SRPBS数据集预处理

数据集介绍

https://www.nature.com/articles/s41597-021-01004-8

Machine learning classifiers for psychiatric disorders using resting-state functional magnetic resonance imaging (rs-fMRI) have recently attracted attention as a method for directly examining relationships between neural circuits and psychiatric disorders. To develop accurate and generalizable classifiers, we compiled a large-scale, multi-site, multi-disorder neuroimaging database. The database comprises resting-state fMRI and structural images of the brain from 993 patients and 1,421 healthy individuals, as well as demographic information such as age, sex, and clinical rating scales. To harmonize the multi-site data, nine healthy participants (“traveling subjects”) visited the sites from which the above datasets were obtained and underwent neuroimaging with 12 scanners. All participants consented to having their data shared and analyzed at multiple medical and research institutions participating in the project, and 706 patients and 1,122 healthy individuals consented to having their data disclosed. Finally, we have published four datasets: 1) the SRPBS Multi-disorder Connectivity Dataset 2), the SRPBS Multi-disorder MRI Dataset (restricted), 3) the SRPBS Multi-disorder MRI Dataset (unrestricted), and 4) the SRPBS Traveling Subject MRI Dataset.

在该数据集中，收集了各种精神类疾病，在我的project中仅使用MDD患者，因此我只处理含有MDD患者及正常对照的中心。在该数据集中rsfMRI数据没有4D的时间nii，我使用fslmerge该命令将COI、HRC、UTO、KUT这4个中心的影响进行处理，注意KUT这个中心在merge单个4D的rsfMRI显示TR为1，需要认为的矫正。在该处理中我排除了HKH中心，在merge的时候报Warning，说可能存在文件确实。因此我一共处理717个被试，其中495个control和222的MDD患者。

构建BIDS结构数据

在构建bids格式的数据时，该数据集不提供相应的json文件，需要自己根据文献中tabel5和table6自行补全。但是在某些中心下，有些参数看起来非常离谱，我选择相信文章中的参数，下面是一个中心的例子。

T1w.json

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{
    "ProtocolName": "UTO Protocol",
    "InstitutionName": "UTO",
    "Manufacturer": "GE",
    "ManufacturersModelName": "GE MR750w",
    "SequenceType": "T1w",
    "VoxelSize": [1, 1, 1.2],
    "RepetitionTime": 0.0077,
    "EchoTime": 0.0031,
    "InversionTime": 0.400,
    "FlipAngle": 11,
    "FieldOfView": 240,
    "MatrixSize": [256, 256]
}

task_rest_bold

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{
    "TaskName": "rest",
    "MagneticFieldStrength": 3,
    "Manufacturer": "GE",
    "ManufacturersModelName": "Discovery MR750w",
    "InstitutionName": "University of Tokyo",
    "ProtocolName": "Protocol #8",
    "ReceiveCoilName": "Head",
    "ReceiveCoilActiveElements": "24",
    "RepetitionTime": 2.5,
    "EchoTime": 0.03,
    "FlipAngle": 80,
    "PhaseEncodingDirection": "j-",
    "MatrixSize": [64, 64],
    "FieldOfView": 212,
    "InPlaneResolution": 3.3,
    "SliceThickness": 3.2,
    "SliceGap": 0.8,
    "NumberOfSlices": 40,
    "SliceAcquisitionOrder": "ascending",
    "NumberOfVolumes": 240,
    "DummyScans": 4,
    "TotalScanTime": "10 min + 10s (dummy)",
    "TaskInstructions": "Fixate"
}

因为都是ascending扫描方式，在这里我直接去掉了slice timing这个属性。

fMRIprep处理

这里直接魔改以前的脚本就可以了，在这里同样没有加入fmap的处理及皮层的处理。