Assess Neural Circuits and Subtypes Underlying Dimensions of Neuropsychiatric Symptoms in Alzheimer's Disease

评估阿尔茨海默病神经精神症状的神经回路和亚型

• 批准号: 10741906
• 负责人: Yu Zhang
• 金额: $ 19.95万
• 依托单位: LEHIGH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-20 至 2025-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741906
• 关键词: Acceleration; Affect; Aggressive behavior; Agitation; Alleles; Alzheimer' s Disease; Amyloid beta-Protein; Anxiety; Behavior; Brain; Characteristics; Cluster Analysis; Communities; Computer software; Data; Dementia; Diagnosis; Dimensions; Disease Progression; Dropout; Early Diagnosis; Etiology; Functional Magnetic Resonance Imaging; Functional disorder; Guidelines; Impaired cognition; Individual; Institutionalization; Intervention; Link; Machine Learning; Maps; Measures; Mental Depression; Modeling; Nerve Degeneration; Neurobiology; Neuropsychology; Outcome; Patients; Pattern; Phenotype; Psychopathology; Psychoses; Publishing; Reproducibility; Research; Rest; Severities; Sleep disturbances; Staging; Symptoms; Testing; Time; Validation; analytical tool; apolipoprotein E-4; associated symptom; clinical diagnosis; clinical predictors; clinically relevant; cognitive function; cohort; common symptom; connectome; improved; mild cognitive impairment; neural circuit; neural patterning; neurobiological mechanism; neuroimaging; neurophysiology; neuropsychiatric symptom; neuropsychiatry; novel; pre-clinical; precision medicine; prevent; response; tool; Acceleration; Affect; Aggressive behavior; Agitation; Alleles; Alzheimer' s Disease; Amyloid beta-Protein; Anxiety; Behavior; Brain; Characteristics; Cluster Analysis; Communities; Computer software; Data; Dementia; Diagnosis; Dimensions; Disease Progression; Dropout; Early Diagnosis; Etiology; Functional Magnetic Resonance Imaging; Functional disorder; Guidelines; Impaired cognition; Individual; Institutionalization; Intervention; Link; Machine Learning; Maps; Measures; Mental Depression; Modeling; Nerve Degeneration; Neurobiology; Neuropsychology; Outcome; Patients; Pattern; Phenotype; Psychopathology; Psychoses; Publishing; Reproducibility; Research; Rest; Severities; Sleep disturbances; Staging; Symptoms; Testing; Time; Validation; analytical tool; apolipoprotein E-4; associated symptom; clinical diagnosis; clinical predictors; clinically relevant; cognitive function; cohort; common symptom; connectome; improved; mild cognitive impairment; neural circuit; neural patterning; neurobiological mechanism; neuroimaging; neurophysiology; neuropsychiatric symptom; neuropsychiatry; novel; pre-clinical; precision medicine; prevent; response; tool

Project Abstract Neuropsychiatric symptoms (NPS) are commonly observed in individuals with mild cognitive impairment or Alzheimer’s disease (AD) dementia. These symptoms affect up to 97% of patients during the course of AD and may cause accelerated declines in cognitive functions and conversion to dementia. Though numerous efforts have been devoted to investigating the etiology of NPS, the neurobiological basis underlying NPS in AD dementia remains unclear. There is an urgent need to advance the mechanistic understanding of these symptoms, which is crucial for early detection and timely intervention to prevent AD progression. Increasing evidence has indicated that differential NPS overlap substantially and are relevant to dysfunctions in distinctive brain networks. Assessing NPS dimensionally and their associated circuit-level dysfunctions would therefore provide significant benefits to deepen our understanding of neural circuits involved in the expression of NPS. In response to the guidelines of the PAR-20-159, the overall objective of the project is to assess neural circuits and identify neurophysiological subtypes (i.e., subtypes) underlying dimensions of NPS in dementia. We hypothesize that distinct patterns of neural circuits will reflect latent dimensions of NPS domains that span from preclinical to severe AD dementia, and interact to define neurophysiological subtypes that are predictive of clinical symptoms and the rate of AD progression. In Aim 1, we will identify interpretable neural circuits and the linked latent dimensions of NPS domains using a sparse multivariate correlation analysis. In Aim 2, we will identify neurophysiological subtypes using statistical clustering with guidance from the NPS dimension-associated circuitry characteristics. We will further evaluate and interpret the neurobiological meanings and clinical relevance underlying these dimensions and subtypes. The proposed approaches will be developed and evaluated by assessing resting-state functional MRI from two independent cohorts (OASIS-3 and ADNI) with more than 1,800 subjects in total. Successful outcomes of the project will lead to an improved understanding of the neurobiological mechanism underlying NPS and its clinical relevance, form a promising new avenue to potentially guide the intervention of NPS and better the management of AD progression, and hence pave the way towards precision medicine of AD dementia.

项目摘要 神经精神症状（NP）通常在患有轻度认知障碍的人或 阿尔茨海默氏病（AD）痴呆。这些症状在AD过程中影响多达97％的患者，并且 可能导致认知功能和转化为痴呆症的加速下降。虽然付出了许多努力 已经致力于研究NP的病因，NP的病因是AD痴呆症中NP的神经生物学基础 仍然不清楚。迫切需要提高对这些符号的机械理解，这些符号 对于早期检测和及时干预至关重要，以防止AD进展。越来越多的证据表明 这种差异NP基本上重叠，并且与独特的大脑网络中的功能障碍有关。 因此，评估NP及其相关的电路级功能障碍将提供重要的 加深我们对NP表达涉及的神经回路的理解。回应 该项目的总体目标是评估神经回路并确定该项目的总体目标 神经生理亚型（即亚型）在痴呆症中NP的基础尺寸。我们假设这一点 神经回路的独特模式将反映从临床前到的NPS域的潜在尺寸 严重的AD痴呆，并相互作用以定义可预测临床症状的神经生理亚型 以及AD进程的速率。在AIM 1中，我们将确定可解释的神经回路和链接的潜在 使用稀疏的多元相关分析的NPS域的维度。在AIM 2中，我们将确定 使用统计聚类与NPS相关的指导使用统计聚类的神经生理亚型 电路特性。我们将进一步评估和解释神经生物学意义和临床 这些维度和亚型的相关性。提出的方法将开发，并 通过评估来自两个独立队列（OASIS-3和ADNI）的静止状态功能MRI 总共有1,800多名受试者。该项目的成功结果将导致人们对 NP的基础神经生物学机制及其临床相关性，构成了有希望的新途径 有可能指导NP的干预措施，更好地管理AD的进展，因此铺平了 探索痴呆症的精确医学的方式。