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@ARTICLE{Doering:281527,
      author       = {Doering, Elena and Hoenig, Merle C and Cole, James H and
                      Drzezga, Alexander},
      title        = {{W}hen {A}ge {I}s {M}ore {T}han a {N}umber: {A}cceleration
                      of {B}rain {A}ging in {N}eurodegenerative {D}iseases.},
      journal      = {Journal of nuclear medicine},
      volume       = {66},
      number       = {10},
      issn         = {0097-9058},
      address      = {New York, NY},
      publisher    = {Soc.},
      reportid     = {DZNE-2025-01145},
      pages        = {1516 - 1521},
      year         = {2025},
      abstract     = {Aging of the brain is characterized by deleterious
                      processes at various levels including cellular/molecular and
                      structural/functional changes. Many of these processes can
                      be assessed in vivo by means of modern neuroimaging
                      procedures, allowing the quantification of brain age in
                      different modalities. Brain age can be measured by suitable
                      machine learning strategies. The deviation (in both
                      directions) between a person's measured brain age and
                      chronologic age is referred to as the brain age gap (BAG).
                      Although brain age, as defined by these methods, generally
                      is related to the chronologic age of a person, this
                      relationship is not always parallel and can also vary
                      significantly between individuals. Importantly, whereas
                      neurodegenerative disorders are not equivalent to
                      accelerated brain aging, they may induce brain changes that
                      resemble those of older adults, which can be captured by
                      brain age models. Inversely, healthy brain aging may involve
                      a resistance or delay of the onset of neurodegenerative
                      pathologies in the brain. This continuing education article
                      elaborates how the BAG can be computed and explores how
                      BAGs, derived from diverse neuroimaging modalities, offer
                      unique insights into the phenotypes of age-related
                      neurodegenerative diseases. Structural BAGs from T1-weighted
                      MRI have shown promise as phenotypic biomarkers for
                      monitoring neurodegenerative disease progression especially
                      in Alzheimer disease. Additionally, metabolic and molecular
                      BAGs from molecular imaging, functional BAGs from functional
                      MRI, and microstructural BAGs from diffusion MRI, although
                      researched considerably less, each may provide distinct
                      perspectives on particular brain aging processes and their
                      deviations from healthy aging. We suggest that BAG
                      estimation, when based on the appropriate modality, could
                      potentially be useful for disease monitoring and offer
                      interesting insights concerning the impact of therapeutic
                      interventions.},
      subtyp        = {Review Article},
      keywords     = {Humans / Neurodegenerative Diseases: diagnostic imaging /
                      Neurodegenerative Diseases: physiopathology /
                      Neurodegenerative Diseases: pathology / Aging: pathology /
                      Brain: diagnostic imaging / Brain: pathology / Brain:
                      physiopathology / Neuroimaging: methods / Magnetic Resonance
                      Imaging / Machine Learning / brain age (Other) / dementia
                      (Other) / machine learning (Other) / neurodegeneration
                      (Other) / neuroimaging (Other)},
      cin          = {AG Boecker},
      ddc          = {610},
      cid          = {I:(DE-2719)1011202},
      pnm          = {353 - Clinical and Health Care Research (POF4-353)},
      pid          = {G:(DE-HGF)POF4-353},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:40841153},
      pmc          = {pmc:PMC12487868},
      doi          = {10.2967/jnumed.125.270325},
      url          = {https://pub.dzne.de/record/281527},
}