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      FMRIPrep: a robust preprocessing pipeline for functional MRI

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          Abstract

          Preprocessing of functional MRI (fMRI) involves numerous steps to clean and standardize data before statistical analysis. Generally, researchers create ad-hoc preprocessing workflows for each new dataset, building upon a large inventory of tools available. The complexity of these workflows has snowballed with rapid advances in acquisition and processing. We introduce fMRIPrep, an analysis-agnostic tool that addresses the challenge of robust and reproducible preprocessing for fMRI data. FMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of virtually any dataset, ensuring high-quality preprocessing with no manual intervention. By introducing visual assessment checkpoints into an iterative integration framework for software-testing, we show that fMRIPrep robustly produces high-quality results on a diverse fMRI data collection. Additionally, fMRIPrep introduces less uncontrolled spatial smoothness than commonly used preprocessing tools. FMRIPrep equips neuroscientists with a high-quality, robust, easy-to-use and transparent preprocessing workflow, which can help ensure the validity of inference and the interpretability of their results.

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          Most cited references70

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          Distributed and overlapping representations of faces and objects in ventral temporal cortex.

          The functional architecture of the object vision pathway in the human brain was investigated using functional magnetic resonance imaging to measure patterns of response in ventral temporal cortex while subjects viewed faces, cats, five categories of man-made objects, and nonsense pictures. A distinct pattern of response was found for each stimulus category. The distinctiveness of the response to a given category was not due simply to the regions that responded maximally to that category, because the category being viewed also could be identified on the basis of the pattern of response when those regions were excluded from the analysis. Patterns of response that discriminated among all categories were found even within cortical regions that responded maximally to only one category. These results indicate that the representations of faces and objects in ventral temporal cortex are widely distributed and overlapping.
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            Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration.

            All fields of neuroscience that employ brain imaging need to communicate their results with reference to anatomical regions. In particular, comparative morphometry and group analysis of functional and physiological data require coregistration of brains to establish correspondences across brain structures. It is well established that linear registration of one brain to another is inadequate for aligning brain structures, so numerous algorithms have emerged to nonlinearly register brains to one another. This study is the largest evaluation of nonlinear deformation algorithms applied to brain image registration ever conducted. Fourteen algorithms from laboratories around the world are evaluated using 8 different error measures. More than 45,000 registrations between 80 manually labeled brains were performed by algorithms including: AIR, ANIMAL, ART, Diffeomorphic Demons, FNIRT, IRTK, JRD-fluid, ROMEO, SICLE, SyN, and four different SPM5 algorithms ("SPM2-type" and regular Normalization, Unified Segmentation, and the DARTEL Toolbox). All of these registrations were preceded by linear registration between the same image pairs using FLIRT. One of the most significant findings of this study is that the relative performances of the registration methods under comparison appear to be little affected by the choice of subject population, labeling protocol, and type of overlap measure. This is important because it suggests that the findings are generalizable to new subject populations that are labeled or evaluated using different labeling protocols. Furthermore, we ranked the 14 methods according to three completely independent analyses (permutation tests, one-way ANOVA tests, and indifference-zone ranking) and derived three almost identical top rankings of the methods. ART, SyN, IRTK, and SPM's DARTEL Toolbox gave the best results according to overlap and distance measures, with ART and SyN delivering the most consistently high accuracy across subjects and label sets. Updates will be published on the http://www.mindboggle.info/papers/ website.
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              Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python

              Current neuroimaging software offer users an incredible opportunity to analyze their data in different ways, with different underlying assumptions. Several sophisticated software packages (e.g., AFNI, BrainVoyager, FSL, FreeSurfer, Nipy, R, SPM) are used to process and analyze large and often diverse (highly multi-dimensional) data. However, this heterogeneous collection of specialized applications creates several issues that hinder replicable, efficient, and optimal use of neuroimaging analysis approaches: (1) No uniform access to neuroimaging analysis software and usage information; (2) No framework for comparative algorithm development and dissemination; (3) Personnel turnover in laboratories often limits methodological continuity and training new personnel takes time; (4) Neuroimaging software packages do not address computational efficiency; and (5) Methods sections in journal articles are inadequate for reproducing results. To address these issues, we present Nipype (Neuroimaging in Python: Pipelines and Interfaces; http://nipy.org/nipype), an open-source, community-developed, software package, and scriptable library. Nipype solves the issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows. Nipype provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, allows rapid comparative development of algorithms and reduces the learning curve necessary to use different packages. Nipype supports both local and remote execution on multi-core machines and clusters, without additional scripting. Nipype is Berkeley Software Distribution licensed, allowing anyone unrestricted usage. An open, community-driven development philosophy allows the software to quickly adapt and address the varied needs of the evolving neuroimaging community, especially in the context of increasing demand for reproducible research.
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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                31 October 2018
                10 December 2018
                January 2019
                10 June 2019
                : 16
                : 1
                : 111-116
                Affiliations
                [1 ]Department of Psychology, Stanford University, California, USA
                [2 ]Max Planck Institute for Empirical Aesthetics, Hesse, Germany
                [3 ]Computational Neuroimaging Lab, Biocruces Health Research Institute, Bilbao, Spain
                [4 ]Neuroscience Program, University of Iowa, USA
                [5 ]McGovern Institute for Brain Research, Massachusetts Institute of Technology: MIT, Cambridge, MA, USA
                [6 ]Montreal Neurological Institute, McGill University
                [7 ]Department of Psychiatry, Stanford Medical School, Stanford University, California, USA
                [8 ]Department of Neurosurgery, University of Iowa Health Care, Iowa City, Iowa
                [9 ]Department of Otolaryngology, Harvard Medical School, Boston, MA, USA
                Author notes

                AUTHOR CONTRIBUTIONS

                OE contributed with conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, and writing (original draft, review, and editing). CJM contributed with conceptualization, data curation, methodology, software, validation, and writing (review, and editing). RWB contributed with software, validation, and writing (review, and editing). CAM contributed with methodology, software, and writing (review, and editing). AII contributed with software, and writing (review, and editing). AE contributed with software, and writing (review, and editing). JDK contributed with investigation, methodology, software, visualization, and writing (review, and editing). MG contributed with software, and writing (review, and editing). EDP contributed with software, and writing (review, and editing). MS contributed with software, and writing (review, and editing). HO contributed with data acquisition, and writing (review, and editing). SSG contributed with conceptualization, software, and writing (review, and editing). JW contributed with conceptualization, and writing (review, and editing). JD contributed with formal analysis, investigation, methodology, software, and writing (review, and editing). RAP contributed with conceptualization, formal analysis, investigation, methodology, validation, supervision, resources, funding acquisition, and writing (original draft, review, and editing). KJG contributed with conceptualization, data curation, formal analysis, investigation, methodology, software, validation, visualization, supervision, resources, funding acquisition, and writing (original draft, review, and editing).

                Article
                NIHMS1511125
                10.1038/s41592-018-0235-4
                6319393
                30532080
                b2519e47-556c-42b8-8d66-6b5d6f4518d5

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