The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
FATCAT: (An Efficient) Functional And Tractographic Connectivity Analysis Toolbox
Open accessAbstract
We present a suite of software tools for facilitating the combination of functional magnetic resonance imaging (FMRI) and diffusion-based tractography from a network-focused point of view. The programs have been designed for investigating functionally derived gray matter networks and related structural white matter networks. The software comprises the Functional and Tractographic Connectivity Analysis Toolbox (FATCAT), now freely distributed with AFNI. This toolbox supports common file formats and has been designed to integrate as easily as possible with existing standard FMRI pipelines and diffusion software, such as AFNI, FSL, and TrackVis. The programs are efficient, run by commandline for facilitating group processing, and produce several visualizable outputs. Here, we present the programs and their underlying methods, and we also provide a test example of resting-state FMRI analysis combined with tractography. Tractography results are compared with existing methods, showing significantly reduced runtime and generally similar connectivity, but with important differences such as more circumscribed tract regions and more physiologically identifiable paths produced between several region-of-interest pairs. Currently, FATCAT uses only diffusion tensor-based tractography (one direction per voxel), but higher-order models will soon be included.
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Cited by
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From State-to-Trait Meditation: Reconfiguration of Central Executive and Default Mode Networks
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A collaborative resource platform for non-human primate neuroimaging
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Self-citation
Metadata
- Authors
- Taylor, P. A., & Saad, Z. S.
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2013-10
- Volume
- 3
- Issue
- 5
- Pages
- 523-535
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- OpenAlex
- W2073690998
- Cited by
- 210
- References
- 19
- FWCI
- 7.84
- Type
- journal article
- Language
- en
- Open access
- green
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