Preprocessed Consortium for Neuropsychiatric Phenomics dataset

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Abstract

Here we present preprocessed MRI data of 265 participants from the Consortium for Neuropsychiatric Phenomics (CNP) dataset. The preprocessed dataset includes minimally preprocessed data in the native, MNI and surface spaces accompanied with potential confound regressors, tissue probability masks, brain masks and transformations. In addition the preprocessed dataset includes unthresholded group level and single subject statistical maps from all tasks included in the original dataset. We hope that availability of this dataset will greatly accelerate research.

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Most cited references 27

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FSL.

Mark Jenkinson, Christian F. Beckmann, Timothy Behrens … (2012)

FSL (the FMRIB Software Library) is a comprehensive library of analysis tools for functional, structural and diffusion MRI brain imaging data, written mainly by members of the Analysis Group, FMRIB, Oxford. For this NeuroImage special issue on "20 years of fMRI" we have been asked to write about the history, developments and current status of FSL. We also include some descriptions of parts of FSL that are not well covered in the existing literature. We hope that some of this content might be of interest to users of FSL, and also maybe to new research groups considering creating, releasing and supporting new software packages for brain image analysis. Copyright © 2011 Elsevier Inc. All rights reserved.

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Cortical surface-based analysis. I. Segmentation and surface reconstruction.

Anders Dale, Bruce Fischl, Martin I. Sereno (1999)

Several properties of the cerebral cortex, including its columnar and laminar organization, as well as the topographic organization of cortical areas, can only be properly understood in the context of the intrinsic two-dimensional structure of the cortical surface. In order to study such cortical properties in humans, it is necessary to obtain an accurate and explicit representation of the cortical surface in individual subjects. Here we describe a set of automated procedures for obtaining accurate reconstructions of the cortical surface, which have been applied to data from more than 100 subjects, requiring little or no manual intervention. Automated routines for unfolding and flattening the cortical surface are described in a companion paper. These procedures allow for the routine use of cortical surface-based analysis and visualization methods in functional brain imaging. Copyright 1999 Academic Press.

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N4ITK: improved N3 bias correction.

Nicholas J Tustison, Brian B Avants, Philip A Cook … (2010)

A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least-squares fitting. We propose the substitution of a recently developed fast and robust B-spline approximation routine and a modified hierarchical optimization scheme for improved bias field correction over the original N3 algorithm. Similar to the N3 algorithm, we also make the source code, testing, and technical documentation of our contribution, which we denote as "N4ITK," available to the public through the Insight Toolkit of the National Institutes of Health. Performance assessment is demonstrated using simulated data from the publicly available Brainweb database, hyperpolarized (3)He lung image data, and 9.4T postmortem hippocampus data.

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Author and article information

Contributors

Krzysztof J. Gorgolewski: Role: ConceptualizationRole: Data CurationRole: Funding AcquisitionRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – Original Draft PreparationRole: Writing – Review & Editing

ORCID: https://orcid.org/0000-0003-3321-7583

Joke Durnez: Role: Data CurationRole: Formal AnalysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – Review & Editing

Russell A. Poldrack: Role: ConceptualizationRole: Funding AcquisitionRole: SupervisionRole: ValidationRole: Writing – Review & Editing

Journal

Journal ID (nlm-ta): F1000Res

Journal ID (iso-abbrev): F1000Res

Journal ID (pmc): F1000Research

Title: F1000Research

Publisher: F1000Research (London, UK )

ISSN (Electronic): 2046-1402

Publication date (Electronic): 22 September 2017

Publication date Collection: 2017

Volume: 6

Electronic Location Identifier: 1262

Affiliations

[1 ]Department of Psychology, Stanford University, Stanford, CA, USA

[2 ]INRIA Parietal, Neurospin, Saclay, Gif-sur-Yvette, France

[1 ]Department of Physics, Florida International University, Miami, FL, USA

[1 ]Hospital Israelita Albert Einstein, São Paulo, SP, Brazil

[2 ]Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

[1 ]Hospital Israelita Albert Einstein, São Paulo, SP, Brazil

[2 ]Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA

Center for Reproducible Neuroscience, Stanford University, USA

[1 ]Department of Physics, Florida International University, Miami, FL, USA

Center for Reproducible Neuroscience, Stanford University, USA

Author notes

[a ] krzysztof.gorgolewski@ 123456gmail.com

[b ] jdurnez@ 123456stanford.edu

*Equal contributors

Competing interests: No competing interests were disclosed.

Author information

Krzysztof J. Gorgolewski https://orcid.org/0000-0003-3321-7583

Article

DOI: 10.12688/f1000research.11964.2

PMC ID: 5664981

PubMed ID: 29152222

SO-VID: 77fa748d-9465-445c-a240-0aefb783e0d5

License:

This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

History

Date accepted : 1 November 2017

Funding

Funded by: Horizon 2020

Award ID: 706561

Funded by: Laura and John Arnold Foundation

This work has been funded by the Laura and John Arnold Foundation. JD has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 706561. The acquisition of the original dataset was supported by the Consortium for Neuropsychiatric Phenomics (NIH Roadmap for Medical Research grants UL1-DE019580, RL1MH083268, RL1MH083269, RL1DA024853, RL1MH083270, RL1LM009833, PL1MH083271, and PL1NS062410).

The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Preprocessed Consortium for Neuropsychiatric Phenomics dataset

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Most cited references 27

FSL.

Cortical surface-based analysis. I. Segmentation and surface reconstruction.

N4ITK: improved N3 bias correction.

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