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Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease

Zhang, Xiuming ; Mormino, Elizabeth C. ; Sun, Nanbo ; Sperling, Reisa A. ; Sabuncu, Mert R. ; Yeo, B. T. Thomas

Proceedings of the National Academy of Sciences - PNAS, 2016-10, Vol.113 (42), p.E6535-E6544 [Periódico revisado por pares]

United States: National Academy of Sciences

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  • Título:
    Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease
  • Autor: Zhang, Xiuming ; Mormino, Elizabeth C. ; Sun, Nanbo ; Sperling, Reisa A. ; Sabuncu, Mert R. ; Yeo, B. T. Thomas
  • Assuntos: Alzheimer Disease - diagnostic imaging ; Alzheimer Disease - pathology ; Alzheimer Disease - physiopathology ; Alzheimer Disease - psychology ; Alzheimer's disease ; Atrophy ; Bayes Theorem ; Bayesian analysis ; Biological Sciences ; Brain ; Brain - pathology ; Brain - physiopathology ; Cognition & reasoning ; Cognitive Dysfunction ; Dementia - etiology ; Dementia - pathology ; Dementia - psychology ; Female ; Humans ; Magnetic Resonance Imaging ; Male ; Memory ; PNAS Plus ; Risk Factors
  • É parte de: Proceedings of the National Academy of Sciences - PNAS, 2016-10, Vol.113 (42), p.E6535-E6544
  • Notas: ObjectType-Article-1
    SourceType-Scholarly Journals-1
    ObjectType-Feature-2
    content type line 23
    Edited by James L. McClelland, Stanford University, Stanford, CA, and approved August 23, 2016 (received for review July 14, 2016)
    Author contributions: X.Z., E.C.M., R.A.S., M.R.S., and B.T.T.Y. designed research; X.Z. and N.S. performed research; X.Z., M.R.S., and A.D.N.I. contributed new reagents/analytic tools; A.D.N.I. contributed to design and implementation of the Alzheimer's Disease Neuroimaging Initiative; X.Z. and N.S. analyzed data; and X.Z., E.C.M., M.R.S., and B.T.T.Y. wrote the paper.
  • Descrição: We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxel-wise structural MRIs of late-onset Alzheimer’s disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid–positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.
  • Editor: United States: National Academy of Sciences
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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