Dynamic connectivity predicts acute motor impairment and recovery post-stroke.
Dynamic connectivity predicts acute motor impairment and recovery post-stroke.
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DOI：
10.1093/braincomms/fcab227
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发表时间：
2021
影响因子：
4.8
通讯作者：
Grefkes C
中科院分区：
文献类型：
--
作者：
Bonkhoff AK;Rehme AK;Hensel L;Tscherpel C;Volz LJ;Espinoza FA;Gazula H;Vergara VM;Fink GR;Calhoun VD;Rost NS;Grefkes C
对急性病变后脑功能障碍的全面评估对于优化预测临床结局至关重要。我们在这里建立了基于随机森林分类器的中风后急性运动障碍和恢复的预测模型。预测依赖于54名中风患者在症状发作的第一天内扫描的结构和静息状态fMRI数据。通过静态和动态的方法来估计功能连接。在急性期和6个月后对运动表现进行表型分析。基于在特定动态连接配置中花费的时间的模型实现了有和没有运动障碍的患者之间的最佳区分（曲线下样本外面积，95%置信区间：0.67 ± 0.01）。相比之下，使用基于动态连接变异性的模型（0.83 ± 0.01），可以将中度至重度损伤患者与轻度缺陷患者区分开来。在这里，同侧感觉运动皮层和壳核之间的连接的变异性最区分患者。最后，运动恢复最好通过在特定连接配置中花费的时间（0.89 ± 0.01）结合初始损伤来预测。在这里，更好的恢复与在功能集成配置中花费的更短时间有关。动态连接性衍生的参数构成了急性损伤和恢复的有效预测因子，这在未来可能会为个性化治疗方案提供信息，以促进卒中恢复。Bonkhoff等人报告了基于54例急性卒中患者的动态连接信息对卒中后前6个月内急性运动损伤和恢复的预测。花费在特定的连接配置和动态连接的变化，涉及壳核的预测性能贡献最大的时间。
Thorough assessment of cerebral dysfunction after acute lesions is paramount to optimize predicting clinical outcomes. We here built random forest classifier-based prediction models of acute motor impairment and recovery post-stroke. Predictions relied on structural and resting-state fMRI data from 54 stroke patients scanned within the first days of symptom onset. Functional connectivity was estimated via static and dynamic approaches. Motor performance was phenotyped in the acute phase and 6 months later. A model based on the time spent in specific dynamic connectivity configurations achieved the best discrimination between patients with and without motor impairments (out-of-sample area under the curve, 95% confidence interval: 0.67 ± 0.01). In contrast, patients with moderate-to-severe impairments could be differentiated from patients with mild deficits using a model based on the variability of dynamic connectivity (0.83 ± 0.01). Here, the variability of the connectivity between ipsilesional sensorimotor cortex and putamen discriminated the most between patients. Finally, motor recovery was best predicted by the time spent in specific connectivity configurations (0.89 ± 0.01) in combination with the initial impairment. Here, better recovery was linked to a shorter time spent in a functionally integrated configuration. Dynamic connectivity-derived parameters constitute potent predictors of acute impairment and recovery, which, in the future, might inform personalized therapy regimens to promote stroke recovery. Bonkhoff et al. report the prediction of acute motor impairments and recovery in the first six months post-stroke based on dynamic connectivity information of 54 acute stroke patients. The time spent in specific connectivity configurations and the variability of dynamic connectivity involving the putamen contributed most to prediction performance.
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影响因子：
4.8
作者：
Lin, Qiu-Hua;Liu, Jingyu;Zheng, Yong-Rui;Liang, Hualou;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
4.8
作者：
Bonkhoff AK;Schirmer MD;Bretzner M;Etherton M;Donahue K;Tuozzo C;Nardin M;Giese AK;Wu O;D Calhoun V;Grefkes C;Rost NS
通讯作者：
Rost NS
影响因子：
3.2
作者：
Haley, SM;Fragala-Pinkham, MA
通讯作者：
Fragala-Pinkham, MA
影响因子：
3.7
作者：
Allen, Elena A.;Damaraju, Eswar;Calhoun, Vince D.
通讯作者：
Calhoun, Vince D.
影响因子：
4.2
作者：
Kundert, Robinson;Goldsmith, Jeff;Luft, Andreas R.
通讯作者：
Luft, Andreas R.