Goncalves, M.; Moser, J.; Madison, T. J.; McCollum, r.; Lundquist, J. T.; Fayzullobekova, B.; Hadera, L.; Pham, H. H. N.; Moore, L. A.; Houghton, A. M.; Conan, G.; Styner, M. A.; Alexopoulos, D.; Smyser, C. D.; Stoyell, S. M.; Koirala, S.; Nelson, S. M.; Weldon, K. B.; Lee, E.; Hermosillo, R. J. M.; Vizioli, L.; Yacoub, E.; Patel, G. H.; Sanchez, J.; Wengler, K.; Salo, T.; Satterthwaite, T. D.; Elison, J. T.; Markiewicz, C. J.; Poldrack, R. A.; Feczko, E.; Esteban, O.; Fair, D. A.

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.