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Original Investigation |

Functional Connectivity in Autosomal Dominant and Late-Onset Alzheimer Disease

Jewell B. Thomas, BA1; Matthew R. Brier, BS1; Randall J. Bateman, MD1; Abraham Z. Snyder, MD, PhD2; Tammie L. Benzinger, MD, PhD2; Chengjie Xiong, PhD3; Marcus Raichle, MD1,2,4; David M. Holtzman, MD1; Reisa A. Sperling, MD5; Richard Mayeux, MD6; Bernardino Ghetti, MD7; John M. Ringman, MD8; Stephen Salloway, MD9; Eric McDade, DO10; Martin N. Rossor, MD11; Sebastien Ourselin, PhD11; Peter R. Schofield, PhD12,13; Colin L. Masters, MD14; Ralph N. Martins, PhD15; Michael W. Weiner, MD16,17,18; Paul M. Thompson, PhD19; Nick C. Fox, MD20; Robert A. Koeppe, PhD21; Clifford R. Jack Jr, MD22; Chester A. Mathis, PhD23; Angela Oliver, RN1; Tyler M. Blazey, BS2; Krista Moulder, PhD24; Virginia Buckles, PhD1; Russ Hornbeck, MS2; Jasmeer Chhatwal, MD, PhD25; Aaron P. Schultz, PhD25; Alison M. Goate, DPhil18; Anne M. Fagan, PhD1; Nigel J. Cairns, PhD1; Daniel S. Marcus, PhD2; John C. Morris, MD1; Beau M. Ances, MD, PhD1
[+] Author Affiliations
1Department of Neurology, Washington University in St Louis, St Louis, Missouri
2Department of Radiology, Washington University in St Louis, St Louis, Missouri
3Division of Biostatistics, Washington University in St Louis, St Louis, Missouri
4Department of Anatomy and Neurobiology, Washington University in St Louis, St Louis, Missouri
5Department of Neurology, Brigham and Women's Hospital, Massachusetts General Hospital, Harvard Medical School, Boston
6Department of Neurology, Columbia University Medical Center, New York, New York
7Department of Pathology and Laboratory Medicine, Indiana University, Bloomington
8Department of Neurology, Easton Center for Alzheimer’s Disease Research, David Geffen School of Medicine, University of California, Los Angeles
9Departments of Neurology and Psychiatry, Warren Alpert Medical School, Brown University, Providence, Rhode Island
10Department of Neurology, University of Pittsburgh, Pittsburgh, Pennsylvania
11Dementia Research Centre, Institute of Neurology, University College London, London, England
12Neuroscience Research Australia, Sydney, Australia
13School of Medical Sciences, University of New South Wales, Sydney, Australia
14Mental Health Research Institute, University of Melbourne, Melbourne, Australia
15School of Medical Sciences, Edith Cowan University, Joondalup, Australia
16Department of Medicine, University of California, San Francisco
17Department of Radiology, University of California, San Francisco
18Department of Psychiatry, University of California, San Francisco
19Departments of Neurology and Psychiatry, Imaging Genetics Center, Laboratory of Neuroimaging, David Geffen School of Medicine at University of California, Los Angeles
20Dementia Research Centre, Department of Neurodegeneration, Institute of Neurology, University College of London, London, England
21Department of Radiology, University of Michigan, Ann Arbor
22Department of Radiology, Mayo Clinic, Rochester, Minnesota
23Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
24Department of Psychiatry, Washington University in St Louis, St Louis, Missouri
25Department of Neurology, Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston
JAMA Neurol. 2014;71(9):1111-1122. doi:10.1001/jamaneurol.2014.1654.
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Importance  Autosomal dominant Alzheimer disease (ADAD) is caused by rare genetic mutations in 3 specific genes in contrast to late-onset Alzheimer disease (LOAD), which has a more polygenetic risk profile.

Objective  To assess the similarities and differences in functional connectivity changes owing to ADAD and LOAD.

Design, Setting, and Participants  We analyzed functional connectivity in multiple brain resting state networks (RSNs) in a cross-sectional cohort of participants with ADAD (n = 79) and LOAD (n = 444), using resting-state functional connectivity magnetic resonance imaging at multiple international academic sites.

Main Outcomes and Measures  For both types of AD, we quantified and compared functional connectivity changes in RSNs as a function of dementia severity measured by the Clinical Dementia Rating Scale. In ADAD, we qualitatively investigated functional connectivity changes with respect to estimated years from onset of symptoms within 5 RSNs.

Results  A decrease in functional connectivity with increasing Clinical Dementia Rating scores were similar for both LOAD and ADAD in multiple RSNs. Ordinal logistic regression models constructed in one type of Alzheimer disease accurately predicted clinical dementia rating scores in the other, further demonstrating the similarity of functional connectivity loss in each disease type. Among participants with ADAD, functional connectivity in multiple RSNs appeared qualitatively lower in asymptomatic mutation carriers near their anticipated age of symptom onset compared with asymptomatic mutation noncarriers.

Conclusions and Relevance  Resting-state functional connectivity magnetic resonance imaging changes with progressing AD severity are similar between ADAD and LOAD. Resting-state functional connectivity magnetic resonance imaging may be a useful end point for LOAD and ADAD therapy trials. Moreover, the disease process of ADAD may be an effective model for the LOAD disease process.

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Figure 1.
Regions of Interest

Individual regions of interest are displayed on brain surfaces along with intranetwork connections in each of the 5 networks analyzed in the current study. CON indicates executive control network; DAN, dorsal attention network; DMN, default mode network; SAL, salience network; and SMN, sensorimotor network.

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Figure 2.
Similarities Within Resting State Network (RSN) Changes in Late-Onset Alzheimer Disease (LOAD) and Autosomal Dominant Alzheimer Disease (ADAD)

Changes in intranetwork resting-state functional connectivity magnetic resonance imaging (rs-fcMRI) composite scores for participants with ADAD and participants with LOAD as a function of the Clinical Dementia Rating (CDR) Scale. For both ADAD and LOAD, a stepwise loss of functional connectivity was seen for most RSNs with an increasing CDR. Whiskers extend to 1.5 × interquartile range. M− indicates mutation negative and M+, mutation positive.aP < .05.bP < .005.cP < .001.

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Figure 3.
Similarities Between Resting State Network (RSN) Changes in Late-Onset Alzheimer Disease (LOAD) and Autosomal Dominant Alzheimer Disease (ADAD)

Changes in internetwork composite scores for participants with ADAD and participants with LOAD as a function of Clinical Dementia Rating (CDR) status. A loss of between-network functional connectivity was seen for the default mode network–dorsal attention network and default mode network–sensorimotor network with an increasing CDR, although for executive control network–sensorimotor network, this pattern was only present in LOAD. Whiskers extend to 1.5 × interquartile range. M− indicates mutation negative and M+, mutation positive.aP < .05.bP < .005.cP < .001.

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Figure 4.
Estimated Years from Symptom Onset Modulates Within Resting State Network Functional Connectivity in Autosomal Dominant Alzheimer Disease

Intranetwork functional connectivity (and standard error bands) as a function of estimated years from symptom onset for all mutation positive (M+) and mutation negative (M−) individuals with autosomal dominant Alzheimer disease.

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Figure 5.
Estimated Years from Symptom Onset Modulates Between Resting State Network Functional Connectivity in Autosomal Dominant Alzheimer Disease

Internetwork functional connectivity (and standard error bands) as a function of estimated years from symptom onset for all mutation positive (M+) and mutation negative (M–) participants with autosomal dominant Alzheimer disease.

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Intranetwork and Internetwork Default Mode Functional Connectivity

A fitted model predicts that whole-brain intranetwork (warm colors) and internetwork (cool colors) default mode functional connectivity declines with estimated years from symptom onset in M+ individuals (n = 57) and begins to disappear approximately at the anticipated age of symptom onset.

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