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

我们提出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者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子领域中的影响 海马体、内侧颞叶附近区域以及功能性全脑连接。为了 为了更全面地了解驱动AD病理学的潜在生理过程， 海马亚区必须进行更深入的研究， fMRI在3 T的局限性。获取数据，将产生一个更准确的看法，这些海马体的相互作用是 7 T技术更容易促进，尽管即使在7 T下，障碍也会使这种研究复杂化。我们 拟议的研究集中在规避这些障碍，特别是数据污染，从过度的系统 噪声、头部运动噪声和与场强成比例的生理噪声，以开发方法 这将使研究人员能够在7 T的参数范围内充分发挥其能力， 更强大的成像生物标志物用于诊断AD。为了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成像设计和成像协议（目标1），自动 分割方法（目标2）、降噪方法（目标3）和多变量分析方法，如 基于约束典型相关分析（约束CCA）的新算法和软件工具， 核CCA、深度CCA和相关的组级分析，使用融合CCA、多集CCA和机器 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 (Aim 5）识别AD风险个体。这项研究将使创造更清晰，更详细的大脑 激活地图，从而促进发现目前未知的方面的大脑功能的前驱AD。 我们的目标的成功完成可能会导致更有效的AD诊断工具，包括 基于fMRI的记忆障碍诊断测试，以表征风险人群的异常记忆功能 对于AD。我们先进的方法，结合7 T高分辨率fMRI，自动分割，数据去噪 和多变量分析，将是必不可少的检测微妙的功能变化，在子领域的海马 以及它与其他皮层区域的联系这项研究的结果预计将广泛影响科学 这是对大脑功能的理解，而不仅仅是加强目前对AD记忆功能的理解。我们 我预计，从我们拟议的研究中获得的发现中开发的方法将具有深远的意义。 对改善功能磁共振成像数据质量的影响，使大脑激活的更准确的检测，开辟了一条道路， 更好的自动化和瞬时海马子场分割，用于许多其他MRI/fMRI应用 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.