Data_Sheet_1_Connectomic insight into unique stroke patient recovery after rTMS treatment.PDF

An improved understanding of the neuroplastic potential of the brain has allowed advancements in neuromodulatory treatments for acute stroke patients. However, there remains a poor understanding of individual differences in treatment-induced recovery. Individualized information on connectivity disturbances may help predict differences in treatment response and recovery phenotypes. We studied the medical data of 22 ischemic stroke patients who received MRI scans and started repetitive transcranial magnetic stimulation (rTMS) treatment on the same day. The functional and motor outcomes were assessed at admission day, 1 day after treatment, 30 days after treatment, and 90 days after treatment using four validated standardized stroke outcome scales. Each patient underwent detailed baseline connectivity analyses to identify structural and functional connectivity disturbances. An unsupervised machine learning (ML) agglomerative hierarchical clustering method was utilized to group patients according to outcomes at four-time points to identify individual phenotypes in recovery trajectory. Differences in connectivity features were examined between individual clusters. Patients were a median age of 64, 50% female, and had a median hospital length of stay of 9.5 days. A significant improvement between all time points was demonstrated post treatment in three of four validated stroke scales utilized. ML-based analyses identified distinct clusters representing unique patient trajectories for each scale. Quantitative differences were found to exist in structural and functional connectivity analyses of the motor network and subcortical structures between individual clusters which could explain these unique trajectories on the Barthel Index (BI) scale but not on other stroke scales. This study demonstrates for the first time the feasibility of using individualized connectivity analyses in differentiating unique phenotypes in rTMS treatment responses and recovery. This personalized connectomic approach may be utilized in the future to better understand patient recovery trajectories with neuromodulatory treatment.

对大脑神经可塑性潜力的深入认知，推动了急性脑卒中患者神经调控治疗的发展。然而，目前学界对治疗诱导的康复过程中的个体差异仍缺乏充分理解。有关连接组紊乱的个体化信息，或可辅助预测治疗响应与康复表型的差异。本研究纳入22名缺血性脑卒中患者，所有患者均接受磁共振成像（MRI）扫描，并于同日启动重复经颅磁刺激（rTMS）治疗。分别于入院当日、治疗后1天、治疗后30天及治疗后90天，采用4项经过验证的标准化脑卒中结局量表，评估患者的功能与运动结局。对每位患者开展详细的基线连接组分析，以识别其结构与功能连接紊乱情况。本研究采用无监督机器学习（ML）系统聚类法，依据四个时间点的结局对患者进行分组，以识别康复轨迹的个体表型，并分析不同聚类组间的连接组特征差异。本研究纳入的患者中位年龄为64岁，女性占比50%，中位住院时长为9.5天。研究所使用的4项标准化脑卒中量表中，有3项显示治疗后各时间点的结局均存在显著改善。基于机器学习的分析识别出了对应各量表的独特患者轨迹聚类组。不同聚类组间的运动网络及皮层下结构的结构与功能连接特征存在量化差异，该差异可解释巴塞尔指数（BI）量表所反映的独特康复轨迹，但无法解释其余脑卒中量表的结果。本研究首次证实，采用个体化连接组分析区分重复经颅磁刺激治疗响应与康复的独特表型具备可行性。未来可借助这种个性化连接组学方法，更深入地理解神经调控治疗下患者的康复轨迹。