KSHAP MRI data

MRI images were acquired using a 3-Tesla MAGNETOM Trio 32 channel coil. Whole-brain T1-weighted images were reconstructed from 224 sagittal slices of 1 mm thickness using an MPRAGE sequence with the following parameters: TR = 2.3 s, TE = 2.3 ms, FOV = 256 × 256 mm2, and FA = 9°. The time between social network measurement and MRI acquisition was 16–21 months. Image preprocessing was carried out using tools implemented in Statistical Parametric Mapping software (SPM12; Wellcome Department of Imaging Neuroscience, London, UK) and executed in Matlab (MathWorks, Natick, Massachusetts). We used the New Segmentation algorithm implemented in SPM12 [43]. T1 images were bias-corrected and segmented into five tissue classes based on non-linearly registered tissue probability maps. The East Asian International Consortium for Brain Mapping template was used for local optimization affine regularization. In order to spatially normalize gray matter images into a standard space with enhanced accuracy of inter-subject registration [44,45], we used Diffeomorphic Anatomical Registration Exponentiated Lie algebra (DARTEL). A customized template was created from imported versions of the gray matter tissue images. Then, the deformation field was applied to previously segmented gray matter images to implement non-linear transformation into standardized Montreal Neurological Institute (MNI) space. During these non-linear transformations, the total volume of gray matter was preserved with modulated images. All images were smoothed using an 8-mm full-width at half-maximum Gaussian kernel.

磁共振成像（MRI，Magnetic Resonance Imaging）图像采用3特斯拉（3-Tesla）MAGNETOM Trio型32通道线圈采集。全脑T1加权像通过MPRAGE序列由224层厚度为1mm的矢状位切片重建得到，扫描参数如下：重复时间（Repetition Time, TR）=2.3s，回波时间（Echo Time, TE）=2.3ms，视野（Field of View, FOV）=256×256 mm²，翻转角（Flip Angle, FA）=9°。社交网络测评与磁共振成像扫描的时间间隔为16至21个月。图像预处理采用英国伦敦威康成像神经科学部开发的统计参数映射软件（SPM12，Statistical Parametric Mapping）中的工具完成，并在MATLAB（美国马萨诸塞州内蒂克市MathWorks公司）环境中运行。本研究使用了SPM12内置的新型分割算法[43]。基于非线性配准的组织概率图谱，完成T1加权图像的偏置场校正并将其分割为5种组织类别。本研究采用东亚国际脑图谱联盟模板实施局部优化的仿射正则化处理。为将灰质图像空间标准化至标准空间并提升被试间配准精度[44,45]，本研究采用微分同胚解剖配准指数李代数（Diffeomorphic Anatomical Registration Exponentiated Lie algebra, DARTEL）算法。基于导入的灰质组织图像构建个体化模板，随后将形变场应用于此前已分割的灰质图像，以实现向标准化蒙特利尔神经学研究所（Montreal Neurological Institute, MNI）空间的非线性变换。在上述非线性变换过程中，通过调制图像保留灰质总容积。所有图像均采用半高全宽为8mm的高斯核进行平滑处理。