BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Gorgolewski, Krzysztof J.; Alfaro-Almagro, Fidel; Auer, Tibor; Bellec, Pierre; Capotă, Mihai; Chakravarty, M. Mallar; Churchill, Nathan W.; Cohen, Alexander L.; Craddock, R. Cameron; Devenyi, Gabriel A.; Eklund, Anders; Estéban, Oscar; Flandin, Guillaume; Ghosh, Samik; Guntupalli, J. Swaroop; Jenkinson, Mark; Keshavan, Anisha; Kiar, Gregory; Liem, Franziskus; Raamana, Pradeep Reddy; Raffelt, David; Steele, Christopher J.; Quirion, Pierre-Olivier; Smith, Robert E.; Strother, Stephen C.; Varoquaux, Gaël; Wang, Yida; Yarkoni, Tal; Poldrack, Russell A.

doi:10.1371/journal.pcbi.1005209

Cited by 253 publications

(181 citation statements)

References 42 publications

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“…Imaging was performed on a Siemens 3T Prisma MRI system at UCLA's Imaging data were preprocessed using the HCP minimal pipelines implemented within the BIDS-App (Gorgolewski et al, 2017). After preprocessing, the functional images were further denoised using FSL's FIX (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FIX).…”

Section: Image Acquisition and Preprocessingmentioning

confidence: 99%

Modulation of amygdala reactivity following rapidly acting interventions for major depression

Loureiro

Leaver

Vasavada

et al. 2020

Human Brain Mapping

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Electroconvulsive therapy (ECT) and ketamine treatment both induce rapidly acting antidepressant effects in patients with major depressive disorder unresponsive to standard treatments, yet their specific impact on emotion processing is unknown. Here, we examined the neural underpinnings of emotion processing within and across patients (N = 44) receiving either ECT (N = 17, mean age: 36.8, 11.0 SD) or repeated subanesthetic (0.5 mg/kg) intravenous ketamine therapy (N = 27, mean age: 37.3, 10.8 SD) using a naturalistic study design. MRI and clinical data were collected before (TP1) and after treatment (TP2); healthy controls (N = 31, mean age: 34.5, 13.5 SD) completed one MRI session (TP1). An fMRI face‐matching task probed negative‐ and positive‐valence systems. Whole‐brain analysis, comparing neurofunctional changes within and across treatment groups, targeted brain regions involved in emotional facial processing, and included regions‐of‐interest analysis of amygdala responsivity. Main findings revealed a decrease in amygdalar reactivity after both ECT and ketamine for positive and negative emotional face processing (p < .05 family wise‐error (FWE) corrected). Subthreshold changes were observed between treatments within the dorsolateral prefrontal cortex and insula (p < .005, uncorrected). BOLD change for positive faces in the inferior parietal cortex significantly correlated with overall symptom improvement, and BOLD change in frontal regions correlated with anxiety for negative faces, and anhedonia for positive faces (p < .05 FWE corrected). Both serial ketamine and ECT treatment modulate amygdala response, while more subtle treatment‐specific changes occur in the larger functional network. Findings point to both common and differential mechanistic upstream systems‐level effects relating to fast‐acting antidepressant response, and symptoms of anxiety and anhedonia, for the processing of emotionally valenced stimuli.

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Section: Image Acquisition and Preprocessingmentioning

confidence: 99%

Modulation of amygdala reactivity following rapidly acting interventions for major depression

Loureiro

Leaver

Vasavada

et al. 2020

Human Brain Mapping

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“…Anatomical images from both samples were preprocessed using the same automated surface-based processing stream of the FreeSurfer Software package (version 6.0.0). For the LHAB sample, this was done via the FreeSurfer BIDS App (v6.0.0-2; Gorgolewski et al (2017). A detailed description of this pipeline is provided by Dale, Fischl, and Sereno (1999) Afterwards, (h) CT was calculated for each vertex as the shortest distance between the white matter surface and the corresponding vertex on the pial surface.…”

Section: Preprocessingmentioning

confidence: 99%

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

Jockwitz

Mérillat

Liem

et al. 2019

Human Brain Mapping

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Normal aging is accompanied by an interindividually variable decline in cognitive abilities and brain structure. This variability, in combination with methodical differences and differences in sample characteristics across studies, pose a major challenge for generalizability of results from different studies. Therefore, the current study aimed at cross‐validating age‐related differences in cognitive abilities and brain structure (measured using cortical thickness [CT]) in two large independent samples, each consisting of 228 healthy older adults aged between 65 and 85 years: the Longitudinal Healthy Aging Brain (LHAB) database (University of Zurich, Switzerland) and the 1000BRAINS (Research Centre Jülich, Germany). Participants from LHAB showed significantly higher education, physical well‐being, and cognitive abilities (processing speed, concept shifting, reasoning, semantic verbal fluency, and vocabulary). In contrast, CT values were larger for participants of 1000BRAINS. Though, both samples showed highly similar age‐related differences in both, cognitive abilities and CT. These effects were in accordance with functional aging theories, for example, posterior to anterior shift in aging as was shown for the default mode network. Thus, the current two‐study approach provides evidence that independently on heterogeneous metrics of brain structure or cognition across studies, age‐related effects on cognitive ability and brain structure can be generalized over different samples, assuming the same methodology is used.

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“…BIDS formatting permitted automatic processing of each of the included OASIS-1 subjects using fMRIPrep version 1.1.1 (Esteban et al, 2018;Gorgolewski et al, 2017) (RRID:SCR_016216) with anatomical image processing only. Briefly, the fMRIPrep pipeline involves linear and deformable registration to the MNI2009cAsym template (Avants, Epstein, Grossman, & Gee, 2008;Fonov et al, 2011) then processing of the structural MRI through Freesurfer for cortical surface and subcortical volumetric labeling (Dale, Fischl, & Sereno, 1999;Bruce Fischl, 2012) (RRID:SCR_001847).…”

Section: Region-of-interest Segmentationmentioning

confidence: 99%

“…Our choice to investigate fMRIPrep registration performance was motivated by their transparent approach to the development of preprocessing software for neuroimaging and BIDS integration (Gorgolewski et al, 2017(Gorgolewski et al, , 2016. The active developer and support base, as well as growing adoption by many end-users were other contributing factors.…”

Section: Subject-to-template Registrationmentioning

confidence: 99%

A framework for evaluating correspondence between brain images using anatomical fiducials

Lau

Parrent

DeMarco

et al. 2018

Preprint

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First Research Excellence Fund to BrainsCAN, Brain Canada, and computational resource through Compute Canada.Abstract: Accurate spatial correspondence between template and subject images is a crucial step in neuroimaging studies and clinical applications like stereotactic neurosurgery. In the absence of a robust quantitative approach, we sought to propose and validate a set of point landmarks, anatomical fiducials (AFIDs), that could be quickly, accurately, and reliably placed on magnetic resonance images of the human brain. Using several publicly available brain templates and individual participant datasets, novice users could be trained to place a set of 32 AFIDs with millimetric accuracy. Furthermore, the utility of the AFIDs protocol is demonstrated for evaluating subject-to-template and template-to-template registration.Specifically, we found that commonly used voxel overlap metrics were relatively insensitive to focal misregistrations compared to AFID point-based measures. Our entire protocol and study framework leverages open resources and tools, and has been developed with full transparency in mind so that others may freely use, adopt, and modify. This protocol holds value for a broad number of applications including alignment of brain images and teaching neuroanatomy.Establishing spatial correspondence between images is a crucial step in neuroimaging studies enabling fusion of multimodal information, analysis of focal morphological differences, and comparison of withinand between-study data in a common coordinate space. Stereotaxy arose as a result of questions raised by scientists and surgeons interested in the physiology and treatment of focal brain structures (A. C. Evans,

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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods

Cited by 253 publications

References 42 publications

Modulation of amygdala reactivity following rapidly acting interventions for major depression

Modulation of amygdala reactivity following rapidly acting interventions for major depression

Generalizing age effects on brain structure and cognition: A two‐study comparison approach

A framework for evaluating correspondence between brain images using anatomical fiducials

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