The relationship between functional MRI (fMRI)-measured brain signal and muscle force and or electromyogram (EMG) is critical in interpreting fMRI data and understanding the control mechanisms of voluntary motor actions. We designed a system that could record joint force and surface EMG online with fMRI data. High-quality force and EMG data were obtained while maintaining the quality of the fMRI brain images. Using this system, we determined the relationship between fMRI-measured brain activation and handgrip force and between fMRI-measured brain signal and EMG of extrinsic finger muscles. Ten volunteers participated in the experiments (only seven subjects' data were analyzed due to excessive noise in the fMRI data of three subjects). The participants exerted 20%, 35%, 50%, 65%, and 80% of the maximal force. During each contraction period, handgrip force, surface EMG of the finger flexor and extensor muscles, and fMRI brain images were acquired. The degree of muscle activation (force and EMG) was directly proportional to the amplitude of the brain signal determined by fMRI in the entire brain and in a number of motor function-related cortical fields, including primary motor, sensory regions, supplementary motor area, premotor, prefrontal, parietal and cingulate cortices, and cerebellum. All the examined brain areas demonstrated a similar relationship between the fMRI signal and force. A stronger fMRI signal during higher force indicates that more cortical output neurons and/or interneurons may participate in generating descending commands and/or processing additional sensory information. The similarity in the relationship between muscle output and fMRI signal in the cortical regions suggests that correlated or networked activation among a number of cortical fields may be necessary for controlling precise static force of finger muscles.

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Aging has a degenerative effect on hand function, including declines in hand and finger strength and ability to control submaximal pinch force and maintain a steady precision pinch posture, manual speed, and hand sensation.

During prolonged submaximal muscle contractions, electromyographic (EMG) signals typically increase as a result of increasing motor unit activities to compensate for fatigue-induced force loss in the muscle. It is thought that cortical signals driving the muscle to higher activation levels also increases, but this has never been experimentally demonstrated. The purpose of this study was to quantify brain activation during submaximal fatigue muscle contractions using functional magnetic resonance imaging (fMRI). Twelve volunteers performed a sustained handgrip contraction for 225 s and 320 intermittent handgrip contractions ( approximately 960 s) at 30% maximal level while their brain was imaged. For the sustained contraction, EMG signals of the finger flexor muscles increased linearly while the target force was maintained. The fMRI-measured cortical activities in the contralateral sensorimotor cortex increased sharply during the first 150 s, then plateaued during the last 75 s. For the intermittent contractions, the EMG signals increased during the first 660 s and then began to decline, while the handgrip force also showed a sign of decrease despite maximal effort to maintain the force. The fMRI signal of the contralateral sensorimotor area showed a linear rise for most part of the task and plateaued at the end. For both the tasks, the fMRI signals in the ipsilateral sensorimotor cortex, prefrontal cortex, cingulate gyrus, supplementary motor area, and cerebellum exhibited steady increases. These results showed that the brain increased its output to reinforce the muscle for the continuation of the performance and possibly to process additional sensory information.

Despite abundant evidence that different nervous system control strategies may exist for human concentric and eccentric muscle contractions, no data are available to indicate that the brain signal differs for eccentric versus concentric muscle actions. The purpose of this study was to evaluate electroencephalography (EEG)-derived movement-related cortical potential (MRCP) and to determine whether the level of MRCP-measured cortical activation differs between the two types of muscle activities. Eight healthy subjects performed 50 voluntary eccentric and 50 voluntary concentric elbow flexor contractions against a load equal to 10% body weight. Surface EEG signals from four scalp locations overlying sensorimotor-related cortical areas in the frontal and parietal lobes were measured along with kinetic and kinematic information from the muscle and joint. MRCP was derived from the EEG signals of the eccentric and concentric muscle contractions. Although the elbow flexor muscle activation (EMG) was lower during eccentric than concentric actions, the amplitude of two major MRCP components-one related to movement planning and execution and the other associated with feedback signals from the peripheral systems-was significantly greater for eccentric than for concentric actions. The MRCP onset time for the eccentric task occurred earlier than that for the concentric task. The greater cortical signal for eccentric muscle actions suggests that the brain probably plans and programs eccentric movements differently from concentric muscle tasks.

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The purpose of this study was to investigate the relationship between EEG-derived motor activity-related cortical potential (MRCP) and voluntary muscle activation. Eight healthy volunteers participated in two experimental sessions. In one session, subjects performed isometric elbow-flexion contractions at four intensity levels [10%, 35%, 60%, and 85% maximal voluntary contraction (MVC)]. In another session, a given elbow-flexion force (35% MVC) was generated at three different rates (slow, intermediate, and fast). Thirty to 40 contractions were performed at each force level or rate. EEG signals were recorded from the scalp overlying the supplementary motor area (SMA) and contralateral sensorimotor cortex, and EMG signals were recorded from the skin surface overlying the belly of the biceps brachii and brachioradialis muscles during all contractions. In each trial, the force was used as the triggering signal for MRCP averaging. MRCP amplitude was measured from the beginning to the peak of the negative slope. The magnitude of MRCP from both EEG recording locations (sensorimotor cortex and SMA) was highly correlated with elbow-flexion force, rate of rising of force, and muscle EMG signals. These results suggest that MRCP represents cortical motor commands that scale the level of muscle activation.

Objective-To investigate the functional connection between motor cortex and muscles, we measured Electroencephalogram-Electromyogram (EEG-EMG) coherence of stroke patients and controls.Methods-Eight healthy controls and 21 patients with shoulder and elbow coordination deficits were enrolled. All subjects performed a reaching task involving shoulder flexion and elbow extension. EMG of the anterior deltoid (AD) and brachii muscles (BB, TB) and 64-channel scalp EEG were recorded during the task. Time-frequency coherence was calculated using the bivariate autoregressive model. Results-Stroke patients had significantly lower corticomuscular coherence compared with healthy controls for the AD and BB muscles at both the beta (20-30 Hz) and lower gamma (30-40 Hz) bands during the movement. BH procedure (FDR) identified a reduced corticomuscular coherence for stroke patients in 11 of 15 scalp area-muscle combinations. There was no statistically significant difference between stroke patients and control subjects according to coherence in other frequency bands.Conclusion-Poorly recovered stroke survivors with persistent upper-limb motor deficits exhibited significantly lower gamma-band corticomuscular coherence in performing a reaching task.Significance-The study suggests poor brain-muscle communication or poor integration of the EEG and EMG signals in higher frequency band during reaching task may reflect an underlying mechanism producing movement deficits post stroke.