Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies
Abstract Background Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole‐brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining ob...
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| Format: | Article |
| Language: | English |
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Wiley
2025-06-01
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| Series: | Brain and Behavior |
| Online Access: | https://doi.org/10.1002/brb3.70589 |
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| author | Renata Rozovsky Maria Wolfe Halimah Abdul‐waalee Mariah Chobany Greeshma Malgireddy Jonathan A. Hart Brianna Lepore Farzan Vahedifard Mary L. Phillips Boris Birmaher Alex Skeba Rasim S. Diler Michele A. Bertocci |
| author_facet | Renata Rozovsky Maria Wolfe Halimah Abdul‐waalee Mariah Chobany Greeshma Malgireddy Jonathan A. Hart Brianna Lepore Farzan Vahedifard Mary L. Phillips Boris Birmaher Alex Skeba Rasim S. Diler Michele A. Bertocci |
| author_sort | Renata Rozovsky |
| collection | DOAJ |
| description | Abstract Background Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole‐brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis. Methods Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13–17 with BD‐I/II (n = 34), other specified BD (OSB) (n = 106), other non‐bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms. Results Whole‐brain classifiers in the model BD‐I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self‐reported mania, negative affect, or anxiety were observed in all inpatient groups. Conclusions These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well‐characterized BD‐I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions. |
| format | Article |
| id | doaj-art-cfdfbc7b76ea4b2080cb12cc9d3977b4 |
| institution | Matheson Library |
| issn | 2162-3279 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley |
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| series | Brain and Behavior |
| spelling | doaj-art-cfdfbc7b76ea4b2080cb12cc9d3977b42025-06-27T17:52:46ZengWileyBrain and Behavior2162-32792025-06-01156n/an/a10.1002/brb3.70589Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other PsychopathologiesRenata Rozovsky0Maria Wolfe1Halimah Abdul‐waalee2Mariah Chobany3Greeshma Malgireddy4Jonathan A. Hart5Brianna Lepore6Farzan Vahedifard7Mary L. Phillips8Boris Birmaher9Alex Skeba10Rasim S. Diler11Michele A. Bertocci12Department of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USADepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USADepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USADepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USAWestern Psychiatric Hospital University of Pittsburgh Medical Center (UPMC) Pittsburgh Pennsylvania USADepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USADepartment of Psychiatry University of Pittsburgh Pittsburgh Pennsylvania USAAbstract Background Bipolar disorder (BD) is among the psychiatric disorders most prone to misdiagnosis, with both false positives and false negatives resulting in treatment delay. We employed a whole‐brain machine learning approach focusing on gray matter volumes (GMVs) to contribute to defining objective biomarkers of BD and discriminating it from other forms of psychopathology, including subthreshold manic presentations without a BD Type I/II diagnosis. Methods Five support vector machine (SVM) models were used to detect differences in GMVs between inpatient adolescents aged 13–17 with BD‐I/II (n = 34), other specified BD (OSB) (n = 106), other non‐bipolar psychopathology (OP) (n = 52), and healthy controls (HC) (n = 27). We examined the most discriminative GMVs and tested their associations with clinical symptoms. Results Whole‐brain classifiers in the model BD‐I/II versus OSB achieved total accuracy of 79%, (AUC = 0.70, p = 0.002); BD versus OP 66%, (AUC = 0.61, p = 0.014); BD versus HC 66%, (AUC = 0.67, p = 0.011); OSB versus HC 77%, (AUC = 0.61, p = 0.01); OP versus HC 68%, (AUC = 0.70, p = 0.001). The most discriminative GMVs that contributed to the classification were in areas associated with movement, sensory processing, and cognitive control. Correlations between these GMVs and self‐reported mania, negative affect, or anxiety were observed in all inpatient groups. Conclusions These findings indicate that pattern recognition models focusing on GMVs in regions associated with movement, sensory processing, and cognitive control can effectively distinguish well‐characterized BD‐I/II from other forms of psychopathology, including other specified BD, in a pediatric population. These results may contribute to enhancing diagnostic accuracy and guiding earlier, more targeted interventions.https://doi.org/10.1002/brb3.70589 |
| spellingShingle | Renata Rozovsky Maria Wolfe Halimah Abdul‐waalee Mariah Chobany Greeshma Malgireddy Jonathan A. Hart Brianna Lepore Farzan Vahedifard Mary L. Phillips Boris Birmaher Alex Skeba Rasim S. Diler Michele A. Bertocci Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies Brain and Behavior |
| title | Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies |
| title_full | Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies |
| title_fullStr | Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies |
| title_full_unstemmed | Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies |
| title_short | Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies |
| title_sort | gray matter differences in adolescent psychiatric inpatients a machine learning study of bipolar disorder and other psychopathologies |
| url | https://doi.org/10.1002/brb3.70589 |
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