Background:

Recovery prediction can assist in the planning for impairment-focused rehabilitation after a stroke. This study investigated a new prediction model based on a lesion network analysis. To predict the potential for recovery, we focused on the next link-step connectivity of the direct neighbors of a lesion.

Methods:

We hypothesized that this connectivity would contribute to recovery after stroke onset. Each lesion in a patient who had suffered a stroke was transferred to a healthy subject. First link-step connectivity was identified by observing voxels functionally connected to each lesion. Next (second) link-step connectivity of the first link-step connectivity was extracted by calculating statistical dependencies between time courses of regions not directly connected to a lesion and regions identified as first link-step connectivity. Lesion impact on second link-step connectivity was quantified by comparing the lesion network and reference network.

Results:

The lower the impact of a lesion was on second link-step connectivity in the brain network, the better the improvement in motor function during recovery. A prediction model containing a proposed predictor, initial motor function, age, and lesion volume was established. A multivariate analysis revealed that this model accurately predicted recovery at 3 months poststroke (R ² = 0.788; cross-validation, R ² = 0.746, RMSE = 13.15).

Conclusion:

This model can potentially be used in clinical practice to develop individually tailored rehabilitation programs for patients suffering from motor impairments after stroke.

中文翻译：

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在缺血性卒中后使用损伤网络中的间接连接预测运动恢复。

背景：

恢复预测可以帮助规划中风后以损伤为中心的康复。本研究调查了一种基于病变网络分析的新预测模型。为了预测恢复的潜力，我们专注于病变直接邻居的下一个链接步骤连接。

方法：

我们假设这种连接将有助于中风发作后的恢复。中风患者的每个病变都转移到健康受试者身上。通过观察功能上连接到每个病变的体素来识别第一个链接步骤连接。通过计算不直接连接到病灶的区域的时间过程与被识别为第一链接步骤连通性的区域之间的统计相关性，提取第一链接步骤连通性的下一个（第二）链接步骤连通性。通过比较病变网络和参考网络来量化病变对第二个链接步骤连接的影响。

结果：

病变对大脑网络中第二个链接步骤连接的影响越低，恢复期间运动功能的改善就越好。建立了一个包含建议的预测因子、初始运动功能、年龄和病变体积的预测模型。多变量分析显示，该模型准确预测了卒中后 3 个月的恢复情况（R  ²  = 0.788；交叉验证，R  ²  = 0.746，RMSE  = 13.15）。

结论：

该模型可以潜在地用于临床实践，为中风后运动障碍的患者开发个性化定制的康复计划。