Quantifying dynamical properties of brain activity using complex systems analysis

Abstract

The human brain exhibits a complex and multiscale dynamical structure, from microscopic cellular crosstalk to macroscopic networks. While complex systems science offers a plethora of analytical tools, brain dynamics are typically explored with only a few hand-picked statistics. This thesis quantifies the brain's intricate dynamical structure by integrating methods from complex systems analysis into a systematic and interpretable framework for comparing across measures sensitive to different aspects of neural activity. Chapter 2 introduces time-series feature analysis through the lens of information theory, bridging distinct measures with common notation and terminology. Chapter 3 introduces and applies the Python library, pyspi, to time-series classification problems (including neuroimaging datasets) to uncover the most salient features for a given task. Building on this foundation, Chapters 4--7 apply these highly comparative methods to compelling questions in modern neuroscience. Chapter 4 comprehensively evaluates measures of inter-areal coupling to characterize its potential role in conscious visual perception. Chapter 5 expands the scope to integrate local activity and pairwise interactions in studying neuropsychiatric disorders, supporting the continued use of linear measures while underscoring the importance of multiscale approaches. Chapter 6 probes functional, structural, and molecular correlates of homotopic connectivity, a robust property of inter-hemispheric network architecture. Finally, Chapter 7 extends this framework to systematically compare algorithms that capture overlapping communities in the structural connectome to test new biological hypotheses. Collectively, this thesis presents a highly comparative framework for capturing the complex and multiscale dynamical structure of the human brain. This work points to exciting future directions in fields from lifespan development to personalized medicine in an era of expanding openly available data.