State-space solutions to the dynamic magnetoencephalography inverse problem using high performance computing

Determining the magnitude and location of neural sources within the brain that are responsible for generating magnetoencephalography (MEG) signals measured on the surface of the head is a challenging problem in functional neuroimaging. The number of potential sources within the brain exceeds by an order of magnitude the number of recording sites. As a consequence, the estimates for the magnitude and location of the neural sources will be ill-conditioned because of the underdetermined nature of the problem. One well-known technique designed to address this imbalance is the minimum norm estimator (MNE). This approach imposes an $L^2$ regularization constraint that serves to stabilize and condition the source parameter estimates. However, these classes of regularizer are static in time and do not consider the temporal constraints inherent to the biophysics of the MEG experiment. In this paper we propose a dynamic state-space model that accounts for both spatial and temporal correlations within and across candidate intracortical sources. In our model, the observation model is derived from the steady-state solution to Maxwell's equations while the latent model representing neural dynamics is given by a random walk process.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS483 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Synchrony, metastability, dynamic integration, and competition in the spontaneous functional connectivity of the human brain

Available online 3 June 2019.The human brain is functionally organized into large-scale neural networks that are dynamically interconnected. Multiple short-lived states of resting-state functional connectivity (rsFC) identified transiently synchronized networks and cross-network integration. However, little is known about the way brain couplings covary as rsFC states wax and wane. In this magnetoencephalography study, we explore the synchronization structure among the spontaneous interactions of well-known resting-state networks (RSNs). To do so, we extracted modes of dynamic coupling that reflect rsFC synchrony and analyzed their spatio-temporal features. These modes identified transient, sporadic rsFC changes characterized by the widespread integration of RSNs across the brain, most prominently in the β band. This is in line with the metastable rsFC state model of resting-state dynamics, wherein our modes fit as state transition processes. Furthermore, the default-mode network (DMN) stood out as being structured into competitive cross-network couplings with widespread DMN-RSN interactions, especially among the β-band modes. These results substantiate the theory that the DMN is a core network enabling dynamic global brain integration in the β band.This work was supported by the Action de Recherche Concert ee (ARC Consolidation 2015–2019, “Characterization of the electrophysiological bases, the temporal dynamics and the functional relevance of resting state network” attributed to X.D.T.) and by the research convention “Les Voies du Savoir” (Fonds Erasme, Brussels, Belgium). M.B. benefited from the program Attract of Innoviris (grant 2015-BB2B-10), the Spanish Ministry of Economy and Competitiveness (grant PSI2016-77175-P), and theMarie Skłodowska-Curie Action of the European Commission (grant 743562). M.V.G. and G.N.were supported by the Fonds Erasme. N.C. benefited from a research grant from the ARC Consolidation (2014–2017, “Characterization of the electrophysiological bases, the temporal dynamics and the functional relevance of resting state network” attributed to X.D.T.) and from the Fonds Erasme (research convention “Les Voies du Savoir”). X.D.T. is Post-doctorate Clinical Master Specialist at the Fonds de la Recherche Scientifique (F.R.S.-FNRS, Brussels, Belgium). The MEG project at the CUB – H^opital Erasme is financially supported by the Fonds Erasme (research convention “Les Voies du Savoir”)

Tensor Analysis and Fusion of Multimodal Brain Images

Current high-throughput data acquisition technologies probe dynamical systems with different imaging modalities, generating massive data sets at different spatial and temporal resolutions posing challenging problems in multimodal data fusion. A case in point is the attempt to parse out the brain structures and networks that underpin human cognitive processes by analysis of different neuroimaging modalities (functional MRI, EEG, NIRS etc.). We emphasize that the multimodal, multi-scale nature of neuroimaging data is well reflected by a multi-way (tensor) structure where the underlying processes can be summarized by a relatively small number of components or "atoms". We introduce Markov-Penrose diagrams - an integration of Bayesian DAG and tensor network notation in order to analyze these models. These diagrams not only clarify matrix and tensor EEG and fMRI time/frequency analysis and inverse problems, but also help understand multimodal fusion via Multiway Partial Least Squares and Coupled Matrix-Tensor Factorization. We show here, for the first time, that Granger causal analysis of brain networks is a tensor regression problem, thus allowing the atomic decomposition of brain networks. Analysis of EEG and fMRI recordings shows the potential of the methods and suggests their use in other scientific domains.Comment: 23 pages, 15 figures, submitted to Proceedings of the IEE

Locally embedded presages of global network bursts

Spontaneous, synchronous bursting of neural population is a widely observed phenomenon in nervous networks, which is considered important for functions and dysfunctions of the brain. However, how the global synchrony across a large number of neurons emerges from an initially non-bursting network state is not fully understood. In this study, we develop a new state-space reconstruction method combined with high-resolution recordings of cultured neurons. This method extracts deterministic signatures of upcoming global bursts in "local" dynamics of individual neurons during non-bursting periods. We find that local information within a single-cell time series can compare with or even outperform the global mean field activity for predicting future global bursts. Moreover, the inter-cell variability in the burst predictability is found to reflect the network structure realized in the non-bursting periods. These findings demonstrate the deterministic mechanisms underlying the locally concentrated early-warnings of the global state transition in self-organized networks

Mapping dynamical properties of cortical microcircuits using robotized TMS and EEG: Towards functional cytoarchitectonics

International audienceBrain dynamics at rest depend on the large-scale interactions between oscillating cortical microcircuits arranged into macrocolumns. Cytoarchitectonic studies have shown that the structure of those microcircuits differs between cortical regions, but very little is known about interregional differences of their intrinsic dynamics at a macro-scale in human. We developed here a new method aiming at mapping the dynamical properties of cortical microcircuits non-invasively using the coupling between robotized transcranial magnetic stimulation and elec-troencephalography. We recorded the responses evoked by the stimulation of 18 cortical targets largely covering the accessible neocortex in 22 healthy volunteers. Specific data processing methods were developed to map the local source activity of each cortical target, which showed interregional differences with very good interhemi-spheric reproducibility. Functional signatures of cortical microcircuits were further studied using spatio-temporal decomposition of local source activities in order to highlight principal brain modes. The identified brain modes revealed that cortical areas with similar intrinsic dynamical properties could be distributed either locally or not, with a spatial signature that was somewhat reminiscent of resting state networks. Our results provide the proof of concept of " functional cytoarchitectonics " , that would guide the parcellation of the human cortex using not only its cytoarchitecture but also its intrinsic responses to local perturbations. This opens new avenues for brain modelling and physiopathology readouts

Dynamics of large-scale brain activity in health and disease

Tese de doutoramento em Engenharia Biomédica e Biofísica, apresentada à Universidade de Lisboa através da Faculdade de Ciências, 2008Cognition relies on the integration of information processed in widely distributed brain regions. Neuronal oscillations are thought to play an important role in the supporting local and global coordination of neuronal activity. This study aimed at investigating the dynamics of the ongoing healthy brain activity and early changes observed in patients with Alzheimer's disease (AD). Electro- and magnetoencephalography (EEG/MEG) were used due to high temporal resolution of these techniques. In order to evaluate the functional connectivity in AD, a novel algorithm based on the concept of generalized synchronization was improved by defining the embedding parameters as a function of the frequency content of interest. The time-frequency synchronization likelihood (TF SL) revealed a loss of fronto-temporal/parietal interactions in the lower alpha (8 10 Hz) oscillations measured by MEG that was not found with classical coherence. Further, long-range temporal (auto-) correlations (LRTC) in ongoing oscillations were assessed with detrended fluctuation analysis (DFA) on times scales from 1 25 seconds. Significant auto-correlations indicate a dependence of the underlying dynamical processes at certain time scales of separation, which may be viewed as a form of "physiological memory". We tested whether the DFA index could be related to the decline in cognitive memory in AD. Indeed, a significant decrease in the DFA exponents was observed in the alpha band (6 13 Hz) over temporo-parietal regions in the patients compared with the age-matched healthy control subjects. Finally, the mean level of SL of EEG signals was found to be significantly decreased in the AD patients in the beta (13 30 Hz) and in the upper alpha (10 13 Hz) and the DFA exponents computed as a measure of the temporal structure of SL time series were larger for the patients than for subjects with subjective memory complaint. The results obtained indicate that the study of spatio-temporal dynamics of resting-state EEG/MEG brain activity provides valuable information about the AD pathophysiology, which potentially could be developed into clinically useful indices for assessing progression of AD or response to medication