# Gray Matter Differences in Adolescent Psychiatric Inpatients: A Machine Learning Study of Bipolar Disorder and Other Psychopathologies

## TL;DR

This study uses machine learning to identify brain differences in adolescents with bipolar disorder, helping to improve early diagnosis and treatment.

## Contribution

The study introduces a machine learning method to distinguish bipolar disorder from other psychopathologies using gray matter volume patterns in adolescents.

## Key findings

- Classification models achieved up to 79% accuracy in distinguishing BD-I/II from other specified BD.
- Key brain regions related to movement, sensory processing, and cognitive control were most discriminative.
- Gray matter volume patterns correlated with symptoms like mania, negative affect, and anxiety in all inpatient groups.

## Abstract

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.

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.

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.

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.

We used a whole‐brain machine learning approach to investigate gray matter volumes in 219 adolescent inpatients with bipolar disorder (BD‐I/II, OSB), other psychopathologies, or healthy controls. Classification accuracies reached up to 79%, with key regions in movement, sensory, and cognitive networks driving the strongest differentiation. These findings highlight potential neurobiological biomarkers for enhancing early diagnostic accuracy and guiding targeted interventions for pediatric BD.