Brain age prediction across the human lifespan using multimodal MRI data

Guan, Sihai; Jiang, Runzhou; Meng, Chun; Biswal, Bharat

doi:10.1007/s11357-023-00924-0

Cited by 8 publications

(6 citation statements)

References 104 publications

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“…It should however be noted that, the observed results from both the 5-HT2AR binding and GM volume based models were on par with the accuracy of other brain age prediction models in the literature (Baecker et al, 2021; J. Cole et al, 2018; Guan et al, 2023). The improved accuracy of predictions based on 5-HT2AR binding over predictions based on GM volumes indicates that with increased sample size, brain age based on 5-HT2AR binding might outperform state-of-the-art MRI-based methods.…”

Section: Discussionsupporting

confidence: 52%

“…Since white matter and the ventricle volumes change with age (Bethlehem et al, 2022), it is likely that including such features would have improved the predictions for the MRI-based models in our study. It should however be noted that, the observed results from both the 5-HT2AR binding and GM volume based models were on par with the accuracy of other brain age prediction models in the literature (Baecker et al, 2021;Guan et al, 2023). The improved accuracy of predictions based on 5-HT2AR binding over predictions based on GM volumes indicates that with increased sample size, brain age based on 5-HT2AR binding might outperform state-of-the-art MRIbased methods.…”

Section: Discussionmentioning

confidence: 74%

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Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

Dörfel,

Arenas-Gomez,

Svarer

et al. 2024

Preprint

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To better assess the pathology of neurodegenerative disorders and to evaluate the efficacy of neuroprotective interventions, it is required to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also widely reduced in, e.g., Alzheimer’s disease. Hence, cerebral 5-HT2AR binding measured in vivo using positron emission tomography (PET) is a potentially useful biomarker for age-related changes in the brain.In this study, we investigate the decline in 5-HT2AR binding to evaluate its usefulness as a biomarker for biological aging. Specifically, we aim to 1) predict brain age using 5-HT2AR binding outcomes, 2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and 3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean=38, std=18). 5-HT2AR binding and GM volume were calculated for 14 cortical and subcortical regions. Different machine learning algorithms were used to predict age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison.We find that both the cerebral 5-HT2AR binding (mean MAE=6.63 years, std=0.78 years) and GM volume (mean MAE=7.76 years, std=0.92 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE=5.93 years, std=0.82). We conclude that when it comes to predicting age, in vivo measurements of the cerebral 5-HT2AR binding are more informative than GM volumes.

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Section: Discussionsupporting

confidence: 52%

Section: Discussionmentioning

confidence: 74%

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

Dörfel,

Arenas-Gomez,

Svarer

et al. 2024

Preprint

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“…This is consistent with related works such as [ 11 , 44 ]. A recent work evaluated brain age prediction accuracy for varying number of time points and observed improvements with the number of time points [ 45 ]. However, the increases were reported as being relatively small.…”

Section: Discussionmentioning

confidence: 99%

Brain age monotonicity and functional connectivity differences of healthy subjects

Sorooshyari

2024

PLoS ONE

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Alterations in the brain’s connectivity or the interactions among brain regions have been studied with the aid of resting state (rs)fMRI data attained from large numbers of healthy subjects of various demographics. This has been instrumental in providing insight into how a phenotype as fundamental as age affects the brain. Although machine learning (ML) techniques have already been deployed in such studies, novel questions are investigated in this work. We study whether young brains develop properties that progressively resemble those of aged brains, and if the aging dynamics of older brains provide information about the aging trajectory in young subjects. The degree of a prospective monotonic relationship will be quantified, and hypotheses of brain aging trajectories will be tested via ML. Furthermore, the degree of functional connectivity across the age spectrum of three datasets will be compared at a population level and across sexes. The findings scrutinize similarities and differences among the male and female subjects at greater detail than previously performed.

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“…Early approaches manually extracted anatomical features from brain magnetic resonance imaging (MRI), such as cortical thickness and regional or tissue-specific volumes, and fed them into traditional regression models, such as linear regression [ 19 ], support vector regression (SVR) [ 20 ], and Gaussian process regression (GPR) [ 3 ]. Gaun et al predicted brain age by partial least squares regression (PLSR) by using cortical thickness features and yielded an MAE of 7.90 years [ 21 ]. Raw image pre-processing for feature extraction involves multiple steps such as field-offset correction, removal of the non-brain region, and tissue segmentation, which may be too time consuming for clinical practice.…”

Section: Introductionmentioning

confidence: 99%

Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network

Lim,

Joo,

et al. 2024

Bioengineering

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Convolutional neural networks (CNNs) have been used widely to predict biological brain age based on brain magnetic resonance (MR) images. However, CNNs focus mainly on spatially local features and their aggregates and barely on the connective information between distant regions. To overcome this issue, we propose a novel multi-hop graph attention (MGA) module that exploits both the local and global connections of image features when combined with CNNs. After insertion between convolutional layers, MGA first converts the convolution-derived feature map into graph-structured data by using patch embedding and embedding-distance-based scoring. Multi-hop connections between the graph nodes are modeled by using the Markov chain process. After performing multi-hop graph attention, MGA re-converts the graph into an updated feature map and transfers it to the next convolutional layer. We combined the MGA module with sSE (spatial squeeze and excitation)-ResNet18 for our final prediction model (MGA-sSE-ResNet18) and performed various hyperparameter evaluations to identify the optimal parameter combinations. With 2788 three-dimensional T1-weighted MR images of healthy subjects, we verified the effectiveness of MGA-sSE-ResNet18 with comparisons to four established, general-purpose CNNs and two representative brain age prediction models. The proposed model yielded an optimal performance with a mean absolute error of 2.822 years and Pearson’s correlation coefficient (PCC) of 0.968, demonstrating the potential of the MGA module to improve the accuracy of brain age prediction.

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Brain age prediction across the human lifespan using multimodal MRI data

Cited by 8 publications

References 104 publications

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

Multimodal brain age prediction using machine learning: combining structural MRI and 5-HT2AR PET derived features

Brain age monotonicity and functional connectivity differences of healthy subjects

Brain Age Prediction Using Multi-Hop Graph Attention Combined with Convolutional Neural Network

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