Dataset Information

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

ABSTRACT:

Introduction

Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).

Methods

Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signatures to cognition and molecular biomarkers of AD.

Results

WMHs were associated with advanced brain aging, AD-like atrophy, poorer cognition, and AD neuropathology in mild cognitive impairment (MCI)/AD and cognitively normal (CN) subjects. High WMH volume was associated with brain aging and cognitive decline occurring in an ≈10-year period in CN subjects. WMHs were associated with doubling the likelihood of amyloid beta (Aβ) positivity after age 65. Brain aging, AD-like atrophy, and WMHs were better predictors of cognition than chronological age in MCI/AD.

Discussion

A Brain Chart quantifying brain-aging trajectories was established, enabling the systematic evaluation of individuals' brain-aging patterns relative to this large consortium.

SUBMITTER: Habes M

PROVIDER: S-EPMC7923395 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature

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Publications

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Habes Mohamad M Pomponio Raymond R Shou Haochang H Doshi Jimit J Mamourian Elizabeth E Erus Guray G Nasrallah Ilya I Launer Lenore J LJ Rashid Tanweer T Bilgel Murat M Fan Yong Y Toledo Jon B JB Yaffe Kristine K Sotiras Aristeidis A Srinivasan Dhivya D Espeland Mark M Masters Colin C Maruff Paul P Fripp Jurgen J Völzk Henry H Johnson Sterling C SC Morris John C JC Albert Marilyn S MS Miller Michael I MI Bryan R Nick RN Grabe Hans J HJ Resnick Susan M SM Wolk David A DA Davatzikos Christos C

Alzheimer's & dementia : the journal of the Alzheimer's Association 20200913 1

<h4>Introduction</h4>Relationships between brain atrophy patterns of typical aging and Alzheimer's disease (AD), white matter disease, cognition, and AD neuropathology were investigated via machine learning in a large harmonized magnetic resonance imaging database (11 studies; 10,216 subjects).<h4>Methods</h4>Three brain signatures were calculated: Brain-age, AD-like neurodegeneration, and white matter hyperintensities (WMHs). Brain Charts measured and displayed the relationships of these signat ...[more]

PMID: 32920988

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Dataset Information

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Introduction

Methods

Results

Discussion

Publications

The Brain Chart of Aging: Machine-learning analytics reveals links between brain aging, white matter disease, amyloid burden, and cognition in the iSTAGING consortium of 10,216 harmonized MR scans.

Similar Datasets

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