Public defence in Neuroscience and Biomedical Engineering, M.Sc. Vladislav Myrov

Title of the thesis: Temporal Coordination and Criticality in Human Neural Dynamics - Bridging Insights from rhythmicity, synchronization, and computational modeling

Doctoral student: Vladislav Myrov
Opponent: Research Director Bertrand Thirion, INRIA, France
Custos: Associate Professor Matias Palva, Aalto University School of Science, Department of Neuroscience and Biomedical Engineering

This thesis explores neural oscillations—rhythmic fluctuations in the excitability of specialized neural populations that are fundamental to coordinating information flow in the brain. Despite extensive research, methods for analyzing neural oscillations are still evolving. In the thesis we introduce a novel pACF method to directly quantify rhythmicity, revealing that cortical oscillations have well-defined anatomical and spectral properties, organized into small frequency islands at the mesoscale level.

But no neuron acts alone and they function within networks, the study examines long-range high frequency phase synchronization in humans. This synchronization displays a modular architecture, differentiates between healthy and epileptic zones, exhibits specific laminar profiles, and shows transient enhancement and suppression in separate frequency bands during a response inhibition task.

The variability in neural dynamics across individuals and cognitive states is addressed through the lens of the critical brain hypothesis. The research proposes that the brain's critical point extends into a broader regime of critical-like dynamics known as Griffith's phase. Within this framework, healthy brain regions operate on the subcritical to critical side, while epileptogenic areas are situated on the critical to supercritical side.

Furthermore, a hierarchical model of critical-like oscillatory activity is developed based on the Kuramoto model, enabling the creation of a digital brain twin tailored to individual neural dynamics. The model's observables are physiologically plausible and closely resemble real human recordings in the subcritical-critical range, demonstrating its effectiveness in reconstructing individual behavior with high accuracy.

Collectively, this work offers new insights into neural oscillation analysis through various approaches, including in vivo connectivity studies, computational modeling, phase-synchronization networks, and examining the criticality of individual neural nodes. By directly quantifying oscillation rhythmicity in alignment with the brain's timing mechanisms, the research expands our understanding of neural dynamics and provides potential new avenues for analyzing information processing in the brain.

Key words: computational neuroscience, brain oscillations, criticality, functional connectome

Thesis available for public display 10 days prior to the defence at: https://aaltodoc.aalto.fi/doc_public/eonly/riiputus/ 

Contact information:

Doctoral theses at the School of Science: https://aaltodoc.aalto.fi/handle/123456789/52 

Zoom Quick Guide: https://www.aalto.fi/en/services/zoom-quick-guide