Repetitive TMS (rTMS) allows for non-invasive and transient disruption of local neuronal functioning. We used machine learning approaches to assess whether brain tumor patients can be accurately classified into aphasic and non-aphasic groups using their rTMS language mapping results as input features. Given that each tumor affects the subject-specific language networks differently, resulting in heterogenous rTMS functional mappings, we propose the use of machine learning strategies to classify potential patterns of rTMS language mapping results. We retrospectively included 90 patients with left perisylvian world health organization (WHO) grade II-IV gliomas that underwent presurgical navigated rTMS language mapping. Within our cohort, 29 of 90 (32.2%) patients suffered from at least mild aphasia as shown in the Aachen Aphasia Test based Berlin Aphasia Score (BAS). After spatial normalization to MNI 152 of all rTMS spots, we calculated the error rate (ER) in each stimulated cortical area (28 regions of interest, ROI) by automated anatomical labeling parcellation (AAL3) and IIT. We used a support vector machine (SVM) to classify significant areas in relation to aphasia. After feeding the ROIs into the SVM model, it revealed that in addition to age (w = 2.98), the ERs of the left supramarginal gyrus (w = 3.64), left inferior parietal gyrus (w = 2.28) and right pars triangularis (w = 1.34) contributed more than other features to the model. The model’s sensitivity was 86.2%, the specificity was 82.0%, the overall accuracy was 85.5% and the AUC was 89.3%. Our results demonstrate an increased vulnerability of right inferior pars triangularis to rTMS in aphasic patients due to left perisylvian gliomas. This finding points towards a functional relevant involvement of the right pars triangularis in response to aphasia. The tumor location feature, specified by calculating overlaps with white and grey matter atlases, did not affect the SVM model. The left supramarginal gyrus as a feature improved our SVM model the most. Additionally, our results could point towards a decreasing potential for neuroplasticity with age.

重复性TMS（rTMS）允许局部神经元功能的非侵入性和暂时性破坏。我们使用机器学习方法来评估脑肿瘤患者是否可以使用他们的rTMS语言映射结果作为输入特征准确地分为失语和非失语组。鉴于每种肿瘤对受试者特定语言网络的影响不同，从而导致异质rTMS功能映射，我们建议使用机器学习策略对rTMS语言映射结果的潜在模式进行分类。我们回顾性研究了90例左围骨世界卫生组织（WHO）的II-IV级神经胶质瘤患者，这些患者接受了术前导航的rTMS语言定位。在我们的同类人群中，排名90的29（32。2％）患者至少患有轻度失语症，如基于亚琛失语症测试的柏林失语症评分（BAS）所示。在对所有rTMS点的MNI 152进行空间归一化后，我们通过自动解剖标记法（AAL3）和IIT计算了每个受刺激皮层区域（28个感兴趣区域，ROI）中的错误率（ER）。我们使用支持向量机（SVM）对与失语症相关的重要区域进行分类。将ROIs输入SVM模型后，发现除了年龄（w = 2.98）外，左上颌上回（w = 3.64），左下顶上回（w = 2.28）和右上三角肌（w = 1.34）对模型的贡献超过其他特征。该模型的灵敏度为86.2％，特异性为82.0％，总准确度为85.5％，AUC为89.3％。我们的结果表明，由于左周围神经胶质瘤，导致失语症患者右下三角肌对rTMS的脆弱性增加。这一发现表明，对失语症患者右三角肌的功能性相关参与。通过计算与白色和灰色物质图谱的重叠指定的肿瘤位置特征不会影响SVM模型。左上颌上回作为一项功能最大地改善了我们的SVM模型。此外，我们的结果可能表明随着年龄的增长，神经可塑性的潜力会逐渐降低。通过计算与白色和灰色物质地图集的重叠指定的值，不会影响SVM模型。左上颌上回作为一项功能最大地改善了我们的SVM模型。此外，我们的结果可能表明随着年龄的增长，神经可塑性的潜力会逐渐降低。通过计算与白色和灰色物质地图集的重叠指定的值，不会影响SVM模型。左上颌上回作为一项功能最大地改善了我们的SVM模型。此外，我们的结果可能表明随着年龄的增长，神经可塑性的潜力会逐渐降低。