  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 machine learning (autoML) has
been gaining attention. Here, we apply an autoML library called TPOT which uses
a tree-based representation of machine learning 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 datasets, 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 accuracy error (MAE):
$4.612 \pm .124$ years) and a Relevance Vector Regression (MAE $5.474 \pm .140$
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.
