The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

Abstract

Researchers working in the field of global connectivity analysis using diffusion magnetic resonance imaging (MRI) can count on a wide selection of software packages for processing their data, with methods ranging from the reconstruction of the local intra-voxel axonal structure to the estimation of the trajectories of the underlying fibre tracts. However, each package is generally task-specific and uses its own conventions and file formats. In this article we present the Connectome Mapper, a software pipeline aimed at helping researchers through the tedious process of organising, processing and analysing diffusion MRI data to perform global brain connectivity analyses. Our pipeline is written in Python and is freely available as open-source at www.cmtk.org.

Related collections

Most cited references 10

Record: found
Abstract: found
Article: not found

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

Krzysztof J. Gorgolewski, Christopher D. Burns, Cindee Madison … (2012)

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.

0 comments Cited 751 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers.

V J Wedeen, R. -P. Wang, J Schmahmann … (2008)

MRI tractography is the mapping of neural fiber pathways based on diffusion MRI of tissue diffusion anisotropy. Tractography based on diffusion tensor imaging (DTI) cannot directly image multiple fiber orientations within a single voxel. To address this limitation, diffusion spectrum MRI (DSI) and related methods were developed to image complex distributions of intravoxel fiber orientation. Here we demonstrate that tractography based on DSI has the capacity to image crossing fibers in neural tissue. DSI was performed in formalin-fixed brains of adult macaque and in the brains of healthy human subjects. Fiber tract solutions were constructed by a streamline procedure, following directions of maximum diffusion at every point, and analyzed in an interactive visualization environment (TrackVis). We report that DSI tractography accurately shows the known anatomic fiber crossings in optic chiasm, centrum semiovale, and brainstem; fiber intersections in gray matter, including cerebellar folia and the caudate nucleus; and radial fiber architecture in cerebral cortex. In contrast, none of these examples of fiber crossing and complex structure was identified by DTI analysis of the same data sets. These findings indicate that DSI tractography is able to image crossing fibers in neural tissue, an essential step toward non-invasive imaging of connectional neuroanatomy.

0 comments Cited 270 times – based on 0 reviews      Review now

Record: found
Abstract: found
Article: not found

A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements.

Geoffrey J M Parker, Hamied A Haroon, Claudia A M Wheeler-Kingshott (2003)

To establish a general methodology for quantifying streamline-based diffusion fiber tracking methods in terms of probability of connection between points and/or regions. The commonly used streamline approach is adapted to exploit the uncertainty in the orientation of the principal direction of diffusion defined for each image voxel. Running the streamline process repeatedly using Monte Carlo methods to exploit this inherent uncertainty generates maps of connection probability. Uncertainty is defined by interpreting the shape of the diffusion orientation profile provided by the diffusion tensor in terms of the underlying microstructure. Two candidates for describing the uncertainty in the diffusion tensor are proposed and maps of probability of connection to chosen start points or regions are generated in a number of major tracts. The methods presented provide a generic framework for utilizing streamline methods to generate probabilistic maps of connectivity. Copyright 2003 Wiley-Liss, Inc.

0 comments Cited 110 times – based on 0 reviews      Review now

All references

Author and article information

Contributors

Christopher P. Hess: Role: Editor

Journal

Journal ID (nlm-ta): PLoS One

Journal ID (iso-abbrev): PLoS ONE

Journal ID (publisher-id): plos

Journal ID (pmc): plosone

Title: PLoS ONE

Publisher: Public Library of Science (San Francisco, USA )

ISSN (Electronic): 1932-6203

Publication date Collection: 2012

Publication date (Electronic): 18 December 2012

Volume: 7

Issue: 12

Electronic Location Identifier: e48121

Affiliations

[1 ]Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

[2 ]Department of Radiology, Lausanne University Hospital, and University of Lausanne, Lausanne, Switzerland

UCSF, United States of America

Author notes

* E-mail: alessandro.daducci@ 123456epfl.ch

Competing Interests: The authors have declared that no competing interests exist.

Conceived and designed the experiments: PH LC XG AD AL JPT. Performed the experiments: SG AD AG LC. Analyzed the data: AL AG AD RM. Wrote the paper: AD AG AL JPT.

Article

Publisher ID: PONE-D-12-19027

DOI: 10.1371/journal.pone.0048121

PMC ID: 3525592

PubMed ID: 23272041

SO-VID: b02de4f8-cb3b-4ab6-a418-22a097369de7

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 29 June 2012

Date accepted : 20 September 2012

Page count

Pages: 9

Funding

This project was partially supported by Swiss National Science Foundation (grant N°320030-130090) and SPUM (grants N°33CM30-124089 and 320030-141165), as well as the Centre d'Imagerie BioMédicale (CIBM) of the Geneva-Lausanne universities and the Swiss Federal Institute of Technology Lausanne (EPFL), the Leenaards and Louis-Jeantet foundations. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

The Connectome Mapper: An Open-Source Processing Pipeline to Map Connectomes with MRI

Read this article at

Abstract

Related collections

PLOS Global Public Health

Most cited references 10

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

Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers.

A framework for a streamline-based probabilistic index of connectivity (PICo) using a structural interpretation of MRI diffusion measurements.

Author and article information

Contributors

Journal

Affiliations

Author notes

Article

History

Page count

Funding

Categories

Comments

Comment on this article

Similar content 16

Cited by 148

Most referenced authors 317