Time and frequency domain methods for quantifying common modulation of motor unit firing patterns

BACKGROUND: In investigations of the human motor system, two approaches are generally employed toward the identification of common modulating drives from motor unit recordings. One is a frequency domain method and uses the coherence function to determine the degree of linear correlation between each frequency component of the signals. The other is a time domain method that has been developed to determine the strength of low frequency common modulations between motor unit spike trains, often referred to in the literature as 'common drive'. METHODS: The relationships between these methods are systematically explored using both mathematical and experimental procedures. A mathematical derivation is presented that shows the theoretical relationship between both time and frequency domain techniques. Multiple recordings from concurrent activities of pairs of motor units are studied and linear regressions are performed between time and frequency domain estimates (for different time domain window sizes) to assess their equivalence. RESULTS: Analytically, it may be demonstrated that under the theoretical condition of a narrowband point frequency, the two relations are equivalent. However practical situations deviate from this ideal condition. The correlation between the two techniques varies with time domain moving average window length and for window lengths of 200 ms, 400 ms and 800 ms, the r(2 )regression statistics (p < 0.05) are 0.56, 0.81 and 0.80 respectively. CONCLUSIONS: Although theoretically equivalent and experimentally well correlated there are a number of minor discrepancies between the two techniques that are explored. The time domain technique is preferred for short data segments and is better able to quantify the strength of a broad band drive into a single index. The frequency domain measures are more encompassing, providing a complete description of all oscillatory inputs and are better suited to quantifying narrow ranges of descending input into a single index. In general the physiological question at hand should dictate which technique is best suited

Multiple frequencies in the basal ganglia in Parkinson's disease

In recent years, the authors have developed what appears to be a very successful phenomenological model for analyzing the role of deep brain stimulation (DBS) in alleviating the symptoms of Parkinson's disease. In this paper, we extend the scope of the model by using it to predict the generation of new frequencies from networks tuned to a specific frequency, or indeed not self-oscillatory at all. We have discussed two principal cases: firstly where the constituent systems are coupled in an excitatory-excitatory fashion, which we designate by ``+/+''; and secondly where the constituent systems are coupled in an excitatory-inhibitory fashion, which we designate ``+/-''. The model predicts that from a basic system tuned to tremor frequency we can generate an unlimited range of frequencies. We illustrate in particular, starting from systems which are initially non-oscillatory, that when the coupling coefficient exceeds a certain value, the system begins to oscillate at an amplitude which increases with the coupling strength. Another very interesting feature, which has been shown by colleagues of ours to arise through the coupling of complicated networks based on the physiology of the basal ganglia, can be illustrated by the root locus method which shows that increasing and decreasing frequencies of oscillation, existing simultaneously, have the property that their geometric mean remains substantially constant as the coupling strength is varied. We feel that with the present approach, we have provided another tool for understanding the existence and interaction of pathological oscillations which underlie, not only Parkinson's disease, but other conditions such as Tourette's syndrome, depression and epilepsy

Analysis of Parkinsonian Surface Electomyography Through Advanced Signal Processing and Nonlinear Methods

Bioengineering in Ireland Annual Conference, NUI Galway, Ireland, 22-23 January 2016Parkinson’s disease (PD) is a neurodegenerative disease that affects approx. 4% of people over 80 years of age [4]. The result of depleted dopaminergic neurons in the substantia nigra, PD is characterised with symptoms such as muscle rigidity, bradykinetic gait, and severe tremor. To distinguish Parkinsonian electromyographic (EMG) signals from those of healthy controls, recent studies have employed nonlinear methods which can capture the underlying activity of the neuromuscular system. Recurrence quantification analysis (RQA) has been shown to effectively characterise the degree of repeated synchronous structure in non-linear dynamical systems including parkinsonian EMG, through parameters such as determinism (%DET) and recurrence rate (%REC) [1]. Additional parameters such as intermuscular coherence and kurtosis have also been used to observe changes in EMG signals under various conditions [2,3]. To date, limited research has examined the potential to discern EMG of individuals with PD from healthy controls using RQA and intermuscular coherence. The work presented here aims to examine differences in Parkinsonian EMG from that of healthy controls using these measures

Non-Linear Analyses of Surface Electromyography in Parkinsons Disease

Insight Student Conference 2015, NUIG, Galway, Ireland, 30 October 2015Non-linear measures, such as recurrence quantification analysis, have been applied to electromyographic (EMG) data to capture the underlying activity of the neuromuscular system. The application of such approaches to EMG data from individuals with Parkinson’s disease (PD) is presented here. Preliminary results indicate differences in the level of determinism and coherence that distinguish Parkinsonian EMG from that of healthy age-matched controls

Analysis of Surface Electromyography in Parkinson's Disease Using Time Frequency and Recurrence Quantification Methods

Bioengineering in Ireland 2015 Conference, Maynooth, Ireland, 16-17 January 2015The work presented here aims to establish the optimal RQA variables for the calculation of RQA parameters (REC, DET, JRP, etc.), and to apply these parameters to EMG data of Parkinson’s disease patients. Additionally, the cross-correlation of these parameters with time frequency features will be assessed with the intention of classifying Parkinson’s patients from healthy controls.Science Foundation Irelan

Feasibility of pair-housing of rats after cranial implant surgery

Rat models employing cranial implants are increasingly employed to facilitate neural stimulation and recording in freely moving animals. Due to possible damage to wound, implant or attached devices, rats with cranial implants are traditionally housed singly, and little information is available on group- or pair-housing. Here we describe a protocol for pair-housing rats following cranial implant surgery and describe our experience with pair-housing during post-surgical recovery and up to 16 weeks following surgery.Thirty-six adult Wistar rats of both sexes were implanted with deep brain stimulation electrodes. Ten rats were equipped with an additional wireless headstage. Rats were housed in stable pairs before surgery and re-introduced 0-18 h post-surgery. Rat grimace scores did not indicate pain after conclusion of the analgesia protocol, physiological parameters were in the normal range three days post-surgery and weight loss did not exceed 10%. Rats with a cement cap only were pair-housed continuously without damage to the headcap. Rats carrying an additional fragile headstage had to be separated during lights-off periods to prevent headstage damage but could be pair-housed during lights-on periods. Pair-housing is a feasible and effective method to facilitate the rats' need for social companionship following cranial implant surgery.European Commission - European Regional Development FundI can't get access to article even through VP

Muscle fatigue increases beta-band coherence between the firing times of simultaneously active motor units in the first dorsal interosseous muscle

Synchronization between the firing times of simultaneously active motor units (MUs) is generally assumed to increase during fatiguing contractions. To date, however, estimates of MU synchronization have relied on indirect measures, derived from surface electromyographic (EMG) interference signals. This study used intramuscular coherence to investigate the correlation between MU discharges in the first dorsal interosseous muscle during and immediately following a submaximal fatiguing contraction, and after rest. Coherence between composite MU spike trains, derived from decomposed surface EMG, were examined in the delta (1–4 Hz), alpha (8–12 Hz), beta (15–30 Hz), and gamma (30–60 Hz) frequency band ranges. A significant increase in MU coherence was observed in the delta, alpha, and beta frequency bands postfatigue. In addition, wavelet coherence revealed a tendency for delta-, alpha-, and beta-band coherence to increase during the fatiguing contraction, with subjects exhibiting low initial coherence values displaying the greatest relative increase. This was accompanied by an increase in MU short-term synchronization and a decline in mean firing rate of the majority of MUs detected during the sustained contraction. A model of the motoneuron pool and surface EMG was used to investigate factors influencing the coherence estimate. Simulation results indicated that changes in motoneuron inhibition and firing rates alone could not directly account for increased beta-band coherence postfatigue. The observed increase is, therefore, more likely to arise from an increase in the strength of correlated inputs to MUs as the muscle fatigues