Longitudinal Changes in Individual <i>BrainAGE</i> in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

Franke, Katja; Gaser, Christian

doi:10.1024/1662-9647/a000074

Cited by 230 publications

(293 citation statements)

References 49 publications

(71 reference statements)

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“…Different ML algorithms have been used for brain age prediction, which include support vector regression (SVR) (Dosenbach et al, 2010;Erus et al, 2015;Franke et al, 2010;Koutsouleris et al, 2014;Liem et al, 2017), Gaussian process regression (GPR) (Aycheh et al, 2018;Cole et al, 2015, relevance vector regression (RVR) (Franke & Gaser, 2012;Gaser et al, 2013;Mwangi et al, 2013), ridge regression (Chung et al, 2018) and elastic net (Khundrakpam, Tohka, & Evans, 2015). Overall, these ML models showed comparable performance on age prediction (Liang et al, 2019).…”

Section: Machine Learning Algorithms For Estimating Brain Agementioning

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). In a longitudinal study, accelerated brain aging was found to be an indicator of conversion from mild cognitive impairment to Alzheimer's disease (Gaser et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Machine Learning Algorithms For Estimating Brain Agementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved prediction of brain age using multimodal neuroimaging data

Niu

Zhang

Kounios

et al. 2019

Human Brain Mapping

106

108

View full text Add to dashboard Cite

show abstract

“…They reported an accuracy value of up to 81% for predicting conversion to AD in MCI patients at the baseline. The authors in (Franke & Gaser, 2012) presented longitudinal alterations in BAS in the 150 AD patients, 112 pMCI patients, 36 sMCI patients, and 108 HCs from the ADNI dataset, where correlation values of r = −0.46, r = 0.39 and r = 0.45 ( p < 0.001) were achieved between baseline BAS and the MMSE, the CDR and the ADAS scores, respectively. In (Koutsouleris et al., 2014), the researchers examined neuroanatomical age estimation in individuals with schizophrenia and other mental disorders.…”

Section: Literate Reviewmentioning

confidence: 99%

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

2018

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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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“…With regard to specific risk factors for dementia, brain‐predicted ageing has been assessed in people with mild cognitive impairment (MCI) and AD. Individuals diagnosed with AD have been shown to have greater apparent brain ageing, observed in several analyses utilizing the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) . In people with MCI, brain‐predicted age was a significant predictor of progression to dementia within three years from a baseline MRI scan .…”

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

confidence: 99%

“…Individuals diagnosed with AD have been shown to have greater apparent brain ageing, observed in several analyses utilizing the data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). [72,80,81] In people with MCI, brain-predicted age was a significant predictor of progression to dementia within three years from a baseline MRI scan. [80][81][82] This is important as it demonstrates that brain-predicted age is sensitive to subtle underlying brain changes that occur prior to outward disease manifestation.…”

Section: What Can "Brain-predicted Age" Tell Us About How Diseases Inmentioning

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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Longitudinal Changes in Individual BrainAGE in Healthy Aging, Mild Cognitive Impairment, and Alzheimer’s Disease

Cited by 230 publications

References 49 publications

Improved prediction of brain age using multimodal neuroimaging data

Improved prediction of brain age using multimodal neuroimaging data

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

Neuroimaging Studies Illustrate the Commonalities Between Ageing and Brain Diseases

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