Functional connectivity signatures of major depressive disorder: machine learning analysis of two multicenter neuroimaging studies.
Mol Psychiatry
; 28(7): 3013-3022, 2023 Jul.
Article
em En
| MEDLINE
| ID: mdl-36792654
ABSTRACT
The promise of machine learning has fueled the hope for developing diagnostic tools for psychiatry. Initial studies showed high accuracy for the identification of major depressive disorder (MDD) with resting-state connectivity, but progress has been hampered by the absence of large datasets. Here we used regular machine learning and advanced deep learning algorithms to differentiate patients with MDD from healthy controls and identify neurophysiological signatures of depression in two of the largest resting-state datasets for MDD. We obtained resting-state functional magnetic resonance imaging data from the REST-meta-MDD (N = 2338) and PsyMRI (N = 1039) consortia. Classification of functional connectivity matrices was done using support vector machines (SVM) and graph convolutional neural networks (GCN), and performance was evaluated using 5-fold cross-validation. Features were visualized using GCN-Explainer, an ablation study and univariate t-testing. The results showed a mean classification accuracy of 61% for MDD versus controls. Mean accuracy for classifying (non-)medicated subgroups was 62%. Sex classification accuracy was substantially better across datasets (73-81%). Visualization of the results showed that classifications were driven by stronger thalamic connections in both datasets, while nearly all other connections were weaker with small univariate effect sizes. These results suggest that whole brain resting-state connectivity is a reliable though poor biomarker for MDD, presumably due to disease heterogeneity as further supported by the higher accuracy for sex classification using the same methods. Deep learning revealed thalamic hyperconnectivity as a prominent neurophysiological signature of depression in both multicenter studies, which may guide the development of biomarkers in future studies.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Transtorno Depressivo Maior
Tipo de estudo:
Clinical_trials
Limite:
Humans
Idioma:
En
Revista:
Mol Psychiatry
Assunto da revista:
BIOLOGIA MOLECULAR
/
PSIQUIATRIA
Ano de publicação:
2023
Tipo de documento:
Article
País de afiliação:
Holanda