ArticleFrontiers in aging neuroscience2019
Deep Learning and Multiplex Networks for Accurate Modeling of Brain Age.
Article in Frontiers in aging neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
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Who cites it
32 citing papers in PubMed.
- From Infancy to Aging: Precise Brain Age Estimation via Hybrid CoTResNet3D and CrossViT Models on T1-Weighted Imaging.Bioengineering (Basel, Switzerland) · 2026Article
- Modeling roles and trade-offs in multiplex networks.Nature communications · 2026Article
- Vision transformer and complex network analysis for autism spectrum disorder classification in T1 structural MRI.Japanese journal of radiology · 2025Article
- Do Transformers and CNNs Learn Different Concepts of Brain Age?Human brain mapping · 2025Article
- Network assortativity for a multidimensional evaluation of socio-economic territorial biases in university rankings.PloS one · 2025Article
- Lifespan brain age prediction based on multiple EEG oscillatory features and sparse group lasso.Frontiers in aging neuroscience · 2025Article
- Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.Brain informatics · 2024Article
- Article
- Multiplex dynamic networks in the newborn brain disclose latent links with neurobehavioral phenotypes.Human brain mapping · 2024Article
- Deep learning approach for denoising low-SNR correlation plenoptic images.Scientific reports · 2023Article
- A systematic review of multimodal brain age studies: Uncovering a divergence between model accuracy and utility.Patterns (New York, N.Y.) · 2023Review
- Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias.Human brain mapping · 2023Article
- Detecting the socio-economic drivers of confidence in government with eXplainable Artificial Intelligence.Scientific reports · 2023Article
- An eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease.Frontiers in aging neuroscience · 2023Article
- Comparative analysis of popular predictors for difficult laryngoscopy using hybrid intelligent detection methods.Heliyon · 2022Article
- Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants.Translational psychiatry · 2022Article
- Mind the gap: Performance metric evaluation in brain-age prediction.Human brain mapping · 2022Article
- Territorial bias in university rankings: a complex network approach.Scientific reports · 2022Article
- Application of medical imaging methods and artificial intelligence in tissue engineering and organ-on-a-chip.Frontiers in bioengineering and biotechnology · 2022Review
- Worldwide impact of lifestyle predictors of dementia prevalence: An eXplainable Artificial Intelligence analysis.Frontiers in big data · 2022Article
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Authors and funding
11 authors.
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Abstract
Recent works have extensively investigated the possibility to predict brain aging from T1-weighted MRI brain scans. The main purposes of these studies are the investigation of subject-specific aging mechanisms and the development of accurate models for age prediction. Deviations between predicted and chronological age are known to occur in several neurodegenerative diseases; as a consequence, reaching higher levels of age prediction accuracy is of paramount importance to develop diagnostic tools. In this work, we propose a novel complex network model for brain based on segmenting T1-weighted MRI scans in rectangular boxes, called patches, and measuring pairwise similarities using Pearson's correlation to define a subject-specific network. We fed a deep neural network with nodal metrics, evaluating both the intensity and the uniformity of connections, to predict subjects' ages. Our model reaches high accuracies which compare favorably with state-of-the-art approaches. We observe that the complex relationships involved in this brain description cannot be accurately modeled with standard machine learning approaches, such as Ridge and Lasso regression, Random Forest, and Support Vector Machines, instead a deep neural network has to be used.
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