Hippocampal atrophy is one of the main hallmarks of Alzheimer's disease (AD). However, there is still controversy about whether this sign is a robust finding during the early stages of the disease, such as in mild cognitive impairment (MCI) and subjective cognitive decline (SCD). Considering this background, we proposed a new marker for assessing hippocampal atrophy: the local surface roughness (LSR). We tested this marker in a sample of 307 subjects (normal control (NC) = 70, SCD = 87, MCI = 137, AD = 13). In addition, 97 patients with MCI were followed-up over a 3-year period and classified as stable MCI (sMCI) (n = 61) or progressive MCI (pMCI) (n = 36). We did not find significant differences using traditional markers, such as normalized hippocampal volumes (NHV), between the NC and SCD groups or between the sMCI and pMCI groups. However, with LSR we found significant differences between the sMCI and pMCI groups and a better ability to discriminate between NC and SCD. The classification accuracy of the LSR for NC and SCD was 68.2%, while NHV had a 57.2% accuracy. In addition, the classification accuracy of the LSR for sMCI and pMCI was 74.3%, and NHV had a 68.3% accuracy. Cox proportional hazards models adjusted for age, sex, and education were used to estimate the relative hazard of progression from MCI to AD based on hippocampal markers and conversion times. The LSR marker showed better prediction of conversion to AD than NHV. These results suggest the relevance of considering the LSR as a new hippocampal marker for the AD continuum.
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http://dx.doi.org/10.1002/hbm.24478 | DOI Listing |
Alzheimers Res Ther
January 2025
Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University, No. 155, Sec. 2, Linong St., Beitou, Taipei, 112304, Taiwan.
Background: Effective treatment for Alzheimer's disease (AD) remains an unmet need. Thus, identifying patients with mild cognitive impairment (MCI) who are at high-risk of progressing to AD is crucial for early intervention.
Methods: Blood-based transcriptomics analyses were performed using a longitudinal study cohort to compare progressive MCI (P-MCI, n = 28), stable MCI (S-MCI, n = 39), and AD patients (n = 49).
Sci Rep
December 2024
Department of Technology Management for Innovation, University of Tokyo, Tokyo, 113-8654, Japan.
Alzheimer's disease (AD) is a neurodegenerative disorder. It causes progressive degeneration of the nervous system, affecting the cognitive ability of the human brain. Over the past two decades, neuroimaging data from Magnetic Resonance Imaging (MRI) scans has been increasingly used in the study of brain pathology related to the birth and growth of AD.
View Article and Find Full Text PDFFront Aging Neurosci
November 2024
Department of Radiology, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
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.
Eur Radiol
October 2024
Department of Nuclear Medicine, University Hospital Bern, Bern, Switzerland.
Objectives: In this study, we propose an interpretable deep learning radiomics (IDLR) model based on [F]FDG PET images to diagnose the clinical spectrum of Alzheimer's disease (AD) and predict the progression from mild cognitive impairment (MCI) to AD.
Methods: This multicentre study included 1962 subjects from two ethnically diverse, independent cohorts (a Caucasian cohort from ADNI and an Asian cohort merged from two hospitals in China). The IDLR model involved feature extraction, feature selection, and classification/prediction.
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