Automated Presurgical Language Mapping via Deep Learning for Multimodal Brain Connectivity

项目总结/摘要 在美国，每年约有10万人被诊断患有原发性脑肿瘤。Neu- 整形外科仍然是这些患者的首选和最常见的治疗选择，其结果与 肿瘤切除范围。然而，较大的切除也增加了术后缺陷的风险，特别是 在运动和语言区域的功能皮层。任务功能磁共振成像（t-fMRI）已成为一个强大的非线性， 这是一种用于术前标测的有创工具，但这些采集时间长，对患者的认知要求高。 此外，如果患者在扫描仪中无法执行任务，t-fMRI是不可靠的。我们的长期目标是 开发一个自动化平台，用于在广泛的患者队列中进行可靠的功能皮层映射， 补充现有的临床工作流程。本提案的总体目标是设计和验证新机器 利用静息态功能磁共振成像（rs-fMRI）和扩散磁共振成像（diffusion MRI）的互补优势的学习算法 （d-MRI），其均为被动模态且易于获取。我们的核心假设是， 这些模态中的结构-功能连接信息将使我们能够定位语言功能 在脑瘤患者中。我们的创新策略使用深度学习的最新进展来捕获COM。 rs-fMRI和d-MRI数据中的复杂相互作用共同定义了语言区域。我们将评估我们的 通过两个具体目标的假设。在目标1中，我们将开发一个图神经网络（GNN）， 卷积滤波器来捕获跨多个尺度的连接数据的拓扑特性。我们的GNN 将在监督下接受培训，并根据t-fMRI激活和术中皮层电图进行评估。 刺激.在目标2中，我们将进行探索性分析，回顾性地将我们的GNN预测与后 语言功能的操作性变化。也就是说，我们假设， 我们的GNN预测将在细粒度语言子域中遇到更大的缺陷。我们将 与其他临床因素相比，我们还评估了GNN预测的预后价值。我们预计 拟议的研究将通过帮助神经外科医生计划更多的手术计划， 有针对性和更安全的手术，从而改善患者的治疗效果和整体护理质量。

Project Summary/Abstract Approximately 100,000 people in the United States are diagnosed with a primary brain tumor each year. Neu- rosurgery remains the first and most common therapeutic option for these patients with outcomes linked to the extent of tumor resection. However, larger resections also increase the risk for postoperative deficits, particularly in the motor and language areas of the eloquent cortex. Task fMRI (t-fMRI) has emerged as a powerful nonin- vasive tool for preoperative mapping, but these acquisitions are lengthy and cognitively demanding for patients. Moreover, t-fMRI is unreliable if the patient cannot perform the tasks while in the scanner. Our long-term goal is to develop an automated platform for reliable eloquent cortex mapping across a broad patient cohort that comple- ments the existing clinical workflow. The overall objective of this proposal is to design and validate new machine learning algorithms that leverage the complementary strengths of resting-state fMRI (rs-fMRI) and diffusion MRI (d-MRI), which are both passive modalities and easy to acquire. Our central hypothesis is that the combined structural-functional connectivity information in these modalities will enable us to localize language functionality in patients with brain tumors. Our innovative strategy uses recent advancements in deep learning to capture com- plex interactions in the rs-fMRI and d-MRI data that collectively define the language areas. We will evaluate our hypothesis via two specific aims. In Aim 1 we will develop a graph neural network (GNN) that employs specialized convolutional filters to capture topological properties of the connectivity data across multiple scales. Our GNN will be trained in a supervised fashion and evaluated against t-fMRI activations and intraoperative electrocortical stimulation. In Aim 2 we will conduct an exploratory analysis to retrospectively link our GNN predictions to post- operative changes in language functionality. Namely, we hypothesize that patients for whom the surgical path intersects our GNN predictions will experience greater deficits across fine-grained language subdomains. We will also assess the prognostic value of our GNN predictions, as compared to other clinical factors. We anticipate the proposed research will have a transformative impact on surgical planning by helping neurosurgeons to plan more targeted and safer surgeries, thus improving patient outcomes and overall quality of care.