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30132 discussions

Unable to run recon all
by Sahil Bajaj 02 Jan '20

02 Jan '20

External Email - Use Caution Hello FS experts, I am trying to run recon all on about 100 subjects. But one out of 100 subjects finishes recon all with error, and doesn't complete the process. Here I am attaching recon all log for your reference. Also, before installing freesurfer on MAC Catalina, I ran sudo spctl - -master -disable I would really appreciate any help in resolving this. Thanks, Sahil 1) FreeSurfer version: freesurfer-Darwin-OSX-stable-pub-v6.0.0-2beb96c 2) Platform: MACOS Catalina 3) recon-all.log: see attached

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Fill holes in BOLD surface
by Marina Fernández 02 Jan '20

02 Jan '20

External Email - Use Caution Hi Freesurfer experts, I have projected 4D-preprocessed BOLD data to Freesurfer cortical surfaces. Resulting individual surfaces have many holes (see attached image) that I would like to fill with nearest neighbors values around each hole (avoiding the medial wall). Could you indicate me how to do this? Thank you in advance. Marina

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mapping the reliability of separating GM/WM
by Liu Y 02 Jan '20

02 Jan '20

External Email - Use Caution Hi Freesurfers, The contrast between GM/WM may vary regions by regions. Some regions have high contrast, some regions have low contrast, is there a method can map the reliability of separating gray and subcortical white matter? Thanks in advance, Yawu

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No Colortable Found
by Baptista, Paulo 02 Jan '20

02 Jan '20

External Email - Use Caution Hello FreeSurfer Developers, I'm helping researchers test Freesurfer 4.5 and Freesurfer 6. Running same code on Freesurfer4.5 environment produces no image with tksurfer. It says "No colortable found." See attached. 1) FreeSurfer version: freesurfer-Linux-centos4_x86_64-stable-pub-v4.5.0 2) Platform: CentOS release 5.11 (Final) We're are running this in a Singularity container. It's getting close to working properly. It would be nice to have a way to run old code on newer Linux systems. Thanks, Paulo

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Obtaining pre&post values from longitudinal dataset without cross-base-long
by Tudor Popescu 02 Jan '20

02 Jan '20

External Email - Use Caution Dear FS experts, From a pre-&post-intervention data set, is it possible to extract thickness values at those two time points just by using recon-all, that is, without running the full longitudinal processing stream (the Cross, Base and Long steps)? I aim not so much to compute the percentage change measure ( pc1 = (post-pre)/pre ), but simply to use the raw pre&post values for other analyses. Many thanks, and happy holidays to all! Tudor

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FSFAST registration
by Star Xi 02 Jan '20

02 Jan '20

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vol2subfield
by stdp82＠virgilio.it 02 Jan '20

02 Jan '20

External Email - Use Caution Hi list, where/how should I add the file listed below? https://surfer.nmr.mgh.harvard.edu/pub/dist/freesurfer/6.0.0-patch/vol2subf… Thanks

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Problem for mri_glmfit
by Gwang-Won Kim 02 Jan '20

02 Jan '20

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recon-all in freesurfer
by Fatemeh Ahmadi 02 Jan '20

02 Jan '20

External Email - Use Caution Dear all,I'm working on brain surface with conformal geometry algorithms, I need to know whether triangular mesh that gets from "recon-all" in Freesurfer is a Delaunay triangular mesh !? Very thanks in advance

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REST-meta-MDD Data Entered Unrestricted Sharing Phase
by YAN Chao-Gan 02 Jan '20

02 Jan '20

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(a)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(a)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

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