Machine and deep learning for finding multimodal imaging biomarkers in prodromal AD

我们建议的研究重点是开发深度神经网络和复杂的多变量分析 研究前驱AD受试者和年龄匹配的正常对照的情景记忆激活的方法。 我们特别感兴趣的是研究空间和对象模式分离在以下子领域中的影响 海马体，内侧颞叶的邻近区域，以及功能正常的全脑连接。按顺序 为了更全面地了解推动AD病理的潜在生理过程，激活 必须进一步深入研究海马亚区，并采用超出当前水平的方法 3T时功能磁共振成像的局限性。获取数据将产生对这些海马体相互作用的更准确的看法 使用7T技术要容易得多，尽管障碍使这样的研究即使在7T也变得复杂。我们的 建议的研究重点是绕过这些障碍，特别是过度系统造成的数据污染 噪声、头部运动噪声和与场强成正比的生理噪声，以开发方法 这将使调查人员能够在7T的参数范围内最大限度地开发 更强大的诊断AD的影像生物标志物。增加成功完成的可能性 我们的研究，我们认为有必要开发更好的任务功能磁共振设计和成像方案(目标1)，自动 分段方法(目标2)、降噪方法(目标3)和多变量分析方法，例如 基于约束典型相关分析的新算法和软件工具(约束CCA)， 使用融合CCA、多集CCA和机器进行内核CCA、深度CCA和相关组级分析 学习和深度学习技术(目标4)用于研究记忆功能以获得新的成像生物标志物 (目标5)确定阿尔茨海默病风险人群。这项研究将使我们能够创造出更清晰、更详细的大脑 激活图，从而促进发现目前未知的脑功能方面的前驱阿尔茨海默病。 成功完成我们的目标可能会带来更有效的AD诊断工具，包括 基于功能磁共振成像的记忆损害诊断测试对高危人群记忆功能异常的表征 对于AD。我们先进的方法，结合7T高分辨率fMRI，自动分割，数据去噪 和多变量分析，对于检测海马亚区的细微功能变化是必不可少的 以及它与其他皮质区域的联系。这项研究的结果预计将对科学产生广泛影响 对大脑功能的理解不仅仅是增强对阿尔茨海默病记忆功能的当前理解。我们 预计根据我们拟议的研究结果开发的方法将产生深远的影响 对提高fMRI数据质量的影响，使更准确地检测大脑激活，为 更好的自动化和瞬时海马子场分割，适用于许多其他MRI/fMRI应用 对神经退行性疾病的研究，并为神经科学领域做出新的重要发现。

Our proposed study focuses on developing deep neural networks and sophisticated multivariate analysis methods for studying episodic memory activations in prodromal AD subjects and age-matched normal controls. We are particularly interested in investigating the effects of spatial and object pattern-separation in subfields of the hippocampus, nearby regions of the medial temporal lobe, and functional whole-brain connections. In order to acquire a fuller understanding of the underlying physiological processes driving AD pathology, activations in hippocampal subfields must be investigated in further depth and with methodologies that exceed the current limitations of fMRI at 3T. Acquiring data that will yield a more accurate view of these hippocampal interactions is far more easily facilitated with 7T technology, although barriers complicate such a study even at 7T. Our proposed study centers on circumventing these barriers, particularly data contamination from excessive system noise, head-motion noise, and physiological noise which is proportional to the field strength, to develop methods that will allow investigators to work within the parameters of 7T at its fullest capacity toward the development of more powerful imaging biomarkers for diagnosing AD. To increase the likelihood of the successful completion of our study, we consider it imperative to develop better task fMRI designs and imaging protocols (Aim 1), automatic segmentation methods (Aim 2), noise-reduction methods (Aim 3), and multivariate analysis methods, such as novel algorithms and software tools based on constrained canonical correlation analysis (constrained CCA), kernel CCA, and deep CCA and relevant group-level analysis using fusion CCA, multiset CCA, and machine learning and deep learning techniques (Aim 4) for studying memory function to obtain novel imaging biomarkers (Aim 5) to identify individuals at risk for AD. This study will enable the creation of clearer and more detailed brain activation maps and thus promote the discovery of currently unknown aspects of brain function in prodromal AD. The successful completion of our objectives could lead to more effective diagnostic tools for AD, including an fMRI-based diagnostic test for memory impairment to characterize abnormal memory function in people at risk for AD. Our advanced methodology, combining 7T high-resolution fMRI, automatic segmentation, data denoising and multivariate analysis, will be essential for detecting subtle functional changes in subfields of the hippocampus and its connections to other cortical regions. Results from this study are expected to broadly impact scientific understanding of brain function beyond only enhancing current understanding of memory function in AD. We anticipate that the methods developed from findings acquired in our proposed study will have a far-reaching influence on improving fMRI data quality, enable more accurate detection of brain activation, open a path toward better automated and instantaneous hippocampal subfield segmentation for many other MRI/fMRI applications of neurodegenerative diseases, and contribute new and vital discoveries to the field of neuroscience in general.