Neuroimaging-derived brain-age: an ageing biomarker?

Cole, James H.

doi:10.18632/aging.101286

Cited by 24 publications

(20 citation statements)

References 7 publications

(7 reference statements)

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“…Using magnetic resonance images (MRI), chronological age can be predicted with mean absolute deviation of 5 years in healthy participants (Franke et al, 2010). Various diseases, including Alzheimer's disease, schizophrenia, epilepsy, traumatic brain injury, bipolar disorder, major depression, cognitive impairment, diabetes mellitus, and HIV, are associated with older brain age than chronological age Cole, 2017;Cole and Franke, 2017;Cole et al, 2016;Franke et al, 2012;Franke et al, 2010). Alongside these biomarkers, EEG-based brain age is a sensible potential complement, which has several advantages: (1) EEG-based brain age could reflect functional changes rather than anatomical changes; (2) EEG is more economical, participant friendly, and in principle could be measured by home-based devices; (3) EEGbased brain age could facilitate within-participant repeated measures to assess the effectiveness of interventions, such as medications (Roehrs and Roth, 2010) or brain stimulation (Tasali et al, 2008) that aim to preserve or improve brain function.…”

Section: Introductionmentioning

confidence: 99%

Brain age from the electroencephalogram of sleep

Sun

Paixao

Oliva

et al. 2019

Neurobiology of Aging

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The human electroencephalogram (EEG) of sleep undergoes profound changes with age. These changes can be conceptualized as "brain age", which can be compared to an age norm to reflect the deviation from normal aging process. Here, we develop an interpretable machine learning model to predict brain age based on two large sleep EEG datasets: the Massachusetts General Hospital sleep lab dataset (MGH, N = 2,621) covering age 18 to 80; and the Sleep Hearth Health Study (SHHS, N = 3,520) covering age 40 to 80. The model obtains a mean absolute deviation of 8.1 years between brain age and chronological age in the healthy participants in the MGH dataset. As validation, we analyze a subset of SHHS containing longitudinal EEGs 5 years apart, which shows a 5.5 years difference in brain age. Participants with neurological and psychiatric diseases, as well as diabetes and hypertension medications show an older brain age compared to chronological age. The findings raise the prospect of using sleep EEG as a biomarker for healthy brain aging.In total, we identify 2,621 EEGs where 189 of them have neurological or psychiatric diseases. Table 1 provides summary statistics for the dataset.

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Section: Introductionmentioning

confidence: 99%

Brain age from the electroencephalogram of sleep

Sun

Paixao

Oliva

et al. 2019

Neurobiology of Aging

View full text Add to dashboard Buy / Rent full text

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“…Despite recent progress in the automated assessment of AD, the development of an automatic approach that addresses the uncertainty of diagnosing AD is still challenging and requires further data. Previous studies have shown that neuroimaging data along with advanced pattern recognition techniques can be used for predicting clinical scores (Moradi, Hallikainen, Hänninen, Tohka, & Neuroimaging, 2017; Shen et al., 2011; Stonnington et al., 2010) as well as chronological age (Cole, 2017). …”

Section: Introductionmentioning

confidence: 99%

The association between “Brain-Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

Beheshti¹,

Maikusa²,

Matsuda³

2018

Brain Behav

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IntroductionWe present the Brain‐Age Score (BAS) as a magnetic resonance imaging (MRI)‐based index for Alzheimer's disease (AD). We developed a fully automated framework for estimating the BAS in healthy controls (HCs) and individuals with mild cognitive impairment (MCI) or AD, using MRI scans.MethodsWe trained the proposed framework using 385 HCs from the IXI and OASIS datasets and evaluated 146 HCs, 102 stable‐MCI (sMCI), 112 progressive‐MCI (pMCI), and 147 AD patients from the J‐ADNI dataset. We used a correlation test to determine the association between the BAS and four traditional screening tools of AD: the Mini‐Mental State Examination (MMSE), Clinical Dementia Ratio (CDR), Alzheimer's Disease Assessment Score (ADAS), and Functional Assessment Questionnaire (FAQ). Furthermore, we assessed the association between BAS and anatomical MRI measurements: the normalized gray matter (nGM), normalized white matter (nWM), normalized cerebrospinal fluid (nCSF), mean cortical thickness as well as hippocampus volume.ResultsThe correlation results demonstrated that the BAS is in line with traditional screening tools of AD (i.e., the MMSE, CDR, ADAS, and FAQ scores) as well as anatomical MRI measurements (i.e., nGM, nCSF, mean cortical thickness, and hippocampus volume).DiscussionThe BAS may be useful for diagnosing the brain atrophy level and can be a reliable automated index for clinical applications and neuropsychological screening tools.

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“…Similar to polygenic risk scores (Dudbridge, 2013), the prediction R 2 of grey-matter BLUP scores increases with the training sample size and is capped by the association R 2 with all vertices (Figure 3). Future application of the grey-matter scores include studying correlates of brain age (Cole, 2017; Cole et al, 2017; Liem et al, 2017), body size and substance use, especially in samples where this information was not collected.…”

Section: Discussionmentioning

confidence: 99%

Widespread associations between grey matter structure and the human phenome

Couvy-Duchesne

Strike

Zhang

et al. 2019

Preprint

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The recent availability of large-scale neuroimaging cohorts (here the UK Biobank [UKB] and the Human Connectome Project [HCP]) facilitates deeper characterisation of the relationship between phenotypic and brain architecture variation in humans. We tested the association between 654,386 vertex-wise measures of cortical and subcortical morphology (from T1w and T2w MRI images) and behavioural, cognitive, psychiatric and lifestyle data. We found a significant association of grey-matter structure with 58 out of 167 UKB phenotypes spanning substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains (UKB discovery sample: N=9,888). Twenty-three of the 58 associations replicated (UKB replication sample: N=4,561; HCP, N=1,110). In addition, differences in body size (height, weight, BMI, waist and hip circumference, body fat percentage) could account for a substantial proportion of the association, providing possible insight into previous MRI case-control studies for psychiatric disorders where case status is associated with body mass index. Using the same linear mixed model, we showed that most of the associated characteristics (e.g. age, sex, body size, diabetes, being a twin, maternal smoking, body size) could be significantly predicted using all the brain measurements in out-of-sample prediction. Finally, we demonstrated other applications of our approach including a Region Of Interest (ROI) analysis that retain the vertex-wise complexity and ranking of the information contained across MRI processing options. Highlights Our linear mixed model approach unifies association and prediction analyses for highly dimensional vertex-wise MRI data Grey-matter structure is associated with measures of substance use, blood assay results, education or income level, diet, depression, being a twin as well as cognition domains Body size (height, weight, BMI, waist and hip circumference) is an important source of covariation between the phenome and grey-matter structure Grey-matter scores quantify grey-matter based risk for the associated traits and allow to study phenotypes not collected The most general cortical processing (“fsaverage” mesh with no smoothing) maximises the brain-morphometricity for all UKB phenotypes

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Neuroimaging-derived brain-age: an ageing biomarker?

Cited by 24 publications

References 7 publications

Brain age from the electroencephalogram of sleep

Brain age from the electroencephalogram of sleep

The association between “Brain-Age Score” (BAS) and traditional neuropsychological screening tools in Alzheimer's disease

Widespread associations between grey matter structure and the human phenome

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