Electrophysiology for biomedical engineering students: a practical and theoretical course in animal electrocorticography

Muscle function alterations in a Parkinson's disease animal model: Electromyographic recordings dataset

Parkinson's disease (PD) is currently diagnosed based on characteristic motor dysfunctions induced by the loss of dopaminergic neurons in the substantia nigra pars compacta [1], [2]. The animal model most commonly used to reproduce PD-related motor deficits in rats is the massive degeneration of nigrostriatal neurons by using intracerebral infusion of 6-hydroxydopamine (6-OHDA) [3], [4].

This article presents data related to the research article “Quantifying muscle alterations in a Parkinson's disease animal model using electromyographic biomarkers” [5]. This study evaluated the effect of PD neurotoxic lesion model on muscle function of freely moving rats. The effects on muscle function considering the time post-lesion have never been described for this Parkinson's disease model. Electromyographic recordings were obtained from control and hemiparkinsonian rats walking in a circular treadmill. Chronic EMG electrodes were implanted subcutaneously in a hindlimb muscle - the biceps femoris muscle - for evaluating muscular activity during the gait. Five dataset of EMG recordings are presented in this article corresponding to control animals and four groups of lesioned animals at different time post-injury (three to six weeks after lesion). Stationarity of the EMG signals were established and the effective muscular contractions were detected by using signal processing methods described in [5]. Power spectrum density was characterized through the mean and median frequencies and signals probability distribution function analysis was also performed.These analyses have shown that PSD frequency contents progressively fall with time post-lesion suggesting muscle function changes along this enclosed time.

This dataset could be reused to investigate muscular activation parameters under control and lesioned conditions in freely moving rats and for evaluating different signal processing methods for EMG patterns detection.

Quantifying muscle alterations in a Parkinson's disease animal model using electromyographic biomarkers

Parkinson's disease (PD) is a neurodegenerative disease currently diagnosed based on characteristic motor dysfunctions. The most common Parkinson's disease animal model induces massive nigrostriatal degeneration by intracerebral infusion of 6-hydroxydopamine (6-OHDA). Motor deficits in rat models of Parkinson's disease were previously addressed in other works. However, an accurate quantification of muscle function in freely moving PD-lesioned rats over time has not been described until now. In this work, we address the muscular activity characterization of a 6-OHDA-lesion model of PD along 6 weeks post-lesion based on spectral and morphological analysis of the signals. Using chronic implanted EMG electrodes in a hindlimb muscle of freely moving rats, we have evaluated the effect of the PD neurotoxic model in the muscular activity during locomotion. EMG signals obtained from animals with different time post-injury were analyzed. Power spectral densities were characterized by the mean and median frequency, and the EMG burst stationarity was previously verified for all animals. Our results show that as the time post-lesion increases both frequency parameters decrease. Probability distribution function analysis was also performed. The results suggest that contractile dynamics of the biceps femoris muscle change with time post-lesion. We have also demonstrated here the usefulness of frequency parameters as biomarkers for monitoring the muscular function changes that could be used for early detection of motor dysfunction.

Winter School on sEMG Signal Processing: An Initiative to Reduce Educational Gaps and to Promote the Engagement of Physiotherapists and Movement Scientists With Science

The application of surface electromyography (sEMG) in neurology is sometimes limited by a scientific background in the use of sEMG. Students frequently use sEMG only when developing their graduate studies. To reduce these barriers, we promoted a free Winter School on sEMG to Latin American students. The school was a 3-day event with theoretical classes and computer programming in Matlab. Lectures were delivered in Portuguese and Spanish to 50 participants. All lectures were recorded and made available on YouTube®. After the School, participants completed a written exam to receive a certificate. The written exam revealed the average effectiveness of 71 ± 20% in the comprehension of topics addressed during the school. Participants rated the School as “excellent” and considered the event as having changed their thoughts about the use of sEMG. Limited mathematical skills or background were the main barriers identified to follow the lectures and to make use of sEMG. We conclude that the Winter School had a positive impact on participant's formation, especially by showing them the importance of continuous involvement with the concepts related to sEMG to become proficient in its use. From the participant's point of view, the activity was excellent and the follow up of the school on YouTube® suggests that combining face-to-face activities followed by the online availability of lectures is a valid strategy to reinforce the learning process and to reduce barriers in the use of sEMG. Whether similar results would be achieved for a paid registration event in an economically developing region, still requires further investigation.

Integrating the Allen Brain Institute Cell Types Database into Automated Neuroscience Workflow

We developed software tools to download, extract features, and organize the Cell Types Database from the Allen Brain Institute (ABI) in order to integrate its whole cell patch clamp characterization data into the automated modeling/data analysis cycle. To expand the potential user base we employed both Python and MATLAB. The basic set of tools downloads selected raw data and extracts cell, sweep, and spike features, using ABI's feature extraction code. To facilitate data manipulation we added a tool to build a local specialized database of raw data plus extracted features. Finally, to maximize automation, we extended our NeuroManager workflow automation suite to include these tools plus a separate investigation database. The extended suite allows the user to integrate ABI experimental and modeling data into an automated workflow deployed on heterogeneous computer infrastructures, from local servers, to high performance computing environments, to the cloud. Since our approach is focused on workflow procedures our tools can be modified to interact with the increasing number of neuroscience databases being developed to cover all scales and properties of the nervous system.