Suo Xueling, Lei Du, Li Wenbin, Yang Jing, Li Lingjiang, Sweeney John A, Gong Qiyong
Huaxi MR Research Center (HMRRC), Department of Radiology, West China Hospital of Sichuan University, Chengdu, China.
Research Unit of Psychoradiology, Chinese Academy of Medical Sciences, Chengdu, China.
Front Behav Neurosci. 2020 Dec 21;14:563152. doi: 10.3389/fnbeh.2020.563152. eCollection 2020.
Previous studies have demonstrated relations between spontaneous neural activity evaluated by resting-state functional magnetic resonance imaging (fMRI) and symptom severity in post-traumatic stress disorder. However, few studies have used brain-based measures to identify imaging associations with illness severity at the level of individual patients. This study applied connectome-based predictive modeling (CPM), a recently developed data-driven and subject-level method, to identify brain function features that are related to symptom severity of trauma survivors. Resting-state fMRI scans and clinical ratings were obtained 10-15 months after the earthquake from 122 earthquake survivors. Symptom severity of post-traumatic stress disorder features for each survivor was evaluated using the Clinician Administered Post-traumatic Stress Disorder Scale (CAPS-IV). A functionally pre-defined atlas was applied to divide the human brain into 268 regions. Each individual's functional connectivity 268 × 268 matrix was created to reflect correlations of functional time series data across each pair of nodes. The relationship between CAPS-IV scores and brain functional connectivity was explored in a CPM linear model. Using a leave-one-out cross-validation (LOOCV) procedure, findings showed that the positive network model predicted the left-out individual's CAPS-IV scores from resting-state functional connectivity. CPM predicted CAPS-IV scores, as indicated by a significant correspondence between predicted and actual values ( = 0.30, = 0.001) utilizing primarily functional connectivity between visual cortex, subcortical-cerebellum, limbic, and motor systems. The current study provides data-driven evidence regarding the functional brain features that predict symptom severity based on the organization of intrinsic brain networks and highlights its potential application in making clinical evaluation of symptom severity at the individual level.
以往的研究已经证明，通过静息态功能磁共振成像（fMRI）评估的自发神经活动与创伤后应激障碍的症状严重程度之间存在关联。然而，很少有研究使用基于脑的测量方法来识别个体患者层面与疾病严重程度相关的影像学关联。本研究应用基于连接组的预测模型（CPM），这是一种最近开发的数据驱动和个体水平的方法，以识别与创伤幸存者症状严重程度相关的脑功能特征。在地震发生10 - 15个月后，对122名地震幸存者进行了静息态fMRI扫描和临床评分。使用临床医生管理的创伤后应激障碍量表（CAPS - IV）评估每位幸存者创伤后应激障碍症状的严重程度。应用一个功能预定义的图谱将人脑划分为268个区域。创建每个人的268×268功能连接矩阵，以反映每对节点之间功能时间序列数据的相关性。在CPM线性模型中探索CAPS - IV评分与脑功能连接之间的关系。使用留一法交叉验证（LOOCV）程序，结果表明正向网络模型可从静息态功能连接预测留出个体的CAPS - IV评分。CPM预测了CAPS - IV评分，预测值与实际值之间存在显著对应关系（ = 0.30， = 0.001），主要利用了视觉皮层、皮层下 - 小脑、边缘系统和运动系统之间的功能连接。本研究提供了基于内在脑网络组织预测症状严重程度的脑功能特征的数据驱动证据，并强调了其在个体水平进行症状严重程度临床评估中的潜在应用。
