Cognitive Assessment and Neuroimaging (CAN) Core E

SUMMARY/ABSTRACT: Cognitive Assessment and Neuroimaging Core E The goal of Cognitive Assessment and Neuroimaging (CAN) Core E, is to provide neurological data with cognitive assessment, MRI, and carotid ultrasound that will directly support Projects 1, 2 and 4, leading to the identification of key neurological signatures of individual differences in cognitive aging. Cognitive assessment protocols will include performance measures of memory, executive functions, processing speed, premorbid function, and overall cognitive function. Cognitive tests derive from the experimental cognitive aging literature that have been demonstrated to be sensitive to individual differences across and within age groups, as well as standardized clinical neuropsychological tests typically used to identify age-related cognitive impairment and potential early dementia. We will standardize and coordinate online cognitive assessments in both English and Spanish, directly supporting Projects 1 and 2, and in-person assessments at four clinical for Project 2 (Tucson, Baltimore, Atlanta, Miami) to ensure quality and consistency throughout the project duration. In addition, we plan to acquire brain MRI and ultrasound carotid images from 1620 participants. Our MRI protocols will be built upon the advanced MRI protocols of ADNI 3, which have been optimized and tested for our MRI platforms. From the acquired data we will produce quantitative measures of brain morphology, white matter hyperintensities, structural and functional connectivity, perfusion, microbleeds, carotid intima-media thickness, plaque presence, size and morphology, and blood flow velocities. The acquired imaging data will be annotated with meta-data that support near real-time quality control, robust mining, query and analyses of imaging data, meeting the meta-data requirements for various MRI software packages, machine learning procedures, and large scale analyses proposed in Project 4. The meta-data management and annotation system will also support incorporation of decentralized data (from the public domain), with which the training of our machine learning modules can be further enhanced. Furthermore, we plan to implement a series of data harmonization and pre-processing pipelines in our XNAT server (integrated with the COINS platform; HPC nodes; and CyVerse), to streamline imaging data QC and quantitative analysis. Our proposed pipelines address limitations of existing software packages (e.g., existing challenge in robustly segmenting hippocampus and other critical brain structures across age groups) through machine learning, and can inherently harmonize multi-modal MRI data (e.g., fast spin-echo MRI in minimally distorted coordinates; and echo-planar imaging in distorted coordinates), enabling streamlined analysis (e.g., from hippocampal segmentation to memory network connectivity analysis) without manual intervention.