Data of the REST-meta-MDD Project from DIRECT Consortium

(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is...

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Hauptverfasser: Chao-Gan Yan, Chen, Xiao, Li, Le, Castellanos, Francisco Xavier, Tong-Jian Bai, Bo, Qi-Jing, Cao, Jun, Guan-Mao Chen, Ning-Xuan Chen, Chen, Wei, Cheng, Chang, Cheng, Yu-Qi, Cui, Xi-Long, Duan, Jia, Fang, Yi-Ru, Gong, Qi-Yong, Guo, Wen-Bin, Hou, Zheng-Hua, Hu, Lan, Kuang, Li, Li, Feng, Li, Tao, Liu, Yan-Song, Zhe-Ning Liu, Long, Yi-Cheng, Luo, Qing-Hua, Meng, Hua-Qing, Dai-Hui Peng, Hai-Tang Qiu, Qiu, Jiang, Shen, Yue-Di, Shi, Yu-Shu, Tang, Yan-Qing, Chuan-Yue Wang, Wang, Fei, Wang, Kai, Wang, Li, Wang, Xiang, Wang, Ying, Wu, Xiao-Ping, Wu, Xin-Ran, Xie, Chun-Ming, Xie, Guang-Rong, Xie, Hai-Yan, Xie, Peng, Xu, Xiu-Feng, Yang, Hong, Yang, Jian, Yao, Jia-Shu, Shu-Qiao Yao, Yin, Ying-Ying, Yong-Gui Yuan, Zhang, Ai-Xia, Zhang, Hong, Zhang, Ke-Rang, Zhang, Lei, Zhang, Zhi-Jun, Ru-Bai Zhou, Zhou, Yi-Ting, Jun-Juan Zhu, Zou, Chao-Jie, Tian-Mei Si, Xi-Nian Zuo, Zhao, Jing-Ping, Zang, Yu-Feng
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Zusammenfassung:(Note: Part of the content of this post was adapted from the original DIRECT Psychoradiology paper (https://academic.oup.com/psyrad/article/2/1/32/6604754) and REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)Major Depressive Disorder (MDD) is the second leading cause of health burden worldwide (1). Unfortunately, objective biomarkers to assist in diagnosis are still lacking, and current first-line treatments are only modestly effective (2, 3), reflecting our incomplete understanding of the pathophysiology of MDD. Characterizing the neurobiological basis of MDD promises to support developing more effective diagnostic approaches and treatments.An increasingly used approach to reveal neurobiological substrates of clinical conditions is termed resting-state functional magnetic resonance imaging (R-fMRI) (4). Despite intensive efforts to characterize the pathophysiology of MDD with R-fMRI, clinical imaging markers of diagnosis and predictors of treatment outcomes have yet to be identified. Previous reports have been inconsistent, sometimes contradictory, impeding the endeavor to translate them into clinical practice (5). One reason for inconsistent results is low statistical power from small sample size studies (6). Low-powered studies are more prone to produce false positive results, reducing the reproducibility of findings in a given field (7, 8). Of note, one recent study demonstrate that sample size of thousands of subjects may be needed to identify reproducible brain-wide association findings (9), calling for larger datasets to boost effect size. Another reason could be the high analytic flexibility (10). Recently, Botvinik-Nezer and colleagues (11) demonstrated the divergence in results when independent research teams applied different workflows to process an identical fMRI dataset, highlighting the effects of “researcher degrees of freedom” (i.e., heterogeneity in (pre-)processing methods) in producing disparate fMRI findings.To address these critical issues, we initiated the Depression Imaging REsearch ConsorTium (DIRECT) in 2017. Through a series of meetings, a group of 17 participating hospitals in China agreed to establish the first project of the DIRECT consortium, the REST-meta-MDD Project, and share 25 study cohorts, including R-fMRI data from 1300 MDD patients and 1128 normal controls. Based on prior work, a standardized preprocessing pipeline adapted from Data Processing Assistant for Res
DOI:10.57760/sciencedb.o00115.00013