脑肿瘤切除前的术前计划对于术后保留神经功能至关重要。神经外科医生越来越多地在术前和术中使用先进的大脑绘图技术来描绘“雄辩”且在切除过程中应保留的大脑区域。功能性 MRI (fMRI) 已成为一种常用的非侵入性方法，用于对个体患者的关键皮质区域（例如运动、语言和视觉皮质）进行绘图。为了绘制运动功能图，患者在执行各种运动任务时使用功能磁共振成像进行扫描，以识别对运动表现至关重要的大脑网络，但由于预先存在的缺陷，某些患者可能很难在扫描仪中执行任务。连接体指纹识别 (CF) 是一种机器学习方法，可学习大脑区域的静息态功能网络与该区域特定任务的激活之间的关联；一旦构建了 CF 模型，就可以根据静息状态数据生成任务激活的个性化预测。在这里，我们利用 CF 根据人类连接组计划 (HCP) 208 名受试者的高质量数据来训练模型，并用它来预测健康对照受试者 (n = 15) 和术前患者 (n = 16) 队列中的任务激活使用静息态功能磁共振成像 (rs-fMRI) 数据。预测质量通过健康对照和患者的任务功能磁共振成像数据进行了验证。我们发现，在大多数健康受试者中，运动区域的任务预测与实际任务激活相当（模型精度约为任务稳定性的 90%-100%），并且一些患者建议可以可靠地替代 CF 模型，而任务数据要么不是可以收集或难以让受试者执行。在没有引发任务相关激活的情况下，我们还能够做出可靠的预测。研究结果证明了 CF 方法在预测样本外受试者、跨站点和扫描仪以及患者群体的激活方面的实用性。这项工作支持了 CF 模型在术前规划中应用的可行性，同时也揭示了未来发展中需要解决的挑战。从业者要点：使用连接组指纹进行精确运动网络预测。精心训练的模型的性能受到任务功能磁共振成像数据稳定性的限制。肿瘤患者的成功跨扫描仪预测和运动网络映射。© 2024 作者。人脑图谱由 Wiley periodicals LLC 出版。

Presurgical planning prior to brain tumor resection is critical for the preservation of neurologic function post-operatively. Neurosurgeons increasingly use advanced brain mapping techniques pre- and intra-operatively to delineate brain regions which are "eloquent" and should be spared during resection. Functional MRI (fMRI) has emerged as a commonly used non-invasive modality for individual patient mapping of critical cortical regions such as motor, language, and visual cortices. To map motor function, patients are scanned using fMRI while they perform various motor tasks to identify brain networks critical for motor performance, but it may be difficult for some patients to perform tasks in the scanner due to pre-existing deficits. Connectome fingerprinting (CF) is a machine-learning approach that learns associations between resting-state functional networks of a brain region and the activations in the region for specific tasks; once a CF model is constructed, individualized predictions of task activation can be generated from resting-state data. Here we utilized CF to train models on high-quality data from 208 subjects in the Human Connectome Project (HCP) and used this to predict task activations in our cohort of healthy control subjects (n = 15) and presurgical patients (n = 16) using resting-state fMRI (rs-fMRI) data. The prediction quality was validated with task fMRI data in the healthy controls and patients. We found that the task predictions for motor areas are on par with actual task activations in most healthy subjects (model accuracy around 90%-100% of task stability) and some patients suggesting the CF models can be reliably substituted where task data is either not possible to collect or hard for subjects to perform. We were also able to make robust predictions in cases in which there were no task-related activations elicited. The findings demonstrate the utility of the CF approach for predicting activations in out-of-sample subjects, across sites and scanners, and in patient populations. This work supports the feasibility of the application of CF models to presurgical planning, while also revealing challenges to be addressed in future developments. PRACTITIONER POINTS: Precision motor network prediction using connectome fingerprinting. Carefully trained models' performance limited by stability of task-fMRI data. Successful cross-scanner predictions and motor network mapping in patients with tumor.© 2024 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.