Department of Physical and Rehabilitation Medicine, Center for Prevention and Rehabilitation, Heart Vascular Stroke Institute, 36626Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Department of Health Sciences and Technology, Department of Medical Device Management and Research, Department of Digital Health, SAIHST, 35017Sungkyunkwan University, Seoul, Republic of Korea.
Neurorehabil Neural Repair. 2022 Mar;36(3):217-226. doi: 10.1177/15459683211070278. Epub 2021 Dec 31.
. Various prognostic biomarkers for upper extremity (UE) motor recovery after stroke have been reported. However, most have relatively low predictive accuracy in severe stroke patients.. This study suggests an imaging biomarker-based model for effectively predicting UE recovery in severe stroke patients.. Of 104 ischemic stroke patients screened, 42 with severe motor impairment were included. All patients underwent structural, diffusion, and functional magnetic resonance imaging at 2 weeks and underwent motor function assessments at 2 weeks and 3 months after stroke onset. According to motor function recovery at 3 months, patients were divided into good and poor subgroups. The value of multimodal imaging biomarkers of lesion load, lesion volume, white matter integrity, and cortical functional connectivity for motor recovery prediction was investigated in each subgroup.. Imaging biomarkers varied depending on recovery pattern. The integrity of the cerebellar tract ( .005, = .432) was the primary biomarker in the good recovery group. In contrast, the sensory-related corpus callosum tract ( .026, = .332) and sensory-related functional connectivity ( .001, = .531) were primary biomarkers in the poor recovery group. A prediction model was proposed by applying each biomarker in the subgroup to patients with different motor evoked potential responses ( .001, = .853, root mean square error = 5.28).. Our results suggest an optimized imaging biomarker model for predicting UE motor recovery after stroke. This model can contribute to individualized management of severe stroke in a clinical setting.
. 已有多种用于预测脑卒中后上肢（UE）运动功能恢复的预后生物标志物被报道。然而，在重度脑卒中患者中，大多数标志物的预测准确性相对较低。. 本研究提出了一种基于影像学生物标志物的模型，可有效预测重度脑卒中患者的 UE 恢复情况。. 在筛选的 104 例缺血性脑卒中患者中，纳入了 42 例运动功能严重受损的患者。所有患者均在发病后 2 周内行结构像、弥散加权像和功能磁共振成像检查，并在发病后 2 周和 3 个月进行运动功能评估。根据 3 个月时的运动功能恢复情况，将患者分为恢复良好和恢复不良亚组。分别在各亚组中探讨了病灶负荷、病灶体积、白质完整性和皮质功能连接等多模态影像学生物标志物对运动功能恢复预测的价值。. 影像学生物标志物的价值因恢复模式而异。在恢复良好的亚组中，小脑束的完整性（.005， =.432）是主要的预测生物标志物。而在恢复不良的亚组中，感觉相关的胼胝体束（.026， =.332）和感觉相关的功能连接（.001， =.531）是主要的预测生物标志物。通过将各亚组中的生物标志物应用于不同运动诱发电位反应的患者中，提出了一种预测模型（.001， =.853，均方根误差=5.28）。. 本研究结果为脑卒中后 UE 运动功能恢复的预测提供了一种优化的影像学生物标志物模型。该模型有望为临床中重度脑卒中的个体化管理提供帮助。
