TY  - JOUR
PY  - 2021//
TI  - Predicting treatment selections for individuals with major depressive disorder according to functional connectivity subgroups
JO  - Brain connectivity
A1  - Wang, Xinyi
A1  - Qin, Jiaolong
A1  - Zhu, Rongxin
A1  - Zhang, Siqi
A1  - Tian, Shui
A1  - Sun, Yurong
A1  - Wang, Qiang
A1  - Zhao, Peng
A1  - Tang, Hao
A1  - Wang, Li
A1  - Si, Tianmei
A1  - Yao, Zhijian
A1  - Lü, Qing
SP  - ePub
EP  - ePub
VL  - ePub
IS  - ePub
N2  - BACKGROUND: Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. Here, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations. <br><br>METHODS: All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery dataset (n=228), we firstly identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic, symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions. <br><br>RESULTS: Three subgroups with specific treatment recommendations were emerged including: (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities. (2) a stimulation-oriented subgroup with more alleviation in suicide. (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing respectively conducted on three testing datasets, results showed an overall accuracy of 72.83%. <br><br>CONCLUSIONS: Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes.<p />  <p>Language: en</p>
LA  - en
SN  - 2158-0014
UR  - http://dx.doi.org/10.1089/brain.2021.0153
ID  - ref1
ER  -