QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Cieslak, Matthew; Cook, Philip A.; He, Xiaosong; Yeh, Fang‐Cheng; Dhollander, Thijs; Adebimpe, Azeez; Aguirre, Geoffrey K.; Bassett, Danielle S.; Betzel, Richard F.; Bourque, Josiane; Cabral, Laura; Davatzikos, Christos; Detre, John A.; Earl, Eric; Elliott, Mark A.; Fadnavis, Shreyas; Fair, Damien A.; Foran, Will; Fotiadis, Panagiotis; Garyfallidis, Eleftherios; Giesbrecht, Barry; Gur, Ruben C.; Gur, Raquel E.; Kelz, Max B.; Keshavan, Anisha; Larsen, Bart; Luna, Beatríz; Mackey, Allyson P.; Milham, Michael P.; Oathes, Desmond J.; Perrone, Anders; Pines, Adam; Roalf, David R.; Richie-Halford, Adam; Rokem, Ariel; Sydnor, Valerie J.; Tapera, Tinashe M.; Tooley, Ursula A.; Vettel, Jean M.; Yeatman, Jason D.; Grafton, Scott T.; Satterthwaite, Theodore D.

doi:10.1038/s41592-021-01185-5

Cited by 181 publications

(162 citation statements)

References 42 publications

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“…Standard processing tools designed for adults are not typically optimized for infant data. In children, adolescents, and adults, neuroimaging processing and analysis have historically been performed using SPM ( Ashburner, 2012 ), FreeSurfer ( Fischl, 2012 ), ANTs ( Tustison et al, 2014 ), FSL ( Smith et al, 2004 ), AFNI ( Cox, 1996 ), or some combination of these packages using an integrated framework combined with custom tools ( Cieslak et al, 2021 , Esteban et al, 2018 , Glasser et al, 2013 , Hagler et al, 2019 ). Unfortunately, these tools and pipelines do not work out of the box for infant neuroimaging.…”

Section: Manuscript Reviewers: Questions That Come Up After Data Are Acquired and Analyzedmentioning

confidence: 99%

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

Korom

Camacho

Filippi

et al. 2022

Developmental Cognitive Neuroscience

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The field of adult neuroimaging relies on well-established principles in research design, imaging sequences, processing pipelines, as well as safety and data collection protocols. The field of infant magnetic resonance imaging, by comparison, is a young field with tremendous scientific potential but continuously evolving standards. The present article aims to initiate a constructive dialog between researchers who grapple with the challenges and inherent limitations of a nascent field and reviewers who evaluate their work. We address 20 questions that researchers commonly receive from research ethics boards, grant, and manuscript reviewers related to infant neuroimaging data collection, safety protocols, study planning, imaging sequences, decisions related to software and hardware, and data processing and sharing, while acknowledging both the accomplishments of the field and areas of much needed future advancements. This article reflects the cumulative knowledge of experts in the FIT’NG community and can act as a resource for both researchers and reviewers alike seeking a deeper understanding of the standards and tradeoffs involved in infant neuroimaging.

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Section: Manuscript Reviewers: Questions That Come Up After Data Are Acquired and Analyzedmentioning

confidence: 99%

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

Korom

Camacho

Filippi

et al. 2022

Developmental Cognitive Neuroscience

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“…Despite potential challenges to segmentation and tracking at the peri-operative timepoints due to mass effect distortions, the AFQ approach generally performed well (Table 2). Given the plethora of increasingly complex dMRI processing tools available, efforts are underway to standardise dMRI pipelines 41 , and AFQ dovetails well with this mission. Ultimately, an automated pipeline avoids manual and subjective ROI placement and is likely more robust to interobserver bias.…”

Section: Discussionmentioning

confidence: 99%

Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome

Toescu

Bruckert

Jabarkheel

et al. 2021

Preprint

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Cerebellar mutism syndrome, characterised by mutism, emotional lability and cerebellar motor signs, occurs in up to 40% of children following resection of medulloblastoma, the most common malignant posterior fossa tumour of childhood. Its pathophysiology remains unclear, but prior studies have implicated damage to the superior cerebellar peduncles. In this study, the objective was to conduct high-resolution spatial profilometry of the cerebellar peduncles and identify anatomic biomarkers of cerebellar mutism syndrome. Twenty-eight children with medulloblastoma (mean age 8.8, s.d. 3.8 years) underwent diffusion MRI at four timepoints over one year. Forty-nine healthy children (mean age 9.0, s.d. 4.2 years), scanned at a single timepoint, served as age- and sex-matched controls. Automated Fibre Quantification was used to segment cerebellar peduncles and compute fractional anisotropy at 30 nodes along each tract. Thirteen patients developed cerebellar mutism syndrome. Fractional anisotropy was significantly lower in the distal segments of the superior cerebellar peduncle pre-operatively in all patients (p=0.01). Pre-operative changes in fractional anisotropy did not predict cerebellar mutism syndrome. However, post-operative reductions in fractional anisotropy were highly specific to the distal left superior cerebellar peduncle, and were most pronounced at follow-up timepoints (p<0.04), in those that developed cerebellar mutism syndrome compared to patients that did not. High spatial resolution cerebellar profilometry identifies a site-specific alteration of the distal segment of the superior cerebellar peduncle unique to cerebellar mutism syndrome with important surgical implications in the treatment of these devastating tumours of childhood.

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“…Requiring a BIDS-like input makes integration with other software in the ecosystem easier. For example, it is fairly straightforward to use the outputs of BIDScompatible preprocessing pipelines, such as QSIprep (26), as inputs to pyAFQ. Furthermore, the modularity of the pyAFQ pipeline means that outputs of other tractography software (e.g., MRTRIX ( 88)) can be used as inputs to bundle recognition, with BIDS filters as the metadata that allows finding and incorporating through the right data.…”

Section: R E S E a R C H A R T I C L Ementioning

confidence: 99%

“…Upper left: pyAFQ requires preprocessed diffusion MRI data in BIDS format. This could be from QSIprep(26) or dMRIprep (https://github.com/nipreps/dmriprep). Bottom right: pyAFQ outputs can serve as inputs to AFQ-Browser for further interaction and visualization(52) or AFQ Insight for statistical analysis(20).…”

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confidence: 99%

Evaluating the Reliability of Human Brain White Matter Tractometry

et al. 2021

Self Cite

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The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.

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QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data

Cited by 181 publications

References 42 publications

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

Dear reviewers: Responses to common reviewer critiques about infant neuroimaging studies

Spatiotemporal changes in along-tract profilometry of cerebellar peduncles in cerebellar mutism syndrome

Evaluating the Reliability of Human Brain White Matter Tractometry

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