Solving large-scale MEG/EEG source localisation and functional connectivity problems simultaneously using state-space models

State-space models are widely employed across various research disciplines to study unobserved dynamics. Conventional estimation techniques, such as Kalman filtering and expectation maximisation, offer valuable insights but incur high computational costs in large-scale analyses. Sparse inverse covariance estimators can mitigate these costs, but at the expense of a trade-off between enforced sparsity and increased estimation bias, necessitating careful assessment in low signal-to-noise ratio (SNR) situations. To address these challenges, we propose a three-fold solution: (1) Introducing multiple penalised state-space (MPSS) models that leverage data-driven regularisation; (2) Developing novel algorithms derived from backpropagation, gradient descent, and alternating least squares to solve MPSS models; (3) Presenting a K-fold cross-validation extension for evaluating regularisation parameters. We validate this MPSS regularisation framework through lower and more complex simulations under varying SNR conditions, including a large-scale synthetic magneto- and electro-encephalography (MEG/EEG) data analysis. In addition, we apply MPSS models to concurrently solve brain source localisation and functional connectivity problems for real event-related MEG/EEG data, encompassing thousands of sources on the cortical surface. The proposed methodology overcomes the limitations of existing approaches, such as constraints to small-scale and region-of-interest analyses. Thus, it may enable a more accurate and detailed exploration of cognitive brain functions

Track D Social Science, Human Rights and Political Science

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138414/1/jia218442.pd

The Haken–Kelso–Bunz (HKB) model: from matter to movement to mind

International audienc

Complex Networks of Urban Environments

Volchenkov D. Complex Networks of Urban Environments. In: Kelso JAS, ed. Mathematical Analysis of Urban Spatial Networks. Springer Series Understanding Complex Systems. Vol 1. Berlin / Heidelberg: Springer; 2008: 1-54

Exploring Community Structure by Diffusion Processes

Volchenkov D. Exploring Community Structure by Diffusion Processes. In: Kelso JAS, ed. Mathematical Analysis of Urban Spatial Networks. Springer Series Understanding Complex Systems. Vol 1. Berlin / Heidelberg: Springer; 2008: 101-117

Spectral Analysis of Directed Graphs and Interacting Networks

Volchenkov D. Spectral Analysis of Directed Graphs and Interacting Networks. In: Kelso JAS, ed. Mathematical Analysis of Urban Spatial Networks. Springer Series Understanding Complex Systems. Vol 1. Berlin / Heidelberg: Springer; 2008: 137-150

Wayfinding and Affine Representations of Urban Environments

Volchenkov D. Wayfinding and Affine Representations of Urban Environments. In: Kelso JAS, ed. Mathematical Analysis of Urban Spatial Networks. Springer Series Understanding Complex Systems. Vol 1. Berlin / Heidelberg: Springer; 2008: 55-99

Urban Area Networks and Beyond

Volchenkov D. Urban Area Networks and Beyond. In: Kelso JAS, ed. Mathematical Analysis of Urban Spatial Networks. Springer Series Understanding Complex Systems. Vol 1. Berlin / Heidelberg: Springer; 2008: 151-157

A Roadmap to Computational Social Neuroscience

International audienceTo complement experimental efforts toward understanding human social interactions at both neural and behavioral levels, two computational approaches are presented: (1) a fully parameterizable mathematical model of a social partner, the Human Dynamic Clamp which, by virtue of experimentally controlled interactions with real people, allows for emergent behaviors to be studied; and (2) a multiscale neurocomputational model of social behavior that enables exploration of social self-organization at all levels—from neuronal patterns to people interacting with each other. These complementary frameworks and the cross product of their analysis aim at understanding the fundamental principles governing social behavior

Critical diversity: Divided or united states of social coordination

<div><p>Much of our knowledge of coordination comes from studies of simple, dyadic systems or systems containing large numbers of components. The huge gap ‘in between’ is seldom addressed, empirically or theoretically. We introduce a new paradigm to study the coordination dynamics of such intermediate-sized ensembles with the goal of identifying key mechanisms of interaction. Rhythmic coordination was studied in ensembles of eight people, with differences in movement frequency (‘diversity’) manipulated within the ensemble. Quantitative change in diversity led to qualitative changes in coordination, a critical value separating régimes of integration and segregation between groups. Metastable and multifrequency coordination between participants enabled communication across segregated groups within the ensemble, without destroying overall order. These novel findings reveal key factors underlying coordination in ensemble sizes previously considered too complicated or 'messy' for systematic study and supply future theoretical/computational models with new empirical checkpoints.</p></div