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

Submitted by YAN Chao-Gan on

(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 Resting-State fMRI (DPARSF) (12, 13) was implemented at each local participating site to minimize heterogeneity in preprocessing methods. R-fMRI metrics can be vulnerable to physiological confounds such as head motion (14, 15). Based on our previous work examination of head motion impact on R-fMRI FC connectomes (16) and other recent benchmarking studies (15, 17), DPARSF implements a regression model (Friston-24 model) on the participant-level and group-level correction for mean frame displacements (FD) as the default setting.

In the REST-meta-MDD Project of the DIRECT consortium, 25 research groups from 17 hospitals in China 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 visit Psychologcial Science Data Bank: (http://doi.org/10.57760/sciencedb.o00115.00013) to download the dataand then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information.

 

 

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.         A. J. Ferrari et al., Burden of Depressive Disorders by Country, Sex, Age, and Year: Findings from the Global Burden of Disease Study 2010. PLOS Medicine 10, e1001547 (2013).

2.         L. M. Williams et al., International Study to Predict Optimized Treatment for Depression (iSPOT-D), a randomized clinical trial: rationale and protocol. Trials 12, 4 (2011).

3.         S. J. Borowsky et al., Who is at risk of nondetection of mental health problems in primary care? J Gen Intern Med 15, 381-388 (2000).

4.         B. B. Biswal, Resting state fMRI: a personal history. Neuroimage 62, 938-944 (2012).

5.         C. G. Yan et al., Reduced default mode network functional connectivity in patients with recurrent major depressive disorder. Proc Natl Acad Sci U S A 116, 9078-9083 (2019).

6.         K. S. Button et al., Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365-376 (2013).

7.         J. P. A. Ioannidis, Why Most Published Research Findings Are False. PLOS Medicine 2, e124 (2005).

8.         R. A. Poldrack et al., Scanning the horizon: towards transparent and reproducible neuroimaging research. Nat Rev Neurosci 10.1038/nrn.2016.167 (2017).

9.         S. Marek et al., Reproducible brain-wide association studies require thousands of individuals. Nature 603, 654-660 (2022).

10.       J. Carp, On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments. Frontiers in Neuroscience 6, 149 (2012).

11.       R. Botvinik-Nezer et al., Variability in the analysis of a single neuroimaging dataset by many teams. Nature 10.1038/s41586-020-2314-9 (2020).

12.       C.-G. Yan, X.-D. Wang, X.-N. Zuo, Y.-F. Zang, DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging. Neuroinformatics 14, 339-351 (2016).

13.       C.-G. Yan, Y.-F. Zang, DPARSF: A MATLAB Toolbox for "Pipeline" Data Analysis of Resting-State fMRI. Frontiers in systems neuroscience 4, 13 (2010).

14.       R. Ciric et al., Mitigating head motion artifact in functional connectivity MRI. Nature protocols 13, 2801-2826 (2018).

15.       R. Ciric et al., Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity. NeuroImage 154, 174-187 (2017).

16.       C.-G. Yan et al., A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. NeuroImage 76, 183-201 (2013).

17.       L. Parkes, B. Fulcher, M. Yücel, A. Fornito, An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI. NeuroImage 171, 415-436 (2018).

18.       L. Wang et al., Interhemispheric functional connectivity and its relationships with clinical characteristics in major depressive disorder: a resting state fMRI study. PLoS One 8, e60191 (2013).

19.       L. Wang et al., The effects of antidepressant treatment on resting-state functional brain networks in patients with major depressive disorder. Hum Brain Mapp 36, 768-778 (2015).

20.       Y. Liu et al., 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 (2017).

21.       X. Zhu et al., Evidence of a dissociation pattern in resting-state default mode network connectivity in first-episode, treatment-naive major depression patients. Biological psychiatry 71, 611-617 (2012).

22.       W. Guo et al., Abnormal default-mode network homogeneity in first-episode, drug-naive major depressive disorder. PLoS ONE 9, e91102 (2014).

23.       W. Guo et al., 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 10.1007/s11682-017-9794-8 (2017).

24.       D. Peng et al., Altered brain network modules induce helplessness in major depressive disorder. Journal of Affective Disorders 168, 21-29 (2014).

25.       D. Peng et al., Dissociated large-scale functional connectivity networks of the precuneus in medication-naïve first-episode depression. Psychiatry Research: Neuroimaging 232, 250-256 (2015).

26.       J. Zhu et al., Default-mode network connectivity in depression: A resting-state fMRI study (in Chinese). Chinese Journal of Nervous and Mental Diseases 40, 454-458 (2014).

27.       Y. Shen et al., 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, 2915-2927 (2015).

28.       Y. Tang et al., 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, 1921-1927 (2013).

29.       H. J. Li et al., Surface-based regional homogeneity in first-episode, drug-naive major depression: a resting-state FMRI study. Biomed Res Int 2014, 374828 (2014).

30.       L. Du et al., 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, 49-54 (2016).

31.       X. Wu et al., Dysfunction of the cingulo-opercular network in first-episode medication-naive patients with major depressive disorder. J Affect Disord 200, 275-283 (2016).

32.       X.-h. Yang et al., 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 (2017).

33.       Z. Hou et al., 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 (2018).

34.       Z. Hou et al., Distinctive pretreatment features of bilateral nucleus accumbens networks predict early response to antidepressants in major depressive disorder. Brain imaging and behavior 12, 1042-1052 (2018).

35.       T. Chen et al., Anomalous single-subject based morphological cortical networks in drug-naive, first-episode major depressive disorder. Hum Brain Mapp 38, 2482-2494 (2017).

36.       J. Cao et al., Resting-state functional MRI of abnormal baseline brain activity in young depressed patients with and without suicidal behavior. J Affect Disord 205, 252-263 (2016).

37.       J. Wang et al., Electroconvulsive therapy selectively enhanced feedforward connectivity from fusiform face area to amygdala in major depressive disorder. Social cognitive and affective neuroscience 12, 1983-1992 (2017).

38.       W. Cheng et al., Medial reward and lateral non-reward orbitofrontal cortex circuits change in opposite directions in depression. Brain 139, 3296-3309 (2016).

39.       M. Ye et al., Changes of Functional Brain Networks in Major Depressive Disorder: A Graph Theoretical Analysis of Resting-State fMRI. PLOS ONE 10, e0133775 (2015).

40.       Q. Luo et al., Frequency Dependant Topological Alterations of Intrinsic Functional Connectome in Major Depressive Disorder. Scientific Reports 5, 9710 (2015).

41.       S. Xue, X. Wang, W. Wang, J. Liu, J. Qiu, Frequency-dependent alterations in regional homogeneity in major depression. Behavioural Brain Research 306, 13-19 (2016).

42.       H. Zheng et al., The dynamic characteristics of the anterior cingulate cortex in resting-state fMRI of patients with depression. Journal of Affective Disorders 227, 391-397 (2018).

43.       B. Jing et al., Difference in amplitude of low-frequency fluctuation between currently depressed and remitted females with major depressive disorder. Brain Research 1540, 74-83 (2013).

44.       X. Yang et al., Anatomical and functional brain abnormalities in unmedicated major depressive disorder. Neuropsychiatr Dis Treat 11, 2415-2423 (2015).

45.       Y. Cheng et al., Resting-state brain alteration after a single dose of SSRI administration predicts 8-week remission of patients with major depressive disorder. Psychological Medicine 47, 438-450 (2017).

46.       Y. Yuan et al., Abnormal neural activity in the patients with remitted geriatric depression: A resting-state functional magnetic resonance imaging study. Journal of Affective Disorders 111, 145-152 (2008).


 

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 (18)/2015 (19)

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 (20)

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 (21)

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 (22)/2017(23)

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 (24)/2015 (25)

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 (26)

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 (27)

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 (28)

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 (29)

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 (30)

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 (31)

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 (32)

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 (33)/2018 (34)

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 (35)

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 (36)

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 (37)

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 (38)/Ye et al., 2015 (39)/Luo et al., 2015 (40)/Xue et al., 2016 (41)

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 (42)/Jing et al., 2013 (43)

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 (44)

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 (45)

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 (46)

Total

1300

1128

 

 

 

 

 

 

 

 

 

 

 

                     
 

Abbreviations: MDD, major depressive disorder; NC, normal control.