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Neuroanatomical and clinical factors predicting future cognitive impairment.
Imms, Phoebe; Chaudhari, Nikhil N; Chowdhury, Nahian F; Wang, Haoqing; Yu, Xiaokun; Amgalan, Anar; Irimia, Andrei.
Afiliación
  • Imms P; Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Chaudhari NN; Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Chowdhury NF; Department of Biomedical Engineering, Viterbi School of Engineering, Corwin D. Denney Research Center, University of Southern California, 1042 Downey Way, Los Angeles, CA, 90089, USA.
  • Wang H; Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Yu X; Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
  • Amgalan A; Computer Science Department, School of Engineering, Columbia University, Mailing Address: 500 West 120 Street, Room 450, New York, NY, MC040110027, USA.
  • Irimia A; Leonard Davis School of Gerontology, University of Southern California, 3715 McClintock Ave, Los Angeles, CA, 90089, USA.
Geroscience ; 2024 Aug 17.
Article en En | MEDLINE | ID: mdl-39153054
ABSTRACT
Identifying cognitively normal (CN) older adults who will convert to cognitive impairment (CI) due to Alzheimer's disease is crucial for early intervention. Clinical and neuroimaging measures were acquired from 301 CN adults who converted to CI within 15 years of baseline, and 294 who did not. Regional volumes and brain age measures were extracted from T1-weighted magnetic resonance images. Linear discriminant analysis compared non-converters' characteristics against those of short-, mid-, and long-term converters. Conversion was associated with clinical measures such as hearing impairment and self-reported memory decline. Converters' brain volumes were smaller than non-converters' across 48 frontal, temporal, and subcortical structures. Brain age measures of 12 structures were correlated with shorter times to conversion. Conversion prediction accuracy increased from 81.5% to 90.5% as time to conversion decreased. Proximity to CI conversion is foreshadowed by anatomic features of brain aging that enhance the accuracy of predicting conversion.
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Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Geroscience Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Idioma: En Revista: Geroscience Año: 2024 Tipo del documento: Article