Hannanu Firdaus Fabrice, Zeffiro Thomas A, Lamalle Laurent, Heck Olivier, Renard Félix, Thuriot Antoine, Krainik Alexandre, Hommel Marc, Detante Olivier, Jaillard Assia
Unité IRM 3T-Recherche- UMS IRMaGe - Centre Hospitalier Universitaire (CHU) Grenoble Alpes, France; Laboratoire MATICE - Pôle Recherche - CHU Grenoble-Alpes, France.
Laboratoire MATICE - Pôle Recherche - CHU Grenoble-Alpes, France; Neurometrika, Potomac, MD, United States.
Neuroimage Clin. 2017 Jan 26;14:518-529. doi: 10.1016/j.nicl.2017.01.023. eCollection 2017.
While motor recovery following mild stroke has been extensively studied with neuroimaging, mechanisms of recovery after moderate to severe strokes of the types that are often the focus for novel restorative therapies remain obscure. We used fMRI to: 1) characterize reorganization occurring after moderate to severe subacute stroke, 2) identify brain regions associated with motor recovery and 3) to test whether brain activity associated with passive movement measured in the subacute period could predict motor outcome six months later. Because many patients with large strokes involving sensorimotor regions cannot engage in voluntary movement, we used passive flexion-extension of the paretic wrist to compare 21 patients with subacute ischemic stroke to 24 healthy controls one month after stroke. Clinical motor outcome was assessed with Fugl-Meyer motor scores (motor-FMS) six months later. Multiple regression, with predictors including baseline (one-month) motor-FMS and sensorimotor network regional activity (ROI) measures, was used to determine optimal variable selection for motor outcome prediction. Sensorimotor network ROIs were derived from a meta-analysis of arm voluntary movement tasks. Bootstrapping with 1000 replications was used for internal model validation. During passive movement, both control and patient groups exhibited activity increases in multiple bilateral sensorimotor network regions, including the primary motor (MI), premotor and supplementary motor areas (SMA), cerebellar cortex, putamen, thalamus, insula, Brodmann area (BA) 44 and parietal operculum (OP1-OP4). Compared to controls, patients showed: 1) lower task-related activity in ipsilesional MI, SMA and contralesional cerebellum (lobules V-VI) and 2) higher activity in contralesional MI, superior temporal gyrus and OP1-OP4. Using multiple regression, we found that the combination of baseline motor-FMS, activity in ipsilesional MI (BA4a), putamen and ipsilesional OP1 predicted motor outcome measured 6 months later (adjusted-R = 0.85; bootstrap p < 0.001). Baseline motor-FMS alone predicted only 54% of the variance. When baseline motor-FMS was removed, the combination of increased activity in ipsilesional MI-BA4a, ipsilesional thalamus, contralesional mid-cingulum, contralesional OP4 and decreased activity in ipsilesional OP1, predicted better motor outcome (djusted-R = 0.96; bootstrap p < 0.001). In subacute stroke, fMRI brain activity related to passive movement measured in a sensorimotor network defined by activity during voluntary movement predicted motor recovery better than baseline motor-FMS alone. Furthermore, fMRI sensorimotor network activity measures considered alone allowed excellent clinical recovery prediction and may provide reliable biomarkers for assessing new therapies in clinical trial contexts. Our findings suggest that neural reorganization related to motor recovery from moderate to severe stroke results from balanced changes in ipsilesional MI (BA4a) and a set of phylogenetically more archaic sensorimotor regions in the ventral sensorimotor trend, in which OP1 and OP4 processes may complement the ipsilesional dorsal motor cortex in achieving compensatory sensorimotor recovery.
虽然轻度中风后的运动恢复已通过神经影像学进行了广泛研究,但对于新型恢复性疗法通常关注的中重度中风后的恢复机制仍不清楚。我们使用功能磁共振成像(fMRI)来:1)描述中重度亚急性中风后发生的重组;2)识别与运动恢复相关的脑区;3)测试亚急性期测量的与被动运动相关的脑活动是否能预测六个月后的运动结果。由于许多涉及感觉运动区域的大面积中风患者无法进行自主运动,我们在中风后一个月,通过对患侧手腕进行被动屈伸,将21例亚急性缺血性中风患者与24名健康对照进行比较。六个月后,用Fugl-Meyer运动评分(motor-FMS)评估临床运动结果。采用多元回归分析,预测指标包括基线(一个月时)的motor-FMS和感觉运动网络区域活动(ROI)测量值,以确定运动结果预测的最佳变量选择。感觉运动网络ROI来自于对手臂自主运动任务的荟萃分析。采用1000次重复的自抽样法进行内部模型验证。在被动运动期间,对照组和患者组在多个双侧感觉运动网络区域均表现出活动增加,包括初级运动区(MI)、运动前区和辅助运动区(SMA)、小脑皮质、壳核、丘脑、岛叶、布罗德曼区(BA)44和顶叶岛盖(OP1 - OP4)。与对照组相比,患者表现为:1)患侧MI、SMA和对侧小脑(小叶V - VI)中与任务相关的活动较低;2)对侧MI、颞上回和OP1 - OP4中的活动较高。通过多元回归分析,我们发现基线motor-FMS、患侧MI(BA4a)、壳核和患侧OP1的活动组合可预测六个月后的运动结果(调整后R = 0.85;自抽样p < 0.001)。仅基线motor-FMS只能预测54%的方差。当去除基线motor-FMS后,患侧MI - BA4a、患侧丘脑、对侧中央扣带、对侧OP4活动增加以及患侧OP1活动减少的组合,能更好地预测运动结果(调整后R = 0.96;自抽样p < 0.001)。在亚急性中风中,在由自主运动期间的活动定义的感觉运动网络中测量的与被动运动相关的fMRI脑活动,比单独的基线motor-FMS能更好地预测运动恢复。此外,单独考虑的fMRI感觉运动网络活动测量值能实现出色的临床恢复预测,并可能为评估临床试验中的新疗法提供可靠的生物标志物。我们的研究结果表明,中重度中风后与运动恢复相关的神经重组是由患侧MI(BA4a)和腹侧感觉运动趋势中一组系统发育上更古老的感觉运动区域的平衡变化导致的,其中OP1和OP4过程可能在实现代偿性感觉运动恢复方面补充患侧背侧运动皮质的功能。