A measure for brain complexity: relating functional segregation and integration in the nervous system
Open accessAbstract
In brains of higher vertebrates, the functional segregation of local areas that differ in their anatomy and physiology contrasts sharply with their global integration during perception and behavior. In this paper, we introduce a measure, called neural complexity (CN), that captures the interplay between these two fundamental aspects of brain organization. We express functional segregation within a neural system in terms of the relative statistical independence of small subsets of the system and functional integration in terms of significant deviations from independence of large subsets. CN is then obtained from estimates of the average deviation from statistical independence for subsets of increasing size. CN is shown to be high when functional segregation coexists with integration and to be low when the components of a system are either completely independent (segregated) or completely dependent (integrated). We apply this complexity measure in computer simulations of cortical areas to examine how some basic principles of neuroanatomical organization constrain brain dynamics. We show that the connectivity patterns of the cerebral cortex, such as a high density of connections, strong local connectivity organizing cells into neuronal groups, patchiness in the connectivity among neuronal groups, and prevalent reciprocal connections, are associated with high values of CN. The approach outlined here may prove useful in analyzing complexity in other biological domains such as gene regulation and embryogenesis.
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Brain processes can best be thought of as coordinated activity of disparate regions across time.
“Two, given that brain processes can best be thought of as coordinated activity of disparate regions across time [14], [15], we necessitated an approach for arriving at precise brain connectivity maps that quantifies relations among brain regions across time for each individual.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [15]
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- Authors
- Tononi, G., Sporns, O., & Edelman, G. M.
- Affiliations
- Neurosciences Institute (US)
- ORCID
- Sporns, O
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 1994-05-24
- Volume
- 91
- Issue
- 11
- Pages
- 5033-5037
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC43925
- OpenAlex
- W2090414491
- Cited by
- 1,626
- References
- 18
- Citations by year
- 2026: 54 · 2025: 79 · 2024: 94 · 2023: 74 · 2022: 92 · 2021: 103 · 2020: 75 · 2019: 100 · 2018: 85 · 2017: 95 · 2016: 77 · 2015: 55 · 2014: 72 · 2013: 70 · 2012: 59
- FWCI
- 4.08
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Neural dynamics and brain function
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Keywords
- neural complexity, functional segregation, functional integration, statistical independence, brain organization, reciprocal connections, brain dynamics, higher vertebrates
- MeSH terms
- Animals, Axons, Brain, Computer Simulation, Humans, Neurons
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