Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.
Identifiable Patterns of Trait, State, and Experience in Chronic Stroke Recovery.
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慢性卒中康复中特质、状态和经历的可识别模式
DOI：
10.1177/1545968320981953
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发表时间：
2021-03
影响因子：
4.2
通讯作者：
Small SL
中科院分区：
文献类型：
--
作者：
Duncan ES;Shereen AD;Gentimis T;Small SL
大量证据表明，健康人脑的功能连接体高度稳定，类似于指纹。我们使用监督式机器学习方法研究了慢性卒中患者队列中任务和会话之间功能连接的稳定性。12名慢性中风患者在18周内接受了7次功能性磁共振成像（fMRI）。中间6周为强化失语治疗。我们收集了两个任务在休息和性能的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。排列检验建立了统计学显著性。全脑功能连接矩阵可以根据参与者（87.1%的准确性; p<0.0001），任务（68.1%的准确性; p=0.002）和时间点（72.1%的准确性; p=0.015）在显著高于偶然性的水平上进行分类。仅使用对侧右半球再现所有显著影响;左半球显示对参与者和任务的显著影响，但不包括时间点。静息状态数据也可用于根据受试者对基于任务的数据进行分类（66.0%; p<0.0001）。虽然最强的治疗后变化发生在假定的语言网络之外的区域，与传统语言相关区域的连接与行为结果指标显着正相关，其他区域有更多的负相关和半球内连接。研究结果表明，在解释中风功能连接研究中的恢复机制时，考虑个体间变异性具有重要意义。
Considerable evidence indicates that the functional connectome of the healthy human brain is highly stable, analogous to a fingerprint. 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. 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 two 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 post-therapy). Permutation testing established statistical significance. Whole brain functional connectivity matrices could be classified at levels significantly greater than chance on the basis of participant (87.1% accuracy; p<0.0001), task (68.1% accuracy; p=0.002), and time point (72.1% accuracy; p=0.015). 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<0.0001). While the strongest post-therapy 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. Findings suggest the profound importance of considering inter-individual variability when interpreting mechanisms of recovery in studies of functional connectivity in stroke.
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影响因子：
2
作者：
Lee J;Fowler R;Rodney D;Cherney L;Small SL
通讯作者：
Small SL
影响因子：
3
作者：
Duncan ES;Small SL
通讯作者：
Small SL
DOI：
10.1073/pnas.1018985108
发表时间：
2011-05-03
影响因子：
11.1
作者：
Bassett, Danielle S.;Wymbs, Nicholas F.;Grafton, Scott T.
通讯作者：
Grafton, Scott T.
影响因子：
4.2
作者：
Duncan, E. Susan;Schmah, Tanya;Small, Steven L.
通讯作者：
Small, Steven L.
影响因子：
--
作者：
Mustaqeem A;Anwar SM;Majid M
通讯作者：
Majid M