33: Machine Learning Evaluation using Semiparametric Correlation in Brain-Psychopathology Associations

Thomas M. Menino Convention & Exhibition Center 

Machine learning (ML) models are used in neuroimaging studies to predict biological and psychometric phenotypes, such as age and psychopathology factor scores. Neuroscientists use Pearson's correlation between the predicted and actual feature to quantify model accuracy for these ML models; however, Pearson's correlation is not accurate when using ML models due to their slow. To address this, we use a model-agnostic semiparametric "one-step" (OS) modification of Pearson's correlation to model associations between functional and structural neuroimaging data with age and psychopathology in the Reproducible Brain Charts (RBC) dataset. We use random forest and ridge regression machine learning models and are able to provide model accuracy scores with confidence intervals that are less biased and more replicable. Our method allows valid model comparisons not possible with Pearson's correlation and shows that functional and structural imaging are highly predictive of age, but they do not improve prediction accuracy for psychopathology.

Machine Learning

Psychopathology

Semiparametric Correlation

Prediction 

ENAR