Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data
Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depre...
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Elsevier
2025-07-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811925002885 |
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| author | Ailing Zhang Haobo Zhang |
| author_facet | Ailing Zhang Haobo Zhang |
| author_sort | Ailing Zhang |
| collection | DOAJ |
| description | Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. Eight MRI features were identified as predictive of depression, including brain regions in the Orbital Gyrus, Superior Frontal Gyrus, Middle Frontal Gyrus, Parahippocampal Gyrus, Cingulate Gyrus, and Inferior Parietal Lobule. The overlaps and the differences between selected features and brain regions with significant between-group differences in t-tests suggest that ML provides a unique perspective on the neural changes associated with depression. Six pairs of prediction models demonstrated varying performance, with accuracies ranging from 0.68 to 0.85 and areas under the curve (AUC) ranging from 0.57 to 0.81. The best-performing model achieved an accuracy of 0.85 and an AUC of 0.80, highlighting the potential of combining sMRI and rs-fMRI features with ML for early depression detection while revealing the potential of overfitting in small-sample and high-dimensional settings. This study necessitates further research to (1) replicate findings in independent larger datasets to address potential overfitting and (2) utilize different advanced ML techniques and multimodal data fusion to improve model performance. |
| format | Article |
| id | doaj-art-68b7080d78ee4fe899b0c412ec79063a |
| institution | OA Journals |
| issn | 1095-9572 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj-art-68b7080d78ee4fe899b0c412ec79063a2025-08-20T02:30:09ZengElsevierNeuroImage1095-95722025-07-0131512128510.1016/j.neuroimage.2025.121285Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging dataAiling Zhang0Haobo Zhang1Faculty of Brain Sciences, University College London, UK, WC1H 0AWSleep and NeuroImaging Center, Faculty of Psychology, Southwest University, Chongqing, 400715, PR China; Key Laboratory of Cognition and Personality (Southwest University), Ministry of Education, Chongqing, 400715, PR China; Corresponding author.Accurate prediction of depressive symptoms in healthy individuals can enable early intervention and reduce both individual and societal costs. This study aimed to develop predictive models for depression in young adults using machine learning (ML) techniques and longitudinal data from the Beck Depression Inventory, structural MRI (sMRI), and resting-state functional MRI (rs-fMRI). Feature selection methods, including the least absolute shrinkage and selection operator (LASSO), Boruta, and VSURF, were applied to identify MRI features associated with depression. Support vector machine and random forest algorithms were then used to construct prediction models. Eight MRI features were identified as predictive of depression, including brain regions in the Orbital Gyrus, Superior Frontal Gyrus, Middle Frontal Gyrus, Parahippocampal Gyrus, Cingulate Gyrus, and Inferior Parietal Lobule. The overlaps and the differences between selected features and brain regions with significant between-group differences in t-tests suggest that ML provides a unique perspective on the neural changes associated with depression. Six pairs of prediction models demonstrated varying performance, with accuracies ranging from 0.68 to 0.85 and areas under the curve (AUC) ranging from 0.57 to 0.81. The best-performing model achieved an accuracy of 0.85 and an AUC of 0.80, highlighting the potential of combining sMRI and rs-fMRI features with ML for early depression detection while revealing the potential of overfitting in small-sample and high-dimensional settings. This study necessitates further research to (1) replicate findings in independent larger datasets to address potential overfitting and (2) utilize different advanced ML techniques and multimodal data fusion to improve model performance.http://www.sciencedirect.com/science/article/pii/S1053811925002885Machine learningDepressionPredictive modelingMagnetic resonance imaging (mri)Support vector machine (svm)Random forest |
| spellingShingle | Ailing Zhang Haobo Zhang Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data NeuroImage Machine learning Depression Predictive modeling Magnetic resonance imaging (mri) Support vector machine (svm) Random forest |
| title | Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data |
| title_full | Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data |
| title_fullStr | Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data |
| title_full_unstemmed | Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data |
| title_short | Predicting depression in healthy young adults: A machine learning approach using longitudinal neuroimaging data |
| title_sort | predicting depression in healthy young adults a machine learning approach using longitudinal neuroimaging data |
| topic | Machine learning Depression Predictive modeling Magnetic resonance imaging (mri) Support vector machine (svm) Random forest |
| url | http://www.sciencedirect.com/science/article/pii/S1053811925002885 |
| work_keys_str_mv | AT ailingzhang predictingdepressioninhealthyyoungadultsamachinelearningapproachusinglongitudinalneuroimagingdata AT haobozhang predictingdepressioninhealthyyoungadultsamachinelearningapproachusinglongitudinalneuroimagingdata |