arXiv subjects

Mengsen Zhang

Publications and source records attributed to Mengsen Zhang.

4 recordsLinked to original sources

arXiv2026

Persistent homology broadens the controllable subspace in human structural connectomes

Network control theory applied to structural connectomes typically ranks brain regions as candidate driver nodes by their structural connectivity strength, and evaluates performance through scalar control energy. We test whether this framing captures the most relevant information about how driver-node selection shapes brain network control. We introduce an alternative criterion based on the persistent topological cycles in which each node participates---a measure of mesoscale integration that captures features beyond local connectivity---and compare it to standard degree-based selection across 70 human structural connectomes at three parcellation scales. Topology- and degree-informed driver sets achieve nearly identical scalar control energy, differing by approximately 0.2%. The geometry of the controllable subspace, however, differs substantially: topology-informed sets distribute controllability across more dimensions of state space and produce better-conditioned controllability matrices. This geometric advantage is preserved when high-degree hub nodes are removed, and it carries a functional signature: because the two criteria place driver nodes in different cortical territory, each most efficiently reaches a different class of target state. The choice of node-ranking criterion therefore shapes which brain-state transitions are energetically favored even when average control cost is unchanged. The results reveal a dissociation between control cost and control geometry, and demonstrate that persistent topology captures information about brain network control that scalar energy summaries miss.

q-bio.NC↗

arXiv2024

Mechanisms and Controversies of tACS (Transcranial Alternating Current Stimulation)

The use of small amplitude electric currents delivered through the scalp, termed transcranial current stimulation (tCS, tDCS / tACS when using DC / AC currents) holds considerable promise for developing safe and effective treatments for central nervous system disorders. Initially welcomed with skepticism due to significant gaps of knowledge in terms of neurophysiology and biophysical mechanisms, tCS is maturing as a technology while its mechanisms of action are gradually being elucidated. However, there remain open questions about the mechanisms of action that warrant clarification to bring tCS to its full potential. In this review focused on tACS, we make an attempt at providing an overview of the converging experimental evidence, from results obtained in various species, regarding the mechanisms of action. We also highlight the remaining points of uncertainty regarding potential confounds, and propose possible experimentally testable solutions to address those issues. Finally, we outline how a continued focus on deepening our understanding of tACS mechanisms might provide significant insights into fundamental, long-standing questions in neuroscience.

q-bio.NC↗

arXiv2019

Topological portraits of multiscale coordination dynamics

Living systems exhibit complex yet organized behavior on multiple spatiotemporal scales. To investigate the nature of multiscale coordination in living systems, one needs a meaningful and systematic way to quantify the complex dynamics, a challenge in both theoretical and empirical realms. The present work shows how integrating approaches from computational algebraic topology and dynamical systems may help us meet this challenge. In particular, we focus on the application of multiscale topological analysis to coordinated rhythmic processes. First, theoretical arguments are introduced as to why certain topological features and their scale-dependency are highly relevant to understanding complex collective dynamics. Second, we propose a method to capture such dynamically relevant topological information using persistent homology, which allows us to effectively construct a multiscale topological portrait of rhythmic coordination. Finally, the method is put to test in detecting transitions in real data from an experiment of rhythmic coordination in ensembles of interacting humans. The recurrence plots of topological portraits highlight collective transitions in coordination patterns that were elusive to more traditional methods. This sensitivity to collective transitions would be lost if the behavioral dynamics of individuals were treated as separate degrees of freedom instead of constituents of the topology that they collectively forge. Such multiscale topological portraits highlight collective aspects of coordination patterns that are irreducible to properties of individual parts. The present work demonstrates how the analysis of multiscale coordination dynamics can benefit from topological methods, thereby paving the way for further systematic quantification of complex, high-dimensional dynamics in living systems.

nlin.AO↗

arXiv2018

Connecting empirical phenomena and theoretical models of biological coordination across scales

Coordination is ubiquitous in living systems. Existing theoretical models of coordination -- from bacteria to brains -- focus on either gross statistics in large-scale systems ($N\rightarrow\infty$) or detailed dynamics in small-scale systems (mostly $N=2$). Both approaches have proceeded largely independent of each other. The present work bridges this gap with a theoretical model of biological coordination that captures key experimental observations of mid-scale social coordination at multiple levels of description. It also reconciles in a single formulation two well-studied models of large- and small-scale biological coordination (Kuramoto and extended Haken-Kelso-Bunz). The model adds second-order coupling (from extended Haken-Kelso-Bunz) to the Kuramoto model. We show that second-order coupling is indispensable for reproducing empirically observed phenomena and gives rise to a phase transition from mono- to multi-stable coordination across scales. This mono-to-multistable transition connects the emergence and growth of behavioral complexity in small and large systems.

physics.bio-ph↗