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      Brain Activity During Unilateral Physical and Imagined Isometric Contractions

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          Abstract

          By convention, it is believed that the ipsilateral side of the body is controlled by the contralateral side of the brain. Past studies measuring brain activity primarily recorded changes before and after an intervention is performed on one side of the body within one hemisphere (usually the contralateral) of the brain. The purpose of this investigation was to observe the brain activity within the left and right hemispheres of the prefrontal and sensorimotor cortices during physical and imagined, dominant and non-dominant unilateral isometric elbow flexion. Fifteen right hand dominant individuals (six males and nine females) between the ages of 18 and 21 performed four different isometric contractions of their biceps brachii at a preacher curl bench: dominant physical contraction (DomCon), non-dominant physical contraction (NonCon), dominant imagined contraction (DomImagine), and non-dominant imagined contraction (NonImagine). Each contraction was sustained for 5 s followed by 30 s of rest. Motor activity-related cortical potential (MRCP) and event-related spectral perturbation (ERSP) within the right and left hemispheres of the sensorimotor and prefrontal cortices were determined for each condition at 500–1,000 ms and 2,000–2,500 ms after initiation of contraction. MRCP and ERSP were both changed at the 500–1,000 ms time window for all conditions. Changes in the 2,000–2,500 ms window were most consistently observed during physical contractions. While the changes during DomCon occurred in the left (contralateral) side of the brain, the greatest changes observed in MRCP and ERSP occurred in both sides of the brain during the NonCon condition. Further understanding of bilateral changes in brain activity during unilateral tasks is valuable for improving rehabilitation practices through mental and physical exercise.

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          Most cited references29

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          Motor imagery: a backdoor to the motor system after stroke?

          Understanding brain plasticity after stroke is important in developing rehabilitation strategies. Active movement therapies show considerable promise but depend on motor performance, excluding many otherwise eligible patients. Motor imagery is widely used in sport to improve performance, which raises the possibility of applying it both as a rehabilitation method and to access the motor network independently of recovery. Specifically, whether the primary motor cortex (M1), considered a prime target of poststroke rehabilitation, is involved in motor imagery is unresolved. We review methodological considerations when applying motor imagery to healthy subjects and in patients with stroke, which may disrupt the motor imagery network. We then review firstly the motor imagery training literature focusing on upper-limb recovery, and secondly the functional imaging literature in healthy subjects and in patients with stroke. The review highlights the difficulty in addressing cognitive screening and compliance in motor imagery studies, particularly with regards to patients with stroke. Despite this, the literature suggests the encouraging effect of motor imagery training on motor recovery after stroke. Based on the available literature in healthy volunteers, robust activation of the nonprimary motor structures, but only weak and inconsistent activation of M1, occurs during motor imagery. In patients with stroke, the cortical activation patterns are essentially unexplored as is the underlying mechanism of motor imagery training. Provided appropriate methodology is implemented, motor imagery may provide a valuable tool to access the motor network and improve outcome after stroke.
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            Strength increases from the motor program: comparison of training with maximal voluntary and imagined muscle contractions.

            G. Yue, K Cole (1992)
            1. This study addressed potential neural mechanisms of the strength increase that occur before muscle hypertrophy. In particular we examined whether such strength increases may result from training-induced changes in voluntary motor programs. We compared the maximal voluntary force production after a training program of repetitive maximal isometric muscle contractions with force output after a training program that did not involve repetitive activation of muscle; that is, after mental training. 2. Subjects trained their left hypothenar muscles for 4 wk, five sessions per week. One group produced repeated maximal isometric contractions of the abductor muscles of the fifth digit's metacarpophalangeal joint. A second group imagined producing these same, effortful isometric contractions. A third group did not train their fifth digit. Maximal abduction force, flexion/extension force and electrically evoked twitch force (abduction) of the fifth digit were measured along with maximal integrated electromyograms (EMG) of the hypothenar muscles from both hands before and after training. 3. Average abduction force of the left fifth digit increased 22% for the Imagining group and 30% for the Contraction group. The mean increase for the Control group was 3.7%. 4. The maximal abduction force of the right (untrained) fifth digit increased significantly in both the Imagining and Contraction groups after training (10 and 14%, respectively), but not in the Control group (2.3%). These results are consistent with previous studies of training effects on contralateral limbs. 5. The abduction twitch force evoked by supramaximal electrical stimulations of the ulnar nerve was unchanged in all three groups after training, consistent with an absence of muscle hypertrophy. The maximal force of the left great toe extensors for individual subjects remained unchanged after training, which argues against strength increases due to general increases in effort level. 6. Increases in abduction and flexion forces of the fifth digit were poorly correlated in subjects of both training groups. The fifth finger abduction force and the hypothenar integrated EMG increases were not well correlated in these subjects either. Together these results indicate that training-induced changes of synergist and antagonist muscle activation patterns may have contributed to force increases in some of the subjects. 7. Strength increases can be achieved without repeated muscle activation. These force gains appear to result from practice effects on central motor programming/planning. The results of these experiments add to existing evidence for the neural origin of strength increases that occur before muscle hypertrophy.
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              Information-based modeling of event-related brain dynamics.

              We discuss the theory and practice of applying independent component analysis (ICA) to electroencephalographic (EEG) data. ICA blindly decomposes multi-channel EEG data into maximally independent component processes (ICs) that typically express either particularly brain generated EEG activities or some type of non-brain artifacts (line or other environmental noise, eye blinks and other eye movements, or scalp or heart muscle activity). Each brain and non-brain IC is identified with an activity time course (its 'activation') and a set of relative strengths of its projections (by volume conduction) to the recording electrodes (its 'scalp map'). Many non-articraft IC scalp maps strongly resemble the projection of a single dipole, allowing the location and orientation of the best-fitting equivalent dipole (or other source model) to be easily determined. In favorable circumstances, ICA decomposition of high-density scalp EEG data appears to allow concurrent monitoring, with high time resolution, of separate EEG activities in twenty or more separate cortical EEG source areas. We illustrate the differences between ICA and traditional approaches to EEG analysis by comparing time courses and mean event related spectral perturbations (ERSPs) of scalp channel and IC data. Comparing IC activities across subjects necessitates clustering of similar Ics based on common dynamic and/or spatial features. We discuss and illustrate such a component clustering strategy. In sum, continued application of ICA methods in EEG research should continue to yield new insights into the nature and role of the complex macroscopic cortical dynamics captured by scalp electrode recordings.
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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                26 November 2019
                2019
                : 13
                : 413
                Affiliations
                [1] 1Department of Exercise Science, Elon University , Elon, NC, United States
                [2] 2Department of Physical Therapy Education, Elon University , Elon, NC, United States
                Author notes

                Edited by: Stephane Perrey, Université de Montpellier, France

                Reviewed by: Athanasios Vourvopoulos, Laboratory for Robotics and Engineering Systems (LARSyS), Instituto Superior Técnico (ISR), Portugal; Muthuraman Muthuraman, University Medical Center of the Johannes Gutenberg University Mainz, Germany

                *Correspondence: Stephen P. Bailey baileys@ 123456elon.edu

                Specialty section: This article was submitted to Motor Neuroscience, a section of the journal Frontiers in Human Neuroscience

                Article
                10.3389/fnhum.2019.00413
                7004234
                9a8365f0-8a5b-4b40-b299-b51b549c5d48
                Copyright © 2019 Martinez, Wittstein, Folger and Bailey.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 26 July 2019
                : 07 November 2019
                Page count
                Figures: 9, Tables: 0, Equations: 0, References: 32, Pages: 12, Words: 6829
                Categories
                Human Neuroscience
                Original Research

                Neurosciences
                imagery,eeg,isometric,prefrontal cortex,sensorimotor cortex
                Neurosciences
                imagery, eeg, isometric, prefrontal cortex, sensorimotor cortex

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