Identification of optimal structural connectivity using functional connectivity and neural modeling
Open accessAbstract
The complex network dynamics that arise from the interaction of the brain's structural and functional architectures give rise to mental function. Theoretical models demonstrate that the structure-function relation is maximal when the global network dynamics operate at a critical point of state transition. In the present work, we used a dynamic mean-field neural model to fit empirical structural connectivity (SC) and functional connectivity (FC) data acquired in humans and macaques and developed a new iterative-fitting algorithm to optimize the SC matrix based on the FC matrix. A dramatic improvement of the fitting of the matrices was obtained with the addition of a small number of anatomical links, particularly cross-hemispheric connections, and reweighting of existing connections. We suggest that the notion of a critical working point, where the structure-function interplay is maximal, may provide a new way to link behavior and cognition, and a new perspective to understand recovery of function in clinical conditions.
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Animal models are proving to be an important and necessary bridge for identifying ground-truth structure-to-function principles.
“animal models are proving to be an important and necessary “bridge” for identifying ground-truth structure-to-function principles (Deco et al., 2014; Grayson et al., 2016; Miranda-Dominguez et al., 2014b; Stafford et al., 2014).”
From Development of large-scale functional networks from birth to adulthood: A guide to the neuroimaging literature (2017) · cites this paper as Deco et al., 2014
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- Authors
- Deco, G., McIntosh, A. R., Shen, K., Hutchison, R. M., Menon, R. S., Everling, S., Hagmann, P., & Jirsa, V. K.
- Affiliations
- Institució Catalana de Recerca i Estudis Avançats (ES)
- University of Toronto (CA)
- Robarts Research Institute (CA)
- Western University (CA)
- Centre Hospitalier Universitaire Vaudois (CH)
- Inserm (FR)
- Venue
- Journal of Neuroscience
- Publisher
- Society for Neuroscience
- Published
- 2014-06-04
- Volume
- 34
- Issue
- 23
- Pages
- 7910-7916
- ISSN
- 0270-6474 (ISSN-L)
- 1529-2401
- PMC
- PMC6608269
- OpenAlex
- W2161460637
- Cited by
- 162
- References
- 33
- Citations by year
- 2026: 6 · 2025: 10 · 2024: 11 · 2023: 7 · 2022: 15 · 2021: 15 · 2020: 13 · 2019: 9 · 2018: 17 · 2017: 20 · 2016: 22 · 2015: 13 · 2014: 4
- FWCI
- 10.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Journal of Neuroscience (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- RECERCAT (Consorci de Serveis Universitaris de Catalunya) (publishedVersion, other-oa)
- IRIS (submittedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (submittedVersion, cc-by-nc-sa)
- SERVAL (Université de Lausanne) (submittedVersion, other-oa)
- Funders
- James S. McDonnell Foundation (220,020,255)
- Fondation Leenaards
- FP7 Ideas: European Research Council (295129)
- FP7 Information and Communication Technologies
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Keywords
- structural connectivity, functional connectivity, structure-function relationship, criticality, network dynamics, macaque, human brain, functional recovery
- MeSH terms
- Algorithms, Animals, Brain, Humans, Macaca, Models, Neurological, Nerve Net, Neural Pathways, Neurons, Nonlinear Dynamics
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