FMRI, PET and the Default Mode Network Classify MCI and AD

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): The purpose of this two year study is to assess the sensitivity and specificity of newer neurophysiological measures, specifically the integrity of the default mode neural network (DMN), to identify amnestic mild cognitive impairment (MCI) and early probable Alzheimer's Disease (AD) and to improve diagnostic accuracy levels. The DMN involves several brain regions, including the anterior and posterior cingulate cortices, lateral parietal cortex, middle frontal lobe, and parahippocampal regions. The DMN is active when a person is awake but resting quietly; it is the network that underlies "stream of consciousness" processing in the brain. In patients with probable AD, FDG PET consistently reveals hypometabolism in lateral temporal cortex, posterior cingulate cortex, entorhinal cortex, and middle temporal lobe structures. Thus there is great overlap in the brain structures involved in the DMN and affected by AD. Recent neuroscience research has shown that evaluation of the integrity of the DMN with fMRI is sufficient to distinguish healthy elderly from those who have probable AD. This method of examining brain activity associated with the DMN provides a new tool for those who study and treat MCI and AD patients and may lead to improving the ability to accurately diagnose MCI/AD in the earliest stages; providing a means of assessing change in status, and increasing interpretability of clinical trials aimed at symptom relief or treatment efficacy. This study furthers the recent findings by comparing data from different neuroimaing methods (quantitative FDG PET glucose uptake ratios, fMRI DMN, and (exploratory) FDG PET DMN) to determine which approach can best classify subjects into their proper diagnostic group (groups defined based on neuropsychological assessment). The additional goal (exploratory aim) is to identify (via path analysis) which pathways within the DMN are most sensitive to the neurodegenerative process. This information will contribute to the benefits listed above by identifying brain regions that may show the earliest signs of dementia, identifying a biomarker for at-risk people, and focusing treatments to stabilize function in the affected regions. To accomplish these goals, we will recruit 20 people ages 60-85 for each of our diagnostic groups: probable AD, MCI, and healthy control. Each will receive diagnostic-quality neuropsychological testing, APOE genotyping, and an fMRI and a FGD PET scan. The important outcome of this project that is specific to the field of public health is the establishment of a tool with which clinicians can identify at risk patients significantly earlier in the disease process, provide noninvasive repeatable testing, evaluate treatment effectiveness, and aid in the process of differential diagnosis. Without advances in early and accurate diagnostic measures, therapeutic treatments, and treatments that delay the onset of the disease, the Alzheimer's Association states that dementia has the potential to become the largest public health problem of this century.