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

机器和深度学习寻找前驱 AD 的多模态成像生物标志物

• 批准号: 10181265
• 负责人: DIETMAR CORDES
• 金额: $ 233.16万
• 依托单位: CLEVELAND CLINIC LERNER COM-CWRU
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10181265
• 关键词: Affect; Age; Algorithmic Software; Algorithms; Alzheimer' s Disease; Alzheimer' s disease diagnosis; Alzheimer' s disease pathology; Alzheimer' s disease risk; Alzheimer’s disease biomarker; Amyloid; Amyloid Proteins; Automobile Driving; Brain; Brain Diseases; Consumption; Data; Data Analyses; Deposition; Detection; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diagnostic tests; Elderly; Encephalitis; Episodic memory; Functional Imaging; Functional Magnetic Resonance Imaging; Functional disorder; Head; Hippocampus (Brain); Image; Individual; Lead; Machine Learning; Magnetic Resonance Imaging; Manuals; Maps; Medial; Memory; Memory impairment; Methodology; Methods; Modality; Modeling; Motion; Multimodal Imaging; Multivariate Analysis; Neurodegenerative Disorders; Neurofibrillary Tangles; Neurosciences; Noise; Pattern; Performance; Physiological; Physiological Processes; Positron-Emission Tomography; Property; Protocols documentation; Public Health; Reproducibility; Research; Research Personnel; Resolution; Rest; Signal Transduction; Software Tools; Specific qualifier value; Statistical Methods; Structure; System; Techniques; Technology; Temporal Lobe; Testing; Time; Weight; Work; amnestic mild cognitive impairment; automated segmentation; base; brain dysfunction; cerebral atrophy; cognitive function; data fusion; data quality; deep learning; deep neural network; denoising; dentate gyrus; design; high resolution imaging; imaging biomarker; improved; interest; mathematical methods; memory process; novel; prevent; prodromal Alzheimer' s disease; three dimensional structure; tool; user friendly software

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.

我们提出的研究重点是开发深层神经网络和复杂的多元分析 研究前驱AD受试者和年龄匹配的正常对照中的情节记忆激活的方法。 我们特别有兴趣调查在子场中的空间和对象模式分开的影响 海马，附近的内侧颞叶区域以及功能性的全脑连接。为了 要对驱动AD病理学的潜在生理过程有更深入的了解 必须进一步研究海马子场，并使用超过电流的方法 fMRI在3T时的局限性。获取将对这些海马相互作用更准确视图的数据是 尽管障碍甚至在7T时，障碍也使这项研究复杂化，但更容易促进了7T技术。我们的 拟议的研究中心是关于规避这些障碍的中心，特别是系统污染的数据污染 噪声，头部运动噪声和生理噪声与现场强度成正比，以开发方法 这将使调查人员能够以最大的能力在7T的参数中进行开发 更强大的成像生物标志物用于诊断AD。增加成功完成的可能性 我们的研究，我们认为必须制定更好的任务fMRI设计和成像协议（AIM 1），自动 分割方法（AIM 2），降噪方法（AIM 3）和多元分析方法，例如 基于约束规范相关分析（约束CCA）的新型算法和软件工具， 内核CCA，以及使用Fusion CCA，MultiSet CCA和机器的深CCA和相关的组级分析 学习和深度学习技术（目标4）用于研究记忆功能以获得新型成像生物标志物 （目标5）确定有AD风险的人。这项研究将使更清晰，更详细的大脑创建 激活图，从而促进前驱AD中脑功能目前未知方面的发现。 我们的目标成功完成可能会为广告提供更有效的诊断工具，包括 基于fMRI的记忆障碍诊断测试，以表征有风险的人的异常记忆功能 对于广告。我们的先进方法结合了7T高分辨率fMRI，自动分割，数据降解 和多元分析对于检测海马子场的细微功能变化至关重要 及其与其他皮质区域的联系。这项研究的结果预计会广泛影响科学 对大脑功能的理解仅仅增强了当前对AD内存功能的理解。我们 预计从我们提出的研究中获得的发现中得出的方法将具有深远的影响 对提高fMRI数据质量的影响，使更准确地检测大脑激活，为通往 许多其他MRI/fMRI应用程序更好的自动化和瞬时海马子场分段 神经退行性疾病的疾病，并为整个神经科学领域贡献了新的和重要的发现。

项目成果

Sex Modulates the Pathological Aging Effect on Caudate Functional Connectivity in Mild Cognitive Impairment.

• DOI: 10.3389/fpsyt.2022.804168
• 发表时间: 2022
• 期刊: FRONTIERS IN PSYCHIATRY
• 影响因子: 4.7
• 作者: Yang, Zhengshi;Caldwell, Jessica Z. K.;Cummings, Jeffrey L.;Ritter, Aaron;Kinney, Jefferson W.;Cordes, Dietmar
• 通讯作者: Cordes, Dietmar

Energy-Period Profiles of Brain Networks in Group fMRI Resting-State Data: A Comparison of Empirical Mode Decomposition With the Short-Time Fourier Transform and the Discrete Wavelet Transform.

• DOI: 10.3389/fnins.2021.663403
• 发表时间: 2021
• 期刊: Frontiers in neuroscience
• 影响因子: 4.3
• 作者: Cordes D;Kaleem MF;Yang Z;Zhuang X;Curran T;Sreenivasan KR;Mishra VR;Nandy R;Walsh RR
• 通讯作者: Walsh RR

Brain functional topology differs by sex in cognitively normal older adults.

• DOI: 10.1093/texcom/tgac023
• 发表时间: 2022
• 期刊: Cerebral cortex communications

Brain Entropy During Aging Through a Free Energy Principle Approach.

• DOI: 10.3389/fnhum.2021.647513
• 发表时间: 2021
• 期刊: Frontiers in human neuroscience
• 影响因子: 2.9
• 作者: Cieri F;Zhuang X;Caldwell JZK;Cordes D
• 通讯作者: Cordes D

Olfaction and Anxiety Are Differently Associated in Men and Women in Cognitive Physiological and Pathological Aging.

• DOI: 10.3390/jcm12062338
• 发表时间: 2023-03-17
• 期刊: JOURNAL OF CLINICAL MEDICINE
• 影响因子: 3.9
• 作者: Cieri, Filippo;Cera, Nicoletta;Ritter, Aaron;Cordes, Dietmar;Caldwell, Jessica Zoe Kirkland
• 通讯作者: Caldwell, Jessica Zoe Kirkland