Emergence of multiple spontaneous coherent subnetworks from a single configuration of human connectome coupled oscillators model
Year: 2024
Type: article
Abstract: Multi-state metastability in neuroimaging signals reflects the brain's flexibility to transition between network configurations in response to changing environments or tasks. We modeled these dynamics with a Kuramoto network of 90 nodes oscillating at an intrinsic frequency of 40 Hz, interconnected using human brain structural connectivity strengths and delays. We simulated this model for 30 min to generate multi-state metastability. We identified global coupling and delay parameters that maximize spectral entropy, a proxy for multi-state metastability. At this operational point, multiple frequency-specific coherent sub-networks spontaneously emerge across oscillatory modes, persisting for periods between 140 and 4300 ms, reflecting flexible and sustained dynamic states. The topography of these sub-networks aligns with empirical resting-state neuroimaging data. Additionally, periodic components of the EEG spectra from young healthy participants correlate with maximal multi-state metastability, while dynamics away from this point correlate with sleep and anesthesia spectra. Our findings suggest that multi-state metastable functional dynamics observed in empirical data emerge from specific interactions of structural topography and connection delays, providing a platform to study mechanisms underlying flexible dynamics of cognition.
Source: Scientific Reports
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,590
Awards: G1131377936, G140296882, G1414836116, COLLECTIVE MIND: FROM COMPUTATIONAL MODELING TO COMPUTATIONAL SOCIOLOGY. UNVEILING THE ROLE OF SELF AND SITUATIONAL AWARENESS IN AGENT-BASED SYSTEMS (ABSS)., G1483213741, G1798610363, NOVEL EEG TOPOGRAPHY ANALYSIS USING GRAPH SIGNAL PROCESSING AND NETWORK CONTROL THEORY, G2532137037, ADVANCED CENTER FOR ELECTRICAL AND ELECTRONIC ENGINEERING, A NEUROCOMPUTATIONAL MODEL TO REMEMBER BRAIN STIMULATION PROTOCOL TO ENHANCE MEMORY IN AGING., FOLLOWING THE BEAT LARGE SCALE BRAIN NETWORK MODELLING OF NEURO-STIMULATION, ADVANCED CENTER FOR ELECTRICAL AND ELECTRONIC ENGINEERING, NETWORK CONTROL MODEL AND OPTIMIZATION OF NON-INVASIVE ELECTRIC BRAIN STIMULATION FOR THE REHABILITATION OF CORTICAL LESIONS, G6213328080, G646719516, DEEPSURF: NETWORK CONTROL FOR A SURFACE ALTERNATIVE TO DEEP BRAIN STIMULATION AND ITS VALIDATION IN A MOUSE MODEL OF OBSESSIVE-COMPULSIVE DISORDER, SENSORY AND ELECTRIC BRAIN STIMULATION FOR NEUROREHABILITATION FROM MECHANISMS TO CLINICAL PRACTICE, A GENERATIVE MODEL OF EEG DYNAMICAL FUNCTIONAL CONNECTIVITY TO PREDICT NEURODEGENERATIVE-DISEASE PROGRESSION CLOSING THE GAP BETWEEN BIOPHYSICAL AND MACHINE LEARNING MODELS, CENTRO CIENTIFICO TECNOLOGICO DE EXCELENCIA CIENCIA & VIDA (CIENCIA & VIDA), SELECTIVE CONTROLLABILITY OF SYNCHRONY AND DYNAMICS OF FUNCTIONAL BRAIN NETWORKS
OpenAlex ID: W4405843191