A Summary Statistics Approach to providing Imaging Results for the Cleveland ADRC Neuroimaging Core
Poster No:
148
Submission Type:
Abstract Submission
Authors:
Jian Lin1, Ken Sakaie1, Wanyong Shin1, Katherine Koenig1, Dan Ma2, Sehong Oh1, Sally Durgerian1, Ajay Nemani1, Pillai Jagan1, Brian Appleby2, Alan Lerner2, James Leverenz1, Mark Lowe1
Institutions:
1The Cleveland Clinic, Cleveland, OH, 2Case Western Reserve University, Cleveland, OH
First Author:
Co-Author(s):
Introduction:
The NIH/NIA supports a network of 33 Alzheimer's Disease Research Centers (ADRC) to promote translation of research to improved patient care. The neuroimaging core in Cleveland (CADRC-NIC) has developed a panel of advanced MRI methods to explore their use in the context of AD and related dementia. We provide an overview of the methods and the overall framework for analysis and distillation of the imaging data into a summary statistics report of regional measures of the brain.
Methods:
Subjects are recruited from the community for annual evaluation. All imaging is performed on a Siemens Prisma 3T MRI with a standard 32 channel head coil (Siemens Healthineers, Erlangen, Germany). Imaging included anatomical, resting state functional MRI (rs-fMRI)1 and diffusion MRI (dMRI) scans based on the ADNI3 advanced protocol (https://adni.loni.usc.edu/). Additional scans including quantitative arterial spin labeling (ASL)2,3, multiecho gradient echo (MGE), myelin-weighted image (MWI)4 using ViSTa5, dynamic contrast enhancement (DCE)6 and magnetic resonance fingerprinting (MRF)7. MGE is used to generate susceptibility-weighted images (SWI) and quantitative susceptibility maps (QSM)8. dMRI is used to calculate both diffusion tensor imaging (DTI)9 and neurite orientation dispersion and density imaging (NODDI)10 maps. Due to concerns related to patient comfort and compliance, scans are acquired in two separate scan sessions, allowing subjects to take a break between sessions. Further details about the imaging and purpose of each scan are provided in Table 1. Histograms of tissue properties of each scan are generated from each scan session as a quality assurance step. Outcome measures consist of average values from within brain parcels defined by FreeSurfer11 that have been coregistered to native space using AFNI12, FSL13, and ANTs14.
Results:
To date, eighty six subjects (36 female) have been scanned. Age was 68±11 years (mean±std). Years of education was 16±3 years. Thirty-seven were cognitively normal, 33 had mild cognitive impairment related to typical AD/atypical AD/DLB/Other etiologies, while 13 had dementia related to typical AD/atypical AD/Down's syndrome/Other etiologies, and 3 were awaiting consensus diagnosis. Figure 1 shows an example of histograms used as part of the quality assurance. Figure 2 depicts an example of outcome measures. Tables 2 and 3 show examples of outcome measures from one subject in regions commonly affected in AD.
Conclusions:
We present a brief overview of the imaging acquired by the CADRC-NIC. While measures of neurodegeneration from structural imaging are well-established for AD, imaging data from other modalities need to be acquired from a large population of subjects in order to evaluate their utility for patient management. Providing a quantitative summary of regional brain measures from an advanced imaging protocol can help to facilitate analyses by the regional and national network of AD and ADRD researchers. These data are made readily available and are intended to help in furthering the development of these advanced measures as biomarkers for AD diagnosis and progression.
Disorders of the Nervous System:
Neurodegenerative/ Late Life (eg. Parkinson’s, Alzheimer’s) 1
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 2
Keywords:
Degenerative Disease
Open Data
Other - Alzheimer's diseases, data sharing
1|2Indicates the priority used for review
·Table 1, 2 and 3
·Figure 1 and 2
Provide references using author date format
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