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

Gorgolewski, Krzysztof J.; Burns, Christopher D.; Madison, Cindee; Clark, Dav; Halchenko, Yaroslav O.; Waskom, Michael; Ghosh, Satrajit

doi:10.3389/fninf.2011.00013

Cited by 1,551 publications

(1,167 citation statements)

References 32 publications

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“…Nipype (Gorgolewski et al 2011) was used to preprocess the fMRI data. Preprocessing consisted of realignment (FSL), artifact rejection (RapidArt) and spatiotemporal filtering to reduce physiological noise (Behzadi et al 2007).…”

Section: Preprocessingmentioning

confidence: 99%

Tinnitus- and Task-Related Differences in Resting-State Networks

Lanting

WoźAniak

Dijk

et al. 2016

Advances in Experimental Medicine and Biology

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We investigated tinnitus-related differences in functional networks in adults with tinnitus by means of a functional connectivity study. Previously it was found that various networks show differences in connectivity in patients with tinnitus compared to controls. How this relates to patients' ongoing tinnitus and whether the ecological sensory environment modulates connectivity remains unknown. Twenty healthy controls and twenty patients suffering from chronic tinnitus were enrolled in this study. Except for the presence of tinnitus in the patient group, all subjects were selected to have normal or near-normal hearing. fMRI data were obtained in two different functional states. In one set of runs, subjects freely viewed emotionally salient movie fragments ("fixed-state") while in the other they were not performing any task ("resting-state"). After data pre-processing, Principal Component Analysis was performed to obtain 25 components for all datasets. These were fed into an Independent Component Analysis (ICA), concatenating the data across both groups and both datasets, to obtain group-level networks of neural origin, each consisting of spatial maps with their respective time-courses. Subject-specific maps and their time-course were obtained by back-projection (Dual Regression). For each of the components a mixed-effects linear model was composed with factors group (tinnitus vs. controls), task (fixed-state vs. resting state) and their interaction. The neural components comprised the visual, sensorimotor, auditory, and limbic systems, the default mode, dorsal attention, executive-control, and frontoparietal networks, and the cerebellum. Most notably, the default mode network (DMN) was less extensive and shows significantly less connectivity in tinnitus patients than in controls. This group difference existed in both paradigms. At the same time, the DMN was stronger during resting-state than during fixed-state in the controls but not the patients. We attribute this pattern to the unremitting engaging effect of the tinnitus percept.

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Section: Preprocessingmentioning

confidence: 99%

Tinnitus- and Task-Related Differences in Resting-State Networks

Lanting

WoźAniak

Dijk

et al. 2016

Advances in Experimental Medicine and Biology

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“…The processing pipeline was developed in Nipype (Gorgolewski et al 2011) and has been described in more detail previously (Ziegler et al 2013). Structural MR images were first segmented using the automated labeling of Freesurfer (Desikan et al 2006).…”

Section: Processingmentioning

confidence: 99%

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

et al. 2015

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This paper studies the link between restingstate functional connectivity (FC), measured by the correlations of fMRI BOLD time courses, and structural connectivity (SC), estimated through fiber tractography. Instead of a static analysis based on the correlation between SC and FC averaged over the entire fMRI time series, we propose a dynamic analysis, based on the time evolution of the correlation between SC and a suitably windowed FC. Assessing the statistical significance of the time series against random phase permutations, our data show a pronounced peak of significance for time window widths around 20-30 TR (40-60 s). Using the appropriate window width, we show that FC patterns oscillate between phases of high modularity, primarily shaped by anatomy, and phases of low modularity, primarily shaped by inter-network connectivity. Building upon recent results in dynamic FC, this emphasizes the potential role of SC as a transitory architecture between different highly connected restingstate FC patterns. Finally, we show that the regions contributing the most to these whole-brain level fluctuations of FC on the supporting anatomical architecture belong to the default mode and the executive control networks Steven Laureys and Rodolphe Sepulchre contributed equally. Electronic supplementary material 123Brain Struct Funct DOI 10.1007/s00429-015-1083-y suggesting that they could be capturing consciousness-related processes such as mind wandering.

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“…The second example uses resting-state fMRI data from 1 out of 102 subjects of the enhanced NKI sample (Nooner et al 2012), which was preprocessed and sampled on the fsaverage5 surface (https://github.com/fliem/nki_nilearn) using Nipype (Gorgolewski et al 2011). A seed region in the left posterior cingulate cortex is extracted from the Destrieux atlas and displayed using the plot_surf_roi function in a medial view (Fig.…”

Section: Results and Limitationsmentioning

confidence: 99%

Loading and plotting of cortical surface representations in Nilearn

Huntenburg¹,

Alexandre²,

Loula³

et al. 2017

RIO

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Processing neuroimaging data on the cortical surface traditionally requires dedicated heavy-weight software suites. Here, we present an initial support of cortical surfaces in Python within the neuroimaging data processing toolbox Nilearn. We provide loading and plotting functions for different surface data formats with minimal dependencies, along with examples of their application. Limitations of the current implementation and potential next steps are discussed.

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

Cited by 1,551 publications

References 32 publications

Tinnitus- and Task-Related Differences in Resting-State Networks

Tinnitus- and Task-Related Differences in Resting-State Networks

Cerebral functional connectivity periodically (de)synchronizes with anatomical constraints

Loading and plotting of cortical surface representations in Nilearn

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