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      Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python


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          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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          Most cited references18

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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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              Smoothing and cluster thresholding for cortical surface-based group analysis of fMRI data.

              Cortical surface-based analysis of fMRI data has proven to be a useful method with several advantages over 3-dimensional volumetric analyses. Many of the statistical methods used in 3D analyses can be adapted for use with surface-based analyses. Operating within the framework of the FreeSurfer software package, we have implemented a surface-based version of the cluster size exclusion method used for multiple comparisons correction. Furthermore, we have a developed a new method for generating regions of interest on the cortical surface using a sliding threshold of cluster exclusion followed by cluster growth. Cluster size limits for multiple probability thresholds were estimated using random field theory and validated with Monte Carlo simulation. A prerequisite of RFT or cluster size simulation is an estimate of the smoothness of the data. In order to estimate the intrinsic smoothness of group analysis statistics, independent of true activations, we conducted a group analysis of simulated noise data sets. Because smoothing on a cortical surface mesh is typically implemented using an iterative method, rather than directly applying a Gaussian blurring kernel, it is also necessary to determine the width of the equivalent Gaussian blurring kernel as a function of smoothing steps. Iterative smoothing has previously been modeled as continuous heat diffusion, providing a theoretical basis for predicting the equivalent kernel width, but the predictions of the model were not empirically tested. We generated an empirical heat diffusion kernel width function by performing surface-based smoothing simulations and found a large disparity between the expected and actual kernel widths.

                Author and article information

                Front Neuroinform
                Front. Neuroinform.
                Frontiers in Neuroinformatics
                Frontiers Research Foundation
                22 August 2011
                : 5
                : 13
                [1] 1simpleNeuroinformatics and Computational Neuroscience Doctoral Training Centre, School of Informatics, University of Edinburgh Edinburgh, UK
                [2] 2simpleHelen Wills Neuroscience Institute, University of California Berkeley, CA, USA
                [3] 3simpleDepartment of Psychology, University of California Berkeley, CA, USA
                [4] 4simpleDepartment of Psychological and Brain Sciences, Dartmouth College Hanover, NH, USA
                [5] 5simpleDepartment of Brain and Cognitive Sciences, Massachusetts Institute of Technology Cambridge, MA, USA
                [6] 6simpleMcGovern Institute for Brain Research, Massachusetts Institute of Technology Cambridge, MA, USA
                [7] 7simpleResearch Laboratory of Electronics, Massachusetts Institute of Technology Cambridge, MA, USA
                Author notes

                Edited by: Andrew P. Davison, CNRS, France

                Reviewed by: Gael Varoquaux, INSERM, France; Ivo Dinov, University of California, USA

                *Correspondence: Krzysztof Gorgolewski, School of Informatics, University of Edinburgh, Informatics Forum, 10 Crichton Street, Edinburgh EH8 9AB, UK. e-mail: krzysztof.gorgolewski@ 123456gmail.com
                Copyright © 2011 Gorgolewski, Burns, Madison, Clark, Halchenko, Waskom, Ghosh.

                This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

                : 23 June 2011
                : 23 July 2011
                Page count
                Figures: 11, Tables: 2, Equations: 0, References: 32, Pages: 15, Words: 9432
                Original Research

                data processing,workflow,reproducible research,python,neuroimaging,pipeline
                data processing, workflow, reproducible research, python, neuroimaging, pipeline


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