From a deep learning model back to the brain—Identifying regional predictors and their relation to aging

Levakov, Gidon; Rosenthal, Gideon; Shelef, Ilan; Raviv, Tammy Riklin; Avidan, Galia

doi:10.1002/hbm.25011

Cited by 74 publications

(85 citation statements)

References 72 publications

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“…These findings are particularly in agreement with studies describing the brain's fluid-filled ventricles as a biomarker of the aging brain ( 64 , 65 ). We found an high overlap with the regions identified in the work of ( 40 ). The authors used CNN models in conjunction with explainable AI techniques to derive explanation map which highlighted a major contribution of ventricles and cisterns.…”

Section: Discussionsupporting

confidence: 57%

“…In addition, we evaluated the ensemble variability as proposed in ( 40 ). This metric is assessed as the standard deviation of the prediction error within the ensemble and is related to the uncertainty in neural networks ( 41 ).…”

Section: Resultsmentioning

confidence: 99%

“…They also used their trained models on the dataset provided by Predictive Analysis Competition 2019 resulting the best team with MAE = 2.90 years. ( 40 ) proposed an ensemble of CNN models trained and tested on an minimally processed T1 MRI scans of 10,176 subjects collected from 15 large-scale open-access databases in order to produce a result that is more robust to scanner's type, field strength, and resolution. The authors showed that by using both CNN models and data augmentation the results improved with MAE = 3.07 years and a correlation between chronological and predicted age of R = 0.98.…”

Section: Discussionmentioning

confidence: 99%

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Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

et al. 2021

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Morphological changes in the brain over the lifespan have been successfully described by using structural magnetic resonance imaging (MRI) in conjunction with machine learning (ML) algorithms. International challenges and scientific initiatives to share open access imaging datasets also contributed significantly to the advance in brain structure characterization and brain age prediction methods. In this work, we present the results of the predictive model based on deep neural networks (DNN) proposed during the Predictive Analytic Competition 2019 for brain age prediction of 2638 healthy individuals. We used FreeSurfer software to extract some morphological descriptors from the raw MRI scans of the subjects collected from 17 sites. We compared the proposed DNN architecture with other ML algorithms commonly used in the literature (RF, SVR, Lasso). Our results highlight that the DNN models achieved the best performance with MAE = 4.6 on the hold-out test, outperforming the other ML strategies. We also propose a complete ML framework to perform a robust statistical evaluation of feature importance for the clinical interpretability of the results.

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Section: Discussionsupporting

confidence: 57%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

et al. 2021

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“…a Second, these methods can produce results that are independent of model and data, and thus inadequate for model debugging and inspection ( 28 ). Finally, additional techniques must be used for combining individual subject saliency maps of into population-level visualizations ( 29 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability

et al. 2021

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Problem: Chronological aging in later life is associated with brain degeneration processes and increased risk for disease such as stroke and dementia. With a worldwide tendency of aging populations and increased longevity, mental health, and psychiatric research have paid increasing attention to understanding brain-related changes of aging. Recent findings suggest there is a brain age gap (a difference between chronological age and brain age predicted by brain imaging indices); the magnitude of the gap may indicate early onset of brain aging processes and disease. Artificial intelligence has allowed for a narrowing of the gap in chronological and predicted brain age. However, the factors that drive model predictions of brain age are still unknown, and there is not much about these factors that can be gleaned from the black-box nature of machine learning models. The goal of the present study was to test a brain age regression approach that is more amenable to interpretation by researchers and clinicians.Methods: Using convolutional neural networks we trained multiple regressor models to predict brain age based on single slices of magnetic resonance imaging, which included gray matter- or white matter-segmented inputs. We evaluated the trained models in all brain image slices to generate a final prediction of brain age. Unlike whole-brain approaches to classification, the slice-level predictions allows for the identification of which brain slices and associated regions have the largest difference between chronological and neuroimaging-derived brain age. We also evaluated how model predictions were influenced by slice index and plane, participant age and sex, and MRI data collection site.Results: The results show, first, that the specific slice used for prediction affects prediction error (i.e., difference between chronological age and neuroimaging-derived brain age); second, the MRI site-stratified separation of training and test sets removed site effects and also minimized sex effects; third, the choice of MRI slice plane influences the overall error of the model.Conclusion: Compared to whole brain-based predictive models of neuroimaging-derived brain age, slice-based approach improves the interpretability and therefore the reliability of the prediction of brain age using MRI data.

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“…Brain ageing study is a recent example of the predictive analysis paradigm (Brown et al, 2012; Cole et al, 2018, 2017; Cole and Franke, 2017; Dosenbach et al, 2010; Franke et al, 2010; Levakov et al, 2020; Neeb et al, 2006). Studies showed that individuals’ chronological age can be predicted accurately from brain MRI scans (Cole et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

Gong

Beckmann

Vedaldi

et al. 2020

Preprint

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Brain age prediction from brain MRI scans not only helps improve brain ageing modelling generally, but also provides benchmarks for predictive analysis methods. Brain-age delta, which is the difference between a subject’s predicted age and true age, has become a meaningful biomarker for the health of the brain. Here, we report the details of our brain age prediction models and results in the Predictive Analysis Challenge 2019. The aim of the challenge was to use T1-weighted brain MRIs to predict a subject’s age in multicentre datasets. We apply a lightweight deep convolutional neural network architecture, Simple Fully Convolutional Neural Network (SFCN), and combined several techniques including data augmentation, transfer learning, model ensemble, and bias correction for brain age prediction. The model achieved first places in both of the two objectives in the PAC 2019 brain age prediction challenge: Mean absolute error (MAE) = 2.90 years without bias removal, and MAE = 2.95 years with bias removal.

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From a deep learning model back to the brain—Identifying regional predictors and their relation to aging

Cited by 74 publications

References 72 publications

Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019

Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability

Optimising a Simple Fully Convolutional Network (SFCN) for accurate brain age prediction in the PAC 2019 challenge

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