External Email - Use Caution
Dear Colleagues,
We are pleased to announce that the REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics.
Major Depressive Disorder (MDD) is the second leading-cause of disability world-wide, with point prevalence exceeding 4% (1). The pathophysiology of MDD remains unknown despite intensive efforts, including neuroimaging studies. However, the small sample size of most MDD neuroimaging studies entails low sensitivity and reliability (2, 3).
Inconsistencies may reflect limited statistical power (2) from small samples, but data analysis flexibility may also contribute, as a large number of preprocessing and analysis operations with many different parameter combinations have been used in fMRI analyses (4). MDD studies have used diverse multiple comparison correction methods, most likely inadequate (5). Data analysis flexibility also impedes large-scale meta-analysis (6, 7). Moreover, clinical characteristics such as number and type of episodes, medication status and illness duration vary across studies, further contributing to heterogeneous results.
To address limited statistical power and analytic heterogeneity, we initiated the REST-meta-MDD Project. We implemented a standardized preprocessing protocol on Data Processing Assistant for Resting-State fMRI (DPARSF) (8) at local sites with only final indices provided to the consortium (please see the SI Appendix, Supplementary Methods of the original PNAS paper for details).
Contributions were requested from users of DPARSF, a MATLAB- and SPM-based R-fMRI preprocessing pipeline (8). Twenty-five research groups from 17 hospitals in China formed the REST-meta-MDD consortium and agreed to share final R-fMRI indices from patients with MDD and matched normal controls (see Supplementary Table; henceforth “site” refers to each cohort for convenience) from studies approved by local Institutional Review Boards. The consortium contributed 2428 previously collected datasets (1300 MDDs and 1128 NCs). On average, each site contributed 52.0±52.4 patients with MDD (range 13-282) and 45.1±46.9 NCs (range 6-251). Most MDD patients were female (826 vs. 474 males), as expected. The 562 patients with first episode MDD included 318 first episode drug-naïve (FEDN) MDD and 160 scanned while receiving antidepressants (medication status unavailable for 84). Of 282 with recurrent MDD, 121 were scanned while receiving antidepressants and 76 were not being treated with medication (medication status unavailable for 85). Episodicity (first or recurrent) and medication status were unavailable for 456 patients.
To improve transparency and reproducibility, our analysis code has been openly shared at https://github.com/Chaogan-Yan/PaperScripts/tree/master/Yan_2019_PNAS. In addition, we would like to share the R-fMRI indices of the 1300 MDD patients and 1128 NCs through the R-fMRI Maps Project ( http://rfmri.org/REST-meta-MDD). These data derivatives will allow replication, secondary analyses and discovery efforts while protecting participant privacy and confidentiality.
According to the agreement of the REST-meta-MDD consortium, there would be 2 phases for sharing the brain imaging data and phenotypic data of the 1300 MDD patients and 1128 NCs. *1) Phase 1: coordinated sharing, before January 1, 2020.* To reduce conflict of the researchers, the consortium will review and coordinate the proposals submitted by interested researchers. The interested researchers first send a letter of intent to rfmrilab@gmail.com. Then the consortium will send all the approved proposals to the applicant. The applicant should submit a new innovative proposal while avoiding conflict with approved proposals. This proposal would be reviewed and approved by the consortium if no conflict. Once approved, this proposal would enter the pool of approved proposals and prevent future conflict. *2) Phase 2: unrestricted sharing, after January 1, 2020.* The researchers can perform any analyses of interest while not violating ethics.
The REST-meta-MDD data entered unrestricted sharing phase since January 1, 2020. The researchers can perform any analyses of interest while not violating ethics. Please sign the Data Use Agreement http://rfmri.org/sites/default/files/REST-meta-MDD_Data_Use_Agreement.pdf and email the scanned signed copy to rfmrilab@gmail.com to get access credentials of the REST-meta-MDD data on the FTP server.
http://rfmri.org/sites/default/files/REST-meta-MDD_Data_Use_Agreement.pdf
*ACKNOWLEDGEMENTS*
This work was supported by the National Key R&D Program of China (2017YFC1309902), the National Natural Science Foundation of China (81671774, 81630031, 81471740 and 81371488), the Hundred Talents Program and the 13th Five-year Informatization Plan (XXH13505) of Chinese Academy of Sciences, Beijing Municipal Science & Technology Commission (Z161100000216152, Z171100000117016, Z161100002616023 and Z171100000117012), Department of Science and Technology, Zhejiang Province (2015C03037) and the National Basic Research (973) Program (2015CB351702).
*REFERENCES*
1. Ferrari AJ*, et al.* (2013) Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. *PLOS Medicine* 10(11):e1001547.
2. Button KS*, et al.* (2013) Power failure: why small sample size undermines the reliability of neuroscience. *Nat Rev Neurosci* 14(5):365-376.
3. Chen X, Lu B, & Yan CG (2018) Reproducibility of R-fMRI metrics on the impact of different strategies for multiple comparison correction and sample sizes. *Hum Brain Mapp* 39(1):300-318.
4. Poldrack RA*, et al.* (2017) Scanning the horizon: towards transparent and reproducible neuroimaging research. *Nat Rev Neurosci* 18(2):115-126.
5. Eklund A, Nichols TE, & Knutsson H (2016) Cluster failure: Why fMRI inferences for spatial extent have inflated false-positive rates. *Proc Natl Acad Sci U S A*.
6. Hamilton JP, Farmer M, Fogelman P, & Gotlib IH (2015) Depressive Rumination, the Default-Mode Network, and the Dark Matter of Clinical Neuroscience. *Biological psychiatry* 78(4):224-230.
7. Kaiser RH, Andrews-Hanna JR, Wager TD, & Pizzagalli DA (2015) Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. *JAMA Psychiatry* 72(6):603-611.
8. Yan CG & Zang YF (2010) DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. *Front Syst Neurosci* 4:13.
9. Wang L*, et al.* (2013) Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. *PLoS One* 8(3):e60191.
10. Wang L*, et al.* (2015) The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. *Hum Brain Mapp* 36(2):768-778.
11. Liu Y*, et al.* (2017) Regional homogeneity associated with overgeneral autobiographical memory of first-episode treatment-naive patients with major depressive disorder in the orbitofrontal cortex: A resting-state fMRI study. *J Affect Disord* 209:163-168.
12. Zhu X*, et al.* (2012) Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. *Biological psychiatry* 71(7):611-617.
13. Guo W*, et al.* (2014) Abnormal default-mode network homogeneity in first-episode, drug-naive major depressive disorder. *PLoS ONE* 9(3):e91102.
14. Guo W*, et al.* (2017) Decreased interhemispheric coordination in the posterior default-mode network and visual regions as trait alterations in first-episode, drug-naive major depressive disorder. *Brain imaging and behavior*.
15. Peng D*, et al.* (2014) Altered brain network modules induce helplessness in major depressive disorder. *Journal of Affective Disorders* 168:21-29.
16. Peng D*, et al.* (2015) Dissociated large-scale functional connectivity networks of the precuneus in medication-naïve first-episode depression. *Psychiatry Research: Neuroimaging* 232(3):250-256.
17. Zhu J*, et al.* (2014) Default-mode network connectivity in depression: A resting-state fMRI study (in Chinese). *Chinese Journal of Nervous and Mental Diseases* 40(8):454-458.
18. Shen Y*, et al.* (2015) Sub-hubs of baseline functional brain networks are related to early improvement following two-week pharmacological therapy for major depressive disorder. *Hum Brain Mapp* 36(8):2915-2927.
19. Tang Y*, et al.* (2013) Decreased functional connectivity between the amygdala and the left ventral prefrontal cortex in treatment-naive patients with major depressive disorder: a resting-state functional magnetic resonance imaging study. *Psychological Medicine* 43(9):1921-1927.
20. Li HJ*, et al.* (2014) Surface-based regional homogeneity in first-episode, drug-naive major depression: a resting-state FMRI study. *Biomed Res Int* 2014:374828.
21. Du L*, et al.* (2016) Changes in Problem-Solving Capacity and Association With Spontaneous Brain Activity After a Single Electroconvulsive Treatment in Major Depressive Disorder. *The journal of ECT* 32(1):49-54.
22. Wu X*, et al.* (2016) Dysfunction of the cingulo-opercular network in first-episode medication-naive patients with major depressive disorder. *J Affect Disord* 200:275-283.
23. Yang X-h*, et al.* (2017) Anhedonia correlates with abnormal functional connectivity of the superior temporal gyrus and the caudate nucleus in patients with first-episode drug-naive major depressive disorder. *Journal of Affective Disorders* 218:284-290.
24. Hou Z*, et al.* (2018) Increased temporal variability of striatum region facilitating the early antidepressant response in patients with major depressive disorder. *Progress in neuro-psychopharmacology & biological psychiatry* 85:39-45.
25. Hou Z*, et al.* (2018) Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. *Brain imaging and behavior* 12(4):1042-1052.
26. Chen T*, et al.* (2017) Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder. *Hum Brain Mapp* 38(5):2482-2494.
27. Cao J*, et al.* (2016) Resting-state functional MRI of abnormal baseline brain activity in young depressed patients with and without suicidal behavior. *J Affect Disord* 205:252-263.
28. Wang J*, et al.* (2017) Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder. *Social cognitive and affective neuroscience* 12(12):1983-1992.
29. Cheng W*, et al.* (2016) Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. *Brain* 139(Pt 12):3296-3309.
30. Ye M*, et al.* (2015) Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI. *PLOS ONE* 10(9):e0133775.
31. Luo Q*, et al.* (2015) Frequency Dependant Topological Alterations of Intrinsic Functional Connectome in Major Depressive Disorder. *Scientific Reports* 5:9710.
32. Xue S, Wang X, Wang W, Liu J, & Qiu J (2016) Frequency-dependent alterations in regional homogeneity in major depression. *Behavioural Brain Research* 306:13-19.
33. Zheng H*, et al.* (2018) The dynamic characteristics of the anterior cingulate cortex in resting-state fMRI of patients with depression. *Journal of Affective Disorders* 227:391-397.
34. Jing B*, et al.* (2013) Difference in amplitude of low-frequency fluctuation between currently depressed and remitted females with major depressive disorder. *Brain Research* 1540:74-83.
35. Yang X*, et al.* (2015) Anatomical and functional brain abnormalities in unmedicated major depressive disorder. *Neuropsychiatr Dis Treat* 11:2415-2423.
36. Cheng Y*, et al.* (2017) Resting-state brain alteration after a single dose of SSRI administration predicts 8-week remission of patients with major depressive disorder. *Psychological Medicine* 47(3):438-450.
37. Yuan Y*, et al.* (2008) Abnormal neural activity in the patients with remitted geriatric depression: A resting-state functional magnetic resonance imaging study. *Journal of Affective Disorders* 111(2):145-152.
*Supplementary Table. *Samples of the REST-meta-MDD project, consortium sites, contributors, sample size, data acquisition parameters, and published studies based on the present cohorts.
Serial Number
Sites (cohorts)
Principal investigators
Data organizer
N
Scanner
Receive (coil)
TR (ms)
TE (ms)
Flip Angle (∘)
Thickness/gap
Slice number
Time points
Voxel size
FOV
Published researches
MDD
NC
1
National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital) & Key Laboratory of Mental Health, Ministry of Health (Peking University)
Tian-Mei Si
Li Wang
74
74
Siemens Tim Trio 3T
32 channel
2000
30
90
4.0mm/0.8mm
30
210
3.28 × 3.28 × 4.80
210 × 210
Wang et al 2013 (9)/2015 (10)
2
Department of Clinical Psychology, Suzhou Suzhou Psychiatric Hospital, The Affiliated Guangji Hospital of Soochow University
Yan-Song Liu
Yan-Song Liu
30
30
Philips Achieva 3T
8-channel
2000
30
90
4.0mm/0 mm
37
200
1.67 × 1.67 × 4.00
240 × 240
Liu et al., 2017 (11)
3
The Second Xiangya Hospital of Central South University
Shu-Qiao Yao / Xiang Wang
Chang Cheng
27
37
Siemens Magnetom Symphony scanner 1.5 T
16 channel
2000
40
90
5.0mm/1.25mm
26
150
3.75 × 3.75 × 6.25
240 × 240
Zhu et al., 2012 (12)
4
The Second Xiangya Hospital of Central South University
Wen-Bin Guo
Wen-Bin Guo
24
24
Siemens Skyra 3T
32 channel
2500
25
90
3.5mm/0mm
39
200
3.75 × 3.75 × 3.50
240 × 240
Guo et al., 2014 (13)/2017(14)
5
Department of Psychiatry, Shanghai Jiao Tong University School of Medicine
Yi-Ru Fang / Dai-Hui Peng
Ru-Bai Zhou
13
11
GE Signa 3T
32 channel
3000
30
90
5.0mm/0mm
22
100
3.75 × 3.75 × 5.00
240 × 240
Peng et al., 2014 (15)/2015 (16)
6
Department of Psychiatry, Shanghai Jiao Tong University School of Medicine
Yi-Ru Fang / Jun-Juan Zhu
Ru-Bai Zhou
15
15
Siemens Tim Trio 3T
32 channel
2000
30
70
4mm/0mm
33
180
3.59 × 3.59 × 4.00
230 × 230
Zhu et al., 2014 (17)
7
Sir Run Run Shaw Hospital, Zhejiang University School of Medicine
Wei Chen
Jia-Shu Yao
38
49
GE discovery MR750
8 channel
2000
30
90
3.2/0
37
184
2.29 × 2.29 × 3.20
220 × 220
Shen et al., 2015 (18)
8
Department of Psychiatry, First Affiliated Hospital, China Medical University
Fei Wang
Jia Duan
75
75
GE Signa 3T
8 channel
2000
30
90
3.0mm/0mm
35
200
3.75 × 3.75 × 3.00
240 × 240
Tang et al., 2013 (19)
9
The First Affiliated Hospital of Jinan University
Ying Wang
Guan-Mao Chen
50
50
GE Discovery MR750 3.0T
8-channel
2000
25
90
3.0/1.0 mm
35
200
3.75 × 3.75 × 4.00
240 × 240
N/A
10
First Hospital of Shanxi Medical University
Ke-Rang Zhang
Ai-Xia Zhang
50
33
Siemens Tim Trio 3T
32 channel
2000
30
90
3.0mm/1.52mm
32
212
3.75 × 3.75 × 4.52
240 × 240
Li et al., 2014 (20)
11
Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University
Qing-Hua Luo / Hua-Qing Meng
Hai-Tang Qiu
32
29
GE Signa 3T
8 channel
2000
30
90
5 mm
33
200
3.75 × 3.75 × 5.00
240 × 240
Du et al., 2016 (21)
12
Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University
Hua-Qing Meng / Qing-Hua Luo
Hai-Tang Qiu
32
6
GE Signa 3T
8 channel
2000
30
90
5 mm
33
240
3.75 × 3.75 × 4.00
240 × 240
N/A
13
The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an Central Hospital
Jian Yang / Xiao-Ping Wu
Hong Zhang
25
17
GE Excite 1.5T
16 channel
2500
35
90
4mm/0
36
150
4.00 × 4.00 × 4.00
256 × 256
Wu et al., 2016 (22)
14
The Second Xiangya Hospital of Central South University
Guang-Rong Xie
Xi-Long Cui
64
32
Siemens Tim Trio 3T
32 channel
2500
25
90
3.5/0
39
200
3.75 × 3.75 × 3.50
240 × 240
Yang et al., 2017 (23)
15
Department of Psychosomatics and Psychiatry, Zhongda Hospital, School of Medicine, Southeast University
Yong-Gui Yuan
Zheng-Hua Hou / Ying-ying Yin
50
50
Siemens Verio 3.0T MRI
12 channel
2000
25
90
4mm/0mm
36
240
3.75 × 3.75 × 4.00
240 × 240
Hou et al., 2018 (24)/2018 (25)
16
Huaxi MR Research Center, West China Hospital of Sichuan University
Qi-Yong Gong / Kai-Ming Li
Kai-Ming Li
31
31
GE Signa 3T
8 channel
2000
30
90
5mm/0mm
30
200
3.75 × 3.75 × 5.00
240 × 240
Chen et al., 2017 (26)
17
Department of Psychiatry, The First Affiliated Hospital of Chongqing Medical University
Li Kuang
Lan Hu
47
44
GE Signa 3T
8 channel
2000
40
90
4.0mm/0mm
33
240
3.75 × 3.75 × 4.00
240 × 240
Cao et al., 2016 (27)
18
Department of Radiology, The First Affiliated Hospital, College of Medicine, Zhejiang University
Hong Yang
Yu-Shu Shi / Hai-Yan Xie
21
20
Philips Achieva 3.0 T scanner (Philips Healthcare, Netherlands)
8-channel SENSE head coil
2000
35
90
5.0/1.0 mm
24
200
1.67 × 1.67 × 6.00
240 × 240
N/A
19
Anhui Medical University
Kai Wang
Tong-Jian Bai
51
36
GE Signa 3T
8 channel
2000
22.5
30
4.0/0.6 mm
33
240
3.44 × 3.44 × 4.60
220 × 220
Wang et al., 2017 (28)
20
Faculty of Psychology, Southwest University
Jiang Qiu
Xin-Ran Wu
282
251
Siemens Tim Trio 3T
12 channel
2000
30
90
3.0mm/1.0mm
32
242
3.44 × 3.44 × 4.00
220 × 220
Cheng et al., 2016 (29)/Ye et al., 2015 (30)/Luo et al., 2015 (31)/Xue et al., 2016 (32)
21
Beijing Anding Hospital, Capital Medical University
Chuan-Yue Wang
Qi-Jing Bo / Feng Li
86
70
Siemens Tim Trio 3T
32 channel
2000
30ms
90
3.5mm/0.7mm
33
240
3.12 × 3.12 × 4.20
200 × 200
Zheng et al., 2018 (33)/Jing et al., 2013 (34)
22
The Institute of Mental Health, Second Xiangya Hospital of Central South University
Zhe-Ning Liu
Yi-Cheng Long
30
20
Philips Gyroscan Achieva 3.0T
32 channel
2000
30
90
4.0mm/0mm
36
250
1.67 × 1.67 × 4.00
240 × 240
N/A
23
Mental Health Center, West China Hospital, Sichuan University
Tao Li
Yi-Ting Zhou
32
30
Philips Achieva 3.0T TX
8 channal
2000
30
90
4.0mm/0mm
38
240
3.75 × 3.75 × 4.00
240 × 240
Yang et al., 2015 (35)
24
First Affiliated Hospital of Kunming Medical University
Xiu-Feng Xu / Yu-Qi Cheng
Chao-Jie Zou
32
31
GE Signa 1.5T
8 channel
2000
40
90
5/1mm
24
160
3.75 × 3.75 × 6.00
240 × 240
Cheng., et al. 2017 (36)
25
Department of Neurology, Affiliated ZhongDa Hospital of Southeast University
Zhi-Jun Zhang
Zhi-Jun Zhang
89
63
Siemens Verio 3T
12 channel head coil
2000
25
90
4.0mm/0mm
36
240
3.75 × 3.75 × 4.00
240 × 240
Yuan et al., 2008 (37)
Total
1300
1128
Abbreviations: MDD, major depressive disorder; NC, normal control.
*(Note: Part of the content of this post was adapted from the original REST-meta-MDD PNAS paper (http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116 http://www.pnas.org/cgi/doi/10.1073/pnas.1900390116) under CC BY-NC-ND license.)*
Best,
Chao-Gan
-- Chao-Gan YAN, Ph.D. Professor, Principal Investigator Director, International Big-Data Center for Depression Research Deputy Director, Magnetic Resonance Imaging Research Center Institute of Psychology, Chinese Academy of Sciences 16 Lincui Road, Chaoyang District, Beijing 100101, China - Initiator DPABI http://rfmri.org/DPABI, http://dpabi.org/DPARSF http://rfmri.org/DPARSF, PRN http://rfmri.org/PRN and The R-fMRI Network http://rfmri.org/ (RFMRI.ORG http://rfmri.org/) http://rfmri.org/yan http://scholar.google.com/citations?user=lJQ9B58AAAAJ
freesurfer@nmr.mgh.harvard.edu