Pypes: Workflows for Processing Multimodal Neuroimaging Data

Savio, Alexandre; Schütte, Michael; Graña, Manuel; Yakushev, Igor

doi:10.3389/fninf.2017.00025

Cited by 10 publications

(8 citation statements)

References 34 publications

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“…DTI volumes were corrected for eddy currents and head motion using FSL [25]. The general processing of the data was performed using Python programming language with the nipype library [26,27].…”

Section: Preprocessing Of Image Datamentioning

confidence: 99%

Mapping covariance in brain FDG uptake to structural connectivity

Yakushev

Ripp

Wang³

et al. 2021

Eur J Nucl Med Mol Imaging

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Purpose Inter-subject covariance of regional 18F-fluorodeoxyglucose (FDG) PET measures (FDGcov) as proxy of brain connectivity has been gaining an increasing acceptance in the community. Yet, it is still unclear to what extent FDGcov is underlied by actual structural connectivity via white matter fiber tracts. In this study, we quantified the degree of spatial overlap between FDGcov and structural connectivity networks. Methods We retrospectively analyzed neuroimaging data from 303 subjects, both patients with suspected neurodegenerative disorders and healthy individuals. For each subject, structural magnetic resonance, diffusion tensor imaging, and FDG-PET data were available. The images were spatially normalized to a standard space and segmented into 62 anatomical regions using a probabilistic atlas. Sparse inverse covariance estimation was employed to estimate FDGcov. Structural connectivity was measured by streamline tractography through fiber assignment by continuous tracking. Results For the whole brain, 55% of detected connections were found to be convergent, i.e., present in both FDGcov and structural networks. This metric for random networks was significantly lower, i.e., 12%. Convergent were 80% of intralobe connections and only 30% of interhemispheric interlobe connections. Conclusion Structural connectivity via white matter fiber tracts is a relevant substrate of FDGcov, underlying around a half of connections at the whole brain level. Short-range white matter tracts appear to be a major substrate of intralobe FDGcov connections.

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Section: Preprocessing Of Image Datamentioning

confidence: 99%

Mapping covariance in brain FDG uptake to structural connectivity

Yakushev

Ripp

Wang³

et al. 2021

Eur J Nucl Med Mol Imaging

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“…There are also software packages that integrate different tools within a single environment. This is for example the case of BCBtoolkit (Foulon et al, 2018), BrainVISA (Cointepas et al, 2001), BrainSuite 27 , BrainLife 28 (Avesani et al, 2019), Flywheel 29 , fMRIPrep (Esteban et al, 2019), MIBCA 30 (Ribeiro et al, 2015), or Pypes (Savio et al, 2017). Clinica falls within this category.…”

Section: Discussionmentioning

confidence: 99%

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

Routier

Burgos

Díaz

et al. 2021

Front. Neuroinform.

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We present Clinica (www.clinica.run), an open-source software platform designed to make clinical neuroscience studies easier and more reproducible. Clinica aims for researchers to (i) spend less time on data management and processing, (ii) perform reproducible evaluations of their methods, and (iii) easily share data and results within their institution and with external collaborators. The core of Clinica is a set of automatic pipelines for processing and analysis of multimodal neuroimaging data (currently, T1-weighted MRI, diffusion MRI, and PET data), as well as tools for statistics, machine learning, and deep learning. It relies on the brain imaging data structure (BIDS) for the organization of raw neuroimaging datasets and on established tools written by the community to build its pipelines. It also provides converters of public neuroimaging datasets to BIDS (currently ADNI, AIBL, OASIS, and NIFD). Processed data include image-valued scalar fields (e.g., tissue probability maps), meshes, surface-based scalar fields (e.g., cortical thickness maps), or scalar outputs (e.g., regional averages). These data follow the ClinicA Processed Structure (CAPS) format which shares the same philosophy as BIDS. Consistent organization of raw and processed neuroimaging files facilitates the execution of single pipelines and of sequences of pipelines, as well as the integration of processed data into statistics or machine learning frameworks. The target audience of Clinica is neuroscientists or clinicians conducting clinical neuroscience studies involving multimodal imaging, and researchers developing advanced machine learning algorithms applied to neuroimaging data.

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“…We here briefly describe them to highlight their relative strengths ( Table 2 ) and discuss how APPIAN compares to these. There are other PET pipelines that carry out at least three of the six steps performed by APPIAN, they are: PMOD ( Mikolajczyk et al, 1998 ), CapAIBL ( Bourgeat et al, 2015 ), MIAKAT ( Gunn et al, 2016 ), Pypes ( Savio et al, 2017 ), and NiftyPET ( Markiewicz et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

“…A recent multi-modal pipeline, Pypes ( Savio et al, 2017 ), combines PET analysis with structural, diffusion, and functional MR images. This pipeline is free, open-source, and it is also written using NiPype ( Gorgolewski et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

APPIAN: Automated Pipeline for PET Image Analysis

Funck

Larcher²,

Toussaint

et al. 2018

Front. Neuroinform.

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APPIAN is an automated pipeline for user-friendly and reproducible analysis of positron emission tomography (PET) images with the aim of automating all processing steps up to the statistical analysis of measures derived from the final output images. The three primary processing steps are coregistration of PET images to T1-weighted magnetic resonance (MR) images, partial-volume correction (PVC), and quantification with tracer kinetic modeling. While there are alternate open-source PET pipelines, none offers all of the features necessary for making automated PET analysis as reliably, flexibly and easily extendible as possible. To this end, a novel method for automated quality control (QC) has been designed to facilitate reliable, reproducible research by helping users verify that each processing stage has been performed as expected. Additionally, a web browser-based GUI has been implemented to allow both the 3D visualization of the output images, as well as plots describing the quantitative results of the analyses performed by the pipeline. APPIAN also uses flexible region of interest (ROI) definition—with both volumetric and, optionally, surface-based ROI—to allow users to analyze data from a wide variety of experimental paradigms, e.g., longitudinal lesion studies, large cross-sectional population studies, multi-factorial experimental designs, etc. Finally, APPIAN is designed to be modular so that users can easily test new algorithms for PVC or quantification or add entirely new analyses to the basic pipeline. We validate the accuracy of APPIAN against the Monte-Carlo simulated SORTEO database and show that, after PVC, APPIAN recovers radiotracer concentrations within 93–100% accuracy.

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Pypes: Workflows for Processing Multimodal Neuroimaging Data

Cited by 10 publications

References 34 publications

Mapping covariance in brain FDG uptake to structural connectivity

Mapping covariance in brain FDG uptake to structural connectivity

Clinica: An Open-Source Software Platform for Reproducible Clinical Neuroscience Studies

APPIAN: Automated Pipeline for PET Image Analysis

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