| Autor: |
Emmanuel D. Meram, Shahira Baajour, Asadur Chowdury, John Kopchick, Patricia Thomas, Usha Rajan, Dalal Khatib, Caroline Zajac-Benitez, Luay Haddad, Alireza Amirsadri, Jeffrey A. Stanley, Vaibhav A. Diwadkar |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
Network Neuroscience, Vol 7, Iss 1, Pp 184-212 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2472-1751 |
| DOI: |
10.1162/netn_a_00278 |
| Popis: |
AbstractThere is a paucity of graph theoretic methods applied to task-based data in schizophrenia (SCZ). Tasks are useful for modulating brain network dynamics, and topology. Understanding how changes in task conditions impact inter-group differences in topology can elucidate unstable network characteristics in SCZ. Here, in a group of patients and healthy controls (n = 59 total, 32 SCZ), we used an associative learning task with four distinct conditions (Memory Formation, Post-Encoding Consolidation, Memory Retrieval, and Post-Retrieval Consolidation) to induce network dynamics. From the acquired fMRI time series data, betweenness centrality (BC), a metric of a node’s integrative value was used to summarize network topology in each condition. Patients showed (a) differences in BC across multiple nodes and conditions; (b) decreased BC in more integrative nodes, but increased BC in less integrative nodes; (c) discordant node ranks in each of the conditions; and (d) complex patterns of stability and instability of node ranks across conditions. These analyses reveal that task conditions induce highly variegated patterns of network dys-organization in SCZ. We suggest that the dys-connection syndrome that is schizophrenia, is a contextually evoked process, and that the tools of network neuroscience should be oriented toward elucidating the limits of this dys-connection. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
