» Articles » PMID: 11755092

Accessory Hardware for Neuromuscular Measurements During Functional MRI Experiments

Overview
Journal MAGMA
Publisher Springer
Date 2002 Jan 5
PMID 11755092
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Functional magnetic resonance imaging (fMRI) is increasingly being used for human sensorimotor function research. Few studies, however, have been able to acquire peripheral neuromuscular data (e.g. joint force and electromyograms [EMG]) online with fMRI measurements. The lack of muscle output information hinders interpretation of fMRI data and prevents investigators from designing more sophisticated experiments. We developed a data-acquisition system that can record force and EMG data simultaneously with fMRI signals. This system included three major components: a hydraulic, pressure transducer-based force measurement device, a well-shielded EMG-recording apparatus, and a visual feedback setup. The three components were integrated with a laptop computer equipped with data acquisition hardware and software. System evaluation experiments demonstrated that no significant mutual interference occurred between the MRI environment and the force-EMG data-acquisition system, i.e. the system can record relatively noise-free force and EMG signals while maintaining the quality of fMRI data. The system has enabled us to study human motor control function involving motor tasks such as handgrip and finger pinch that require precision control of force and EMG. This accessory equipment can facilitate fMRI investigations of human sensorimotor function.

Citing Articles

Sophisticated Study of Time, Frequency and Statistical Analysis for Gradient-Switching-Induced Potentials during MRI.

Bouzrara K, Fokapu O, Fakhfakh A, Derbel F Bioengineering (Basel). 2023; 10(11).

PMID: 38002408 PMC: 10669119. DOI: 10.3390/bioengineering10111282.


Nonlinear features of surface EEG showing systematic brain signal adaptations with muscle force and fatigue.

Yao B, Liu J, Brown R, Sahgal V, Yue G Brain Res. 2009; 1272:89-98.

PMID: 19332036 PMC: 2683909. DOI: 10.1016/j.brainres.2009.03.042.


Assessing time-dependent association between scalp EEG and muscle activation: A functional random-effects model approach.

Wang X, Yang Q, Fan Z, Sun C, Yue G J Neurosci Methods. 2008; 177(1):232-40.

PMID: 18977246 PMC: 2659537. DOI: 10.1016/j.jneumeth.2008.09.030.


Multivariate Granger causality analysis of fMRI data.

Deshpande G, LaConte S, James G, Peltier S, Hu X Hum Brain Mapp. 2008; 30(4):1361-73.

PMID: 18537116 PMC: 6870657. DOI: 10.1002/hbm.20606.


Ranking and averaging independent component analysis by reproducibility (RAICAR).

Yang Z, LaConte S, Weng X, Hu X Hum Brain Mapp. 2007; 29(6):711-25.

PMID: 17598162 PMC: 6870671. DOI: 10.1002/hbm.20432.


References
1.
Baudendistel K, Schad L, Wenz F, Essig M, Schroder J, Jahn T . Monitoring of task performance during functional magnetic resonance imaging of sensorimotor cortex at 1.5 T. Magn Reson Imaging. 1996; 14(1):51-8. DOI: 10.1016/0730-725x(95)02052-u. View

2.
Kim S, Ugurbil K . Functional magnetic resonance imaging of the human brain. J Neurosci Methods. 1997; 74(2):229-43. DOI: 10.1016/s0165-0270(97)02252-8. View

3.
Kwong K, Belliveau J, Chesler D, Goldberg I, Weisskoff R, Poncelet B . Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation. Proc Natl Acad Sci U S A. 1992; 89(12):5675-9. PMC: 49355. DOI: 10.1073/pnas.89.12.5675. View

4.
Dettmers C, Connelly A, Stephan K, Turner R, Friston K, Frackowiak R . Quantitative comparison of functional magnetic resonance imaging with positron emission tomography using a force-related paradigm. Neuroimage. 1996; 4(3 Pt 1):201-9. DOI: 10.1006/nimg.1996.0071. View

5.
Ogawa S, Tank D, Menon R, Ellermann J, Kim S, Merkle H . Intrinsic signal changes accompanying sensory stimulation: functional brain mapping with magnetic resonance imaging. Proc Natl Acad Sci U S A. 1992; 89(13):5951-5. PMC: 402116. DOI: 10.1073/pnas.89.13.5951. View