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Brain. 2018 May 1;141(5):1486-1500. doi: 10.1093/brain/awy053.

Preferential degradation of cognitive networks differentiates Alzheimer's disease from ageing.

Author information

Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA.
Department of Neurology, Center for Alzheimer Research and Treatment, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Department of Radiology, Section of Neuroradiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO 63110, USA.
Department of Radiology, Mayo Clinic, Rochester, MN 55905, USA.
Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA.
Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA.
Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Department of Neurology, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Department of Neurology, Ludwig-Maximilians Universität, Postbox 701260, 81377 Munich, Germany.
German Center for Neurodegenerative Diseases (DZNE), Munich, Germany.
German Center for Neurodegenerative Diseases (DZNE), 72076 Tuebingen, Germany.
Department of Nuclear Medicine and NeuroImaging Center (TUM-NIC) at Technische Universität München, 81675 Munich, Germany.
Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tuebingen, Tuebingen, 72076, Germany.
Department of Radiology, University of Michigan, Ann Arbor, MI 48109, USA.
Department of Neurology and Perlman Neurology Clinic, University of California at San Diego, La Jolla, CA 92093, USA.
Division of Biostatistics, Washington University School of Medicine, St. Louis, MO 63110, USA.
Florey Institute of Neuroscience, University of Melbourne, Parkville, Victoria 3010, Australia.
Neuroscience Research Australia, Sydney NSW 2031, Australia.
School of Medical Sciences, University of New South Wales, Sydney NSW 2052, Australia.
Dementia Research Centre, Department of Neurodegenerative Disease, UCL Institute of Neurology, University College London, London WC1N 3BG, UK.
Butler Hospital, Providence, RI 02906, USA.
Alpert Medical School, Brown University, Providence, RI 02903 USA.
Centre of Excellence for Alzheimer's Disease Research, School of Medical Sciences, Edith Cowan University, Joondalup, WA 6027, Australia.


Converging evidence from structural, metabolic and functional connectivity MRI suggests that neurodegenerative diseases, such as Alzheimer's disease, target specific neural networks. However, age-related network changes commonly co-occur with neuropathological cascades, limiting efforts to disentangle disease-specific alterations in network function from those associated with normal ageing. Here we elucidate the differential effects of ageing and Alzheimer's disease pathology through simultaneous analyses of two functional connectivity MRI datasets: (i) young participants harbouring highly-penetrant mutations leading to autosomal-dominant Alzheimer's disease from the Dominantly Inherited Alzheimer's Network (DIAN), an Alzheimer's disease cohort in which age-related comorbidities are minimal and likelihood of progression along an Alzheimer's disease trajectory is extremely high; and (ii) young and elderly participants from the Harvard Aging Brain Study, a cohort in which imaging biomarkers of amyloid burden and neurodegeneration can be used to disambiguate ageing alone from preclinical Alzheimer's disease. Consonant with prior reports, we observed the preferential degradation of cognitive (especially the default and dorsal attention networks) over motor and sensory networks in early autosomal-dominant Alzheimer's disease, and found that this distinctive degradation pattern was magnified in more advanced stages of disease. Importantly, a nascent form of the pattern observed across the autosomal-dominant Alzheimer's disease spectrum was also detectable in clinically normal elderly with clear biomarker evidence of Alzheimer's disease pathology (preclinical Alzheimer's disease). At the more granular level of individual connections between node pairs, we observed that connections within cognitive networks were preferentially targeted in Alzheimer's disease (with between network connections relatively spared), and that connections between positively coupled nodes (correlations) were preferentially degraded as compared to connections between negatively coupled nodes (anti-correlations). In contrast, ageing in the absence of Alzheimer's disease biomarkers was characterized by a far less network-specific degradation across cognitive and sensory networks, of between- and within-network connections, and of connections between positively and negatively coupled nodes. We go on to demonstrate that formalizing the differential patterns of network degradation in ageing and Alzheimer's disease may have the practical benefit of yielding connectivity measurements that highlight early Alzheimer's disease-related connectivity changes over those due to age-related processes. Together, the contrasting patterns of connectivity in Alzheimer's disease and ageing add to prior work arguing against Alzheimer's disease as a form of accelerated ageing, and suggest multi-network composite functional connectivity MRI metrics may be useful in the detection of early Alzheimer's disease-specific alterations co-occurring with age-related connectivity changes. More broadly, our findings are consistent with a specific pattern of network degradation associated with the spreading of Alzheimer's disease pathology within targeted neural networks.

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