abnormal activities (interictal spikes) and also by modified functional properties. The introduction of novel approaches to large-scale modeling of these networks offers new methods in the goal of better predicting the effects of epilepsy surgery. The epileptogenic network concept is a key factor in identifying the anatomic distribution of the epileptogenic process, which is particularly important in the context of epilepsy surgery.
Publisher: Frontiers Media SA
Date: 23-06-2015
Publisher: eLife Sciences Publications, Ltd
Date: 09-07-2021
DOI: 10.7554/ELIFE.67400
Abstract: Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r = 0.37, p .0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
Publisher: Cold Spring Harbor Laboratory
Date: 25-11-2020
DOI: 10.1101/2020.11.25.393017
Abstract: Brain activity during rest displays complex, rapidly evolving patterns in space and time. Structural connections comprising the human connectome are hypothesized to impose constraints on the dynamics of this activity. Here, we use magnetoencephalography (MEG) to quantify the extent to which fast neural dynamics in the human brain are constrained by structural connections inferred from diffusion MRI tractography. We characterize the spatio-temporal unfolding of whole-brain activity at the millisecond scale from source-reconstructed MEG data, estimating the probability that any two brain regions will significantly deviate from baseline activity in consecutive time epochs. We find that the structural connectome relates to, and likely affects, the rapid spreading of neuronal avalanches, evidenced by a significant association between these transition probabilities and structural connectivity strengths (r=0.37, p .0001). This finding opens new avenues to study the relationship between brain structure and neural dynamics.
Publisher: Elsevier BV
Date: 09-2010
Publisher: Springer Berlin Heidelberg
Date: 2007
Publisher: Public Library of Science (PLoS)
Date: 29-08-2008
Publisher: Elsevier BV
Date: 2016
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: eLife Sciences Publications, Ltd
Date: 29-05-2020
Publisher: Springer Berlin Heidelberg
Date: 2008
Publisher: Springer Science and Business Media LLC
Date: 08-2017
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 22-11-2022
DOI: 10.1212/WNL.0000000000201200
Abstract: Amyotrophic lateral sclerosis (ALS) is a multisystem disorder, as supported by clinical, molecular, and neuroimaging evidence. As a consequence, predicting clinical features requires a description of large-scale neuronal dynamics. Normally, brain activity dynamically reconfigures over time, recruiting different brain areas. Brain pathologies induce stereotyped dynamics which, in turn, are linked to clinical impairment. Hence, based on recent evidence showing that brain functional networks become hyperconnected as ALS progresses, we hypothesized that the loss of flexible dynamics in ALS would predict the symptoms severity. To test this hypothesis, we quantified flexibility using the “functional repertoire” (i.e., the number of configurations of active brain areas) as measured from source-reconstructed magnetoencephalography (MEG) in patients with ALS and healthy controls. The activity of brain areas was reconstructed in the classic frequency bands, and the functional repertoire was estimated to quantify spatiotemporal fluctuations of brain activity. Finally, we built a k-fold cross-validated multilinear model to predict the in idual clinical impairment from the size of the functional repertoire. Comparing 42 patients with ALS and 42 healthy controls, we found a more stereotyped brain dynamics in patients with ALS ( p 0.05), as conveyed by the smaller functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group showed significant correlations in both the delta and the theta frequency bands. Furthermore, through a k -fold cross-validated multilinear regression model, we found that the functional repertoire predicted both clinical staging ( p 0.001 and p 0.01, in the delta and theta bands, respectively) and symptoms severity ( p 0.001, in both the delta and theta bands). Our work shows that (1) ALS pathology reduces the flexibility of large-scale brain dynamics, (2) subcortical regions play a key role in determining brain dynamics, and (3) reduced brain flexibility predicts disease stage and symptoms severity. Our approach provides a noninvasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new opportunities in disease management and a framework to test, in the near future, the effects of disease-modifying interventions at the whole-brain level.
Publisher: Elsevier BV
Date: 2017
DOI: 10.1016/J.NEUROIMAGE.2016.04.049
Abstract: In idual variability has clear effects upon the outcome of therapies and treatment approaches. The customization of healthcare options to the in idual patient should accordingly improve treatment results. We propose a novel approach to brain interventions based on personalized brain network models derived from non-invasive structural data of in idual patients. Along the ex le of a patient with bitemporal epilepsy, we show step by step how to develop a Virtual Epileptic Patient (VEP) brain model and integrate patient-specific information such as brain connectivity, epileptogenic zone and MRI lesions. Using high-performance computing, we systematically carry out parameter space explorations, fit and validate the brain model against the patient's empirical stereotactic EEG (SEEG) data and demonstrate how to develop novel personalized strategies towards therapy and intervention.
Publisher: Cold Spring Harbor Laboratory
Date: 07-02-2022
DOI: 10.1101/2022.02.07.22270581
Abstract: Amyotrophic lateral sclerosis (ALS) is a multisystem disorder. This view is widely supported by clinical, molecular and neuroimaging evidence. As a consequence, predicting clinical features requires a comprehensive description of large-scale brain activity. Flexible dynamics is key to support complex adaptive responses. In health, brain activity reconfigures over time, involving different brain areas. Brain pathologies can induce more stereotyped dynamics, which, in turn, are linked to clinical impairment. Hence, based on recent evidence that brain functional networks become more connected as ALS progresses, we hypothesized that loss of flexible dynamics in ALS would predict their clinical condition. To test this hypothesis, we quantified flexibility utilizing the “functional repertoire” (i.e. the number of unique patterns) expressed during the magnetoencephalography (MEG) recording, based on source-reconstructed signals. Specifically, 42 ALS patients and 42 healthy controls underwent MEG and MRI recordings. The activity of the brain areas was reconstructed in the classical frequency bands, and the functional repertoire was estimated to quantify spatio-temporal fluctuations of brain activity. In order to verify if the functional repertoire predicted disease severity, we built a multilinear model and validated it using a k -fold cross validation scheme. The comparison between the two groups revealed that ALS patients showed more stereotyped brain dynamics ( P 0.05), with reduced size of the functional repertoire. The relationship between the size of the functional repertoire and the clinical scores in the ALS group was investigated using Spearman’s coefficient, showing significant correlations in both the delta and the theta frequency bands. In order to prove the robustness of our results, the k -fold cross validation model was used. We found that the functional repertoire significantly predicted both clinical staging ( P 0.001 and P 0.01, in delta and theta bands, respectively) and impairment ( P 0.001, in both delta and theta bands). In conclusion, our work shows that: 1) ALS pathology reduces the flexibility of brain dynamics 2) sub-cortical regions play a key role in determining brain dynamics 3) reduced brain flexibility predicts the stage of the disease as well as the severity of the symptoms. Based on these findings, our approach provides a non-invasive tool to quantify alterations in brain dynamics in ALS (and, possibly, other neurodegenerative diseases), thus opening new diagnostic opportunities as well as a framework to test disease-modifying interventions.
Publisher: Society for Neuroscience
Date: 05-11-2014
DOI: 10.1523/JNEUROSCI.1570-14.2014
Abstract: Brain regions generating seizures in patients with refractory partial epilepsy are referred to as the epileptogenic zone (EZ). During a seizure, paroxysmal activity is not restricted to the EZ, but may recruit other brain regions and propagate activity through large brain networks, which comprise brain regions that are not necessarily epileptogenic. The identification of the EZ is crucial for candidates for neurosurgery and requires unambiguous criteria that evaluate the degree of epileptogenicity of brain regions. To obtain such criteria and investigate the mechanisms of seizure recruitment and propagation, we develop a mathematical framework of coupled neural populations, which can interact via signaling through a slow permittivity variable. The permittivity variable captures effects evolving on slow timescales, including extracellular ionic concentrations and energy metabolism, with time delays of up to seconds as observed clinically. Our analyses provide a set of indices quantifying the degree of epileptogenicity and predict conditions, under which seizures propagate to nonepileptogenic brain regions, explaining the responses to intracerebral electric stimulation in epileptogenic and nonepileptogenic areas. In conjunction, our results provide guidance in the presurgical evaluation of epileptogenicity based on electrographic signatures in intracerebral electroencephalograms.
Publisher: eLife Sciences Publications, Ltd
Date: 21-07-2020
DOI: 10.7554/ELIFE.55632
Abstract: Seizures are a disruption of normal brain activity present across a vast range of species and conditions. We introduce an organizing principle that leads to the first objective Taxonomy of Seizure Dynamics (TSD) based on bifurcation theory. The ‘dynamotype’ of a seizure is the dynamic composition that defines its observable characteristics, including how it starts, evolves and ends. Analyzing over 2000 focal-onset seizures from multiple centers, we find evidence of all 16 dynamotypes predicted in TSD. We demonstrate that patients’ dynamotypes evolve during their lifetime and display complex but systematic variations including hierarchy (certain types are more common), non-bijectivity (a patient may display multiple types) and pairing preference (multiple types may occur during one seizure). TSD provides a way to stratify patients in complement to present clinical classifications, a language to describe the most critical features of seizure dynamics, and a framework to guide future research focused on dynamical properties.
Publisher: Mary Ann Liebert Inc
Date: 12-2014
Abstract: Spontaneous brain activity, that is, activity in the absence of controlled stimulus input or an explicit active task, is topologically organized in multiple functional networks (FNs) maintaining a high degree of coherence. These "resting state networks" are constrained by the underlying anatomical connectivity between brain areas. They are also influenced by the history of task-related activation. The precise rules that link plastic changes and ongoing dynamics of resting-state functional connectivity (rs-FC) remain unclear. Using the framework of the open source neuroinformatics platform "The Virtual Brain," we identify potential computational mechanisms that alter the dynamical landscape, leading to reconfigurations of FNs. Using a spiking neuron model, we first demonstrate that network activity in the absence of plasticity is characterized by irregular oscillations between low- litude asynchronous states and high- litude synchronous states. We then demonstrate the capability of spike-timing-dependent plasticity (STDP) combined with intrinsic alpha (8-12 Hz) oscillations to efficiently influence learning. Further, we show how alpha-state-dependent STDP alters the local area dynamics from an irregular to a highly periodic alpha-like state. This is an important finding, as the cortical input from the thalamus is at the rate of alpha. We demonstrate how resulting rhythmic cortical output in this frequency range acts as a neuronal tuner and, hence, leads to synchronization or de-synchronization between brain areas. Finally, we demonstrate that locally restricted structural connectivity changes influence local as well as global dynamics and lead to altered rs-FC.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Viktor Jirsa.