Recent studies have shown that it is important to understand the brain mechanism specifically by focusing on the common and unique functional connectivity in each disorder including depression.

To specify the biomarker of major depressive disorder (MDD), we applied the sparse machine learning algorithm to classify several types of affective disorders using the resting state fMRI data collected in multiple sites, and this study shows the results of depression as a part of those results.

The aim of this study is to understand some specific pattern of functional connectivity in MDD, which would support diagnosis of depression and development of focused and personalized treatments in the future.

The neuroimaging data from patients with major depressive disorder (MDD, n = 100) and healthy control adults (HC: n = 100) from multiple sites were used for the training dataset. A completely separate dataset (n = 16) was kept aside for testing. After all preprocessing of fMRI data, based on one hundred and forty anatomical region of interests (ROIs), 9730 functional connectivities during resting states were prepared as the input of the sparse machine-learning algorithm.

As results, 20 functional connectivities were selected with the classification performance of Accuracy: 83.0% (Sensitivity: 81.0%, Specificity: 85.0%). The test data, which was completely separate from the training data, showed the performance accuracy of 83.3%.

The selected functional connectivities based on the sparse machine learning algorithm included the brain regions which have been associated with depression.

The authors have not supplied their declaration of competing interest.