Recent resting-state fMRI studies in stroke patients have identified two robust biomarkers of acute brain dysfunction: a reduction of inter-hemispheric functional connectivity (FC) between homotopic regions of the same network, and an abnormal increase of ipsilesional FC between task-negative and task-positive resting-state networks (RSNs). Whole-brain computational modeling studies, at the individual subject level, using undirected effective connectivity (EC) derived from empirically measured FC, have shown a reduction of measures of integration and segregation in stroke as compared to healthy brains. Here we employ a novel method, first, to infer whole-brain directional EC from zero-lagged and lagged FC, then, to compare it to empirically measured FC for predicting stroke vs. healthy status, and patient performance (zero, one, multiple deficits) across neuropsychological tests. We also investigated the accuracy of FC vs. model EC in predicting the long-term outcome from acute measures. Both FC and EC predicted healthy from stroke individuals significantly better than the chance-level, however, EC accuracy was significantly higher than that of FC at 1-2 weeks, three months, and one-year post-stroke. The predictive FC links mainly included those reported in previous studies (within-network inter-hemispheric, and between task-positive and -negative networks intra-hemispherically). Predictive EC links included additional between-network links. EC was a better predictor than FC of the number of behavioral domains in which patients suffered deficits, both at two weeks and one-year post-onset of stroke. Interestingly, patient deficits at the one-year time point were better predicted by EC values at two weeks rather than at the one-year time point. Our results thus demonstrate that the second-order statistics of fMRI resting-state activity at an early stage of stroke, derived from a whole-brain EC, estimated in a model fitted to reproduce the propagation of BOLD activity, has pertinent information for clinical prognosis.

中文翻译：

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有效的连通性从中风的静止状态活动中提取临床相关的预后信息

最近在卒中患者中进行的静息态功能磁共振成像研究已经确定了急性脑功能障碍的两个强有力的生物标志物：同一网络同位区域之间的半球之间功能连接（FC）减少，以及负任务和负任务之间的同侧FC异常增加。任务阳性静止状态网络（RSN）。在单个受试者水平上的全脑计算模型研究，使用从经验测得的FC得出的无方向有效连通性（EC），与健康的大脑相比，显示出卒中整合和分离的测量值减少了。在这里，我们采用一种新颖的方法，首先从零延迟和滞后FC推断出全脑定向EC，然后将其与根据经验测得的FC进行比较，以预测中风与健康状况以及患者的表现（零，一个，跨神经心理学测试）。我们还研究了FC与模型EC在预测急性措施的长期预后方面的准确性。FC和EC预测卒中患者的健康状况均明显好于机会水平，但是，在卒中后1-2周，三个月和一年内，EC的准确性显着高于FC。预测性FC链接主要包括先前研究中所报告的链接（半球网络内部，以及半球内任务阳性和阴性网络之间）。预测性EC链接包括其他网络间链接。在中风发作后的两周和一年内，EC比行为缺陷的行为域数量更好，比FC更好。有趣的是 两年时间的EC值比一年时间点的EC值更好地预测了一年时间点的患者赤字。因此，我们的结果表明，在适合重现BOLD活动传播的模型中，从全脑EC估计的中风早期fMRI静止状态活动的二级统计具有相关的临床预后信息。 。