COBRE preprocessed with NIAK 0.12.4

<strong>!!! WIP !!! Because of the 1Gb quota, I need to upload this dataset bits by bits. The upload is not currently complete !!</strong> <strong><br></strong> <strong>### Content</strong> This work is a derivative from the COBRE sample found in the International Neuroimaging Data-sharing Initiative (INDI, http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html), originally released under Creative Commons -- Attribution Non-Commercial. It includes preprocessed resting-state functional magnetic resonance images for 72 patients diagnosed with schizophrenia (58 males, age range = 18-65 yrs) and 74 healthy controls (51 males, age range = 18-65 yrs). The fMRI dataset for each subject are single nifti files (.nii.gz), featuring 150 EPI blood-oxygenation level dependent (BOLD) volumes were obtained in 5 mns (TR = 2 s, TE = 29 ms, FA = 75°, 32 slices, voxel size = 3x3x4 mm3 , matrix size = 64x64, FOV = mm2 ). The COBRE preprocessed fMRI release more specifically contains the following files:<br><strong>* README.md</strong>: a markdown (text) description of the release.<br><strong>* fmri_szxxxSUBJECT_session1_run1.nii.gz</strong>: a 3D+t nifti volume at 3 mm isotropic resolution, in the MNI non-linear 2009a symmetric space<br>(http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009). Note that the number of time samples may vary, as some samples have been removed if tagged with excessive motion. See the _extra.mat file below for more info.<br><strong>* fmri_szxxxSUBJECT_session1_run1_extra</strong>.mat: a matlab/octave file for each subject. Each .mat file contains the following variables:<br><strong>* confounds</strong>: a TxK array. Each row corresponds to a time sample, and each column to one confound that was regressed out from the time series during preprocessing.<br><strong>* labels_confounds</strong>: cell of strings. Each entry is the label of a confound that was regressed out from the time series.<br><strong>* mask_suppressed</strong>: a T2x1 vector. T2 is the number of time samples in the raw time series (before preprocessing), T2=119. Each entry corresponds to a time sample, and is 1 if the corresponding sample was removed due to excessive motion (or to wait for magnetic equilibrium at the beginning of the series). Samples that were kept are tagged with 0s.<br><strong>* time_frames</strong>: a Tx1 vector. Each entry is the time of acquisition (in s) of the corresponding volume. <strong>### Preprocessing</strong> The datasets were analysed using the NeuroImaging Analysis Kit (NIAK https://github.com/SIMEXP/niak) version 0.12.14, under CentOS version 6.3 with Octave(http://gnu.octave.org) version 3.8.1 and the Minc toolkit (http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) version 0.3.18. Each fMRI dataset was corrected for inter-slice difference in acquisition time and the parameters of a rigid-body motion were estimated for each time frame. Rigid-body motion was estimated within as well as between runs, using the median volume of the first run as a target. The median volume of one selected fMRI run for each subject was coregistered with a T1 individual scan using Minctracc (Collins and Evans, 1998), which was itself non-linearly transformed to the Montreal Neurological Institute (MNI) template (Fonov et al., 2011) using the CIVET pipeline (Ad-Dabbagh et al., 2006). The MNI symmetric template was generated from the ICBM152 sample of 152 young adults, after 40 iterations of non-linear coregistration. The rigid-body<br>transform, fMRI-to-T1 transform and T1-to-stereotaxic transform were all combined, and the functional volumes were resampled in the MNI space at a 3 mm isotropic resolution. The “scrubbing” method of (Power et al., 2012), was used to remove the volumes with excessive motion (frame displacement greater than 0.5 mm). A minimum number of 60 unscrubbed volumes per run, corresponding to ~180 s of acquisition, was then required for further analysis. For this reason, 16 controls and 29 schizophrenia patients were<br>rejected from the subsequent analyses. The following nuisance parameters were regressed out from the time series at each voxel: slow time drifts (basis of discrete cosines with a 0.01 Hz high-pass cut-off), average signals in conservative masks of the white matter and the lateral ventricles as well as the first principal components (95% energy) of the six rigid-body motion parameters and their squares (Giove et al., 2009). The fMRI volumes were finally spatially smoothed with a 6 mm isotropic Gaussian blurring kernel. <strong>### References</strong> Ad-Dab’bagh, Y., Einarson, D., Lyttelton, O., Muehlboeck, J. S., Mok, K., Ivanov, O., Vincent, R. D., Lepage, C., Lerch, J., Fombonne, E., Evans, A. C., 2006. <em>The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research</em>. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping<br>Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. <em>Multi-level bootstrap analysis of stable clusters in resting-state fMRI.</em> NeuroImage 51 (3), 1126–1139.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. <em>Animal: validation and applications of nonlinear registration-based segmentation</em>. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011. <em>Unbiased average age-appropriate atlases for pediatric studies</em>. NeuroImage 54 (1), 313–327.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033 Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. <em>Images-based suppression of unwanted global signals in resting-state functional connectivity studies</em>. Magnetic resonance imaging 27 (8), 1058–1064.<br>URL http://dx.doi.org/10.1016/j.mri.2009.06.004 Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E.,<br>Feb. 2012. <em>Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion</em>. NeuroImage 59 (3), 2142–2154.<br>URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018 ### Other derivatives This dataset was used in a publication, see the link below.<br>https://github.com/SIMEXP/glm_connectome

<strong>!!! 开发中（WIP）！！！ 由于1GB配额限制，本数据集需分批次上传，目前尚未上传完成！</strong> <strong><br></strong> <strong>### 数据集内容</strong> 本数据集源自国际神经影像数据共享倡议（INDI, http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html）中的COBRE样本，原作品采用知识共享署名-非商业性使用许可协议发布。本数据集包含72名精神分裂症患者（58名男性，年龄范围18~65岁）与74名健康对照者（51名男性，年龄范围18~65岁）的预处理静息态功能磁共振成像（resting-state functional magnetic resonance imaging）数据。每位受试者的fMRI数据集为单个NIfTI文件（.nii.gz格式），包含150帧回波平面成像（EPI）血氧水平依赖（blood-oxygenation level dependent, BOLD）影像帧，采集时长5分钟（重复时间TR=2s，回波时间TE=29ms，翻转角FA=75°，共32层，体素尺寸3×3×4 mm³，矩阵尺寸64×64，视野FOV：mm²）。本次发布的COBRE预处理fMRI数据集具体包含以下文件：<br><strong>* README.md</strong>：本次发布的Markdown（文本）说明文档。<br><strong>* fmri_szxxxSUBJECT_session1_run1.nii.gz</strong>：采用3mm各向同性分辨率的3D+T NIfTI影像文件，位于MNI 2009a非线性对称空间（http://www.bic.mni.mcgill.ca/ServicesAtlases/ICBM152NLin2009）。请注意，由于存在过度运动标记的样本被移除，各文件的时间采样帧数可能存在差异，详细信息请参见下方的_extra.mat文件。<br><strong>* fmri_szxxxSUBJECT_session1_run1_extra.mat</strong>：每位受试者对应的MATLAB/Octave格式文件。每个.mat文件包含以下变量：<br><strong>* confounds</strong>：T×K数组。每一行对应一个时间采样点，每一列对应预处理阶段从时间序列中回归去除的一项混淆变量。<br><strong>* labels_confounds</strong>：字符串元胞数组。每个元素对应一项被回归去除的混淆变量的名称。<br><strong>* mask_suppressed</strong>：T2×1向量。T2为原始时间序列（预处理前）的时间采样点数，固定为119。每个元素对应一个原始时间采样点，若该采样点因过度运动（或序列初始阶段等待磁场平衡）被移除，则标记为1；保留的采样点标记为0。<br><strong>* time_frames</strong>：T×1向量。每个元素对应对应影像帧的采集时刻（单位：秒）。<strong>### 预处理流程</strong> 本数据集的预处理采用神经影像分析工具包（NeuroImaging Analysis Kit, NIAK https://github.com/SIMEXP/niak）0.12.14版本完成，运行环境为CentOS 6.3系统，搭配Octave(http://gnu.octave.org) 3.8.1版本与Minc工具包(http://www.bic.mni.mcgill.ca/ServicesSoftware/ServicesSoftwareMincToolKit) 0.3.18版本。针对每一份fMRI数据集，首先校正了层间采集时间差异，并为每个时间帧估计刚性体运动参数。运动校正以首个扫描序列的中间帧为基准，同时估算序列内与序列间的刚性体运动。每位受试者选定的fMRI序列的中间帧，通过Minctracc工具（Collins与Evans, 1998）与个体T1影像进行配准；随后该个体T1影像通过CIVET流水线（Ad-Dabbagh等, 2006）非线性配准至蒙特利尔神经研究所（Montreal Neurological Institute, MNI）标准模板（Fonov等, 2011）。本次使用的MNI对称模板由152名青年成年人的ICBM152样本经过40次非线性配准迭代生成。将刚性体配准变换、fMRI到T1影像的配准变换以及T1影像到立体定向空间的变换进行组合，随后将功能影像重采样至MNI空间，分辨率设为3mm各向同性。采用Power等（2012）提出的scrubbing法移除帧位移超过0.5mm的过度运动影像帧。为保证后续分析质量，要求每个序列保留至少60帧未被移除的影像（对应约180秒采集时长）。基于该标准，共有16名健康对照者与29名精神分裂症患者的数据被排除在后续分析之外。针对每个体素的时间序列，回归去除以下干扰参数：慢时间漂移（采用截止频率0.01Hz的离散余弦变换基进行校正）、白质与侧脑室保守掩模内的平均信号，以及6项刚性体运动参数及其平方项的第一主成分（覆盖95%能量，Giove等, 2009）。最后，采用6mm各向同性高斯平滑核对fMRI影像进行空间平滑处理。<strong>### 参考文献</strong> Ad-Dab’bagh Y, Einarson D, Lyttelton O, Muehlboeck JS, Mok K, Ivanov O, Vincent RD, Lepage C, Lerch J, Fombonne E, Evans AC. CIVET影像处理环境：面向解剖神经影像研究的全自动综合流水线[C]//Corbetta M (编). 第12届人类大脑映射组织年会论文集. 意大利佛罗伦萨: Neuroimage, 2006.<br>Bellec P, Rosa-Neto P, Lyttelton OC, Benali H, Evans AC. 静息态fMRI稳定簇的多层Bootstrap分析[J]. NeuroImage, 2010, 51(3): 1126-1139. URL: http://dx.doi.org/10.1016/j.neuroimage.2010.02.082<br>Collins DL, Evans AC. ANIMAL：基于非线性配准的分割方法的验证与应用[J]. 国际模式识别与人工智能期刊, 1997, 11: 1271-1294.<br>Fonov V, Evans AC, Botteron K, Almli CR, McKinstry RC, Collins DL. 适用于儿科研究的无偏平均年龄适配脑图谱[J]. NeuroImage, 2011, 54(1): 313-327. URL: http://dx.doi.org/10.1016/j.neuroimage.2010.07.033<br>Giove F, Gili T, Iacovella V, Macaluso E, Maraviglia B. 静息态功能连接研究中基于影像的无关全局信号抑制方法[J]. Magnetic Resonance Imaging, 2009, 27(8): 1058-1064. URL: http://dx.doi.org/10.1016/j.mri.2009.06.004<br>Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. 受试者运动导致功能连接MRI网络中出现虚假但系统性的相关性[J]. NeuroImage, 2012, 59(3): 2142-2154. URL: http://dx.doi.org/10.1016/j.neuroimage.2011.10.018<strong>### 衍生应用</strong> 本数据集曾被用于一项已发表研究，详情参见链接：https://github.com/SIMEXP/glm_connectome