Can structure predict function in the human brain?
Abstract
Over the past decade, scientific interest in the properties of large-scale spontaneous neural dynamics has intensified. Concurrently, novel technologies have been developed for characterizing the connective anatomy of intra-regional circuits and inter-regional fiber pathways. It will soon be possible to build computational models that incorporate these newly detailed structural network measurements to make predictions of neural dynamics at multiple scales. Here, we review the practicality and the value of these efforts, while at the same time considering in which cases and to what extent structure does determine neural function. Studies of the healthy brain, of neural development, and of pathology all yield examples of direct correspondences between structural linkage and dynamical correlation. Theoretical arguments further support the notion that brain network topology and spatial embedding should strongly influence network dynamics. Although future models will need to be tested more quantitatively and against a wider range of empirical neurodynamic features, our present large-scale models can already predict the macroscopic pattern of dynamic correlation across the brain. We conclude that as neuroscience grapples with datasets of increasing completeness and complexity, and attempts to relate the structural and functional architectures discovered at different neural scales, the value of computational modeling will continue to grow.
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The close relationship of the DMN and other resting state networks to task-based activation patterns implies an interconnectedness between brain networks and the underlying structural connectivity.
“The close relationship of the DMN, and other resting state networks, to task-based activation patterns 84 implies an interconnect-edness between brain networks and the underlying structural connectivity.27,50”
From Graph theory analysis of complex brain networks: new concepts in brain mapping applied to neurosurgery (2016) · cites this paper as [50]
Both brain structure and function may serve as susceptibility mechanisms.
“Thus, both brain structure and function, which may interrelate and whose development inform each other (Hao et al., 2013, Honey et al., 2010, Paus, 2013, Power et al., 2010, Zielinski et al., 2010), may both serve as susceptibility mechanisms.”
From Adolescent neurobiological susceptibility to social context (2016) · cites this paper as Honey et al., 2010
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- Authors
- Honey, C. J., Thivierge, J.-P., & Sporns, O.
- Affiliations
- Princeton University (US)
- Institute for Biological Sciences (CA)
- Indiana University Bloomington (US)
- Corresponding author
- Sporns, Olaf
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2010-09
- Volume
- 52
- Issue
- 3
- Pages
- 766-776
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2152556184
- Cited by
- 629
- References
- 145
- Citations by year
- 2026: 20 · 2025: 49 · 2024: 44 · 2023: 35 · 2022: 43 · 2021: 44 · 2020: 50 · 2019: 33 · 2018: 40 · 2017: 44 · 2016: 42 · 2015: 39 · 2014: 41 · 2013: 49 · 2012: 21
- FWCI
- 2.32
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- NPARC (submittedVersion)
- 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
- brain structure-function relationship, structural connectivity, functional connectivity, brain network topology, spatial embedding, computational models of the brain, neural development, neuropathology, large-scale brain networks
- MeSH terms
- Brain, Humans, Models, Neurological, Nerve Net, Neural Networks, Computer
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