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BAX: A Toolbox for the Dynamic Analysis of Functional MRI Datasets

BAX: A Toolbox for the Dynamic Analysis of Functional MRI Datasets We developed a toolbox called BAX (brain activation explorer) for the dynamic analysis of functional magnetic resonance imaging (fMRI) datasets using the general linear model. The toolbox provides a graphical user interface where several routines can be accessed to extract different sets of information from a given series of functional images. The dynamic analysis can be implemented using either an incremental approach or a sliding window approach. In particular, BAX can be used to construct dynamic activation maps that can be used to assess the contribution of newly added volumes in the final activation map, detect problematic segments in the dataset, or localize in time dynamic changes in brain activity. Consistency maps, which graphically represent the number of times voxels are consecutively detected as active in a given analysis, can also be constructed using either incremental or sliding window analysis. BAX runs under Matlab (MathWorks, Inc.) and requires some routines from SPM2 (Wellcome Department of Cognitive Neurology, London, UK) for its operation. It can be freely downloaded at http://www.medgrid.org/ website. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

BAX: A Toolbox for the Dynamic Analysis of Functional MRI Datasets

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Publisher
Springer Journals
Copyright
Copyright © 2008 by Humana Press
Subject
Biomedicine; Computational Biology/Bioinformatics; Biotechnology; Neurology ; Computer Appl. in Life Sciences ; Neurosciences
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-008-9017-y
pmid
18465269
Publisher site
See Article on Publisher Site

Abstract

We developed a toolbox called BAX (brain activation explorer) for the dynamic analysis of functional magnetic resonance imaging (fMRI) datasets using the general linear model. The toolbox provides a graphical user interface where several routines can be accessed to extract different sets of information from a given series of functional images. The dynamic analysis can be implemented using either an incremental approach or a sliding window approach. In particular, BAX can be used to construct dynamic activation maps that can be used to assess the contribution of newly added volumes in the final activation map, detect problematic segments in the dataset, or localize in time dynamic changes in brain activity. Consistency maps, which graphically represent the number of times voxels are consecutively detected as active in a given analysis, can also be constructed using either incremental or sliding window analysis. BAX runs under Matlab (MathWorks, Inc.) and requires some routines from SPM2 (Wellcome Department of Cognitive Neurology, London, UK) for its operation. It can be freely downloaded at http://www.medgrid.org/ website.

Journal

Neuroinformatics – Springer Journals

Published: May 9, 2008

References

  • Estimation of general linear model coefficients for real-time application
    Bagarinao, E.; Matsuo, K.; Nakai, T.; Sato, S.
  • Consistency constraint improves specificity in sliding window real-time functional MRI analysis
    Bagarinao, E.; Matsuo, K.; Nakai, T.; Tanaka, Y.
  • Real-time functional magnetic resonance imaging
    Cox, R. W.; Jesmanowics, A.; Hyde, J. S.
  • Real-time independent component analysis of fMRI time-series
    Esposito, F.; Seifritz, E.; Formisano, E.; Morrone, R.; Scarabino, T.; Tedeschi, G.
  • Statistical parametric maps in functional imaging: A general linear approach
    Friston, K.; Holmes, A. P.; Worsley, K. J.; Poline, J. B.; Frith, C. D.; Frackowiak, R. S.
  • Functional magnetic resonance imaging in real time (FIRE): Sliding-window correlation analysis and reference-vector optimization
    Gembris, D.; Taylor, J. G.; Schor, S.; Frings, W.; Suter, D.; Posse, S.
  • Dynamic monitoring of brain activation under visual stimulation using fMRI—The advantage of real-time fMRI with sliding window GLM analysis
    Nakai, T.; Bagarinao, E.; Matsuo, K.; Ohgami, Y.; Kato, C.
  • A new approach to measure single-event related brain activity using real-time fMRI: Feasibility of sensory, motor, and higher cognitive tasks
    Posse, S.; Binkofski, F.; Schneider, F.; Gembris, D.; Frings, W.; Habel, U.
  • Real-time multiple linear regression for fMRI supported by time-aware acquisition and processing
    Smyser, C.; Grabowski, T. J.; Frank, R. J.; Haller, J. W.; Boliner, L.