Objective: The newly proposed National Institute on Aging-Alzheimer's Association (NIA-AA) criteria for mild cognitive impairment (MCI) due to Alzheimer disease (AD) suggest a combination of clinical features and biomarker measures, but their performance in the community is not known.
Methods: The Mayo Clinic Study of Aging (MCSA) is a population-based longitudinal study of nondemented subjects in Olmsted County, Minnesota. A sample of 154 MCI subjects from the MCSA was compared to a sample of 58 amnestic MCI subjects from the Alzheimer's Disease Neuroimaging Initiative 1 (ADNI-1) to assess the applicability of the criteria in both settings and to assess their outcomes.
Results: Fourteen percent of MCSA and 16% of ADNI-1 of subjects were biomarker negative. In addition, 14% of MCSA and 12% of ADNI-1 subjects had evidence for amyloid deposition only, whereas 43% of MCSA and 55% of ADNI-1 subjects had evidence for amyloid deposition plus neurodegeneration (magnetic resonance imaging atrophy, fluorodeoxyglucose positron emission tomography hypometabolism, or both). However, a considerable number of subjects had biomarkers inconsistent with the proposed AD model; for example, 29% of MCSA subjects and 17% of ADNI-1 subjects had evidence for neurodegeneration without amyloid deposition. These subjects may not be on an AD pathway. Neurodegeneration appears to be a key factor in predicting progression relative to amyloid deposition alone.
Interpretation: The NIA-AA criteria apply to most MCI subjects in both the community and clinical trials settings; however, a sizeable proportion of subjects had conflicting biomarkers, which may be very important and need to be explored.
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http://dx.doi.org/10.1002/ana.23931 | DOI Listing |
J Alzheimers Dis
November 2024
School of Biomedical Engineering, Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical university, Beijing, Beijing, China.
Background: Accurately differentiating stable mild cognitive impairment (sMCI) from progressive MCI (pMCI) is clinically relevant, and identification of pMCI is crucial for timely treatment before it evolves into Alzheimer's disease (AD).
Objective: To construct a convolutional neural network (CNN) model to differentiate pMCI from sMCI integrating features from structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images.
Methods: We proposed a multi-modal and multi-stage region of interest (ROI)-based fusion network (m2ROI-FN) CNN model to differentiate pMCI from sMCI, adopting a multi-stage fusion strategy to integrate deep semantic features and multiple morphological metrics derived from ROIs of sMRI and PET images.
IEEE J Biomed Health Inform
June 2024
Early diagnosis of Alzheimer's disease (AD) is crucial for its prevention, and hippocampal atrophy is a significant lesion for early diagnosis. The current DL-based AD diagnosis methods only focus on either AD classification or hippocampus segmentation independently, neglecting the correlation between the two tasks and lacking pathological interpretability. To address this issue, we propose a Reliable Hippo-guided Learning model for Alzheimer's Disease diagnosis (RLAD), which employs multi-task learning for AD classification as a main task supplemented by hippocampus segmentation.
View Article and Find Full Text PDFJ Prev Alzheimers Dis
March 2023
Michael J. Dolton, Roche Products Pty Limited, Sydney, NSW, Australia; Telephone: +612 9454 9000; Email:
Background: Progression in Alzheimer's disease manifests as changes in multiple biomarker, cognitive, and functional endpoints. Disease progression modeling can be used to integrate these multiple measures into a synthesized metric of where a patient lies within the disease spectrum, allowing for a more dynamic measure over the range of the disease.
Objectives: This study aimed to combine modeling techniques from psychometric research (e.
Front Aging Neurosci
April 2022
National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.
Alzheimer's disease (AD) is an age-related disease that affects a large proportion of the elderly. Currently, the neuroimaging techniques [e.g.
View Article and Find Full Text PDFFront Aging Neurosci
April 2022
Chongqing Key Laboratory of Big Data and Intelligent Computing, Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China.
Numerous artificial intelligence (AI) based approaches have been proposed for automatic Alzheimer's disease (AD) prediction with brain structural magnetic resonance imaging (sMRI). Previous studies extract features from the whole brain or individual slices separately, ignoring the properties of multi-view slices and feature complementarity. For this reason, we present a novel AD diagnosis model based on the multiview-slice attention and 3D convolution neural network (3D-CNN).
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