Automated Presurgical Language Mapping via Deep Learning for Multimodal Brain Connectivity

项目摘要/摘要 在美国，每年大约有10万人被诊断出患有原发性脑瘤。新大学- 对于这些患者，RoSurgery仍然是fi第一和最常见的治疗选择，其结果与 肿瘤切除范围。然而，更大的切除也增加了术后deficits的风险，尤其是 在能言善辩的大脑皮层的运动和语言区域。任务功能磁共振成像(t-fMRI)已成为一种强大的非核磁共振成像技术。 用于术前标测的广泛工具，但这些采集对患者来说是漫长的和认知要求的。 此外，如果患者在扫描仪中不能执行任务，t-fMRI是不可靠的。我们的长期目标是 为了开发一个自动化平台，在广泛的患者队列中进行可靠的口才皮质映射，包括- 集合了现有的临床工作flow。该提案的总体目标是设计和验证新机器 利用静息功能磁共振成像(RS-fMRI)和扩散磁共振成像优势互补的学习算法 (d-MRI)，这既是被动的，又容易获得。我们的中心假设是 这些模式中的结构-功能连接信息将使我们能够本地化语言功能 在脑瘤患者身上。我们的创新战略利用深度学习方面的最新进展来捕获COM- Rs-fmri和d-mri数据中的复杂交互作用共同导致了语言区域的Defi。我们将评估我们的 假设通过两个特定的fic目标。在目标1中，我们将开发一个图神经网络(GNN)，它使用专门的 卷积fi用于捕获多个尺度上的连通性数据的拓扑属性。我们的GNN 将在监督下接受训练，并对照t-fMRI激活和术中皮质电检查进行评估 刺激。在目标2中，我们将进行探索性分析，将我们的GNN预测与后 语言功能的可操作性变化。也就是说，我们假设手术路径对患者来说 交集我们的GNN预测将经历更大的defi跨fiNe粒度语言子域的Cit。我们会 与其他临床因素相比，也评估我们的GNN预测的预后价值。我们期待着 拟议的研究将帮助神经外科医生计划更多，从而对手术计划产生变革性的影响 有针对性和更安全的手术，从而改善患者的预后和整体护理质量。

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.