Muscle spindles as pain receptors.

Background: Muscle membranes have a sensation of pain, but within the muscle tissue, the origin of pain is unclear. We present a hypothesis that the pain receptors of the muscle tissue are situated principally in the muscle spindles. A recent report reintroduced that 'end plate spikes' in needle electromyography (EMG) are fusimotor unit potentials of the intrafusal muscle fibres, and thus represent a marker of muscle spindles. Methods: We studied four relaxed muscles with 50 EMG needle insertions in each and mapped the appearance of pain and spontaneous EMG activity. Results: Only 4.0% of the needle insertions in muscle tissue elicited pain. However, needle insertions in local active points showing 'end plate spikes' and, thus, fusimotor unit potentials of the muscle spindles elicited pain in 86% of the insertions, whereas needle insertions in points without 'end plate spikes' elicited pain in only 1.0% of the insertions (p<0.001). Conclusions: Muscle spindles have pain receptors. The extrafusal muscle tissue is practically pain-free for the needle insertions. This demonstrates a scarcity of extrafusal pain receptors. How this observation is put into perspective with the muscle pain syndromes was discussed.

Electromyography of the muscle spindle.

In needle electromyography, there are two spontaneous waveforms, miniature end plate potentials and "end plate spikes", appearing usually together. Miniature end plate potentials are local, non-propagating postsynaptic waves, caused by spontaneous exocytosis of acetylcholine in the neuromuscular junction. The prevailing hypothesis states that "end plate spikes" are propagated postsynaptic action potentials of muscle fibers, caused by presynaptic irritation of the motor nerve or nerve terminal. Using several small concentric needle electrodes in parallel with the muscle fibers, most "end plate spikes" are strictly local or propagating for 2-4 mm. At the end plate zone, there are miniature end plate potentials without "end plate spikes". Local "end plate spikes" are junctional potentials of intrafusal gamma neuromuscular junctions of the nuclear bag fibers, and propagated "end plate spikes" are potentials of nuclear chain muscle fibers of muscle spindles. Miniature end plate potentials without "end plate spikes" at the end plate zone derive from alpha neuromuscular junctions. These findings contrast with the prevailing hypothesis. The history of observations and different hypotheses of the origin of end plate spikes are described.

Automatic and robust noise suppression in EEG and MEG: The SOUND algorithm.

Electroencephalography (EEG) and magnetoencephalography (MEG) often suffer from noise- and artifact-contaminated channels and trials. Conventionally, EEG and MEG data are inspected visually and cleaned accordingly, e.g., by identifying and rejecting the so-called "bad" channels. This approach has several shortcomings: data inspection is laborious, the rejection criteria are subjective, and the process does not fully utilize all the information in the collected data. Here, we present noise-cleaning methods based on modeling the multi-sensor and multi-trial data. These approaches offer objective, automatic, and robust removal of noise and disturbances by taking into account the sensor- or trial-specific signal-to-noise ratios. We introduce a method called the source-estimate-utilizing noise-discarding algorithm (the SOUND algorithm). SOUND employs anatomical information of the head to cross-validate the data between the sensors. As a result, we are able to identify and suppress noise and artifacts in EEG and MEG. Furthermore, we discuss the theoretical background of SOUND and show that it is a special case of the well-known Wiener estimators. We explain how a completely data-driven Wiener estimator (DDWiener) can be used when no anatomical information is available. DDWiener is easily applicable to any linear multivariate problem; as a demonstrative example, we show how DDWiener can be utilized when estimating event-related EEG/MEG responses. We validated the performance of SOUND with simulations and by applying SOUND to multiple EEG and MEG datasets. SOUND considerably improved the data quality, exceeding the performance of the widely used channel-rejection and interpolation scheme. SOUND also helped in localizing the underlying neural activity by preventing noise from contaminating the source estimates. SOUND can be used to detect and reject noise in functional brain data, enabling improved identification of active brain areas.

Optimal digital filters for analyzing the mid-latency auditory P50 event-related potential in patients with Alzheimer's disease.

BACKGROUND: Filtering is an effective pre-processing technique for improving the signal-to-noise ratio of ERP waveforms. Filters can, however, introduce substantial distortions into the time-domain representations of ERP waveforms. Inappropriate filter parameters may lead to the presence of statistically significant but artificial effects, whereas true effects may appear as insignificant. NEW METHOD: The present study aimed to determine the optimal digital filters for analyzing the auditory P50 component in patients with Alzheimer's disease. To provide evidence of the optimal filter settings, different high-pass and low-pass filters were applied to ERP waveforms obtained from a conditioning-testing paradigm. The results facilitate practical recommendations for selection of filters that maximize the signal-to-noise ratio of the P50 components without introducing significant distortions. RESULTS: The present study confirms that filter parameters have a significant effect on the amplitude and gating measures of the P50 component. Setting the high-pass cut-off at 0.1Hz and the low-pass cut-off at 90Hz (or above) is recommended for P50 component analyses. COMPARISON WITH EXISTING METHODS: The majority of ERP studies on sensory gating report using high-pass filters with 10-Hz cut-offs to measure P50 suppression. Such a high cut-off appeared to induce significant distortions into the ERP waveforms; thus, the authors advise against using these excessive high-pass cut-offs. CONCLUSIONS: Filtering broadband signals, such as ERP signals, necessary results in time-domain distortions. However, by adjusting the filter parameters carefully according to the components of interest, it is possible to minimize filter artifacts and obtain more easily interpretable ERP waveforms.

Multimodal event-related potentials in occupational chronic solvent encephalopathy.

The aim of this study was to test a multimodal event-related potential (ERP) paradigm in chronic solvent encephalopathy (CSE) to develop a sensitive method for the clinical diagnostics to CSE. The study comprised 11 CSE patients and 13 healthy controls. We used three tasks: an auditory odd-ball (AUD), a visual detection (VIS), and a recognition memory (MEM) task. The auditory and visual stimuli were presented in single- and dual-task conditions. The auditory P300 amplitude in single-task condition was smaller in the patient group than in the control group at the parietal (Pz) but not at the frontal midline electrode location. The auditory P300 response in the dual task condition AUD+VIS was unrecognizable in 8 of 11 patients and in 1 of 13 controls and in the AUD+MEM condition in 10 of 11 patients and in 4 of 13 controls. In the AUD+MEM condition, the auditory P300 amplitude at Pz was smaller in the patient group than in the control group. Reaction time for auditory stimuli in both dual conditions as well as for visual stimuli in AUD+VIS condition were in the patient group prolonged. The ERP results indicate that CSE patients present with slowed performance speed and difficulties in allocation of attention. Based on ERP results, the disturbance in brain activity in CSE seems to affect posterior aspects of the frontoparietal continuity. The multimodal paradigm seems promising as a tool for the clinical diagnostics of CSE.