Jing Z. Liu

Relationship between motor activity-related cortical potential and voluntary muscle activation.

Abstract. 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.

A dynamical model of muscle activation, fatigue, and recovery.

A dynamical model is presented as a framework for muscle activation, fatigue, and recovery. By describing the effects of muscle fatigue and recovery in terms of two phenomenological parameters (F, R), we develop a set of dynamical equations to describe the behavior of muscles as a group of motor units activated by voluntary effort. This model provides a macroscopic view for understanding biophysical mechanisms of voluntary drive, fatigue effect, and recovery in stimulating, limiting, and modulating the force output from muscles. The model is investigated under the condition in which brain effort is assumed to be constant. Experimental validation of the model is performed by fitting force data measured from healthy human subjects during a 3-min sustained maximal voluntary handgrip contraction. The experimental results confirm a theoretical inference from the model regarding the possibility of maximal muscle force production, and suggest that only 97% of the true maximal force can be reached under maximal voluntary effort, assuming that all motor units can be recruited voluntarily. The effects of different motor unit types, time-dependent brain effort, sources of artifacts, and other factors that could affect the model are discussed. The applications of the model are also discussed.

Automated histogram-based brain segmentation in T1-weighted three-dimensional magnetic resonance head images.

Current semiautomated magnetic resonance (MR)-based brain segmentation and volume measurement methods are complex and not sufficiently accurate for certain applications. We have developed a simpler, more accurate automated algorithm for whole-brain segmentation and volume measurement in T(1)-weighted, three-dimensional MR images. This histogram-based brain segmentation (HBRS) algorithm is based on histograms and simple morphological operations. The algorithms three steps are foreground/background thresholding, disconnection of brain from skull, and removal of residue fragments (sinus, cerebrospinal fluid, dura, and marrow). Brain volume was measured by counting the number of brain voxels. Accuracy was determined by applying HBRS to both simulated and real MR data. Comparing the brain volume rendered by HBRS with the volume on which the simulation is based, the average error was 1.38%. By applying HBRS to 20 normal MR data sets downloaded from the Internet Brain Segmentation Repository and comparing them with expert segmented data, the average Jaccard similarity was 0.963 and the kappa index was 0.981. The reproducibility of brain volume measurements was assessed by comparing data from two sessions (four total data sets) with human volunteers. Intrasession variability of brain volumes for sessions 1 and 2 was 0.55 +/- 0.56 and 0.74 +/- 0.56%, respectively; the mean difference between the two sessions was 0.60 +/- 0.46%. These results show that the HBRS algorithm is a simple, fast, and accurate method to determine brain volume with high reproducibility. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving MRI data are needed.

Simultaneous measurement of human joint force, surface electromyograms, and functional MRI-measured brain activation

Functional magnetic resonance imaging (fMRI) has been increasingly used in studying human brain function given its non-invasive feature and good spatial resolution. However, difficulties in acquiring data from peripheral (e.g. information from muscle) during fMRI studies of motor function hinder interpretation of fMRI data and designing more sophisticated investigations. Here we describe a system that was designed to concurrently measure handgrip force, surface electromyograms (EMG) of finger flexor and extensor muscles, and fMRI of human brain. The system included a pressure transducer built in a hydraulic environment, a heavily shielded EMG recording element, and a visual feedback structure for online monitoring of force and/or EMG signal, by the subject positioned in the scanner during an fMRI experiment. System evaluation and subsequent fMRI motor function studies have indicated that by using this system, high quality force and EMG signals can be recorded without sacrificing the quality of the fMRI data.

A three-dimensional fractal analysis method for quantifying white matter structure in human brain

Fractal dimension (FD) is increasingly used to quantify complexity of brain structures. Previous research that analyzed FD of human brain mainly focused on two-dimensional measurements. In this study, we developed a three-dimensional (3D) box-counting method to measure FD of human brain white matter (WM) interior structure, WM surface and WM general structure simultaneously. This method, which firstly incorporates a shape descriptor (3D skeleton) representing interior structure and combines the three features, provides a more comprehensive characterization of WM structure. WM FD of different brain segments was computed to test robustness of the method. FDs of fractal phantoms were computed to test the accuracy of the method. The consistency of the computed and theoretical FD values suggests that our method is accurate in measuring FDs of fractals. Statistical analysis was performed to examine sensitivity of the method in detecting WM structure differences in a number of young and old subjects. FD values of the WM skeleton and surface were significantly greater in young than old individuals, indicating more complex WM structures in young people. These results suggest that our method is accurate in quantifying three-dimensional brain WM structures and sensitive in detecting age-related degeneration of the structures.

Nonlinear cortical modulation of muscle fatigue: A functional MRI study

Muscle fatigue has been studied for over a century, but almost no data are available to indicate how the brain perceives fatigue and modulates its signals to the fatiguing muscle. In this study, brain activation was measured by functional magnetic resonance imaging (fMRI) during a sustained (2-min) maximal-effort handgrip contraction while handgrip force and finger muscle electromyographic (EMG) data were recorded simultaneously by a magnetic resonance environment-adapted force-EMG measurement system. The results showed decoupled progresses in brain and muscle activities when muscle was fatigued and correlated behaviors among the cortical areas being analyzed. While handgrip force and EMG signals declined in parallel during the course of muscle fatigue, fMRI-measured brain activities first substantially increased and then decreased. This similar signal modulation occurred not only in the primary sensorimotor areas but also in the secondary and association cortices (supplementary motor, prefrontal, and cingulate areas). The nonlinear changes of brain signal may reflect an early adjustment to strengthen the descending command for force-loss compensation and subsequent inhibition by sensory feedback as fatigue became more severe. The close association in the activation pattern in many cortical regions may reflect integrated processing of information in the brain.

Quantifying degeneration of white matter in normal aging using fractal dimension

Although degeneration of brain white matter (WM) in aging is a well-recognized problem, its quantification has mainly relied on volumetric measurements, which lack detail in describing the degenerative adaptation. In this study, WM structural complexity was evaluated in healthy old and young adults by analyzing the three-dimensional fractal dimension (FD) of WM segmented from magnetic resonance images of brain. FDs detected in the old were significantly smaller than in the young subjects. Specifically, WM interior structure complexity degenerated in the left hemisphere in old men but in the right hemisphere in old women. Men showed more complex WM patterns than women. An asymmetrical (right-greater-than-left-hemisphere) complexity pattern was observed in the interior and general structures of WM, yet the surface complexity was symmetrical across WM structures of the two hemispheres. WM volumes were also measured, but no significant decline was found with aging. These results suggest that the deterioration of WM complexity is not uniformly distributed between the genders and across brain hemispheres.

Fractal Dimension in Human Cerebellum Measured by Magnetic Resonance Imaging

Fractal dimension has been used to quantify the structures of a wide range of objects in biology and medicine. We measured fractal dimension of human cerebellum (CB) in magnetic resonance images of 24 healthy young subjects (12 men and 12 women). CB images were resampled to a series of image sets with different 3D resolutions. At each resolution, the skeleton of the CB white matter was obtained and the number of pixels belonging to the skeleton was determined. Fractal dimension of the CB skeleton was calculated using the box-counting method. The results indicated that the CB skeleton is a highly fractal structure, with a fractal dimension of 2.57 +/- 0.01. No significant difference in the CB fractal dimension was observed between men and women.

Effects of surface EMG rectification on power and coherence analyses: an EEG and MEG study.

Coherence between electromyography (EMG) and electroencephalography (EEG) or magnetoencephalography (MEG) is frequently examined to gain insights on neuromuscular binding. Commonly, EMG signals are rectified before coherence is computed. However, the appropriateness of EMG rectification in computing EMG-EEG/MEG coherence has never been validated. Since rectification is a non-linear operation and alters the EMG power spectrum, such a validation is important to ensure the accuracy of coherence calculation. In this study we experimentally investigated the effects of EMG rectification on EMG power spectra and its coherence with EEG/MEG signals. Subjects performed sustained isometric index finger abduction at approximately 5-10% maximal voluntary force (in both EEG-EMG and MEG-EMG experiments) and index finger tapping at approximately 2-4Hz (in EEG-EMG experiment only). Bipolar surface EMG data from the first dorsal interosseus (FDI) and EEG/MEG signals from the contralateral primary sensorimotor area (C3) were recorded simultaneously. Power spectra and coherence with the EEG/MEG were calculated before and after EMG rectification. The results show that rectification shifts EMG power to lower frequencies, possibly enhancing peaks of motor unit firing. Coherences with the EEG/MEG signals were not significantly changed by EMG rectification, indicating EMG rectification is overall an appropriate procedure in power and coherence analyses.

Brain activation during human finger extension and flexion movements

Corticospinal projections to the motor neuron pool of upper-limb extensor muscles have been reported to differ from those of the flexor muscles in humans and other primates. The influence of this difference on the central nervous system control for extension and flexion movements is unknown. Cortical activation during thumb extension and flexion movements of eight human volunteers was measured using functional magnetic resonance imaging (fMRI), which detects signal changes caused by an alteration in the local blood oxygenation level. Although the relative activity of the extensor and flexor muscles of the thumb was similar, the brain volume activated during extension was substantially larger than that during flexion. These fMRI results were confirmed by measurements of EEG-derived movement-related cortical potential. Higher brain activity during thumb extension movement may be a result of differential corticospinal, and possibly other pathway projections to the motoneuron pools of extensor and flexor muscles of upper the extremities.