@article{621bc21372784c76b0ed62792c84a8f1,
title = "Comparing cortical signatures of atrophy between late-onset and autosomal dominant Alzheimer disease",
abstract = "Defining a signature of cortical regions of interest preferentially affected by Alzheimer disease (AD) pathology may offer improved sensitivity to early AD compared to hippocampal volume or mesial temporal lobe alone. Since late-onset Alzheimer disease (LOAD) participants tend to have age-related comorbidities, the younger-onset age in autosomal dominant AD (ADAD) may provide a more idealized model of cortical thinning in AD. To test this, the goals of this study were to compare the degree of overlap between the ADAD and LOAD cortical thinning maps and to evaluate the ability of the ADAD cortical signature regions to predict early pathological changes in cognitively normal individuals. We defined and analyzed the LOAD cortical maps of cortical thickness in 588 participants from the Knight Alzheimer Disease Research Center (Knight ADRC) and the ADAD cortical maps in 269 participants from the Dominantly Inherited Alzheimer Network (DIAN) observational study. Both cohorts were divided into three groups: cognitively normal controls (nADRC = 381; nDIAN = 145), preclinical (nADRC = 153; nDIAN = 76), and cognitively impaired (nADRC = 54; nDIAN = 48). Both cohorts underwent clinical assessments, 3T MRI, and amyloid PET imaging with either 11C-Pittsburgh compound B or 18F-florbetapir. To generate cortical signature maps of cortical thickness, we performed a vertex-wise analysis between the cognitively normal controls and impaired groups within each cohort using six increasingly conservative statistical thresholds to determine significance. The optimal cortical map among the six statistical thresholds was determined from a receiver operating characteristic analysis testing the performance of each map in discriminating between the cognitively normal controls and preclinical groups. We then performed within-cohort and cross-cohort (e.g. ADAD maps evaluated in the Knight ADRC cohort) analyses to examine the sensitivity of the optimal cortical signature maps to the amyloid levels using only the cognitively normal individuals (cognitively normal controls and preclinical groups) in comparison to hippocampal volume. We found the optimal cortical signature maps were sensitive to early increases in amyloid for the asymptomatic individuals within their respective cohorts and were significant beyond the inclusion of hippocampus volume, but the cortical signature maps performed poorly when analyzing across cohorts. These results suggest the cortical signature maps are a useful MRI biomarker of early AD-related neurodegeneration in preclinical individuals and the pattern of decline differs between LOAD and ADAD.",
keywords = "Alzheimer disease, Amyloid, Autosomal dominant Alzheimer disease, Cortical signature, Cortical thickness, Preclinical",
author = "{for the Dominantly Inherited Alzheimer Network DIAN} and Aylin Dincer and Gordon, {Brian A.} and Amrita Hari-Raj and Keefe, {Sarah J.} and Shaney Flores and McKay, {Nicole S.} and Paulick, {Angela M.} and {Shady Lewis}, {Kristine E.} and Feldman, {Rebecca L.} and Hornbeck, {Russ C.} and Ricardo Allegri and Ances, {Beau M.} and Berman, {Sarah B.} and Brickman, {Adam M.} and Brooks, {William S.} and Cash, {David M.} and Chhatwal, {Jasmeer P.} and Farlow, {Martin R.} and {la Foug{\`e}re}, Christian and Fox, {Nick C.} and Fulham, {Michael J.} and Jack, {Clifford R.} and Nelly Joseph-Mathurin and Karch, {Celeste M.} and Athene Lee and Johannes Levin and Masters, {Colin L.} and McDade, {Eric M.} and Hwamee Oh and Perrin, {Richard J.} and Cyrus Raji and Salloway, {Stephen P.} and Schofield, {Peter R.} and Yi Su and Villemagne, {Victor L.} and Qing Wang and Weiner, {Michael W.} and Chengjie Xiong and Igor Yakushev and Morris, {John C.} and Bateman, {Randall J.} and {L.S. Benzinger}, Tammie",
note = "Funding Information: This work was supported by The Dominantly Inherited Alzheimer Network (DIAN, UF1AG032438) and funded by the National Institutes of Health (U19AG03243808, P01AG003991, P01AG026276, P01AG005681, K01AG053474, R01AG03158, and P30AG019610), the German Center for Neurodegenerative Diseases (DZNE), XNAT (R01EB009352), Neuroimaging Informatics and Analysis Center (P30NS098577), the Center for High-Performance Computing (1S10RR022984-01A1 and 1S10OD018091-0), ADHS Grant No. CTR040636 (previously ADHS Grant No. ADHS14-052688), Alzheimer{\textquoteright}s Association (AARG-17-532945), the BrightFocus Foundation (ADR A2017272S), Alzheimer Association International Research Program (AARFD-20-681815), and the Raul Carrea Institute for Neurological Research (FLENI). This work was also supported by the generous support of Barnes-Jewish Hospital, the Paula and Rodger O. Riney Fund, the Danial J Brennan MD Fund, the Fred Simmons and Olga Mohan Fund, and the Willman Fund, the Arizona Alzheimer{\textquoteright}s Consortium, DHS of the State of Arizona, the Research and Development Grants for Dementia from Japan Agency for Medical Research and Development, AMED, and the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI). This manuscript has been reviewed by DIAN Study investigators for scientific content and consistency of data interpretation with previous DIAN Study publications. Funding Information: We acknowledge the altruism of the participants and their families and contributions of the DIAN and the Knight ADRC research and support staff at each of the participating sites for their contributions to this work. Without the generous contribution and time the participants gave for these studies, this work would not be possible. Publisher Copyright: {\textcopyright} 2020 The Authors",
year = "2020",
month = jan,
doi = "10.1016/j.nicl.2020.102491",
language = "English",
volume = "28",
journal = "NeuroImage: Clinical",
issn = "2213-1582",
}