Structural and functional connectome from 70 young healthy adults

<strong><em>Data Acquisition</em></strong> Informed written consent in accordance with institutional guidelines (protocol approved by the Ethics Committee of Clinical Research of the Faculty of Biology and Medicine, University of Lausanne, Switzerland, #82/14, #382/11, #26.4.2005) was obtained for all subjects. Data provided are fully anonymized. A total of 70 healthy participants (age 28.8 +- 9.1 years, 27 females) were scanned in a 3-Tesla MRI scanner (Trio, Siemens Medical, Germany) using a 32-channel head-coil. The session protocol was comprised of (1) a magnetization-prepared rapid acquisition gradient echo (MPRAGE) sequence sensitive to white/gray matter contrast (1-mm in-plane resolution, 1.2-mm slice thickness), (2) a DSI sequence (128 diffusion-weighted volumes and a single b0 volume, maximum b-value 8,000 s/mm², 2.2x2.2x3.0 mm voxel size), and (3) a gradient echo EPI sequence sensitive to BOLD contrast (3.3-mm in-plane resolution and slice thickness with a 0.3-mm gap, TR 1,920 ms, resulting in 280 images per participant). During the fMRI scan, participants were not engaged in any overt task, and the scan was treated as eyes-open resting-state fMRI (rs-fMRI). <strong><em>Data Pre-processing </em></strong> Initial signal processing of all MPRAGE, DSI, and rs-fMRI data was performed using the Connectome Mapper pipeline (Daducci<em> et al.</em>, 2012). Gray and white matter were segmented from the MPRAGE volume using freesurfer (Desikan<em> et al.</em>, 2006) and parcellated into 83 cortical and subcortical areas. The parcels were then further subdivided into 129, 234, 463 and 1015 approximately equally sized parcels according to the Lausanne anatomical atlas following the method proposed by (Cammoun<em> et al.</em>, 2012). DSI data were reconstructed following the protocol described by (Wedeen<em> et al.</em>, 2005), allowing us to estimate multiple diffusion directions per voxel. The diffusion probability density function was reconstructed as the discrete 3D Fourier transform of the signal modulus. The orientation distribution function (ODF) was calculated as the radial summation of the normalized 3D probability distribution function. Thus, the ODF is defined on a discrete sphere and captures the diffusion intensity in every direction. <strong><em>Structural Connectivity</em></strong> Structural connectivity matrices were estimated for individual participants using deterministic streamline tractography on reconstructed DSI data, initiating 32 streamline propagations per diffusion direction, per white matter voxel (Wedeen<em> et al.</em>, 2008). Within each voxel, the starting points were spatially random. For each starting point, a fiber streamline was grown in two opposite directions with a fixed step of 1 mm. Once the fiber entered a new voxel, the fiber growth continued along the ODF maximum direction that produces the least curvature for the fiber (i.e., was most similar to the trajectory of the fiber to that point). Fibers were stopped if the change in direction was greater than 60 degrees/mm. The process was complete when both ends of the fiber left the white matter mask. Structural connectivity between pairs of regions was measured in terms of fiber density, defined as the number of streamlines between the two regions, normalized by the average length of the streamlines and average surface area of the two regions (Hagmann<em> et al.</em>, 2008). The goal of this normalization was to compensate for the bias toward longer fibers inherent in the tractography procedure, as well as differences in region size. <strong><em>Functional Connectivity</em></strong> Functional data were pre-processed using routines designed to facilitate subsequent network exploration (Murphy<em> et al.</em>, 2009; Power<em> et al.</em>, 2012). fMRI volumes were corrected for physiological variables, including regression of white matter, cerebrospinal fluid, as well as motion (three translations and three rotations, estimated by rigid body co-registration). BOLD time series were then subjected to a lowpass filter (temporal Gaussian filter with full width half maximum equal to 1.92 s). The first four time points were excluded from subsequent analysis to allow the time series to stabilize. Motion ‘‘scrubbing’’ was performed as described by (Power<em> et al.</em>, 2012). A group-average functional connectivity matrix was constructed from the fMRI BOLD time series by concatenating the regional time series from all participants and estimating a single correlation matrix. To threshold this matrix, we sampled at random 276 points from the concatenated times series and calculated a full correlation matrix from these points. We repeated this analysis 1,000 times. From these bootstrapped samples, we estimated confidence intervals for the correlation magnitude between every pair of brain regions. Pairs whose correlation was consistently positive or negative across the 1,000 samples were retained (along with the sign and weight of the correlation) as putative functional connections.<strong><em>数据采集</em></strong> 所有受试者均已签署符合机构规范的知情同意书（本研究方案已获得瑞士洛桑大学生物与医学学院临床研究伦理委员会批准，编号：#82/14、#382/11、#26.4.2005）。所提供的数据已完全匿名化处理。本研究共纳入70名健康受试者，年龄为28.8±9.1岁，其中女性27名；所有受试者均使用德国西门子医疗（Siemens Medical）生产的3特斯拉磁共振成像扫描仪（型号：Trio），搭配32通道头部线圈进行扫描。本次扫描流程包含：（1）对灰白质对比度敏感的磁化准备快速采集梯度回波（MPRAGE）序列，其平面内分辨率为1mm，层厚为1.2mm；（2）扩散谱成像（DSI）序列，包含128个扩散加权容积与1个b0容积，最大b值为8000 s/mm²，体素大小为2.2×2.2×3.0 mm；（3）对血氧水平依赖（BOLD）对比度敏感的梯度回波回波平面成像（EPI）序列，其平面内分辨率与层厚均为3.3mm，层间距0.3mm，重复时间（TR）为1920ms，每名受试者可获得280幅图像。在功能磁共振成像（fMRI）扫描过程中，受试者未执行任何显性任务，本次扫描为睁眼静息态功能磁共振成像（rs-fMRI）。 <strong><em>数据预处理</em></strong> 所有MPRAGE、DSI及rs-fMRI数据的初始信号处理均采用连接组映射器（Connectome Mapper）流程（Daducci等，2012）。研究人员使用FreeSurfer软件（Desikan等，2006）从MPRAGE容积数据中分割出灰质与白质，并将其划分为83个皮层及皮层下脑区。随后根据洛桑解剖图谱（Lausanne anatomical atlas），按照Cammoun等（2012）提出的方法，将这些脑区进一步划分为129、234、463及1015个尺寸近似均等的亚分区。 DSI数据的重建遵循Wedeen等（2005）描述的流程，可实现每个体素内多个扩散方向的估计。扩散概率密度函数通过信号模值的离散三维傅里叶变换进行重建。方位分布函数（ODF）通过对归一化三维概率分布函数进行径向求和计算得到，其定义于离散球面之上，可捕捉每个方向上的扩散强度。 <strong><em>结构连接性</em></strong> 针对每名受试者，研究人员基于重建后的DSI数据，采用确定性流线束追踪技术构建结构连接矩阵：每个白质体素的每个扩散方向均启动32条流线传播（Wedeen等，2008）。每个体素内的起始点均为空间随机分布。针对每个起始点，纤维流线将以1mm的固定步长沿两个相反方向延伸；当纤维进入新的体素后，纤维延伸将沿方位分布函数（ODF）的最大方向继续，该方向可使纤维的弯曲程度最小（即与纤维至此的轨迹最为相似）。若方向变化率超过60°/mm，则终止纤维追踪；当纤维的两端均离开白质掩码区域时，追踪过程完成。脑区之间的结构连接性以纤维密度进行量化：纤维密度指两个脑区之间的流线数量，经流线平均长度与两个脑区的平均表面积归一化处理（Hagmann等，2008）。该归一化处理的目的是抵消追踪流程本身固有的长纤维偏向性，以及脑区尺寸差异带来的偏差。 <strong><em>功能连接性</em></strong> 功能数据的预处理采用专为后续网络探索设计的流程（Murphy等，2009；Power等，2012）。fMRI容积数据已针对生理变量进行校正，包括对白质、脑脊液信号的回归处理，以及对头部运动的校正（通过刚体共配准估计的3个平移与3个旋转参数）。随后对血氧水平依赖（BOLD）时间序列施加低通滤波（半高全宽为1.92s的时域高斯滤波器）。为使时间序列趋于稳定，后续分析将排除前4个时间点的数据。按照Power等（2012）描述的方法执行运动剔除（motion scrubbing）处理。研究人员将所有受试者的脑区时间序列进行拼接，据此估计得到单个相关矩阵，进而构建组平均功能连接矩阵。为对该矩阵进行阈值处理，研究人员从拼接后的时间序列中随机抽取276个时间点，基于这些点计算完整的相关矩阵；该分析过程重复执行1000次。基于这些自助抽样样本，研究人员估计每对脑区之间相关系数的置信区间。将在1000次抽样中相关系数始终为正或负的脑区对（保留相关系数的符号与权重）作为潜在功能连接予以保留。