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@ARTICLE{Habes:153378,
      author       = {Habes, Mohamad and Grothe, Michel J and Tunc, Birkan and
                      McMillan, Corey and Wolk, David A and Davatzikos, Christos},
      title        = {{D}isentangling {H}eterogeneity in {A}lzheimer's {D}isease
                      and {R}elated {D}ementias {U}sing {D}ata-{D}riven
                      {M}ethods.},
      journal      = {Biological psychiatry},
      volume       = {88},
      number       = {1},
      issn         = {0006-3223},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DZNE-2020-01375},
      pages        = {70 - 82},
      year         = {2020},
      abstract     = {Brain aging is a complex process that includes atrophy,
                      vascular injury, and a variety of age-associated
                      neurodegenerative pathologies, together determining an
                      individual's course of cognitive decline. While Alzheimer's
                      disease and related dementias contribute to the
                      heterogeneity of brain aging, these conditions themselves
                      are also heterogeneous in their clinical presentation,
                      progression, and pattern of neural injury. We reviewed
                      studies that leveraged data-driven approaches to examining
                      heterogeneity in Alzheimer's disease and related dementias,
                      with a principal focus on neuroimaging studies exploring
                      subtypes of regional neurodegeneration patterns. Over the
                      past decade, the steadily increasing wealth of clinical,
                      neuroimaging, and molecular biomarker information collected
                      within large-scale observational cohort studies has allowed
                      for a richer understanding of the variability of disease
                      expression within the aging and Alzheimer's disease and
                      related dementias continuum. Moreover, the availability of
                      these large-scale datasets has supported the development and
                      increasing application of clustering techniques for studying
                      disease heterogeneity in a data-driven manner. In
                      particular, data-driven studies have led to new discoveries
                      of previously unappreciated disease subtypes characterized
                      by distinct neuroimaging patterns of regional
                      neurodegeneration, which are paralleled by heterogeneous
                      profiles of pathological, clinical, and molecular biomarker
                      characteristics. Incorporating these findings into novel
                      frameworks for more differentiated disease stratification
                      holds great promise for improving individualized diagnosis
                      and prognosis of expected clinical progression, and provides
                      opportunities for development of precision medicine
                      approaches for therapeutic intervention. We conclude with an
                      account of the principal challenges associated with
                      data-driven heterogeneity analyses and outline avenues for
                      future developments in the field.},
      subtyp        = {Review Article},
      keywords     = {Alzheimer Disease: diagnostic imaging / Alzheimer Disease:
                      pathology / Atrophy: pathology / Brain: diagnostic imaging /
                      Brain: pathology / Cognitive Dysfunction: pathology / Humans
                      / Neuroimaging},
      cin          = {Rostock / Greifswald common / AG Teipel},
      ddc          = {610},
      cid          = {I:(DE-2719)6000017 / I:(DE-2719)1510100},
      pnm          = {344 - Clinical and Health Care Research (POF3-344)},
      pid          = {G:(DE-HGF)POF3-344},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32201044},
      pmc          = {pmc:PMC7305953},
      doi          = {10.1016/j.biopsych.2020.01.016},
      url          = {https://pub.dzne.de/record/153378},
}