PDF(Pubmed)
Abstract：
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.
摘要：
脑肿瘤切除前的术前计划对于术后神经功能的保留至关重要。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”的大脑区域，这些区域在切除过程中应幸免。功能磁共振成像（fMRI）已成为一种常用的非侵入性方式，用于对患者的关键皮质区域进行个体映射，例如运动，语言,和视觉皮层。要映射运动功能，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，一些患者可能难以在扫描仪中执行任务。Connectome指纹识别（CF）是一种机器学习方法，可以学习大脑区域的静息状态功能网络与该区域针对特定任务的激活之间的关联；一旦构建了CF模型，可以从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对来自人类连接体项目（HCP）中208名受试者的高质量数据进行模型训练，并使用静息状态fMRI（rs-fMRI）数据预测我们的健康对照受试者（n=15）和术前患者（n=16）队列中的任务激活。通过健康对照和患者的任务fMRI数据验证了预测质量。我们发现，运动区域的任务预测与大多数健康受试者的实际任务激活相当（模型准确性约为任务稳定性的90％-100％），并且一些患者建议CF模型可以可靠地替换，其中任务数据不可能收集或受试者难以执行。在没有与任务相关的激活引起的情况下，我们还能够做出可靠的预测。研究结果表明，CF方法可用于预测样本外受试者的激活，跨站点和扫描仪，在患者人群中。这项工作支持CF模型应用于术前规划的可行性，同时也揭示了未来发展中需要应对的挑战。实践要点：使用连接体指纹进行精确的运动网络预测。精心训练的模型性能受任务功能磁共振成像数据稳定性的限制。成功的跨扫描仪预测和肿瘤患者的运动网络映射。
