Extracting Multiscale Pattern Information of fMRI Based Functional Brain Connectivity with Application on Classification of Autism Spectrum Disorders
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We employed a multi-scale clustering methodology known as "data cloud geometry" to extract functional connectivity patterns derived from functional magnetic resonance imaging (fMRI) protocol. The method was applied to correlation matrices of 106 regions of interest (ROIs) in 29 individuals with autism spectrum disorders (ASD), and 29 individuals with typical development (TD) while they completed a cognitive control task. Connectivity clustering geometry was examined at both "fine" and "coarse" scales. At the coarse scale, the connectivity clustering geometry produced 10 valid clusters with a coherent relationship to neural anatomy. A supervised learning algorithm employed fine scale information about clustering motif configurations and prevalence, and coarse scale information about intra- and inter-regional connectivity; the algorithm correctly classified ASD and TD participants with sensitivity of 82.8% and specificity of 82.8%. Most of the predictive power of the logistic regression model resided at the level of the fine-scale clustering geometry, suggesting that cellular versus systems level disturbances are more prominent in individuals with ASD. This article provides validation for this multi-scale geometric approach to extracting brain functional connectivity pattern information and for its use in classification of ASD.
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Wang et al. used whole-brain functional connectivity to correctly predict ASD with 83% accuracy in a sample of 58 participants.
“Wang et al., using whole-brain functional connectivity, correctly predicted ASD with 83% accuracy in a sample of 58 participants 63.”
From Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm (2018) · cites this paper as [63]
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- Authors
- Wang, H., Chen, C., & Fushing, H.
- Affiliations
- University of California, Davis (US)
- Wake Forest University (US)
- ORCID
- Wang, Hui
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-10-08
- Volume
- 7
- Issue
- 10
- Pages
- e45502
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3466274
- OpenAlex
- W2026493785
- Cited by
- 33
- References
- 37
- Citations by year
- 2023: 3 · 2022: 2 · 2021: 5 · 2020: 5 · 2019: 3 · 2018: 2 · 2017: 3 · 2016: 3 · 2015: 1 · 2014: 3 · 2013: 3
- FWCI
- 1.15
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (1007219)
- University of California, Davis
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- autism spectrum disorder, brain functional connectivity, multi-scale clustering, autism classification, regional connectivity
- MeSH terms
- Adolescent, Algorithms, Autistic Disorder, Brain, Brain Mapping, Female, Humans, Learning, Magnetic Resonance Imaging, Male, Neural Pathways, Cluster Analysis, Logistic Models
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