Predicting treatment response in individuals with major depressive disorder using structural MRI-based similarity features
Abstract Background Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Structural magnetic resonance imaging (sMRI) and machine learning have shown promise in psychiatry, offering insights into brain abnormalities in MDD. However, predicting trea...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2025-05-01
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| Series: | BMC Psychiatry |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12888-025-06945-7 |
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| Summary: | Abstract Background Major Depressive Disorder (MDD) is a prevalent mental health condition with significant societal impact. Structural magnetic resonance imaging (sMRI) and machine learning have shown promise in psychiatry, offering insights into brain abnormalities in MDD. However, predicting treatment response remains challenging. This study leverages inter-brain similarity from sMRI as a novel feature to enhance prediction accuracy and explore disease mechanisms. The method’s generalizability across adult and adolescent cohorts is also evaluated. Methods The study included 172 participants. Based on remission status, 39 participants from the Hangzhou Dataset and 34 from the Jinan Dataset were selected for further analysis. Three methods were used to extract brain similarity features, followed by a statistical test for feature selection. Six machine learning classifiers were employed to predict treatment response, and their generalizability was tested using the Jinan Dataset. Group analyses between remission and non-remission groups were conducted to identify brain regions associated with treatment response. Results Brain similarity features outperformed traditional metrics in predicting treatment outcomes, with the highest accuracy achieved by the model using these features. Between-group analyses revealed that the remission group had lower gray matter volume and density in the right precentral gyrus, but higher white matter volume (WMV). In the Jinan Dataset, significant differences were observed in the right cerebellum and fusiform gyrus, with higher WMV and density in the remission group. Conclusions This study demonstrates that brain similarity features combined with machine learning can predict treatment response in MDD with moderate success across age groups. These findings emphasize the importance of considering age-related differences in treatment planning to personalize care. Trial registration Clinical trial number: not applicable. |
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| ISSN: | 1471-244X |