An fMRI dataset for whole-body somatotopic mapping in humans
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# An fMRI dataset for whole-body somatotopic mapping in humans ## Raw data A total of 62 healthy adults (34 females), ranging in age between 19 and 29 years (mean ± standard deviation[SD], 22.76 ± 2.22 years), participated in this study. Participants were instructed to perform movements of various body parts, including the toe, ankle, leg, finger, wrist, forearm, upper arm, jaw, lip, tongue, and eyes. MRI was performed on a Siemens 3 Tesla (3T) MAGNETOM Prisma MRI scanner at the BNU Imaging Center for Brain Research, Beijing, China, using a 64-channel phased-array head coil. ## Pipeline description 1. The DICOM images acquired from the Siemens scanner were converted into the NIfTI format and then reorganized into the BIDS using HeuDiConv (https://github.com/nipy/heudiconv). 2. The NIfTI images were anonymized by removing facial features using the PyDeface (https://github.com/poldracklab/pydeface). 3. The data were preprocessed using fMRIPrep 20.2.1 (https://fmriprep.org). 4. A spatial ICA was performed on each run from each participant in the individual native space using MELODIC (version 3.15) from the FSL with default parameters. Artifact-related ICs and signal ICs were manually identified according to their spatial maps, time courses, and power spectrum of the time courses using melview (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview). 5. The manually denoised data were adapted to a CIFTI-based grayordinate format using Ciftify (https://github.com/edickie/ciftify). ## Data records **Structural MRI**: <Sub-ID>/ses-1/anat/<SUB-ID>_ses-1_run-01_T1w.nii.gz **Functional MRI**: <Sub-ID>/ses-1/func/<Sub-ID>_ses-1_task-motor_<Run-ID>_bold.nii.gz **Field mapping**: <Sub-ID>/ses-1/fmap/<Sub-ID>_ses-1_run-01_<magnitude/phasediff>.nii.gz **Preprocessed functional MRI**: derivatives/fmriprep/<Sub-ID>/<Sub-ID>_ses-1_task-motor_<Run-ID>_space-T1w_desc-preproc_bold.nii.gz **Denoised fMRI**: derivatives/fmriprep/<Sub-ID>/<Sub-ID>_ses-1_task-motor_<Run-ID>_space-T1w_desc-preproc_bold_denoised.nii.gz **Spatial maps from ICA**: derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/melodic_IC.nii.gz **Time series from ICA**: derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/melodic_mix **Manually classified labels**: derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/results_suggest.csv **Native surface**: derivatives/ciftify/<Sub-ID>/native_surface **Results of task analysis**: derivatives/ciftify/<Sub-ID>/GLM
# 用于人类全身体位拓扑映射的功能磁共振成像（fMRI）数据集 ## 原始数据 本研究共招募62名健康成年人（其中女性34名），年龄区间为19至29岁（平均值±标准差[SD]：22.76±2.22岁）。要求参与者完成多组身体部位运动，涵盖脚趾、脚踝、腿部、手指、手腕、前臂、上臂、下颌、嘴唇、舌头及眼部。扫描工作在中国北京师范大学脑成像研究中心的西门子3特斯拉（3T）MAGNETOM Prisma磁共振扫描仪上完成，采用64通道相控阵头线圈。 ## 处理流程说明 1. 将从西门子扫描仪获取的DICOM图像转换为NIfTI格式，随后通过HeuDiConv（https://github.com/nipy/heudiconv）按照BIDS标准进行数据重组。 2. 使用PyDeface（https://github.com/poldracklab/pydeface）移除图像中的面部特征，完成NIfTI图像的匿名化处理。 3. 借助fMRIPrep 20.2.1（https://fmriprep.org）完成数据预处理。 4. 采用FSL套件中的MELODIC（版本3.15），以默认参数对每位参与者的每一次扫描任务在个体原生空间中执行空间独立成分分析（spatial ICA）。通过melview（https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Melview），依据独立成分的空间分布图、时间进程曲线及时间进程功率谱，手动甄别与伪影相关的独立成分及信号独立成分。 5. 使用Ciftify（https://github.com/edickie/ciftify）将手动去噪后的数据转换为基于CIFTI格式的灰质坐标数据。 ## 数据记录 **结构磁共振成像**：<Sub-ID>/ses-1/anat/<SUB-ID>_ses-1_run-01_T1w.nii.gz **功能磁共振成像**：<Sub-ID>/ses-1/func/<Sub-ID>_ses-1_task-motor_<Run-ID>_bold.nii.gz **场图**：<Sub-ID>/ses-1/fmap/<Sub-ID>_ses-1_run-01_<magnitude/phasediff>.nii.gz **预处理功能磁共振成像**：derivatives/fmriprep/<Sub-ID>/<Sub-ID>_ses-1_task-motor_<Run-ID>_space-T1w_desc-preproc_bold.nii.gz **去噪功能磁共振成像**：derivatives/fmriprep/<Sub-ID>/<Sub-ID>_ses-1_task-motor_<Run-ID>_space-T1w_desc-preproc_bold_denoised.nii.gz **独立成分分析空间图谱**：derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/melodic_IC.nii.gz **独立成分分析时间序列**：derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/melodic_mix **手动分类标签**：derivatives/melodic/<Sub-ID>/ses-1/<Sub-ID>_ses-1_task-motor_<Run-ID>.ica/results_suggest.csv **原生表面数据**：derivatives/ciftify/<Sub-ID>/native_surface **任务分析结果**：derivatives/ciftify/<Sub-ID>/GLM
