Connectivity-Optimized Individualized Localization (COIL)

连接优化的个性化定位 (COIL)

• 批准号: 519094028
• 负责人: Professor Dr. Simon Eickhoff
• 金额: --
• 依托单位: Lehrstuhl für Psychiatrie und Psychotherapie
• 依托单位国家: 德国
• 项目类别: Research Grants
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/519094028?language=en
• 关键词: Connectivity; Optimized; Individualized; Localization; COIL

The overarching goal of this project is to develop a tool for connectivity- guided targeting the dorsolateral prefrontal cortex (DLPFC) with transcranial magnetic stimulation (TMS). For broad applicability, the underlying algorithm needs to robustly provide individually optimized targets for high-precision personalized stimulation. To balance individual precision and robustness, large-scale information from normative datasets and data from patients with depression will serve to define the critical connectivity profiles of the DLPFC. We will apply machine-learning algorithms to these profiles to identify the optimal hotspot within this region in individual subjects. Implementation of the algorithm into a user-friendly software package will yield an impactful tool for TMS therapy of treatment-resistant depression.

该项目的总体目标是开发一种工具，通过经颅磁刺激 (TMS) 来针对背外侧前额叶皮层 (DLPFC) 进行连接引导。为了获得广泛的适用性，底层算法需要稳健地提供单独优化的目标，以实现高精度的个性化刺激。为了平衡个体精度和鲁棒性，来自规范数据集和抑郁症患者数据的大规模信息将有助于定义 DLPFC 的关键连接配置文件。我们将对这些配置文件应用机器学习算法，以识别各个受试者的该区域内的最佳热点。将该算法实施到用户友好的软件包中将为 TMS 治疗难治性抑郁症提供有效的工具。