Brain activity during a motor learning task: An fMRI and skin conductance study

Authors

  • Bradley J. MacIntosh,

    Corresponding author
    1. Imaging Research, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
    2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
    • Imaging Research, Sunnybrook Health Sciences Centre, Research Building, Room S635, 2075 Bayview Avenue, Toronto, Ontario, Canada M4N 3M5
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  • Richard Mraz,

    1. Imaging Research, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
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  • William E. McIlroy,

    1. Department of Physical Therapy, University of Toronto, Toronto, Ontario, Canada
    2. Graduate Department of Rehabilitation Science, University of Toronto, Toronto, Ontario, Canada
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  • Simon J. Graham

    1. Imaging Research, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
    2. Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada
    3. Rotman Research Institute at Baycrest, University of Toronto, Toronto, Ontario, Canada
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Abstract

Measuring electrodermal activity (EDA) during fMRI is an effective means of studying the influence of task-related arousal, inferred from autonomic nervous system activity, on brain activation patterns. The goals of this study were: (1) to measure reliable EDA from healthy individuals during fMRI involving an effortful unilateral motor task, (2) to explore how EDA recordings can be used to augment fMRI data analysis. In addition to conventional hemodynamic modeling, skin conductance time series data were used as model waveforms to generate activation images from fMRI data. Activations from the EDA model produced significantly different brain regions from those obtained with a standard hemodynamic model, primarily in the insula and cingulate cortices. Onsets of the EDA changes were synchronous with the hemodynamic model, but EDA data showed additional transient features, such as a decrease in amplitude with time, and helped to provide behavioral evidence suggesting task difficulty decreased with movement repetition. Univariate statistics also confirmed that several brain regions showed early versus late session effects. Partial least squares (PLS) multivariate analysis of EDA and fMRI data provided complimentary, additional insight on how the motor network varied over the course of a single fMRI session. Brain regions identified in this manner included the insula, cingulate gyrus, pre- and postcentral gyri, putamen and parietal cortices. These results suggest that recording EDA during motor fMRI experiments provides complementary information that can be used to improve the fMRI analysis, particularly when behavioral or task effects are difficult to model a priori. Hum Brain Mapp, 2007. © 2007 Wiley-Liss, Inc.

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