DOI: 10.57760/sciencedb.o00115.00013
title: Data of the REST-meta-MDD Project from DIRECT Consortium
title: Data of the REST-meta-MDD Project from DIRECT Consortium

description: (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 Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information.   ACKNOWLEDGEMENTSThis 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). REFERENCES1.        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).Given the huge size of the files, please use FTP to download the data. You may need to resume from break-point sometimes. You need to sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information.
description: (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 Psychological Science Data Bank to download the data, and then sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information.   ACKNOWLEDGEMENTSThis 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). REFERENCES1.        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).Given the huge size of the files, please use FTP to download the data. You may need to resume from break-point sometimes. You need to sign the Data Use Agreement and email the scanned signed copy to rfmrilab@gmail.com to get unzip password and phenotypic information.