Predicting Brain Age at Slice Level: Convolutional Neural Networks and Consequences for Interpretability
| Autor: | Felipe Meneguzzi, Benicio N. Frey, Augusto Buchweitz, Laura Tomaz da Silva, Nathalia Bianchini Esper, Pedro Ballester, Matheus Marcon |
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| Rok vydání: | 2021 |
| Předmět: |
lcsh:RC435-571
Separation (statistics) Convolutional neural network 03 medical and health sciences 0302 clinical medicine Neuroimaging lcsh:Psychiatry convolutional neural networks medicine Dementia Stroke Original Research brain age 030304 developmental biology Interpretability Psychiatry 0303 health sciences neuroimaging medicine.diagnostic_test business.industry Deep learning deep learning model interpretability Magnetic resonance imaging medicine.disease Psychiatry and Mental health Artificial intelligence Psychology business Neuroscience 030217 neurology & neurosurgery |
| Zdroj: | Frontiers in Psychiatry, Vol 12 (2021) Frontiers in Psychiatry |
| ISSN: | 1664-0640 |
| DOI: | 10.3389/fpsyt.2021.598518 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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