Brain structure, chemistry and function investigations in aging using MRI/MRS

使用 MRI/MRS 研究衰老过程中的脑结构、化学和功能

• 批准号: 8931645
• 负责人: Dimitrios Kapogiannis
• 金额: $ 18.77万
• 依托单位: NATIONAL INSTITUTE ON AGING
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/8931645
• 关键词: Affective; Age; Aging; Agonist; Alzheimer' s Disease; Amyloid deposition; Anterior; Area; Atrophic; Biological Markers; Brain; CNR1 gene; Caloric Restriction; Cephalic; Chemistry; Clinical Research; Cognition; Cognitive; Collaborations; Data; Data Set; Databases; Disease; Disease Progression; Enrollment; Etiology; Financial compensation; Food; Functional Magnetic Resonance Imaging; Future; Generations; Genetic Crossing Over; Glucose; Glutamates; Glutamine; Glycine; Goals; Hippocampus (Brain); Hypothalamic structure; Image; Individual; Insula of Reil; Insulin; Insulin Resistance; Investigation; Journals; Knowledge; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Manuscripts; Measurement; Measures; Medial; Mentors; Metabolic; Metabolic Control; Metabolism; Methodology; Metric; Mind; Nerve Degeneration; Neurology; Neurotransmitters; Obesity; Participant; Pathogenesis; Pathway interactions; Patients; Pattern; Performance; Peripheral; Placebo Control; Play; Positron-Emission Tomography; Postdoctoral Fellow; Prefrontal Cortex; Process; Psychiatry; Publishing; Randomized; Religious Belief; Reporting; Research; Rest; Role; Staging; Structure; Techniques; Temporal Lobe; Ventral Tegmental Area; age group; aging brain; brain metabolism; brain volume; data reduction; disease diagnosis; exenatide; frontal lobe; gamma-Aminobutyric Acid; glucose metabolism; independent component analysis; insulin secretion; interest; memory encoding; memory retrieval; mild cognitive impairment; neurochemistry; neuroimaging; neurotransmission; novel; response; sex; systematic review; theories; trait; uptake; visual stimulus

Volumetric measures obtained with analysis of high-definition 3D structural MRI images capture neuroanatomical variability which may be associated with long-standing traits or may help explain why certain brain areas or individuals are vulnerable to neurodegenerative processes. This last year, I applied a data reduction technique called group-level independent component analysis (ICA) to structural MRI images to uncover patterns of structural covariance (Independent Components). We applied ICA to MRI data collected in the ADNI study and examined how they can discriminate between participants with normal cognition, mild cognitive impairment (MCI) and Alzheimer's disease (AD). We showed that ICs can be useful as classifiers and predictors of future AD diagnosis or conversion to AD from MCI (the manuscript is currently in press in "Psychiatry Research: Neuroimaging"). In addition, given the association between insulin resistance and Alzheimer's disease, we examined how volumetric and FDG-PET uptake measures for several key regions of interest for AD relate to peripheral insulin resistance. We found that insulin resistance promotes two abnormal and pathogenic compensations: it increases glucose metabolism in the hippocampus and medial temporal lobe at the stage of mild cognitive impairment and increases glucose metabolism in default mode network nodes at the stage of AD. Prior fMRI studies suggest that these compensatory increases in metabolism are associated with disease progression; therefore, insulin resistance seems to promote a maladaptive compensation. The manuscript is currently under review in the journal "Neurology". Together with a post-doctoral fellow I mentor, Dr. Auriel Willette, we conducted a systematic review of neuroimaging studies looking at associations of obesity and brain volume across the age-span. We found that increased obesity is associated with atrophy mainly in the frontal lobes. Our review was published by the journal "Aging Research Reviews". I conducted an fMRI study using a novel methodology for effective connectivity analysis to fMRI data based on the principles of Granger causality. This methodology allows us to uncover causal pathways between nodes of a brain network. I implemented this analysis in a dataset from a fMRI study on religious beliefs conducted in 2009. The findings support the theory that areas implicated in Theory of Mind are key to generation of religious beliefs. The study was published in the journal "Brain Connectivity". Finally, in collaboration with Drs. Chia and Egan, we completed the "RISE study", a multi-faceted randomized placebo-controlled cross-over clinical study of the effects of a CB1 receptor agonist and antagonist on peripheral metabolism, brain function and brain metabolic control. The study included a robust neuroimaging component including fMRI and MRS. We performed two activation-paradigm fMRI studies, one to discover brain correlates of cephalic insulin secretion and the effects of CB1 receptors, the second to assess the effects of CB1 receptors on food appetitiveness. The goal of the first study was to demonstrate a rise in insulin levels in response to food visual stimuli (cephalic insulin response) as a result of activation of certain brain areas (insula, anterior cingulate, hypothalamus, ventral tegmental area, etc). Moreover, given the presence of CB1 receptors in the candidate areas, we aimed to demonstrate a difference in their level of activation with CB1 agonists and antagonists. The goal of the second study was to demonstrate dissociable effects of CB1 receptor stimulation on food value (food choices) and salience (intensity of such choices). In addition, we performed a resting fMRI study to assess CB1 modulation of functional connectivity of the various brain networks. Finally, we performed MRS to assess CB1 modulation of brain metabolism (glucose, lactate) and neurotransmission (glutamate, GABA, glycine). We are currently in the process of analyzing and interpreting the data from these studies.

通过分析高清3D结构MRI图像获得的体积测量可以捕获神经解剖学的变异，这可能与长期存在的特征有关，也可能有助于解释为什么某些大脑区域或个体容易受到神经退行性过程的影响。去年，我将一种称为组级独立成分分析（ICA）的数据简化技术应用于结构MRI图像，以揭示结构协方差（独立成分）的模式。我们将ICA应用于ADNI研究中收集的MRI数据，并检查它们如何区分正常认知，轻度认知障碍（MCI）和阿尔茨海默病（AD）的参与者。我们发现ic可以作为分类器和预测器，用于未来AD诊断或从MCI转化为AD（手稿目前在“精神病学研究：神经影像学”上出版）。