Integrating Multimodal Brain Imaging Data to Assess Subtle Cognitive Impairment

整合多模态脑成像数据来评估细微的认知障碍

• 批准号: 8637220
• 负责人: Jeffrey S Spence
• 金额: $ 2.9万
• 依托单位: UNIVERSITY OF TEXAS DALLAS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-04-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8637220
• 关键词: Integrating; Multimodal; Brain; Imaging; Data

DESCRIPTION (provided by applicant): Functional neuroimaging studies of the human brain have become increasingly important in the understanding of normal and pathological processes of cognition. Sophisticated statistical analytic frameworks have been developed to locate signal change and define brain networks involved in various tasks. However, in subtle cognitive impairment-e.g., exposure-related illness, early stages of degenerative diseases, injury, secondary illness following adjuvant therapy for cancer-these methods tend to have low sensitivity for detecting small changes in brain states resulting from mild brain dysfunction. An understanding of disease mechanism or progression of subtle cognitive dysfunction requires a novel statistical analytic framework with improved sensitivity to measure small changes in brain states. We have developed an innovative methodology that we successfully applied in measures of regional cerebral blood flow experiments. These methods use well established spatial modeling procedures, new to the functional brain imaging field, to derive statistically optimal spatial summaries within effective resolution groups or "kriging", shown by preliminary studies to improve signal detection sensitivity and mitigate the multiple testing burden. Within this new spatial modeling framework, we propose to extend the kriging methodology to fMRI and EEG, modify existing techniques for characterizing brain networks of connectivity (e.g., kriging-based independent components analysis), and integrate the imaging modalities using a statistical classifier based on derived inputs of data driven effective resolution groups. Our primary goal is to develop this analysis framework to provide insight into the neurophysiological mechanisms of mild cognitive dysfunction. Achieving this goal may suggest treatments to alleviate symptoms, prevent progression, or at minimum, provide an informed clinical management of cognitively impaired patients.

描述（由申请人提供）：人类大脑的功能性神经影像学研究在理解认知的正常和病理过程中变得越来越重要。已经开发出复杂的统计分析框架来定位信号变化并定义参与各种任务的大脑网络。然而，在轻微的认知障碍中，手术相关疾病、退行性疾病的早期阶段、损伤、癌症辅助治疗后的继发性疾病-这些方法对于检测由轻度脑功能障碍引起的脑状态的微小变化往往具有低灵敏度。理解疾病机制或微妙的认知功能障碍的进展需要一种新的统计分析框架，该框架具有提高的灵敏度以测量大脑状态的微小变化。我们已经开发出一种创新的方法，我们成功地应用于区域脑血流实验的措施。这些方法使用完善的空间建模程序，新的功能性脑成像领域，以获得有效的分辨率组或“克里格”内的统计最佳空间摘要，初步研究表明，以提高信号检测灵敏度和减轻多重测试负担。在这个新的空间建模框架内，我们建议将克里金方法扩展到fMRI和EEG，修改现有的用于表征大脑连接网络的技术（例如，基于克里金法的独立分量分析），并且基于数据驱动的有效分辨率组的导出输入，使用统计分类器来整合成像模态。我们的主要目标是开发这个分析框架，以提供对轻度认知功能障碍的神经生理机制的见解。实现这一目标可能会建议治疗，以减轻症状，防止进展，或至少，提供一个知情的认知受损患者的临床管理。