Cobre (for machine learning)

COBRE dataset, preprocessed and functional connectivity features extracted at 7 resolutions (7,12,20,36,64,122,197,325,444). Pearson correlation was used to compute functional connectivity between time series. The resolution are based on a partition using Cambridge dataset availlable at http://dx.doi.org/10.6084/m9.figshare.1285615 ### Content 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 ). * cobre_model_group.csv A comma-separated value file, with the sz (1: patient with schizophrenia, 0: control), age, sex, and FD (frame displacement, as defined by Power et al. 2012) variables. Each column codes for one variable, starting with the label, and each line has the label of the corresponding subject. * cobre_resolution_xx.mat: a .mat (octave/matlab) structure with two variables: data a NxF (N subjects x F features) and a subj_idx the subject id of each row. The features are a vetororized for of the connectome. ### Preprocessing 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.<br>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 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. ### References 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. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. Neu-<br>roImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082 Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. 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.Unbiased average age-appropriate atlases for pediatric studies. 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. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. 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., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018

COBRE数据集，已完成预处理，并在9种分辨率（7、12、20、36、64、122、197、325、444）下提取了功能连接特征。采用皮尔逊相关系数计算时间序列间的功能连接。该分辨率方案基于剑桥数据集的分区，该数据集可从http://dx.doi.org/10.6084/m9.figshare.1285615获取。
### 数据集内容
本数据集衍生自国际神经影像数据共享倡议（International Neuroimaging Data-sharing Initiative, INDI）中的COBRE样本，原始数据集采用知识共享署名非商业性（Creative Commons -- Attribution Non-Commercial）许可协议发布。数据集包含72例精神分裂症患者（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 images）数据。每名受试者的功能磁共振成像（fMRI）数据均为单个NIfTI格式压缩文件（.nii.gz），包含150幅回波平面成像（EPI）血氧水平依赖（blood oxygenation level dependent, BOLD）序列影像，采集时长为5分钟，具体参数如下：重复时间（TR）=2秒，回波时间（TE）=29毫秒，翻转角（FA）=75°，共32层切片，体素尺寸为3×3×4 mm³，矩阵尺寸为64×64，视场（FOV）= mm²。
* cobre_model_group.csv：逗号分隔值文件，包含sz（1：精神分裂症患者，0：健康对照）、年龄、性别以及帧位移（frame displacement, FD，参照Power等人2012年的定义）变量。每一列对应一个变量，首行为变量标签，每一行对应一名受试者的标签信息。
* cobre_resolution_xx.mat：Octave/MATLAB格式的结构文件，包含两个变量：`data`为N×F矩阵（N为受试者数量，F为特征数量），`subj_idx`为每一行对应的受试者ID；特征为连接组的向量化表示。
### 预处理流程
本数据集采用神经影像分析工具包（NeuroImaging Analysis Kit, NIAK）v0.12.14进行分析，运行环境为CentOS 6.3，搭配Octave v3.8.1与Minc工具包v0.3.18。
针对每一份fMRI数据，首先校正不同切片间的采集时间差异，并估计每个时间帧的刚体运动参数。刚体运动参数的估计同时在单次扫描内与扫描间进行，以首次扫描的中间帧作为配准目标。每名受试者选取一次fMRI扫描的中间帧，通过MincTracc（Collins与Evans, 1998）与个体T1影像完成配准；随后利用CIVET流水线（Ad-Dabbagh等人, 2006）将T1影像非线性配准至蒙特利尔神经研究所（Montreal Neurological Institute, MNI）模板（Fonov等人, 2011）。
MNI对称模板由152名年轻成年人的ICBM152样本经过40次非线性配准生成。将刚体变换、fMRI到T1的变换以及T1到立体定位空间的变换进行组合，随后将功能影像重采样至MNI空间，分辨率为3mm各向同性。
采用Power等人2012年提出的“剔除（scrubbing）”方法，移除帧位移大于0.5mm的异常影像帧。后续分析要求单次扫描至少保留60帧未被剔除的影像，对应约180秒的采集时长。因此，16名健康对照与29例精神分裂症患者被排除在后续分析之外。
从每个体素的时间序列中去除以下混杂参数：缓慢时间漂移（采用截止频率0.01Hz的离散余弦变换基函数校正）、白质与侧脑室的保守掩膜平均信号，以及6个刚体运动参数及其平方项的前95%能量主成分（Giove等人, 2009）。
最后，使用6mm各向同性高斯卷积核对fMRI影像进行空间平滑。
### 参考文献
1. 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. The CIVET Image-Processing Environment: A Fully Automated Comprehensive Pipeline for Anatomical Neuroimaging Research. In: Corbetta, M. (Ed.), Proceedings of the 12th Annual Meeting of the Human Brain Mapping Organization. Neuroimage, Florence, Italy.
2. Bellec, P., Rosa-Neto, P., Lyttelton, O. C., Benali, H., Evans, A. C., Jul. 2010. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage 51 (3), 1126–1139. URL http://dx.doi.org/10.1016/j.neuroimage.2010.02.082
3. Collins, D. L., Evans, A. C., 1997. Animal: validation and applications of nonlinear registration-based segmentation. International Journal of Pattern Recognition and Artificial Intelligence 11, 1271–1294.
4. Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., Collins, D. L., Jan. 2011.Unbiased average age-appropriate atlases for pediatric studies. NeuroImage 54 (1), 313–327. URL http://dx.doi.org/10.1016/j.neuroimage.2010.07.033
5. Giove, F., Gili, T., Iacovella, V., Macaluso, E., Maraviglia, B., Oct. 2009. Images-based suppression of unwanted global signals in resting-state functional connectivity studies. Magnetic resonance imaging 27 (8), 1058–1064. URL http://dx.doi.org/10.1016/j.mri.2009.06.004
6. Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., Petersen, S. E., Feb. 2012. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. NeuroImage 59 (3), 2142–2154. URL http://dx.doi.org/10.1016/j.neuroimage.2011.10.018