Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers

Cole, James R.; Franke, Katja

doi:10.1016/j.tins.2017.10.001

Cited by 684 publications

(707 citation statements)

References 62 publications

(70 reference statements)

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“…The idea of generating a “brain‐predicted age,” in other words a biological‐age prediction derived using measurements of the brain (commonly structural neuroimaging), is becoming increasingly common . This is thanks to the adoption of sophisticated machine learning methods for statistical analysis.…”

Section: Neuroimaging Can Be Used To Model Brain Ageing In Health Andmentioning

confidence: 99%

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

Cole

2018

BioEssays

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The lack of specificity in neuroimaging studies of neurological and psychiatric diseases suggests that these different diseases have more in common than is generally considered. Potentially, features that are secondary effects of different pathological processes may share common neurobiological underpinnings. Intriguingly, many of these mechanisms are also observed in studies of normal (i.e., non-pathological) brain ageing. Different brain diseases may be causing premature or accelerated ageing to the brain, an idea that is supported by a line of "brain ageing" research that combines neuroimaging data with machine learning analysis. In reviewing this field, I conclude that such observations could have important implications, suggesting that we should shift experimental paradigm: away from characterizing the average case-control brain differences resulting from a disease toward methods that place individuals in their age-appropriate context. This will also lead naturally to clinical applications, whereby neuroimaging can contribute to a personalized-medicine approach to improve brain health.

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Section: Neuroimaging Can Be Used To Model Brain Ageing In Health Andmentioning

confidence: 99%

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

Cole

2018

BioEssays

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“…Cross-sectional histological analyses suggest that the atrophy results from a combination of dendritic regression and neuronal death (Dumitriu et al, 2010). While there is inter-individual variability in the rate of brain atrophy during aging, it has been suggested that brain imaging data can be used to establish a “biological age” of one’s brain (Cole and Franke, 2017). Environmental factors can influence the rate of brain structural changes during aging.…”

Section: Introductionmentioning

confidence: 99%

Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States

2018

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During aging, the cellular milieu of the brain exhibits tell-tale signs of compromised bioenergetics, impaired adaptive neuroplasticity and resilience, aberrant neuronal network activity, dysregulation of neuronal Ca2+ homeostasis, the accrual of oxidatively modified molecules and organelles, and inflammation. These alterations render the aging brain vulnerable to Alzheimer’s and Parkinson’s diseases and stroke. Emerging findings are revealing mechanisms by which sedentary overindulgent lifestyles accelerate brain aging, whereas lifestyles that include intermittent bioenergetic challenges (exercise, fasting, and intellectual challenges) foster healthy brain aging. Here we provide an overview of the cellular and molecular biology of brain aging, how those processes interface with disease-specific neurodegenerative pathways, and how metabolic states influence brain health.

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“…Deep neural networks (DNNs) have also been applied to brain age prediction recently. With recent advances in deep learning, DNNs are expected to improve prediction performance (Cole & Franke, ). However, most studies showed DNN yielded similar prediction performance to traditional ML methods (Aycheh et al, ; Cole et al, ).…”

Section: Introductionmentioning

confidence: 99%

“…As an index of deviation from a healthy brain-aging trajectory, the brain age gap has the potential to suggest age-associated brain disease or cognitive aging with both neuroscientific and clinical implications (Cole & Franke, 2017). For example, accelerated brain aging was found in patients with Alzheimer's disease (Franke et al, 2010;Franke & Gaser, 2012), traumatic brain injury (Cole, Leech, & Sharp, 2015), and psychiatric disorders such as schizophrenia and major depression disorders (Chung et al, 2018;Koutsouleris et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

Improved prediction of brain age using multimodal neuroimaging data

Niu

Zhang

Kounios

et al. 2019

Human Brain Mapping

106

108

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Brain age prediction based on imaging data and machine learning (ML) methods has great potential to provide insights into the development of cognition and mental disorders. Though different ML models have been proposed, a systematic comparison of ML models in combination with imaging features derived from different modalities is still needed. In this study, we evaluate the prediction performance of 36 combinations of imaging features and ML models including deep learning. We utilize single and multimodal brain imaging data including MRI, DTI, and rs‐fMRI from a large data set with 839 subjects. Our study is a follow‐up to the initial work (Liang et al., 2019. Human Brain Mapping) to investigate different analytic strategies to combine data from MRI, DTI, and rs‐fMRI with the goal to improve brain age prediction accuracy. Additionally, the traditional approach to predicting the brain age gap has been shown to have a systematic bias. The potential nonlinear relationship between the brain age gap and chronological age has not been thoroughly tested. Here we propose a new method to correct the systematic bias of brain age gap by taking gender, chronological age, and their interactions into consideration. As the true brain age is unknown and may deviate from chronological age, we further examine whether various levels of behavioral performance across subjects predict their brain age estimated from neuroimaging data. This is an important step to quantify the practical implication of brain age prediction. Our findings are helpful to advance the practice of optimizing different analytic methodologies in brain age prediction.

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Predicting Age Using Neuroimaging: Innovative Brain Ageing Biomarkers

Cited by 684 publications

References 62 publications

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

Hallmarks of Brain Aging: Adaptive and Pathological Modification by Metabolic States

Improved prediction of brain age using multimodal neuroimaging data

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