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Publications

Journal Article Preprint Epublish 6 citations

PubMed: 40462975 DOI: 10.1101/2025.05.14.654069

fMRIPrep Lifespan: Extending A Robust Pipeline for Functional MRI Preprocessing to Developmental Neuroimaging.

bioRxiv

Authors: Mathias Goncalves, Julia Moser, Thomas J Madison, Rae McCollum, Jacob T Lundquist, Begim Fayzullobekova, Lidia Hadera, Han H N Pham, Lucille A Moore, Audrey Houghton, Greg Conan, Martin A Styner, Dimitrios Alexopoulos, Christopher D Smyser, Sally M Stoyell, Sanju Koirala, Steven M Nelson, Kimberly B Weldon, Erik Lee, Robert J M Hermosillo, Luca Vizioli, Essa Yacoub, Gaurav H Patel, Juan Sanchez, Kenneth Wengler, Taylor Salo, Theodore D Satterthwaite, Jed T Elison, Christopher J Markiewicz, Russell A Poldrack, Eric Feczko, Oscar Esteban, Damien A Fair

Affiliations

  • Department of Psychology, Stanford University, Stanford, CA, USA.
  • Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA.
  • Department of Psychiatry and Computer Science, University of North Carolina at Chapel Hill.
  • Department of Neurology, Washington University School of Medicine.
  • Institute of Child Development, University of Minnesota.
  • Department of Pediatrics, University of Minnesota.
  • Center for Magnetic resonance Research, University of Minnesota.
  • Department of Psychiatry, Columbia University Irving Medical Center and New York State Psychiatric Institute.
  • Departments of Psychiatry and Radiology, Icahn School of Medicine at Mount Sinai.
  • Penn Lifespan Informatics and Neuroimaging Center, University of Pennsylvania.
  • Penn-CHOP Lifespan Brain Institute.
  • Department of Radiology, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland.

Abstract

The adoption of a standardized preprocessing workflow is vital for fostering community, sharing, and reproducibility. fMRIPrep has been a critical advancement towards this end, however, it is limited in its capacity to be applied to data across the lifespan, starting from infancy. Here, we introduce fMRIPrep Lifespan, an extension of fMRIPrep that extends the standardized processing from childhood to senescence to include neonatal, infant, and toddler structural and functional MRI data preprocessing. This effort involves a NiPreps integration of 1) a workflow akin to fMRIPrep optimized for MRI data in the first years of life (previously NiBabies) and 2) upstream enhancements to the entire NiPreps suite, including multi-echo data processing, modularization of workflow components, and convergence of processing with other popular workflows (ABCD-BIDS, Human Connectome Project Pipelines). Using data from the Baby Connectome Project (participants 1-43 months of age), we demonstrate that fMRIPrep Lifespan produces high-quality outputs across a wide age range. Moving forward, the scalable, modular infrastructure of fMRIPrep Lifespan will ensure adaptability to data from birth to old age while maintaining robust and reproducible frameworks for functional MRI research across the lifespan.

MeSH

Neuroimaging, Workflow