Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via
Hooman Rokham1,2, Haleh Falakshahi1,2, Zening Fu2
1Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA.
Human Brain Mapping
|March 15, 2023
Summary
This study explored how brain connectivity patterns differ between psychosis diagnostic systems. Biology-based categories showed less distinct brain connectivity differences compared to symptom-based ones, yet both methods accurately classified individuals.
Area of Science:
- Neuroscience
- Psychiatry
- Artificial Intelligence
Background:
- Psychiatric diagnoses, primarily symptom-based, lack biological validation, posing reliability challenges.
- The Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP) proposed biology-based reclassification of psychotic disorders.
- Limited research exists on integrating B-SNIP's approach with neuroimaging, such as resting-state fMRI.
Purpose of the Study:
- To investigate the relationship between different psychotic disorder classification methods and dynamic functional network connectivity (dFNC).
- To apply advanced artificial intelligence (AI) methods to resting-state fMRI data for analyzing diagnostic categories.
- To compare diagnostic validity using both the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) and B-SNIP's biomarker-based (Biotype) approach.
Main Methods:
- Utilized resting-state fMRI data from 613 subjects, including individuals with psychosis and healthy controls.
- Classified subjects using both DSM-IV criteria and B-SNIP's Biotype approach.
- Applied AI to analyze statistical group differences and cross-validated classifiers based on dynamic functional network connectivity (dFNC) within each classification framework.
Main Results:
- Both DSM-IV and Biotype categories exhibited distinct differences in transient connectivity states compared to healthy individuals.
- Biotype categories showed less distinctiveness in connectivity state occupancy and fewer cellwise differences than DSM-IV categories.
- Classification accuracy for psychosis was significantly above chance for both DSM-IV and Biotype classifications.
Conclusions:
- Both symptom-based (DSM-IV) and biology-based (Biotype) categorization methods offer valuable insights into psychosis.
- Dynamic functional network connectivity reveals differences across diagnostic frameworks, highlighting the complexity of psychotic disorders.
- Future research should integrate diverse data types to further refine biological psychiatric classifications.
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