ArticleHuman brain mapping2020
An automated machine learning approach to predict brain age from cortical anatomical measures.
Article in Human brain mapping, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- A dual track predictive model for assessing crow's feet aging across different clinical severity grades.Scientific reports · 2026Article
- Early Detection of Autism Spectrum Disorder Through Automated Machine Learning.Diagnostics (Basel, Switzerland) · 2025Article
- SAGEFusionNet: An Auxiliary Supervised Graph Neural Network for Brain Age Prediction as a Neurodegenerative Biomarker.Brain sciences · 2025Article
- Identification of testicular cancer with T2-weighted MRI-based radiomics and automatic machine learning.BMC cancer · 2025Article
- Use Test of Automated Machine Learning in Cancer Diagnostics.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Accelerated functional brain aging in major depressive disorder: evidence from a large scale fMRI analysis of Chinese participants.Translational psychiatry · 2022Article
- Automated Classification of Atherosclerotic Radiomics Features in Coronary Computed Tomography Angiography (CCTA).Diagnostics (Basel, Switzerland) · 2022Article
- Automatic machine learning based on native T1 mapping can identify myocardial fibrosis in patients with hypertrophic cardiomyopathy.European radiology · 2022Article
- Multimodal brain age prediction fusing morphometric and imaging data and association with cardiovascular risk factors.Frontiers in neurology · 2022Article
- Using machine learning to estimate the calendar age based on autonomic cardiovascular function.Frontiers in aging neuroscience · 2022Article
- Advances in Using MRI to Estimate the Risk of Future Outcomes in Mental Health - Are We Getting There?Frontiers in psychiatry · 2022Article
- Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM).Computational and mathematical methods in medicine · 2022Article
- Image-Based Differentiation of Intracranial Metastasis From Glioblastoma Using Automated Machine Learning.Frontiers in neuroscience · 2022Article
- A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- An automated machine learning approach to predict brain age from cortical anatomical measures.Human brain mapping · 2020Article
- A Computational Model of the Brain Cortex and Its Synchronization.BioMed research international · 2020Article
- Brain-Age Prediction Using Shallow Machine Learning: Predictive Analytics Competition 2019.Frontiers in psychiatry · 2020Article
- Brain Age Prediction With Morphological Features Using Deep Neural Networks: Results From Predictive Analytic Competition 2019.Frontiers in psychiatry · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The use of machine learning (ML) algorithms has significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the target variable? What are the hyperparameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated ML (autoML) has been gaining attention. Here, we apply an autoML library called Tree-based Pipeline Optimisation Tool (TPOT) which uses a tree-based representation of ML pipelines and conducts a genetic programming-based approach to find the model and its hyperparameters that more closely predicts the subject's true age. To explore autoML and evaluate its efficacy within neuroimaging data sets, we chose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT was able to scan through the model space and create pipelines that outperformed the state-of-the-art accuracy for Freesurfer-based models using only thickness and volume information for anatomical structure. In particular, we compared the performance of TPOT (mean absolute error [MAE]: 4.612 ± .124 years) and a relevance vector regression (MAE 5.474 ± .140 years). TPOT also suggested interesting combinations of models that do not match the current most used models for brain prediction but generalise well to unseen data. AutoML showed promising results as a data-driven approach to find optimal models for neuroimaging applications.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.