A minimum variance adaptive technique for parameter estimation and hypothesis testing

An automated machine learning approach to predict brain age from cortical anatomical measures

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.

Selection of an adaptive test statistic for use with multiple comparison analyses of neuroimaging data

Statistical analysis of neuroimages is commonly approached with intergroup comparisons made by repeated application of univariate or multivariate tests performed on the set of the regions of interest sampled in the acquired images. The use of such large numbers of tests requires application of techniques for correction for multiple comparisons. Standard multiple comparison adjustments (such as the Bonferroni) may be overly conservative when data are correlated and/or not normally distributed. Resampling-based step-down procedures that successfully account for unknown correlation structures in the data have recently been introduced. We combined resampling step-down procedures with the Minimum Variance Adaptive method, which allows selection of an optimal test statistic from a predefined class of statistics for the data under analysis. As shown in simulation studies and analysis of autoradiographic data, the combined technique exhibits a significant increase in statistical power, even for small sample sizes (n = 8, 9, 10).