118 citations · 152 across the 7 of their papers we have counts for
7 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…
Deep neural network heatmaps capture Alzheimer's disease patterns reported in a large meta-analysis of neuroimaging studies
Di Wang, Nicolas Honnorat, Peter T. Fox +4
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer's disease and h…
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…
JTrack: A Digital Biomarker Platform for Remote Monitoring in Neurological and Psychiatric Diseases
Mehran Sahandi Far, Michael Stolz, Jona M. Fischer +2
Objective: Health-related data being collected by smartphones offer a promising complementary approach to in-clinic assessments. Here we introduce the JTrack platform as a secure,…
Systematic Misestimation of Machine Learning Performance in Neuroimaging Studies of Depression
Claas Flint, Micah Cearns, Nils Opel +15
We currently observe a disconcerting phenomenon in machine learning studies in psychiatry: While we would expect larger samples to yield better results due to the availability of m…
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…