Making the Science of the Brain Reliable and Reproducible
How Differences in Processing Reliability Can Hinder Advancement in Neuroimaging
Examining how cross-tool differences can distort our ability to detect individual variations, and advance the field.
Embracing Computational Errors to Create More Predictive and Generalizable Biomarkers
Exploring how computational errors can be leveraged to improve models of brain networks.
Computational Errors Could Have a Negative Impact on the Ability to Study Brain Networks
Identifying the effects that random unavoidable computing errors have on developing reliable models of brain-phenotype relationships.
Preprint on Phenotypic Reliability
Achieving better biomarker discovery for a fraction of the cost of large-scale samples.