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47 Assessing the Feasibility of Cardiorespiratory Exercise During Functional MRI

Published online by Cambridge University Press:  21 December 2023

J A Cloud*
Affiliation:
The Ohio State University, Department of Psychology, Columbus, Ohio, USA.
K J Hiersche
Affiliation:
The Ohio State University, Department of Psychology, Columbus, OH, USA.
A N Hasselbach
Affiliation:
The Ohio State University, Department of Psychology, Columbus, OH, USA.
A N Shin
Affiliation:
The Ohio State University, Department of Psychology, Columbus, OH, USA.
V J Williams
Affiliation:
Division of Geriatrics and Gerontology, Department of Medicine, University of Wisconsin at Madison, School of Medicine and Public Health, Madison, WI, USA. Wisconsin Alzheimer's Disease Research Center, University of Wisconsin at Madison, School of Medicine and Public Health, Madison, WI, USA.
D Salat
Affiliation:
Neuroimaging Research for Veterans Center, VA Boston Healthcare System, Boston, MA, USA. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard University, Boston, MA, USA
S M Hayes
Affiliation:
The Ohio State University, Department of Psychology, Columbus, Ohio, USA.
*
Correspondence: J.A. Cloud The Ohio State University, Department of Psychology [email protected]
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Abstract

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Objective:

To examine the feasibility of implementing a cardiorespiratory exercise stimulus during functional Magnetic Resonance Imaging (fMRI).

Participants and Methods:

12 young adults (age: 18-22 years) completed progressive maximal exercise testing and a brain MRI scan. During scanning, participants completed three runs of functional MRI (volumes = 619; TR = 800 ms; multiband = 4; voxel size = 3 mm3). During each 8 minute fMRI run, participants completed an exercise challenge consisting of alternating blocks of exercise and rest. Exercise was implemented with a cardiostepper, an MRI-compatible device (similar to a Stairmaster) capable of generating a cardiorespiratory exercise stimulus. During exercise blocks, participants stepped at a rate of 60 Hz with pedal resistance determined by participants' fitness level. Heart rate and respiration data were collected during MRI. fMRI data were processed and analyzed using FMRIB Software Library (FSL). The ARtifact Detection Toolbox (ART) software was also used to identify volumes with significant artifact, and ICA-AROMA was used to remove motion-related BOLD signal components.

Results:

During exercise blocks, heart rate increased (mean = 131 beats per minute) compared to rest (mean = 87 beats per minute; t(34) = 4.3; p < .001). The mean heart rate during exercise blocks corresponds to an exercise intensity in the light to moderate intensity range for this age group. Motion (median framewise displacement) was significantly higher during exercise (mean = .53 mm) than rest (mean = .36 mm). Across all blocks, ART classified 19.8% of brain volumes as artifact-containing outliers, with 69% of the outliers occurring during exercise blocks. Although greater head motion was observed during exercise, the use of ICA-AROMA reduced the impact of motion considerably, recovering an additional 25% of the task-related signal, relative to noise. Comparison of fMRI activity during exercise versus rest revealed significant associations with primary and supplementary motor cortices, hippocampus, and the insula, among other regions.

Conclusions:

The current study demonstrates the feasibility of eliciting light to moderate intensity cardiorespiratory exercise (using a lower body stepping exercise) during functional MRI. Although increased head motion was observed during exercise compared to rest, the degree of head motion was roughly approximate to the values published in previous fMRI studies and post image acquisition processing improved task-related signal. During exercise, increased brain activation was observed in regions associated with the central command network, which regulates autonomic nervous system and musculoskeletal function during exercise.

Type
Poster Session 10: Late Breaking Science
Copyright
Copyright © INS. Published by Cambridge University Press, 2023