P1-232: Longitudinal progression of Alzheimer's disease-like patterns of brain atrophy in a normal elderly cohort and in mild cognitive impairment: A high-dimensional pattern classification study

AbstractThe entorhinal-hippocampal circuit is a strategic hub for memory but also the first site to be affected in the Alzheimer’s Disease (AD)-related pathology. We investigated MRI patterns of brain atrophy and functional connectivity in a study cohort obtained from the Alzheimer’s Disease Neuroimaging Initiative database including healthy control (HC), Mild Cognitive Impairment (MCI), and AD subjects. MCI individuals were clinically evaluated 24 months after the MRI scan, and the group further divided into a subset of subjects who either did (c-MCI) or did not (nc-MCI) convert to AD. Compared to HC subjects, AD patients exhibited a collapse of long-range connectivity from the hippocampus and entorhinal cortex, pronounced cortical/sub-cortical atrophy, and a dramatic decline in cognitive performances. c-MCI patients showed entorhinal and hippocampal hypo-connectivity, no signs of cortical thinning but evidence of right hippocampus atrophy. On the contrary, nc-MCI patients showed lack of brain atrophy, largely preserved cognitive functions, hippocampal and entorhinal hyper-connectivity with selected neocortical/sub-cortical regions mainly involved in memory processing and brain meta-stability. This hyper-connectivity can represent an early compensatory strategy to overcome the progression of cognitive impairment. This functional signature can also be employed for the diagnosis of c-MCI subjects.

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An MRI-Based Semiquantitative Index for the Evaluation of Brain Atrophy and Lesions in Alzheimer’s Disease, Mild Cognitive Impairment and Normal Aging

Dementia and Geriatric Cognitive Disorders ◽

10.1159/000319537 ◽

2010 ◽

Vol 30 (2) ◽

pp. 121-130 ◽

Cited By ~ 14

Author(s):

Wei Chen ◽

Xiaowei Song ◽

Yunting Zhang ◽

Sultan Darvesh ◽

Ningnannan Zhang ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Atrophy ◽

Normal Aging

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Comparing regional brain atrophy in mild cognitive impairment and Alzheimer’s disease

Neurodegenerative Disease Management ◽

10.2217/nmt.13.49 ◽

2013 ◽

Vol 3 (5) ◽

pp. 417-426

Author(s):

Marina Weiler ◽

Fernando Cendes ◽

Marcio LF Balthazar

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Atrophy ◽

Regional Brain ◽

Regional Brain Atrophy

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Gaussian Discriminant Analysis for Optimal Delineation of Mild Cognitive Impairment in Alzheimer’s Disease

International Journal of Neural Systems ◽

10.1142/s012906571850017x ◽

2018 ◽

Vol 28 (08) ◽

pp. 1850017 ◽

Cited By ~ 8

Author(s):

Chen Fang ◽

Chunfei Li ◽

Mercedes Cabrerizo ◽

Armando Barreto ◽

Jean Andrian ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Discriminant Analysis ◽

Classification Problem ◽

Classification Performance ◽

High Dimensional ◽

Support Vector ◽

Brain Hemispheres

Over the past few years, several approaches have been proposed to assist in the early diagnosis of Alzheimer’s disease (AD) and its prodromal stage of mild cognitive impairment (MCI). Using multimodal biomarkers for this high-dimensional classification problem, the widely used algorithms include Support Vector Machines (SVM), Sparse Representation-based classification (SRC), Deep Belief Networks (DBN) and Random Forest (RF). These widely used algorithms continue to yield unsatisfactory performance for delineating the MCI participants from the cognitively normal control (CN) group. A novel Gaussian discriminant analysis-based algorithm is thus introduced to achieve a more effective and accurate classification performance than the aforementioned state-of-the-art algorithms. This study makes use of magnetic resonance imaging (MRI) data uniquely as input to two separate high-dimensional decision spaces that reflect the structural measures of the two brain hemispheres. The data used include 190 CN, 305 MCI and 133 AD subjects as part of the AD Big Data DREAM Challenge #1. Using 80% data for a 10-fold cross-validation, the proposed algorithm achieved an average F1 score of 95.89% and an accuracy of 96.54% for discriminating AD from CN; and more importantly, an average F1 score of 92.08% and an accuracy of 90.26% for discriminating MCI from CN. Then, a true test was implemented on the remaining 20% held-out test data. For discriminating MCI from CN, an accuracy of 80.61%, a sensitivity of 81.97% and a specificity of 78.38% were obtained. These results show significant improvement over existing algorithms for discriminating the subtle differences between MCI participants and the CN group.

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Estimation of the risk of conversion of mild cognitive impairment of Alzheimer type to Alzheimer's disease in a south Brazilian population-based elderly cohort: the PALA study

International Psychogeriatrics ◽

10.1017/s1041610211002043 ◽

2011 ◽

Vol 24 (4) ◽

pp. 674-681 ◽

Cited By ~ 7

Author(s):

Cláudia Godinho ◽

Ana Luiza Camozzato ◽

Diego Onyszko ◽

Márcia Lorena Chaves

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Population Based ◽

Alzheimer Type ◽

Risk Of Progression ◽

Elderly Cohort ◽

The Impact ◽

Cognitively Intact

ABSTRACTBackground: Higher mild cognitive impairment (MCI) prognostic variability has been related to sample characteristics (community-based or specialized clinic) and to diverse operationalization criteria. The aim of the study was to evaluate the trajectory of MCI of Alzheimer type in a population-based elderly cohort in Southern Brazil. We also estimated the risk for the development of probable Alzheimer's disease (AD) in comparison with healthy subjects.Methods: Data were derived from a population-based cohort (the PALA study). MCI outcomes were sub-classified into three categories: conversion, stabilization, and reconversion. The risk of progression to dementia was compared between MCI and normal participants. The analysis was based on 21 MCI subjects and 220 cognitively intact participants (N = 241).Results: Of the 21 MCI subjects, 38% developed dementia, 24% remained stable and 38% improved. The MCI annual conversion rate to AD was 8.5%. MCI was associated with significantly higher risk of conversion to AD (HR = 49.83, p = 0.004), after adjustment for age, education, sex and Mini-Mental State Examination score.Conclusions: Independent of the heterogeneity of the outcomes, MCI of the Alzheimer type participants showed significantly higher risk of developing probable AD, demonstrating the impact of the use of these MCI criteria that emphasize long-term episodic memory impairment.

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3D characterization of brain atrophy in Alzheimer's disease and mild cognitive impairment using tensor-based morphometry

NeuroImage ◽

10.1016/j.neuroimage.2008.02.010 ◽

2008 ◽

Vol 41 (1) ◽

pp. 19-34 ◽

Cited By ~ 117

Author(s):

X HUA ◽

A LEOW ◽

S LEE ◽

A KLUNDER ◽

A TOGA ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Brain Atrophy

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Automated Brain Volumetric Program Measuring Regional Brain Atrophy in Diagnosis of Mild Cognitive Impairment and Alzheimer’s Disease Dementia

10.21203/rs.3.rs-640508/v1 ◽

2021 ◽

Author(s):

Dong-Woo Ryu ◽

Yun Jeong Hong ◽

Jung Hee Cho ◽

Kichang Kwak ◽

Jong-Min Lee ◽

...

Keyword(s):

Alzheimer’S Disease ◽

Alzheimer's Disease ◽

Cognitive Impairment ◽

Mild Cognitive Impairment ◽

Diagnostic Accuracy ◽

Predictive Model ◽

Brain Atrophy ◽

Brain Volume ◽

Volume Ratio ◽

Rater Reliability

Abstract A quantitative analysis of brain volume can assist in diagnosis of Alzheimer’s disease (AD) ususally accompannied by brain atrophy. With an automated analysis program Quick Brain Volumetry (QBraVo) developed for volumetric measurements, we measured regional volumes and ratios to evaluate their performance in discriminating AD dementia (ADD) and mild cognitive impairment (MCI) patients from normal controls (NC). Validation of QBraVo was based on intra-rater and inter-rater reliability with a manual measurement. The regional volumes and ratios to total intracranial volume (TIV) and to total brain volume (TBV) or total cerebrospinal fluid volume (TCV) were compared among subjects. The regional volume to total cerebellar volume ratio named Standardized Atrophy Volume Ratio (SAVR) was calculated to compare brain atrophy. Diagnostic performances to distinguish among NC, MCI, and ADD were compared between MMSE, SAVR, and the predictive model. In total, 56 NCs, 44 MCI, and 45 ADD patients were enrolled. The average run time of QBraVo was 5 minutes 36 seconds. Intra-rater reliability was 0.999. Inter-rater reliability were high for TBV, TCV, and TIV (R = 0.97, 0.89 and 0.93, respectively). The medial temporal SAVR showed the highest performance for discriminating ADD from NC (AUC = 0.808, diagnostic accuracy = 80.2%). The predictive model using both MMSE and medial temporal SAVR improved the diagnostic performance for MCI in NC (AUC = 0.844, diagnostic accuracy = 79%). Our results demonstrated QBraVo as a fast and accurate method to measure brain volume. The regional volume calculated as SAVR could help to diagnose ADD and MCI and increase diagnostic accuracy for MCI.

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