FMRIPrep-next: Preprocessing as a fit-transform model

2257 

Abstract Submission 

Christopher Markiewicz¹, Mathias Goncalves¹, Ma Feilong², Lea Waller³, John Kruper⁴, Robert Smith⁵, Joseph Wexler¹, Juan Sanchez-Pena⁶, Gaurav Patel⁷, Russell Poldrack¹, Oscar Esteban⁸

¹Stanford University, Stanford, CA, ²Dartmouth College, Hanover, NH, ³Charité Universitätsmedizin Berlin, Berlin, Berlin, ⁴University of Washington, Seattle, WA, ⁵Florey Institute of Neuroscience and Mental Health, Melbourne, VIC, ⁶NYSPI Columbia, New York, NY, ⁷Columbia University, New York, NY, ⁸Lausanne University Hospital and University of Lausanne, Lausanne, VD

Christopher Markiewicz, PhD  
Stanford University
Stanford, CA

Mathias Goncalves  
Stanford University
Stanford, CA

Ma Feilong  
Dartmouth College
Hanover, NH

Lea Waller  
Charité Universitätsmedizin Berlin
Berlin, Berlin

John Kruper  
University of Washington
Seattle, WA

Robert Smith  
Florey Institute of Neuroscience and Mental Health
Melbourne, VIC

Joseph Wexler  
Stanford University
Stanford, CA

Juan Sanchez-Pena, M.S.  
NYSPI Columbia
New York, NY

Gaurav Patel, MD/PhD  
Columbia University
New York, NY

Russell Poldrack  
Stanford University
Stanford, CA

Oscar Esteban  
Lausanne University Hospital and University of Lausanne
Lausanne, VD

The value of publicly shared neuroimaging data depends on the level of processing applied to the data. While raw data provide the greatest opportunity for asking novel questions, each step of processing left to secondary researchers is a potential source of analytical variation that can lead to conflicting results from the same source data [0]. A researcher wishing to share the data they have collected can reduce sources of variability in downstream analyses by providing a canonical set of preprocessed data for reuse [2].

Publication of data that have been resampled into several spaces can enable different analyses while limiting analytical variability, but this requires significantly more storage and bandwidth. Generation of many derivatives may also be inefficient on shared, high-performance computing systems suited to computationally intensive tasks with relatively little storage use. It would thus be beneficial to calculate and distribute a compact set of preprocessing derivatives that permit the remaining derivatives to be generated cheaply and deterministically at the time of analysis.

Here we present recent changes in the architecture of fMRIPrep [1], a preprocessing pipeline for functional MRI. These changes separate the typical processing pipeline into two discrete, user-accessible workflows. Firstly, a computationally expensive "fit" stage performs steps such as segmentation, registration, and surface reconstruction, the derivatives of which are small and therefore easy to distribute. A second "transform" workflow then utilizes the raw input data and the derivatives from the "fit" workflow to deterministically generate dense pre-processed fMRI data in any desired target space with minimal additional computational cost. We discuss the practical consequences of these software changes for fMRI data processing and distribution.

The fit-transform architecture has been published in version 23.2.0a2. To test the impact of the described changes, fMRIPrep 23.2.0a2 and the prior release, 23.1.4, were run on two subjects from two different datasets.

Dataset A: 6 T1-weighted, 3 T2-weighted scans, 2 phase-difference fieldmaps, 4 single-echo BOLD runs with 195 volumes, and 1 single-band reference volume per BOLD series.

Dataset B: 2 T1-weighted scans, 6 spin-echo fieldmaps, and 8 single-echo BOLD runs with varying lengths, for a total of 4274 volumes.

The commands tested requested outputs registered to MNI152NLin2009cAsym volumetric template and the fsLR "grayordinate" template. All processes were run on a single, 20-core Intel i9-10900 2.8GHz processor.

Table 1 compares runtimes and the storage usage in each version. Running fit-only workflows resulted in 34-66%, 94-98% and 84-92% reductions in runtime, data size and file counts, compared to the previous version. These reductions reflect the compute/storage utilization of the prior transform processes.

Running fit and transform workflows resulted in 25-52%, 43-54% and 72-87% reductions in runtime, data size and file counts.

Some efficiency was achieved through changes incidental to the structural changes described here. The increase in output sizes reflects additional outputs needed for resampling, present in the fit-only derivatives, as well as some unintended outputs which will be removed in future revisions.

Here we describe changes which result in a significant decrease in computational time and storage utilization for users of fMRIPrep. These changes particularly benefit researchers and data stewards interested in ensuring access to large scale data repositories.

We also anticipate that these changes will simplify the process of resolving errors in preprocessing, as errors of fit and transformation can be addressed separately, and researchers will have the option of providing alternative fit results to be used in transformation. At the same time, the full workflow continues to provide the full range of derivatives that make fMRIPrep an attractive option.

Motion Correction and Preprocessing ²

Workflows ¹

Computing

Data analysis

FUNCTIONAL MRI

Informatics

Open-Source Software

Workflows