Literature DB >> 10407059

Continuous functional magnetic resonance imaging reveals dynamic nonlinearities of "dose-response" curves for finger opposition.

G S Berns1, A W Song, H Mao.   

Abstract

Linear experimental designs have dominated the field of functional neuroimaging, but although successful at mapping regions of relative brain activation, the technique assumes that both cognition and brain activation are linear processes. To test these assumptions, we performed a continuous functional magnetic resonance imaging (MRI) experiment of finger opposition. Subjects performed a visually paced bimanual finger-tapping task. The frequency of finger tapping was continuously varied between 1 and 5 Hz, without any rest blocks. After continuous acquisition of fMRI images, the task-related brain regions were identified with independent components analysis (ICA). When the time courses of the task-related components were plotted against tapping frequency, nonlinear "dose- response" curves were obtained for most subjects. Nonlinearities appeared in both the static and dynamic sense, with hysteresis being prominent in several subjects. The ICA decomposition also demonstrated the spatial dynamics with different components active at different times. These results suggest that the brain response to tapping frequency does not scale linearly, and that it is history-dependent even after accounting for the hemodynamic response function. This implies that finger tapping, as measured with fMRI, is a nonstationary process. When analyzed with a conventional general linear model, a strong correlation to tapping frequency was identified, but the spatiotemporal dynamics were not apparent.

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Year:  1999        PMID: 10407059      PMCID: PMC6783069     

Source DB:  PubMed          Journal:  J Neurosci        ISSN: 0270-6474            Impact factor:   6.167


  8 in total

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Authors:  Tara L Alvarez; Vincent R Vicci; Yelda Alkan; Eun H Kim; Suril Gohel; Anna M Barrett; Nancy Chiaravalloti; Bharat B Biswal
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2.  Pneumatically driven finger movement: a novel passive functional MR imaging technique for presurgical motor and sensory mapping.

Authors:  S Shriver; K E Knierim; J P O'Shea; G H Glover; A J Golby
Journal:  AJNR Am J Neuroradiol       Date:  2011-07-21       Impact factor: 3.825

3.  Dynamic modeling of neuronal responses in fMRI using cubature Kalman filtering.

Authors:  Martin Havlicek; Karl J Friston; Jiri Jan; Milan Brazdil; Vince D Calhoun
Journal:  Neuroimage       Date:  2011-03-09       Impact factor: 6.556

4.  Dynamic Granger causality based on Kalman filter for evaluation of functional network connectivity in fMRI data.

Authors:  Martin Havlicek; Jiri Jan; Milan Brazdil; Vince D Calhoun
Journal:  Neuroimage       Date:  2010-06-01       Impact factor: 6.556

5.  Imaging Brain Dynamics Using Independent Component Analysis.

Authors:  Tzyy-Ping Jung; Scott Makeig; Martin J McKeown; Anthony J Bell; Te-Won Lee; Terrence J Sejnowski
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2001-07-01       Impact factor: 10.961

6.  Differentiation between vergence and saccadic functional activity within the human frontal eye fields and midbrain revealed through fMRI.

Authors:  Yelda Alkan; Bharat B Biswal; Tara L Alvarez
Journal:  PLoS One       Date:  2011-11-02       Impact factor: 3.240

7.  Is the brain's inertia for motor movements different for acceleration and deceleration?

Authors:  Bhim M Adhikari; Kristen M Quinn; Mukesh Dhamala
Journal:  PLoS One       Date:  2013-10-21       Impact factor: 3.240

8.  Age-related functional brain changes in FMR1 premutation carriers.

Authors:  Stephanie S G Brown; Shinjini Basu; Heather C Whalley; Peter C Kind; Andrew C Stanfield
Journal:  Neuroimage Clin       Date:  2017-12-09       Impact factor: 4.881

  8 in total

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