Technical Program

Paper Detail

Paper: PS-1B.50
Session: Poster Session 1B
Location: H Fl├Ąche 1.OG
Session Time: Saturday, September 14, 16:30 - 19:30
Presentation Time:Saturday, September 14, 16:30 - 19:30
Presentation: Poster
Publication: 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany
Paper Title: Automated Machine Learning in Brain Predictive Modelling: A data-driven approach to Predict Brain Age from Cortical Anatomical Measures
Manuscript:  Click here to view manuscript
License: Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
DOI: https://doi.org/10.32470/CCN.2019.1080-0
Authors: Jessica Dafflon, James H. Cole, Federico Turkheimer, Robert Leech, King's College London, United Kingdom; Mathew A. Harris, Simon R. Cox, Heather C. Whalley, Andrew M. McIntosh, Peter J. Hellyer, University of Edinburgh, United Kingdom
Abstract: The use of machine learning (ML) algorithms significantly increased in neuroscience. However, from the vast extent of possible ML algorithms, which one is the optimal model to predict the feature of interest? What are the best parameters for such a model? Given the plethora of possible answers to these questions, in the last years, automated machine learning (autoML) has been gaining attention. Here, we used TPOT which is a tree-based pipeline optimisation tool that scans a model space of models, their hyperparameters and finds the model with the highest accuracy. To explore autoML approaches and evaluate their efficacy within neuroimaging datasets, we choose a problem that has been the focus of previous extensive study: brain age prediction. Without any prior knowledge, TPOT could scan through the model space and create pipelines that outperform the state-of-the-art accuracy for Freesurfer-based models (MAE: 4.89 years) using only the cortical thickness and subcortical volume information. It also suggests interesting ensembles that do not match the current most used models for brain prediction but generalise well to an unseen dataset (MAE: 4.94 years). Thus, TPOT can be used as a data-driven approach to find ML models that accurately predict brain age.