Background Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint. Objective We investigated the stability of functional connectivity across tasks and sessions in a cohort of individuals with chronic stroke using a supervised machine learning approach. Methods Twelve individuals with chronic stroke underwent functional magnetic resonance imaging (fMRI) seven times over 18 weeks. The middle 6 weeks consisted of intensive aphasia therapy. We collected fMRI data during rest and performance of 2 tasks. We calculated functional connectivity metrics for each imaging run, then applied a support vector machine to classify data on the basis of participant, task, and time point (pre- or posttherapy). Permutation testing established statistical significance. Results Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; P < .0001), task (68.1% accuracy; P = .002), and time point (72.1% accuracy; P = .015). All significant effects were reproduced using only the contralesional right hemisphere; the left hemisphere revealed significant effects for participant and task, but not time point. Resting state data could also be used to classify task-based data according to subject (66.0%; P < .0001). While the strongest posttherapy changes occurred among regions outside putative language networks, connections with traditional language-associated regions were significantly more positively correlated with behavioral outcome measures, and other regions had more negative correlations and intrahemispheric connections. Conclusions Findings suggest the profound importance of considering interindividual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.

中文翻译：

---

慢性中风康复的特征、状态和经验的可识别模式

背景 大量证据表明，健康人脑的功能连接组高度稳定，类似于指纹。目标我们使用有监督的机器学习方法研究了慢性中风患者队列中跨任务和会话的功能连接的稳定性。方法 12 名慢性卒中患者在 18 周内接受了 7 次功能性磁共振成像 (fMRI)。中间 6 周包括强化失语症治疗。我们在休息和执行 2 个任务期间收集了 fMRI 数据。我们计算了每次成像运行的功能连接指标，然后应用支持向量机根据参与者、任务和时间点（治疗前或治疗后）对数据进行分类。排列测试建立了统计显着性。结果 根据参与者（87.1% 准确率；P < .0001）、任务（68.1% 准确率；P = .002）和时间点（72.1% 准确率），全脑功能连接矩阵可以分类为显着大于机会的水平; P = .015）。仅使用对侧右半球再现所有显着效果；左半球显示出对参与者和任务的显着影响，但不是时间点。静息状态数据也可用于根据主题对基于任务的数据进行分类 (66.0%; P < .0001)。虽然最强烈的治疗后变化发生在假定的语言网络之外的区域，但与传统语言相关区域的联系与行为结果测量显着更正相关，和其他地区有更多的负相关和半球内的联系。结论 研究结果表明，在解释卒中功能连接研究中的恢复机制时，考虑个体差异非常重要。