18 citations · 29 across the 4 of their papers we have counts for
4 papers
Confound-leakage: Confound Removal in Machine Learning Leads to Leakage
Sami Hamdan, Bradley C. Love, Georg G. von Polier +4
Machine learning (ML) approaches to data analysis are now widely adopted in many fields including epidemiology and medicine. To apply these approaches, confounds must first be remo…
Predictive Data Calibration for Linear Correlation Significance Testing
Kaustubh R. Patil, Simon B. Eickhoff, Robert Langner
Inferring linear relationships lies at the heart of many empirical investigations. A measure of linear dependence should correctly evaluate the strength of the relationship as well…
More Alike than Different: Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder across Neuroimaging Modalities
Nils R. Winter, Ramona Leenings, Jan Ernsting +28
Introduction: Identifying neurobiological differences between patients suffering from Major Depressive Disorder (MDD) and healthy individuals has been a mainstay of clinical neuros…
Benchmarking confound regression strategies for the control of motion artifact in studies of functional connectivity
Rastko Ciric, Daniel H. Wolf, Jonathan D. Power +11
Since initial reports regarding the impact of motion artifact on measures of functional connectivity, there has been a proliferation of confound regression methods to limit its imp…