Wavelet-Based Muscle Artefact Noise Reduction for Short Latency rTMS Evoked Potentials.

This paper presents a new method of reducing the noise in the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artefact and millivolt amplitude compound muscle action potentials (CMAP) recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 20-ms post stimulus. The proposed method uses the wavelet transform with a fourth-order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds.

Quantitative input-output relationships between human soleus muscle spindle afferents and motoneurons.

A method is described that, for the first time, allows instantaneous estimation of the Ia fiber input to human soleus motoneurons following electrical stimulation of the tibial nerve. The basis of the method is to determine the thresholds of the most and least excitable 1a fibers to electrical stimulation, and to treat the intervening thresholds as having a normal distribution about the mean; the validity of this approach is discussed. It was found that, for the same Ia fiber input, the percentage of soleus motoneurons contributing to the H (Hoffmann)-reflex differed considerably among subjects; when the results were pooled, however, there was an approximately linear relationship between Ia input and motoneuron output.

Sensory nerve cross-anastomosis and electrical muscle stimulation synergistically enhance functional recovery of chronically denervated muscle.

Background: Long-term muscle denervation leads to severe and irreversible atrophy coupled with loss of force and motor function. These factors contribute to poor functional recovery following delayed reinnervation. The authors' previous work demonstrated that temporarily suturing a sensory nerve to the distal motor stump (called sensory protection) significantly reduces muscle atrophy and improves function following reinnervation.

A machine learning approach using EEG data to predict response to SSRI treatment for major depressive disorder.

Objective: The problem of identifying, in advance, the most effective treatment agent for various psychiatric conditions remains an elusive goal. To address this challenge, we investigate the performance of the proposed machine learning (ML) methodology (based on the pre-treatment electroencephalogram (EEG)) for prediction of response to treatment with a selective serotonin reuptake inhibitor (SSRI) medication in subjects suffering from major depressive disorder (MDD).

Methods: A relatively small number of most discriminating features are selected from a large group of candidate features extracted from the subject's pre-treatment EEG, using a machine learning procedure for feature selection.

Electrical muscle stimulation after immediate nerve repair reduces muscle atrophy without affecting reinnervation.

Introduction: Electrical stimulation of denervated muscle has been shown to minimize atrophy and fibrosis and increase force in animal and human models. However, electrical stimulation after nerve repair is controversial due to questions of efficacy.

Methods: Using a rat model, we investigated the efficacy of short-term electrical muscle stimulation for increasing reinnervation and preventing muscle atrophy.

Application of wavelet based denoising techniques to rTMS evoked potentials.

This paper presents a new method of removing noise from the EEG response signal recorded during repetitive transcranial magnetic stimulation (rTMS). This noise is principally composed of the residual stimulus artifact and mV amplitude compound muscle action potentials recorded from the scalp muscles and precludes analysis of the cortical evoked potentials, especially during the first 15 ms post stimulus. The method uses the wavelet transform with a fourth order Daubechies mother wavelet and a novel coefficient reduction algorithm based on cortical amplitude thresholds.

Using pre-treatment electroencephalography data to predict response to transcranial magnetic stimulation therapy for major depression.

We investigate the use of machine learning methods based on the pre-treatment electroencephalograph (EEG) to predict response to repetitive transcranial magnetic stimulation (rTMS), which is a non-pharmacological form of therapy for treating major depressive disorder (MDD). The learning procedure involves the extraction of a large number of candidate features from EEG data, from which a very small subset of most statistically relevant features is selected for further processing. A statistical prediction model based on mixture of factor analysis (MFA) model is constructed from a training set that classifies the respective subject into responder and non-responder classes.

Dorsolateral prefrontal cortex sensitivity to rTMS.

This paper presents the preliminary results of a study to determine dorsolateral prefrontal cortex sensitivity to rTMS stimulation presented at clinically accepted amplitudes, frequencies and locations. A specially developed EEG system with 10-20 electrode locations was used to record the short latency magnetically evoked potentials. Sixteen normal subjects were stimulated using 10 Hz for the left hemisphere and 1 Hz for the right.

Determining the effects of electrical stimulation on functional recovery of denervated rat gastrocnemius muscle using motor unit number estimation.

The use of electrical muscle stimulation to treat denervated muscle prior to delayed reinnervation has been widely debated. There is evidence showing both positive and negative results following different protocols of electrical stimulation. In this study we investigated the role electrical stimulation has on muscle reinnervation following immediate and delayed nerve repair using motor unit estimation techniques.

A New System and Paradigm for Chronic Stimulation of Denervated Rat Muscle.

Traditionally, animal studies employing electrical stimulation for conditioning denervated muscle rely on 24-hour-based stimulation paradigms, most employing implantable stimulators. While these stimulators provide the necessary current to cause muscular contraction, they have problems with battery life, programmability, and long-term robustness. Continuous 24-hour stimulation, while shown to be effective in animals, is not easily translatable to a clinical setting.

A pilot study to determine whether machine learning methodologies using pre-treatment electroencephalography can predict the symptomatic response to clozapine therapy.

Objective: To investigate whether applying advanced machine learning (ML) methodologies to pre-treatment electroencephalography (EEG) data can predict the response to clozapine therapy in adult subjects suffering from chronic schizophrenia.

Methods: Pre-treatment EEG data are collected in 23+14 schizophrenic adults. Treatment outcome, after at least one year follow-up, is determined using clinical ratings by a trained clinician blind to EEG results.

A novel approach using tendon vibration of the human flexor carpi radialis muscle to study spinal reflexes.

Although most muscle spindle investigations have used the cat model and invasive measurement techniques, several investigators have used microneurography to record from the Ia and II fibres in humans during tendon vibration. In these studies the muscle spindle primary endings are stimulated using transverse vibration of the tendon at reflex sub-threshold amplitudes. Others have used low amplitude vibration and the stretch evoked M-wave response to determine reflex properties during both agonist and antagonist voluntary contractions.

Design and testing of an instrumentation system to reduce stimulus pulse amplitude requirements during FES.

Functional Electrical Stimulation (FES) has been used for decades to restore muscle function following neural trauma. Another promising use has been to maintain or increase muscle strength following injury. Unfortunately in the latter case there is considerable stimulation pain for the sensorially intact subject during effective levels of stimulation using surface electrodes.

The use of the wavelet transform in EMG M-wave pattern classification.

A system was previously designed to obtain estimates of the number of motor units (MUNE) in a superficial muscle and hence number of functioning motor neurons to that muscle. This method uses incremental stimulation of a motor nerve and subsequent recognition and classification of the elicited M-waves. In this earlier work we used the Fourier power coefficients as pattern classifiers.

Tendon reflexes elicited using a computer controlled linear motor tendon hammer.

We present a novel instrumentation system for studying tendon and spinal reflexes using a commercial linear servo-motor as a precisely controlled tendon hammer. The system uses a LabVIEW-based program to both control electrical or mechanical stimuli and record and measure the resulting M and H waves. The hammer can deliver tendon taps with selected velocities, durations, frequencies and excursions.

Speculations surrounding a spinal reflex.

A method has been developed for measuring the Ia fibre input/motoneurone output relationship for the soleus H-reflex in healthy human volunteers. The shift in the relationship during weak toe extension, and in some subjects during weak plantar flexion, indicates the imposition of an inhibitory mechanism, presumably presynaptic. From these observations, and others previously made on long-loop reflexes, it is argued that the inhibitory mechanism may have evolved to suppress unwanted information from the periphery, not only during movement but in the resting state, and that this development was a necessary accompaniment of encephalisation.

The effects of pulse configuration on magnetic stimulation.

A study is presented in which the authors have examined the effects of pulse configuration, stimulation intensity, and coil current direction during magnetic stimulation. Using figure-8 and circular coils, the median nerve was stimulated at the cubital fossa and at the wrist of 10 healthy volunteers, and the response amplitude and site of stimulation were determined. The key findings of this study are in agreement with other researchers' findings and confirm that biphasic stimulating pulses produce significantly higher M-wave amplitudes than monophasic stimulating pulses for the same stimulus intensity.