UH Biocomputation Group - network neuroscience

Brain Network Dynamics during Working Memory Are Modulated by Dopamine and Diminished in Schizophrenia

2021-11-03T10:06:29+00:00

This week on Journal Club session Emil Dmitruk will talk about a paper "Brain Network Dynamics during Working Memory Are Modulated by Dopamine and Diminished in Schizophrenia".

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory performance entails brain-wide switching between activity states using a combination of functional magnetic resonance imaging in healthy controls and individuals with schizophrenia, pharmacological fMRI, genetic analyses and network control theory. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Individuals with schizophrenia show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations. Our results demonstrate the relevance of dopamine signaling for the steering of whole-brain network dynamics during working memory and link these processes to schizophrenia pathophysiology.

Papers:

U. Braun, A. Harneit, G. Pergola, T. Menara, A. Schäfer, R. Betzel, Z. Zang, J. Schweiger, X. Zhang, K. Schwarz, J. Chen, G. Blasi, A. Bertolino, D. Durstewitz, F. Pasqualetti, E. Schwarz, A. Meyer-Lindenberg, D. Bassett, H. Tost, "Brain Network Dynamics during Working Memory Are Modulated by Dopamine and Diminished in Schizophrenia", 2021, Nature Communications, 12, 3478
J. Kim, D. Bassett, "Linear Dynamics & Control of Brain Networks", 2019, arXiv:1902.03309 [physics, q-bio],

Date: 2021/11/05
Time: 14:00
Location: online

Cliques and cavities in human connectome

2020-12-02T13:11:34+00:00

This week on Journal Club session Emil Dmitruk will talk about a paper "Cliques and cavities in human connectome".

Encoding brain regions and their connections as a network of nodes and edges captures many of the possible paths along which information can be transmitted as humans order relations, concepts naturally expressed in the language of algebraic topology. These tools can be used to study mesoscale network structures that arise from the arrangement of densely connected substructures called cliques in otherwise sparsely connected brain networks. We detect cliques (all-to-all connected sets of brain regions) in the average structural connectomes of 8 healthy adults scanned in triplicate and discover the presence of more large cliques than expected in null networks constructed via wiring minimization, providing architecture through which brain network can perform rapid, local processing. We then locate topological cavities of different dimensions, around which information may flow in either diverging or converging patterns. These cavities exist consistently across subjects, differ from those observed in null model networks, and - importantly - link regions of early and late evolutionary origin in long loops, underscoring their unique role in controlling brain function. These results offer a first demonstration that techniques from algebraic topology offer a novel perspective on structural connectomics, highlighting loop-like paths as crucial features in the human brain’s structural architecture.

Papers:

Ann E. Sizemore, Chad Giusti, Ari Kahn, Jean M. Vettel, Richard F. Betzel1, Danielle S. Bassett "Cliques and cavities in human connectome" , J Comput Neurosci 44, 115–145 (2018).

Date: 04/12/2020
Time: 16:00
Location: online