Literature DB >> 25311276

Disentangling normal aging from Alzheimer's disease in structural magnetic resonance images.

Marco Lorenzi1, Xavier Pennec2, Giovanni B Frisoni3, Nicholas Ayache2.   

Abstract

The morphology observed in the brains of patients affected by Alzheimer's disease (AD) is a combination of different biological processes, such as normal aging and the pathological matter loss specific to AD. The ability to differentiate between these biological factors is fundamental to reliably evaluate pathological AD-related structural changes, especially in the earliest phase of the disease, at prodromal and preclinical stages. Here we propose a method based on non-linear image registration to estimate and analyze from observed brain morphologies the relative contributions from aging and pathology. In particular, we first define a longitudinal model of the brain's normal aging process from serial T1-weight magnetic resonance imaging scans of 65 healthy participants. The longitudinal model is then used as a reference for the cross-sectional analysis. Given a new brain image, we then estimate its anatomical age relative to the aging model; this is defined as a morphological age shift with respect to the average age of the healthy population at baseline. Finally, we define the specific morphological process as the remainder of the observed anatomy after the removal of the estimated normal aging process. Experimental results from 105 healthy participants, 110 subjects with mild cognitive impairment (MCI), 86 with MCI converted to AD, and 134 AD patients provide a novel description of the anatomical changes observed across the AD time span: normal aging, normal aging at risk, conversion to MCI, and the latest stages of AD. More advanced AD stages are associated with an increased morphological age shift in the brain and with strong disease-specific morphological changes affecting mainly ventricles, temporal poles, the entorhinal cortex, and hippocampi. Our model shows that AD is characterized by localized disease-specific brain changes as well as by an accelerated global aging process. This method may thus represent a more precise instrument to identify potential clinical outcomes in clinical trials for disease modifying drugs.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  CSF Abeta42; Deformation based morphometry; Healthy aging; Longitudinal atrophy; Nonlinear registration

Mesh:

Year:  2014        PMID: 25311276     DOI: 10.1016/j.neurobiolaging.2014.07.046

Source DB:  PubMed          Journal:  Neurobiol Aging        ISSN: 0197-4580            Impact factor:   4.673


  22 in total

1.  Simulating the outcome of amyloid treatments in Alzheimer's disease from imaging and clinical data.

Authors:  Clément Abi Nader; Nicholas Ayache; Giovanni B Frisoni; Philippe Robert; Marco Lorenzi
Journal:  Brain Commun       Date:  2021-04-28

Review 2.  Membrane Aging as the Real Culprit of Alzheimer's Disease: Modification of a Hypothesis.

Authors:  Qiujian Yu; Chunjiu Zhong
Journal:  Neurosci Bull       Date:  2017-11-24       Impact factor: 5.203

3.  Disentangling Normal Aging From Severity of Disease via Weak Supervision on Longitudinal MRI.

Authors:  Jiahong Ouyang; Qingyu Zhao; Ehsan Adeli; Greg Zaharchuk; Kilian M Pohl
Journal:  IEEE Trans Med Imaging       Date:  2022-09-30       Impact factor: 11.037

4.  Simultaneous Longitudinal Registration with Group-Wise Similarity Prior.

Authors:  Greg M Fleishman; Boris A Gutman; P Thomas Fletcher; Paul M Thompson
Journal:  Inf Process Med Imaging       Date:  2015

5.  Subject-Specific Longitudinal Shape Analysis by Coupling Spatiotemporal Shape Modeling with Medial Analysis.

Authors:  Sungmin Hong; James Fishbaugh; Morteza Rezanejad; Kaleem Siddiqi; Hans Johnson; Jane Paulsen; Eun Young Kim; Guido Gerig
Journal:  Proc SPIE Int Soc Opt Eng       Date:  2017-04

6.  HIV infection and age effects on striatal structure are additive.

Authors:  Erin E O'Connor; Timothy Zeffiro; Oscar L Lopez; James T Becker; Thomas Zeffiro
Journal:  J Neurovirol       Date:  2019-04-26       Impact factor: 2.643

7.  Early diagnosis of Alzheimer's disease on ADNI data using novel longitudinal score based on functional principal component analysis.

Authors:  Haolun Shi; Da Ma; Yunlong Nie; Mirza Faisal Beg; Jian Pei; Jiguo Cao; The Alzheimer's Disease Neuroimaging Initiative
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-21

8.  In-depth insights into Alzheimer's disease by using explainable machine learning approach.

Authors:  Bojan Bogdanovic; Tome Eftimov; Monika Simjanoska
Journal:  Sci Rep       Date:  2022-04-20       Impact factor: 4.996

9.  Early neurone loss in Alzheimer's disease: cortical or subcortical?

Authors:  Thomas Arendt; Martina K Brückner; Markus Morawski; Carsten Jäger; Hermann-Josef Gertz
Journal:  Acta Neuropathol Commun       Date:  2015-02-10       Impact factor: 7.801

10.  Learning Biomarker Models for Progression Estimation of Alzheimer's Disease.

Authors:  Alexander Schmidt-Richberg; Christian Ledig; Ricardo Guerrero; Helena Molina-Abril; Alejandro Frangi; Daniel Rueckert
Journal:  PLoS One       Date:  2016-04-20       Impact factor: 3.240

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