Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophrenia Article Swipe
Charles A. Ellis
,
Robyn L. Miller
,
Vince D. Calhoun
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyt.2024.1165424
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3389/fpsyt.2024.1165424
Introduction Dynamic functional network connectivity (dFNC) analysis of resting state functional magnetic resonance imaging data has yielded insights into many neurological and neuropsychiatric disorders. A common dFNC analysis approach uses hard clustering methods like k-means clustering to assign samples to states that summarize network dynamics. However, hard clustering methods obscure network dynamics by assuming (1) that all samples within a cluster are equally like their assigned centroids and (2) that samples closer to one another in the data space than to their centroids are well-represented by their centroids. In addition, it can be hard to compare subjects, as in some cases an individual may not manifest a state strongly enough to enter a hard cluster. Approaches that allow a dimensional approach to connectivity patterns (e.g., fuzzy clustering) can mitigate these issues. In this study, we present an explainable fuzzy clustering framework by combining fuzzy c-means clustering with several explainability metrics and novel summary features. Methods We apply our framework for schizophrenia (SZ) default mode network analysis. Namely, we extract dFNC from individuals with SZ and controls, identify 5 dFNC states, and characterize the dFNC features most crucial to those states with a new perturbation-based clustering explainability approach. We then extract several features typically used in hard clustering and further present a variety of unique features specially designed for use with fuzzy clustering to quantify state dynamics. We examine differences in those features between individuals with SZ and controls and further search for relationships between those features and SZ symptom severity. Results Importantly, we find that individuals with SZ spend more time in states of moderate anticorrelation between the anterior and posterior cingulate cortices and strong anticorrelation between the precuneus and anterior cingulate cortex. We further find that individuals with SZ tend to transition more rapidly than controls between low-magnitude and high-magnitude dFNC states. Conclusion We present a novel dFNC analysis framework and use it to identify effects of SZ upon network dynamics. Given the ease of implementing our framework and its enhanced insight into network dynamics, it has great potential for use in future dFNC studies.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fpsyt.2024.1165424
- https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1165424/pdf
- OA Status
- gold
- Cited By
- 2
- References
- 90
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391841909
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391841909Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3389/fpsyt.2024.1165424Digital Object Identifier
- Title
-
Explainable fuzzy clustering framework reveals divergent default mode network connectivity dynamics in schizophreniaWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-14Full publication date if available
- Authors
-
Charles A. Ellis, Robyn L. Miller, Vince D. CalhounList of authors in order
- Landing page
-
https://doi.org/10.3389/fpsyt.2024.1165424Publisher landing page
- PDF URL
-
https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1165424/pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/journals/psychiatry/articles/10.3389/fpsyt.2024.1165424/pdfDirect OA link when available
- Concepts
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Default mode network, Schizophrenia (object-oriented programming), Fuzzy logic, Cluster analysis, Computer science, Psychology, Artificial intelligence, Functional connectivity, Neuroscience, PsychiatryTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2Per-year citation counts (last 5 years)
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90Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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