Eric Feczko

Eric Feczko is an Assistant Professor in the Department of Clinical Behavioral Neuroscience within the Medical School at the University of Minnesota. His research focuses on advancing neuroimaging methodologies and applying them to understand brain development in children and adolescents, with particular emphasis on pediatric populations.

Dr. Feczko's primary research interests include:

• Functional Connectivity and Resting-State Functional Connectivity
• Functional Magnetic Resonance Imaging (fMRI) methodology and analysis
• Child and Adolescent Brain Development
• Behavioral Neuroscience with a focus on pediatric applications
• Impact of social disadvantage on brain development and substance use trajectories
• Neuroimaging methodology for high-motion populations, particularly minoritized youth

His recent research has focused on developing precision neuroimaging techniques to better understand individual differences in brain network organization. Dr. Feczko has made significant contributions to creating advanced preprocessing pipelines like NiBabies for infant fMRI and sMRIPrep for structural MRI data. His work addresses critical methodological challenges in neuroimaging, particularly related to motion artifacts in pediatric populations, and examines how environmental factors shape brain development. His publications in high-impact journals like Nature Neuroscience demonstrate the significance of his methodological innovations in the field.

Dr. Feczko is actively involved in several major research initiatives:

• Healthy Brain and Child Development National Consortium (as Co-Investigator)
• Neurobehavioral mechanisms linking childhood social disadvantage with substance use trajectories in adolescence and adulthood (NIH-funded project as Co-Investigator)
• Healthy brain and child development national consortium data coordinating center

He has developed and contributed to significant neuroimaging resources including:

• NiBabies: a robust preprocessing pipeline for infant functional MRI
• sMRIPrep: Structural MRI PREProcessing workflows