Literature DB >> 17946850

Directed transfer function analysis of fMRI data to investigate network dynamics.

Gopikrishna Deshpande1, Stephen LaConte, Scott Peltier, Xiaoping Hu.   

Abstract

In this work, we have adapted the directed transfer function (DTF) to fMRI for the analysis of cortical network dynamics. While modern fMRI sequences are capable of sampling at second or sub-second rates, the underlying hemodynamic response limits the true temporal resolution to the order of 6-12 seconds. Therefore, DTF analysis of fMRI is appropriate for characterizing dynamics in brain response which evolves more slowly than the fMRI response, such as those during learning, fatigue and habituation. In such cases, the response to repeated trials will change with time and a summary measure from each trial can be used as input to the DTF analysis because these summary measures are of appropriate sampling rates and are not affected by the sluggishness of the hemodynamic response. As an example, we investigated the dynamic effects of muscle fatigue on the motor network. Specifically, DTF was used as a multivariate measure of the strength and direction of information flow between the various nodes of the network. We found that the primary motor area had a causal influence on the supplementary motor area, pre-motor area and cerebellum, and this influence initially increased with time and diminished towards the end of the experiment, probably as a result of fatigue.

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Year:  2006        PMID: 17946850     DOI: 10.1109/IEMBS.2006.259969

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  5 in total

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Authors:  Anna Korzeniewska; Piotr J Franaszczuk; Ciprian M Crainiceanu; Rafał Kuś; Nathan E Crone
Journal:  Neuroimage       Date:  2011-03-16       Impact factor: 6.556

2.  Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

Authors:  Catie Chang; Gary H Glover
Journal:  Neuroimage       Date:  2009-12-16       Impact factor: 6.556

3.  Quantifying auditory event-related responses in multichannel human intracranial recordings.

Authors:  Dana Boatman-Reich; Piotr J Franaszczuk; Anna Korzeniewska; Brian Caffo; Eva K Ritzl; Sarah Colwell; Nathan E Crone
Journal:  Front Comput Neurosci       Date:  2010-03-19       Impact factor: 2.380

4.  Multivariate Granger causality analysis of fMRI data.

Authors:  Gopikrishna Deshpande; Stephan LaConte; George Andrew James; Scott Peltier; Xiaoping Hu
Journal:  Hum Brain Mapp       Date:  2009-04       Impact factor: 5.038

5.  Dynamic fMRI networks predict success in a behavioral weight loss program among older adults.

Authors:  Fatemeh Mokhtari; W Jack Rejeski; Yingying Zhu; Guorong Wu; Sean L Simpson; Jonathan H Burdette; Paul J Laurienti
Journal:  Neuroimage       Date:  2018-02-19       Impact factor: 6.556

  5 in total

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