Journal
CEREBRAL CORTEX
Volume 32, Issue 24, Pages 5637-5653Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/cercor/bhac042
Keywords
core-periphery organization; lower-higher-order network topography; neural complexity; slow-fast frequency balance; spatiotemporal neuroscience
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Funding
- EJLB-Michael Smith Foundation
- Canadian Institutes of Health Research
- Ministry of Science and Technology of China
- National Key R&D Program of China [2016YFC1306700]
- Hope of Depression Foundation (HDRF)
- Start-Up Research Grant in Hangzhou Normal University
- European Union [785907]
- Canada-UK Artificial Intelligence (AI) Initiative The self as agent-environment nexus: crossing disciplinary boundaries to help human selves and anticipate artificial selves [ES/T01279X/1]
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Using functional magnetic resonance imaging data, this study investigates the topographical hierarchy and temporal dynamics of lower- and higher-order networks in the brain. The results show differences in complexity and frequency distribution between these networks at rest, as well as task-related changes. Additionally, there is a hierarchical and causal relationship between frequency distribution and complexity, with the rest state acting as a mediator. These findings highlight the importance of neural dynamics at rest in shaping complexity during task states.
The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.
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