Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI

Simfukwe, Chanda; Youn, Young Chul

doi:10.12779/dnd.2022.21.4.138

Cited by 6 publications

(10 citation statements)

References 21 publications

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“…However, the finding that the younger by brain age, low-educated female group had a very high survival probability (Fig. 3 , pink line) implies that brain age should be used as an additional predictive index in estimating dementia conversion 23 . As the number of patients in each subgroup was small, further evaluation with a larger sample size is warranted to validate this result.…”

Section: Discussionmentioning

confidence: 98%

Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment

Kim,

Lee

et al. 2023

Sci Rep

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Patients with amyloid-negative amnestic mild cognitive impairment (MCI) have a conversion rate of approximately 10% to dementia within 2 years. We aimed to investigate whether brain age is an important factor in predicting conversion to dementia in patients with amyloid-negative amnestic MCI. We conducted a retrospective cohort study of patients with amyloid-negative amnestic MCI. All participants underwent detailed neuropsychological evaluation, brain magnetic resonance imaging (MRI), and [18F]-florbetaben positron emission tomography. Brain age was determined by the volumetric assessment of 12 distinct brain regions using an automatic segmentation software. During the follow-up period, 38% of the patients converted from amnestic MCI to dementia. Further, 73% of patients had a brain age greater than their actual chronological age. When defining ‘survival' as the non-conversion of MCI to dementia, these groups differed significantly in survival probability (p = 0.036). The low-educated female group with a brain age greater than their actual age had the lowest survival rate among all groups. Our findings suggest that the MRI-based brain age used in this study can contribute to predicting conversion to dementia in patients with amyloid-negative amnestic MCI.

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Section: Discussionmentioning

confidence: 98%

Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment

Kim,

Lee

et al. 2023

Sci Rep

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“…There was a similar study that investigated the performance of age prediction using ML regression models. Simfukwe and Youn applied ML regression models to features of brain region volumes in order to predict brain age [36]. In their study, the Bayesian ridge regression model showed the highest R 2 value of 0.3 compared to other regression models, including the gradient boosting regression, support vector regression, and linear regression models.…”

Section: Discussionmentioning

confidence: 99%

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

Yoon,

Oh,

Kim

2023

Brain Sciences

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This study aimed to develop and validate machine learning (ML) models that predict age using intracranial vessels’ tortuosity and diameter features derived from magnetic resonance angiography (MRA) data. A total of 171 subjects’ three-dimensional (3D) time-of-flight MRA image data were considered for analysis. After annotations of two endpoints in each arterial segment, tortuosity features such as the sum of the angle metrics, triangular index, relative length, and product of the angle distance, as well as the vessels’ diameter features, were extracted and used to train and validate the ML models for age prediction. Features extracted from the right and left internal carotid arteries (ICA) and basilar arteries were considered as the inputs to train and validate six ML regression models with a four-fold cross validation. The random forest regression model resulted in the lowest root mean square error of 14.9 years and the highest average coefficient of determination of 0.186. The linear regression model showed the lowest average mean absolute percentage error (MAPE) and the highest average Pearson correlation coefficient (0.532). The mean diameter of the right ICA vessel segment was the most important feature contributing to prediction of age in two out of the four regression models considered. An ML of tortuosity descriptors and diameter features extracted from MRA data showed a modest correlation between real age and ML-predicted age. Further studies are warranted for the assessment of the model’s age predictions in patients with intracranial vessel diseases.

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“…In [7], disentangling AD neurodegeneration from typical brain aging was performed using machine learning. In this work [8] prediction is performed based on the East Asian brain age dataset, which is trained based on community-based healthy brain MRI. Considering the work [9], there was a prediction of individual brain age based on an ensemble deep learning model that was involved in improvising the analysis.…”

Section: Related Studymentioning

confidence: 99%

“…Grey matter volume declines with age, whereas cerebrospinal fluid volume rises and white matter volume experiences relatively little change, according to research that has examined changes in brain tissues such as white matter, grey matter, and cerebrospinal fluid. In brain research [1] - [8], volumetric changes have been analyzed using T1weighted MRI Studying the brain is difficult because of its three-dimensional structure and numerous components. Even minor changes in these areas can signal a variety of changes, and transformations occur in the mind and other body regions.…”

Section: Introductionmentioning

confidence: 99%

Power of Ensemble Techniques for Brain Age Prediction Using Machine Learning Models

Ravi,

Baghel,

Chinamsetty

et al. 2024

Preprint

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Aging has a profound impact on brain structure and function, resulting in cognitive decline and an increased susceptibility to neurodegenerative diseases. The brain age gap is defined as the difference between an individual's estimated brain age and their actual age, which is considered a potential marker of overall brain health and may indicate structural abnormalities. Considering this work, machine learning models are used to estimate the brain age based on brain imaging data from four prominent repositories namely IXI dataset, Calgary Campinas, Sparse Linear Method, and Sign Agnostic Learning with Derivatives respectively This work is done based on five regression models such as XGBoost (Extreme Gradient Boosting), Support Vector Regression (SVR), Gradient Boosting Regression (GBR), Random Forest Regression (RFR), and K-Nearest Neighbors (KNN) regression, and also considering ensemble models such as Stacking, Bagging, and Boosting. The dataset contains T1-weighted Magnetic resonance imaging (MRI) images from over 1800 patients, and approximately 143 features were extracted using the reliable tool FreeSurfer (6.0). In this study, a major analysis was performed using grid search and cross-validation to train the models and optimize the hyperparameters to prevent overfitting. The results exhibit that a stacked ensemble model of SVR and XGBoost outperformed the other models, with a mean absolute error (MAE) of 4.65 and R2 value of 0.92 on the training dataset while 6.62 and 0.85 on the test set respectively. The validation results indicated that regression models and ensemble techniques for brain age prediction provide a powerful combination of interpretability, accuracy, and robustness.

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Prediction of East Asian Brain Age using Machine Learning Algorithms Trained With Community-based Healthy Brain MRI

Cited by 6 publications

References 21 publications

Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment

Magnetic resonance image-based brain age as a discriminator of dementia conversion in patients with amyloid-negative amnestic mild cognitive impairment

Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information

Power of Ensemble Techniques for Brain Age Prediction Using Machine Learning Models

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