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Deep Learning for Brain Age Estimation: A Systematic Review
M. Tanveer, M. A. Ganaie, Iman Beheshti, Tripti Goel, Nehal Ahmad, Kuan-Ting Lai, Kaizhu Huang, Yu-Dong Zhang, Javier Del Ser, Chin-Teng Lin
TL;DR
Brain-age deviations are associated with brain abnormalities, and existing surveys had largely focused on traditional machine learning rather than systematically reviewing deep learning. This review searches and classifies deep-learning studies using neuroimaging, synthesizes their approaches and reported performance, and identifies research needs including unified benchmarking.
Problem
Earlier brain-age surveys mostly focused on traditional machine-learning methods, and the review reports no prior systematic review of deep learning for brain-age estimation.
Source: §1 · p. 3Method
The authors searched Google Scholar and PubMed in March 2022, retained 35 studies, and organized the literature by architecture and slice- or voxel-based input.
Source: §3.1 · p. 7, §3.2 · p. 7Results
The review finds growing interest in deep-learning brain-age estimation while identifying research niches that still warrant attention.
Source: §4 · p. 19Takeaways & Limitations
The field lacks a comprehensive benchmark using public datasets, unified metrics, evaluation protocols, and multiple datasets.
Source: §4.5 · p. 21Most deep-learning studies tested their techniques on Alzheimer’s disease patients with extensive brain atrophy.
Source: §4.2 · p. 20
Abstract
from arXiv · showhide
Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated to the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required for eliciting accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models
1. Introduction
Brain-age estimation uses neuroimaging to predict individual age trajectories, and deviations from typical trajectories are associated with brain health. This review addresses the lack of a systematic survey focused on deep learning for brain-age estimation.
1. Introduction
- Brain-age delta is estimated brain age minus real age, and positive values are associated with brain abnormalities and mortality while negative values are linked to a younger brain.Frameworks typically train supervised regression models on cognitively healthy participants and apply them to unseen subjects.Source: §1 · p. 2
- Deep learning is motivated by its ability to combine feature extraction, feature reduction, and prediction for complex data, while accurate frameworks are needed for robust clinical interpretation.The review notes deep learning’s growing use in neuroimaging.Source: §1 · pp. 2–3
- The review identifies a gap: earlier brain-age surveys mostly covered traditional machine learning, and none had systematically reviewed deep learning for brain-age estimation.It presents its literature analysis and future directions as addressing that niche.Source: §1 · p. 3
- The review classifies brain-age models by deep-learning architecture and by whether inputs are modeled as slices or voxels.It also examines modalities, data size, reported performance, and evaluation datasets.Source: §1 · pp. 3–4
- The review examines architectural hyperparameters, including hidden-layer counts, activation and loss functions, and dense-layer choices, and identifies unresolved challenges and future research directions.These are among the contributions the authors describe for advancing data-based brain-age prediction from image data.Source: §1 · p. 4
2. Deep Learning Architectures
The section introduces feed-forward networks, CNN variants, and ensembles as architectures relevant to brain-age estimation. It outlines their basic structures and the design choices intended to support feature learning or generalization.
2. Deep Learning Architectures
- The review surveys feed-forward networks, CNNs, VGGNet, ResNet, and ensembles to orient readers to architectures used in brain-age estimation.It introduces these models to ease understanding of the literature examination.Source: §2 · p. 4
2.1. Feed-forward neural network
- Feed-forward networks chain functions with one-way data flow; their depth is the number of layers, and training adjusts parameters by backpropagation to minimize loss.Their primary objective is to approximate a function.Source: §2.1 · pp. 4–5
2.2. Convolutional Neural Networks, ResNet and VGGNet
- CNNs use convolutional, pooling, and dense layers; convolutional layers model spatial correlations and automate feature extraction from multidimensional data.The review describes CNNs as originating in computer vision and being applied to regression as well as classification.Source: §2.2 · p. 5
- ResNet uses skip connections to link activations across intervening layers in residual blocks, addressing exploding and vanishing gradients in deep convolutional networks.The passage says this architecture enhances accuracy without loss of accuracy or increased training and testing error.Source: §2.2 · p. 5
- VGGNet omits local response normalization, which the review says reduces memory consumption and model time complexity.Its variants differ in depth and hyperparameter configurations.Source: §2.2 · p. 5
2.3. Ensemble Deep Learning
- Ensembles train and combine multiple models to improve generalization by combining predictions from networks that capture diversified knowledge.The review groups deep-learning ensembles into bagging, boosting, and stacking.Source: §2.3 · p. 6
3. Taxonomy and Literature Review
This section reviews screened studies of deep-learning approaches to brain-age estimation, organizing them by architecture and slice- or voxel-based input. It also surveys miscellaneous approaches, including clinical imaging, age-sensitive interpretation, multimodal autoencoders, transfer learning, and image-pair relation learning.
3.1. Search Strategy
- The March 2022 search of Google Scholar and PubMed screened keyword combinations around deep learning, brain-age estimation or prediction, MRI, brain imaging, and neuroimaging, retaining 35 studies.Studies not focused on deep learning and neuroimaging were excluded at initial screening.Source: §3.1 · p. 7
3.2. Taxonomy and Literature Review
- The taxonomy (Figure 2) classifies studies by architecture and slice/voxel input: 2D slices reduce parameters but omit inter-slice context, while registered 3D inputs model it at higher parameter cost.The review also summarizes the analyzed studies’ modalities, models, datasets, and reported results in Table 2.Source: §3.2 · p. 7, Figure 2 · p. 8
3.2.1. Convolutional Neural Networks
- CNN studies included gray-matter models that performed best among tested tissue inputs, dementia-related age-gap analysis, and pediatric models where 3D-CNN outperformed 2D-CNN and predictions were more accurate under age 2.Reported scope and limitations varied: the dementia study excluded dementia and stroke subjects during training, while the pediatric comparison underscored the importance of inter-slice correlations.Source: §3.2.1 · pp. 9–10
3.2.2. VGGNet
- Voxel-based VGGNet studies examined regional, lifestyle/clinical, and network-specific age findings, while reporting limits in cohort coverage or brain-region coverage.Examples included associations with frontal cortical thickness, attention gates and multimodal variables, and lower mean absolute error in three of seven cortical networks.Source: §3.2.2 · pp. 10–11
3.2.3. ResNet
- ResNet work ranged from fetal age estimation associated with anomalies to voxel-based models linking predicted age differences with genetic and neuropsychological measures.The reviewed studies also used transfer learning to test generalization across datasets [54]; limitations included computational complexity, restricted imaging planes or outcomes, and omitted lifestyle or disease factors [54].Source: §3.2.3 · pp. 11–12
3.2.4. Ensemble Deep Learning
- 3.07 years MAE on unseen data was reported for a 10,176-subject 3D-CNN ensemble, while multilevel ensembles outperformed individual base learners.Other reviewed voxel-based ensembles combined feature sets or classifiers to improve performance, but ensembles incur additional training complexity; work also identified ventricular and hippocampal findings.Source: §3.2.4 · pp. 13–14
3.2.5. Miscellaneous Deep Learning Models
- 3.13 years for MCI and 6.08 years for AD were the predicted age differences reported with AlexNet; other work explored SHAP/LIME and U-Net local-age maps.SHAP was reported as the most reliable descriptor for morphological aging, while U-Net local predictions showed moderate between-scanner reliability and required a healthy same-site population.Source: §3.2.5 · pp. 14–15
- The DenseNet-based model in used routine clinical head examinations, spanning scanners and acquisition protocols, but was trained only on radiologically normal-for-age brains.Its interpretability was attributed to brain parenchyma without systematic analysis of which brain features drove predictions; behavior in gross abnormalities remained unknown.Source: §3.2.5 · p. 15
- Lee et al. found age-dependent occlusion-sensitive regions and greater prediction sensitivity for metabolic than MRI data.Posterior regions, peaking at the posterior cingulate, contributed more for ages 30–50; inferior frontal, orbitofrontal, and olfactory regions predominated for ages 50–60 and 70–80. Evaluation covered neurodegenerative pathology, not chronic systemic or vascular disease.Source: §3.2.5 · p. 15
- Mouches et al. used a deterministic autoencoder for brain-age prediction and age-conditional templates showing age-related ventricular enlargement and wider sulci.Their later multimodal model combined MRI and angiography; combining brain tissue and artery information improved performance, with the lateral sulcus, fourth ventricle, and medial temporal regions identified as important morphological features.Source: §3.2.5 · pp. 15–16
- Varatharajah et al. used pretrained Inception-V1 3D features followed by a regressor and bucketed classification.Source: §3.2.5 · p. 16
- 2.38 was the lowest MAE achieved by ’s deep-relation model, which paired EfficientNet feature extraction with a transformer for image-pair relations.The model evaluated brain-age estimation using different images, known-age references, and the same image pairs.Source: §3.2.5 · pp. 16, 18
4. Challenges and future directions
The review identifies research niches in deep-learning brain-age estimation and outlines future directions to address them. These include broader data and disorder coverage, more practical and interpretable models, and stronger evaluation of architectures.
4.1. Integration of Multimodal Data
- Most published approaches use T1-weighted images, while multimodal integration could incorporate connectivity, iron deposition, and metabolic variation.The review points to multimodal fusion as a route to explore additional biomarkers affecting brain aging.Source: §4.1 · p. 19
4.2. Association of Brain Age Difference with Cognitive Decline
- Models trained on cognitively healthy participants may be biased by mental-health, life-satisfaction, or metabolic factors, and need validation beyond Alzheimer’s disease.The review calls for checking dependability in disorders with smaller volumetric alterations, including Parkinson’s disease, Huntington’s disease, epilepsy, and Down syndrome.Source: §4.2 · p. 20
4.3. Computational Complexity
- Many estimators use computationally intensive preprocessing, and few are designed for routine MRI scans; efficient, accurate models should address routine-scan artifacts.The review also notes 2D CNNs as a way to reduce computational complexity and suggests comparing MRI planes.Source: §4.3 · p. 20
4.4. Significant Brain Region Extraction
- Only a few studies analyze which brain regions drive age estimates; reported affected regions differ by age group, motivating analyses by age and gender.Source: §4.4 · p. 20
4.5. New Deep Learning Architectures
- Negative correlation learning and other ensemble-diversity approaches remain unexplored, while the field also lacks a comprehensive benchmark using shared metrics, protocols, and datasets.Such benchmarking could inform consensus on state-of-the-art models and support work on precision, trustworthiness, and efficiency.Source: §4.5 · p. 21
4.6. Adversarial Perturbations and Uncertainty Estimation
- Brain-age studies seldom consider adversarial robustness; the review also calls for uncertainty estimates that communicate confidence in individual predictions.Suggested uncertainty approaches include conformal prediction, quantile regression, evidential losses, Monte Carlo dropout, and Bayesian networks.Source: §4.6 · p. 21
4.7. Explainability in Deep Learning for Brain Age Estimation
- Deep models are treated as black boxes, and the review calls for post-hoc explanations to clarify relevant inputs and surface biomarkers associated with brain aging.SHAP and LIME are cited as examples; the review argues explanations matter for clinical decisions and model trustworthiness.Source: §4 · p. 19, §4.7 · pp. 21–22
5. Concluding Remarks and Outlook
The review organizes deep-learning brain-age estimation studies and their reported performance and architectural choices. It proposes a taxonomy to support critical assessment and identifies future research directions.
5. Concluding Remarks and Outlook
- The review examines how published studies frame brain-age estimation, their modeling patterns, reported predictive performance, and architectural choices.Source: §5 · p. 22
- A proposed taxonomy organizes the literature and is intended to guide critical assessment of the reviewed works’ strengths and weaknesses.Source: §5 · p. 22
- The review enumerates research directions for future work and aims to serve as a reference for researchers, practitioners, and newcomers.Source: §5 · p. 22