Evaluation of boundaries between mood and psychosis disorder using dynamic functional network connectivity (dFNC) via deep learning classification

• Published in: Human brain mapping Vol. 44; no. 8; pp. 3180 - 3195
• Main Authors: Rokham, Hooman, Falakshahi, Haleh, Fu, Zening, Pearlson, Godfrey, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2023
• Subjects: Artificial Intelligence; Biomarkers; Biotypes; Bipolar disorder; Brain; Brain - diagnostic imaging; Categories; Classification; Deep Learning; Diagnostic systems; dynamic functional connectivity; Electroencephalography; Emotional disorders; Evaluation; Functional magnetic resonance imaging; Humans; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Mental disorders; Mental health; Mood disorders; Neural networks; Neuroimaging; Phenotypes; Psychiatry; Psychosis; psychosis disorders; Psychotic Disorders - diagnostic imaging; Reproducibility of Results; resting‐state functional MRI; Schizophrenia; Signs and symptoms; Statistical analysis; Statistics; deep learning; dynamic functional connectivity; classification; machine learning; psychosis disorders; resting-state functional MRI
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26273

The validity and reliability of diagnoses in psychiatry is a challenging topic in mental health. The current mental health categorization is based primarily on symptoms and clinical course and is not biologically validated. Among multiple ongoing efforts, neurological observations alongside clinical evaluations are considered to be potential solutions to address diagnostic problems. The Bipolar‐Schizophrenia Network on Intermediate Phenotypes (B‐SNIP) has published multiple papers attempting to reclassify psychotic illnesses based on biological rather than symptomatic measures. However, the effort to investigate the relationship between this new categorization approach and other neuroimaging techniques, including resting‐state fMRI data, is still limited. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches. We applied our method to 613 subjects, including individuals with psychosis and healthy controls, which were classified using both the Diagnostic and Statistical Manual of Mental Disorders (DSM‐IV) and the B‐SNIP biomarker‐based (Biotype) approach. Statistical group differences and cross‐validated classifiers were performed within each framework to assess how different categories. Results highlight interesting differences in occupancy in both DSM‐IV and Biotype categorizations compared to healthy individuals, which are distributed across specific transient connectivity states. Biotypes tended to show less distinctiveness in occupancy level and included fewer cellwise differences. Classification accuracy obtained by DSM‐IV and Biotype categories were both well above chance. Results provided new insights and highlighted the benefits of both DSM‐IV and biology‐based categories while also emphasizing the importance of future work in this direction, including employing further data types. This study focused on investigating the relationship between different psychotic disorders categorization methods and resting‐state fMRI‐based measures called dynamic functional network connectivity (dFNC) using state‐of‐the‐art artificial intelligence (AI) approaches.