% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@ARTICLE{Chhatwal:139950,
      author       = {Chhatwal, Jasmeer P and Schultz, Aaron P and Johnson, Keith
                      A and Hedden, Trey and Jaimes, Sehily and Benzinger, Tammie
                      L S and Jack, Clifford and Ances, Beau M and Ringman, John M
                      and Marcus, Daniel S and Ghetti, Bernardino and Farlow,
                      Martin R and Danek, Adrian and Levin, Johannes and Yakushev,
                      Igor and Laske, Christoph and Koeppe, Robert A and Galasko,
                      Douglas R and Xiong, Chengjie and Masters, Colin L and
                      Schofield, Peter R and Kinnunen, Kirsi M and Salloway,
                      Stephen and Martins, Ralph N and McDade, Eric and Cairns,
                      Nigel J and Buckles, Virginia D and Morris, John C and
                      Bateman, Randall and Sperling, Reisa A and Network,
                      Dominantly Inherited Alzheimer},
      title        = {{P}referential degradation of cognitive networks
                      differentiates {A}lzheimer's disease from ageing.},
      journal      = {Brain},
      volume       = {141},
      number       = {5},
      issn         = {0006-8950},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DZNE-2020-06272},
      pages        = {1486-1500},
      year         = {2018},
      abstract     = {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.},
      keywords     = {Adult / Aged / Aged, 80 and over / Aging / Alzheimer
                      Disease: complications / Alzheimer Disease: diagnostic
                      imaging / Alzheimer Disease: genetics / Aniline Compounds:
                      pharmacokinetics / Brain Mapping / Cognition Disorders:
                      diagnostic imaging / Cognition Disorders: etiology / Female
                      / Fluorodeoxyglucose F18: pharmacokinetics / Humans /
                      Magnetic Resonance Imaging / Male / Middle Aged / Models,
                      Neurological / Neural Pathways: diagnostic imaging / Neural
                      Pathways: drug effects / Positron-Emission Tomography /
                      Thiazoles: pharmacokinetics /
                      2-(4'-(methylamino)phenyl)-6-hydroxybenzothiazole (NLM
                      Chemicals) / Aniline Compounds (NLM Chemicals) / Thiazoles
                      (NLM Chemicals) / Fluorodeoxyglucose F18 (NLM Chemicals)},
      cin          = {U Clinical Researchers - München / Clinical Dementia
                      Research München / München Pre 2020 / AG Jucker},
      ddc          = {610},
      cid          = {I:(DE-2719)7000003 / I:(DE-2719)1111016 /
                      I:(DE-2719)6000016 / I:(DE-2719)1210001},
      pnm          = {342 - Disease Mechanisms and Model Systems (POF3-342) / 344
                      - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-342 / G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29522171},
      pmc          = {pmc:PMC5917745},
      doi          = {10.1093/brain/awy053},
      url          = {https://pub.dzne.de/record/139950},
}