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

• Published in: PloS one Vol. 7; no. 12; p. e48121
• Main Authors: Daducci, Alessandro, Gerhard, Stephan, Griffa, Alessandra, Lemkaddem, Alia, Cammoun, Leila, Gigandet, Xavier, Meuli, Reto, Hagmann, Patric, Thiran, Jean-Philippe
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.12.2012; Public Library of Science (PLoS)
• Subjects: Algorithms; Biology; Brain; Brain - physiology; Brain Mapping - methods; Computer Graphics; Computer programs; Computer Science; Computers; Connectivity analysis; Connectome; Data Interpretation, Statistical; Data processing; Diffusion; Diffusion Magnetic Resonance Imaging - methods; Engineering; Freeware; Humans; Image Processing, Computer-Assisted - methods; Information sharing; Internet; Laboratories; Magnetic resonance; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medicine; Models, Statistical; Morphology; Neural networks; Neuroimaging; Neurosciences; NMR; Nuclear magnetic resonance; Open source software; Pipelines; Programming Languages; Researchers; Signal processing; Software; Software packages; Source code; Statistical analysis; Studies; Trajectory analysis; User-Computer Interface; Switzerland
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0048121

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.