RESUMO
Accurate quantification of amyloid positron emission tomography (PET) is essential for early detection of and intervention in Alzheimer's disease (AD) but there is still a lack of studies comparing the performance of various automated methods. This study compared the PET-only method and PET-and-MRI-based method with a pre-trained deep learning segmentation model. A large sample of 1180 participants in the Catholic Aging Brain Imaging (CABI) database was analyzed to calculate the regional standardized uptake value ratio (SUVR) using both methods. The logistic regression models were employed to assess the discriminability of amyloid-positive and negative groups through 10-fold cross-validation and area under the receiver operating characteristics (AUROC) metrics. The two methods showed a high correlation in calculating SUVRs but the PET-MRI method, incorporating MRI data for anatomical accuracy, demonstrated superior performance in predicting amyloid-positivity. The parietal, frontal, and cingulate importantly contributed to the prediction. The PET-MRI method with a pre-trained deep learning model approach provides an efficient and precise method for earlier diagnosis and intervention in the AD continuum.
Assuntos
Doença de Alzheimer , Encéfalo , Imageamento por Ressonância Magnética , Tomografia por Emissão de Pósitrons , Humanos , Tomografia por Emissão de Pósitrons/métodos , Imageamento por Ressonância Magnética/métodos , Feminino , Doença de Alzheimer/diagnóstico por imagem , Doença de Alzheimer/metabolismo , Masculino , Idoso , Encéfalo/diagnóstico por imagem , Encéfalo/metabolismo , Amiloide/metabolismo , Aprendizado Profundo , Idoso de 80 Anos ou mais , Pessoa de Meia-Idade , Curva ROCRESUMO
INTRODUCTION: Despite prior research on the association between sarcopenia and cognitive impairment in the elderly, a comprehensive model that integrates various brain pathologies is still lacking. METHODS: We used data from 528 non-demented older adults with or without sarcopenia in the Catholic Aging Brain Imaging (CABI) database, containing magnetic resonance imaging scans, positron emission tomography scans, and clinical data. We also measured three key components of sarcopenia: skeletal muscle index (SMI), hand grip strength (HGS), and the five times sit-to-stand test (5STS). RESULTS: All components of sarcopenia were significantly correlated with global cognitive function, but cortical thickness and amyloid-beta (Aß) retention had distinctive relationships with each measure. In the path model, brain atrophy resulting in cognitive impairment was mediated by Aß retention for SMI and periventricular white matter hyperintensity for HGS, but directly affected by the 5STS. DISCUSSION: Treatments targeting each sub-domain of sarcopenia should be considered to prevent cognitive decline. HIGHLIGHTS: We identified distinct impacts of three sarcopenia measures on brain structure and Aß. Muscle mass is mainly associated with Aß and has an influence on the brain atrophy. Muscle strength linked with periventricular WMH and brain atrophy. Muscle function associated with cortical thinning in specific brain regions. Interventions on sarcopenia may be important to ease cognitive decline in the elderly.