Résultats de recherche pour « Brain Dynamics Decomposition »

Functional dissociation of brain neural activity induced by opening or closing the eyes has been well established. However, how the temporal dynamics of the underlying neuronal modulations differ between these eye conditions during movement-related behaviours is less known. Using a robotic-assisted motor imagery brain-computer interface (MI BCI), we measured neural activity over the motor regions with electroencephalography (EEG) in a stroke survivor during his longitudinal rehabilitation training. We investigated lateralized oscillatory sensorimotor rhythm modulations while the patient imagined moving his hemiplegic hand with closed and open eyes to control an external robotic splint. In order to precisely identify the main profiles of neural activation affected by MI with eyes-open (MIEO) and eyes-closed (MIEC), a data-driven approach based on parallel factor analysis (PARAFAC) tensor decomposition was employed. Using the proposed framework, a set of narrow-band, subject-specific sensorimotor rhythms was identified; each of them had its own spatial and time signature. When MIEC trials were compared with MIEO trials, three key narrow-band rhythms whose peak frequencies centred at ∼8.0 Hz, ∼11.5 Hz, and ∼15.5 Hz, were identified with differently modulated oscillatory dynamics during movement preparation, initiation, and completion time frames. Furthermore, we observed that lower and higher sensorimotor oscillations represent different functional mechanisms within the MI paradigm, reinforcing the hypothesis that rhythmic activity in the human sensorimotor system is dissociated. Leveraging PARAFAC, this study achieves remarkable precision in estimating latent sensorimotor neural substrates, aiding the investigation of the specific functional mechanisms involved in the MI process.

Abstract The human brain is a complex, hierarchical system operating far from equilibrium, yet the mechanisms linking its directed network architecture to temporal irreversibility remain largely unknown. While prior studies have revealed functional and structural hierarchies, no framework has captured the hierarchical organization of the brain’s directed network in relation to its non-equilibrium dynamics. Here, we present the first large-scale eigendecomposition of whole-brain effective connectivity (EC), estimated from resting-state fMRI using sparse Dynamic Causal Modeling. We isolate the irreversible component of EC, which encodes the directionality of information flow and forms a comprehensive hierarchical network of forward and backward interactions. This hierarchy strengthens in brain states approaching criticality, where slow, oscillatory modes dominate and reflects the influence of long-range anatomical connections that scaffold whole-brain information flow. Our decomposition provides the first multiscale quantification of temporal irreversibility, revealing dual counterpropagating streams along the unimodal–transmodal axis operating at distinct frequencies. Crucially, these hierarchical dynamics carry robust, individual-specific signatures, with unimodal networks contributing disproportionately to subject identifiability. Altogether, this work delivers the first dynamic, whole-brain characterization of effective connectivity-derived hierarchies across spatiotemporal scales and individuals, offering a unified framework for studying brain hierarchy, non-equilibrium dynamics, and subject identifiability.