Resolving anatomical and functional structure in human brain organization: identifying mesoscale organization in weighted network representations
Open accessAbstract
Human brain anatomy and function display a combination of modular and hierarchical organization, suggesting the importance of both cohesive structures and variable resolutions in the facilitation of healthy cognitive processes. However, tools to simultaneously probe these features of brain architecture require further development. We propose and apply a set of methods to extract cohesive structures in network representations of brain connectivity using multi-resolution techniques. We employ a combination of soft thresholding, windowed thresholding, and resolution in community detection, that enable us to identify and isolate structures associated with different weights. One such mesoscale structure is bipartivity, which quantifies the extent to which the brain is divided into two partitions with high connectivity between partitions and low connectivity within partitions. A second, complementary mesoscale structure is modularity, which quantifies the extent to which the brain is divided into multiple communities with strong connectivity within each community and weak connectivity between communities. Our methods lead to multi-resolution curves of these network diagnostics over a range of spatial, geometric, and structural scales. For statistical comparison, we contrast our results with those obtained for several benchmark null models. Our work demonstrates that multi-resolution diagnostic curves capture complex organizational profiles in weighted graphs. We apply these methods to the identification of resolution-specific characteristics of healthy weighted graph architecture and altered connectivity profiles in psychiatric disease.
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A core property of human brain activity is modularity: unexpectedly strong functional connectivity within resting-state networks compared with between them.
“A defining feature of human brain activity is “modularity,” which refers to an unexpectedly high level of within-RSN FC relative to between-RSN FC (Lohse 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 Lohse et al., 2014
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- Authors
- Lohse, C., Bassett, D. S., Lim, K. O., & Carlson, J. M.
- Affiliations
- Heidelberg University (DE)
- University of California, Santa Barbara (US)
- University of Minnesota (US)
- Corresponding author
- Bassett, Danielle S.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-10-02
- Volume
- 10
- Issue
- 10
- Pages
- e1003712
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC4183375
- OpenAlex
- W2168189894
- Cited by
- 62
- References
- 83
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 5 · 2023: 1 · 2022: 6 · 2021: 1 · 2020: 6 · 2019: 6 · 2018: 5 · 2017: 8 · 2016: 16 · 2015: 4 · 2014: 2
- FWCI
- 5.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- arXiv (Cornell University)
- Funders
- Alfred P. Sloan Foundation
- Errett Fisher Foundation
- National Institutes of Health (-0001)
- National Institute of Mental Health (R01MH060662)
- Army Research Office (W911NF-09-0001, W911NF)
- Institute for Collaborative Biotechnologies (W911NF‐09‐0001)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- human brain connectivity, weighted networks, soft thresholding, modularity, network fault diagnosis, null models, mental illness, brain architecture
- MeSH terms
- Adult, Brain, Female, Humans, Male, Middle Aged, Models, Neurological, Nerve Net, Neural Pathways, Schizophrenia, Computational Biology
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