You can also find my articles on my Google Scholar profile.
We evaluate screening surveys called Prevention Targeting Tools (PTTs) currently used by homelessness prevention service providers in the City and County of Los Angeles.
In our preregistered study, we used anatomical MRI data from the NIMH/NHLBI Data Sharing Project (NNDSP) dataset to compare accuracy in prediction of age for a complex machine learning model with a large number of features to a simple machine learning model with only four features: white matter fraction, grey matter fraction, CSF fraction and intracranial volume, chosen a priori.
In this manuscript, we have explored scanner-related differences in the dataset recently released by the Adolescent Brain Cognitive Development (ABCD) project, a multi-site, longitudinal study of children age 9-10. We demonstrate that scanner manufacturer, model, as well as the individual scanner itself, are detectable in the resting and task-based fMRI results of the ABCD dataset.