Individualized diagnosis of major depressive disorder via multivariate pattern analysis of thalamic sMRI features

Abstract

Background Magnetic resonance imaging (MRI) studies have found thalamic abnormalities in major depressive disorder (MDD). Although there are significant differences in the structure and function of the thalamus between MDD patients and healthy controls (HCs) at the group level, it is not clear whether the structural and functional features of the thalamus are suitable for use as diagnostic prediction aids at the individual level. Here, we were to test the predictive value of gray matter density (GMD), gray matter volume (GMV), amplitude of low-frequency fluctuations (ALFF), and fractional amplitude of low-frequency fluctuations (fALFF) in the thalamus using multivariate pattern analysis (MVPA). Methods Seventy-four MDD patients and 44 HC subjects were recruited. The Gaussian process classifier (GPC) was trained to separate MDD patients from HCs, Gaussian process regression (GPR) was trained to predict depression scores, and Multiple Kernel Learning (MKL) was applied to explore the contribution of each subregion of the thalamus. Results The primary findings were as follows: [1] The balanced accuracy of the GPC trained with thalamic GMD was 96.59% (P < 0.001). The accuracy of the GPC trained with thalamic GMV was 93.18% (P < 0.001). The correlation between Hamilton Depression Scale (HAMD) score targets and predictions in the GPR trained with GMD was 0.90 (P < 0.001, r2 = 0.82), and in the GPR trained with GMV, the correlation between HAMD score targets and predictions was 0.89 (P < 0.001, r2 = 0.79). [2] The models trained with ALFF and fALFF in the thalamus failed to discriminate MDD patients from HC participants. [3] The MKL model showed that the left lateral prefrontal thalamus, the right caudal temporal thalamus, and the right sensory thalamus contribute more to the diagnostic classification. Conclusions The results suggested that GMD and GMV, but not functional indicators of the thalamus, have good potential for the individualized diagnosis of MDD. Furthermore, the thalamus shows the heterogeneity in the structural features of thalamic subregions for predicting MDD. To our knowledge, this is the first study to focus on the thalamus for the prediction of MDD using machine learning methods at the individual level.

Treatment Response Prediction for Major Depressive Disorder Patients via Multivariate Pattern Analysis of Thalamic Features

“…The thalamus might be a proper biomarker of MDD that could be used to predict patients’ antidepressant treatment responses. Our previous study and many other thalamic imaging studies have found thalamic abnormalities in patients with MDD, and these results show that the thalamus may play an important role in MDD ( Li et al, 2021 ). Whether the thalamus is related to the prognosis of depression has also attracted the attention of researchers.…”

“…The participants’ GMV, GMD, ALFF, and fALFF values in the thalamus were extracted as regression and classification features. More information were the same as previously reported ( Li et al, 2021 ).…”

“…MDD patients (49 female patients and 25 male patients with an average age of 26.53 ± 8.56 years) were recruited from the Department of Psychiatry of the Seventh People’s Hospital of Hangzhou and the Department of Psychiatry of the Second People’s Hospital of Hangzhou. Study participants were the same as previously reported ( Li et al, 2021 ). All enrolled patients met the following criteria: (1) met the International Classification of Diseases, 10th Revision (ICD-10) criteria for MDD; (2) had no history of medicine or physiotherapy for at least one month before recruitment or taking only SSRI antidepressants ≤1 week; (3) had a HAMD (Version: 24 Items, HAMD-24) total score ≥20; and (4) aged 18–65 years.…”

“…Three-dimensional MR imaging was acquired using a GE 3T scanner (MR750, GE Medical Systems, Milwaukee, WI, United States) with a 32-channel radio frequency coil at the Center for Cognition and Brain Disorders (CCBD), Hangzhou Normal University (HZNU). MRI data acquisition were the same as previously reported ( Li et al, 2021 ).…”

“…All datasets were preprocessed via DPABI_V3.1 (a toolbox for Data Processing and Analysis for Brain Imaging) ( Yan et al, 2016 ). More MRI data processing details were the same as previously reported ( Li et al, 2021 ).…”