Nipype: A Flexible, Lightweight and Extensible Neuroimaging Data Processing Framework in Python
Krzysztof J. Gorgolewski,Christopher Burns,Cindee Madison,Dav Clark,Yaroslav O. Halchenko,Michael Waskom,Satrajit S. Ghosh +6 more
TLDR
Nipype solves issues by providing Interfaces to existing neuroimaging software with uniform usage semantics and by facilitating interaction between these packages using Workflows, and provides an environment that encourages interactive exploration of algorithms, eases the design of Workflows within and between packages, and reduces the learning Curve.Abstract:
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 BSD 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.read more
Citations
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Human es-fMRI Resource: Concurrent deep-brain stimulation and whole-brain functional MRI
William Hedley Thompson,William Hedley Thompson,Remya Nair,Hiroyuki Oya,Oscar Esteban,James M. Shine,Christopher I. Petkov,Russell A. Poldrack,Matthew A. Howard,Ralph Adolphs +9 more
TL;DR: A unique resource is established and data is presented from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI) with medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations.
Posted ContentDOI
Distributed representation of context by intrinsic subnetworks in prefrontal cortex
Michael Waskom,Anthony D. Wagner +1 more
TL;DR: It is found that pairs of voxels with similar context preferences exhibited spontaneous correlations that were approximately twice as large as those between pairs with opposite context preferences, suggesting that abstract context representations are constrained by an intrinsic functional architecture.
Posted ContentDOI
Multi-modal and multi-subject modular organization of human brain networks
TL;DR: This work investigates and characterize the relationship between anatomical and functional modular organization of the human brain, developing a novel multi- layer framework that expands the classical concept of multi-layer modularity optimization.
Journal ArticleDOI
Biomarker Localization, Analysis, Visualization, Extraction, and Registration (BLAzER) Methodology for Research and Clinical Brain PET Applications.
Fabio Raman,Sameera Grandhi,Charles F. Murchison,Richard E. Kennedy,Susan M. Landau,Erik D. Roberson,Jonathan McConathy,Alzheimer’s Disease Neuroimaging Initiative +7 more
TL;DR: BLAzER provides an efficient methodology for regional brain PET quantification and FDA-cleared components and visualization of registration reduce barriers between research and clinical applications.
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Deep Learning for Predicting Cognitive Gap as a Reliable Biomarker of Dementia
TL;DR: Results indicate that PCG can accurately separate healthy subjects from demented ones and thus, the structure of the brain contributes to the level of human cognition and their functional abilities and therefore, PCG could be used as a biomarker for dementia.
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