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.

Increase in serum prolactin levels in females improves the performance of spatial learning by promoting changes in the circuital dynamics of the hippocampus.

Beyond the direct physiological functions associated with motherhood in mammals, previous studies have suggested the potential role of prolactin (Prl) in distinct brain processes such as neuroprotection, neurogenesis, and stress responses. However, the cognitive influence of Prl remains unclear, particularly regarding the mechanisms of acquisition, consolidation and retrieval of information in the brain. Using chronic implanted electrodes in freely moving female mice combined with behavioral tests, we investigated the rhythmic activity changes induced by Prl in a model of hippocampus-dependent learning and memory. Our results show that Prl improves the learning of a spatial memory task in the acquisition stage. The main variations at the circuitry level were in the theta frequency band (4-8 Hz and 8-12 Hz), marked by a faster change in oscillatory activity with no modifications to higher frequencies. These results show that Prl plays a significant role in the acquisition of information during learning of a spatial memory task, suggesting that an increase in Prl levels may induce changes in circuital network plasticity.

Identification of Functionally Interconnected Neurons Using Factor Analysis.

The advances in electrophysiological methods have allowed registering the joint activity of single neurons. Thus, studies on functional dynamics of complex-valued neural networks and its information processing mechanism have been conducted. Particularly, the methods for identifying neuronal interconnections are in increasing demand in the area of neurosciences. Here, we proposed a factor analysis to identify functional interconnections among neurons via spike trains. This method was evaluated using simulations of neural discharges from different interconnections schemes. The results have revealed that the proposed method not only allows detecting neural interconnections but will also allow detecting the presence of presynaptic neurons without the need of the recording of them.

Toward an Improvement of the Analysis of Neural Coding.

Machine learning and artificial intelligence have strong roots on principles of neural computation. Some examples are the structure of the first perceptron, inspired in the retina, neuroprosthetics based on ganglion cell recordings or Hopfield networks. In addition, machine learning provides a powerful set of tools to analyze neural data, which has already proved its efficacy in so distant fields of research as speech recognition, behavioral states classification, or LFP recordings. However, despite the huge technological advances in neural data reduction of dimensionality, pattern selection, and clustering during the last years, there has not been a proportional development of the analytical tools used for Time-Frequency (T-F) analysis in neuroscience. Bearing this in mind, we introduce the convenience of using non-linear, non-stationary tools, EMD algorithms in particular, for the transformation of the oscillatory neural data (EEG, EMG, spike oscillations…) into the T-F domain prior to its analysis with machine learning tools. We support that to achieve meaningful conclusions, the transformed data we analyze has to be as faithful as possible to the original recording, so that the transformations forced into the data due to restrictions in the T-F computation are not extended to the results of the machine learning analysis. Moreover, bioinspired computation such as brain-machine interface may be enriched from a more precise definition of neuronal coding where non-linearities of the neuronal dynamics are considered.

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

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Biomedical Engineering career, we offer short and optional courses to complement studies for students as they initiate their Graduation Project. The objective of these theoretical and practical courses is to introduce students to the topics of their projects. The present work describes an experience in electrophysiology to teach undergraduate students how to extract cortical information using electrocorticographic techniques. Students actively participate in some parts of the experience and then process and analyze the data obtained with different signal processing tools. In postlaboratory evaluations, students described the course as an exceptional opportunity for students interested in following a postgraduate science program and fully appreciated their contents.

Functional specificity of rat vibrissal primary afferents.

In this study, we propose to analyze the peripheral vibrissal system specificity through its neuronal responses. Receiver operating characteristics (ROC) curve analyses were used, which required the implementation of a binary classifier (artificial neural network) trained to identify the applied stimulus. The training phase consisted of the observation of a predetermined amount of vibrissal sweeps on two surfaces of different texture and similar roughness. Our results suggest that the specificity of the peripheral vibrissal system easily permits the discrimination between perceived stimuli, quantified through neuronal responses, and that it can be evaluated through an ROC curve analysis. We found that such specificity makes a linear binary classifier capable of detecting differences between stimuli with five sweeps at most.

Discriminability measures and time-frequency features: an application to vibrissal tactile discrimination.

BACKGROUND: Often, the first problem that the neuroscientist must face is to determine if a specific stimulus set applied to biological system produces specific, precise and well differentiated responses. NEW METHOD: In the present study we have proposed four discriminability measures to evaluate the feasibility of differentiating experimental conditions: information measures based on information theory, percentage overlap based on Linacre method, Bhattacharyya distance and univariate standard distance. All discriminability measures were evaluated on experimental protocols related to vibrissal tactile discrimination. RESULTS: Time-frequency features were extracted from afferent discharges and then, pairwise comparisons were realized by using the proposed discriminability measures. Our results reveal the existence of time-frequency patterns which allows differentiating of sweep conditions from multifiber recordings. COMPARISON WITH EXISTING METHODS: Currently, statistical methods used to justify significant differences in experimental conditions have rigorous criteria that must be met for correct validation of results. Discriminability measures proposed here are robust and can be adjusted to different experimental conditions (time series, repeated measures, specific variables and other). CONCLUSIONS: Discriminability measures allowed determining the time intervals where two sweep situations have the highest probability to be differentiated from each other. High discriminability percentages were observed into protraction phase, although to a lesser degree, it was also observed in retraction phase. It was demonstrated that sensibility of discriminability measures are different. This revealing a greater ability to highlight percentage changes of pairwise comparisons. Finally, the methods here proposed can be adapted to other features of biological responses.

Neural encoding schemes of tactile information in afferent activity of the vibrissal system.

When rats acquire sensory information by actively moving their vibrissae, a neural code is manifested at different levels of the sensory system. Behavioral studies in tactile discrimination agree that rats can distinguish different roughness surfaces by whisking their vibrissae. The present study explores the existence of neural encoding in the afferent activity of one vibrissal nerve. Two neural encoding schemes based on "events" were proposed (cumulative event count and median inter-event time). The events were detected by using an event detection algorithm based on multiscale decomposition of the signal (Continuous Wavelet Transform). The encoding schemes were quantitatively evaluated through the maximum amount of information which was obtained by the Shannon's mutual information formula. Moreover, the effect of difference distances between rat snout and swept surfaces on the information values was also studied. We found that roughness information was encoded by events of 0.8 ms duration in the cumulative event count and event of 1.0 to 1.6 ms duration in the median inter-event count. It was also observed that an extreme decrease of the distance between rat snout and swept surfaces significantly reduces the information values and the capacity to discriminate among the sweep situations.

Electrophysiological characterization of texture information slip-resistance dependent in the rat vibrissal nerve.

BACKGROUND: Studies in tactile discrimination agree that rats are able to learn a rough-smooth discrimination task by actively touching (whisking) objects with their vibrissae. In particular, we focus on recent evidence of how neurons at different levels of the sensory pathway carry information about tactile stimuli. Here, we analyzed the multifiber afferent discharge of one vibrissal nerve during active whisking. Vibrissae movements were induced by electrical stimulation of motor branches of the facial nerve. We used sandpapers of different grain size as roughness discrimination surfaces and we also consider the change of vibrissal slip-resistance as a way to improve tactile information acquisition. The amplitude of afferent activity was analyzed according to its Root Mean Square value (RMS). The comparisons among experimental situation were quantified by using the information theory. RESULTS: We found that the change of the vibrissal slip-resistance is a way to improve the roughness discrimination of surfaces. As roughness increased, the RMS values also increased in almost all cases. In addition, we observed a better discrimination performance in the retraction phase (maximum amount of information). CONCLUSIONS: The evidence of amplitude changes due to roughness surfaces and slip-resistance levels allows to speculate that texture information is slip-resistance dependent at peripheral level.

Laboratory experience for teaching sensory physiology.

The major challenge in laboratory teaching is the application of abstract concepts in simple and direct practical lessons. However, students rarely have the opportunity to participate in a laboratory that combines practical learning with a realistic research experience. In the Bioengineering Department, we started an experiential laboratory physiology to teach graduated students some aspects of sensorial physiology and exposes them to laboratory skills in instrumentation and physiological measurements. Students were able to analyze and quantify the effects of activation of mechanoreceptors in multifiber afferent discharges using equipment that was not overly sophisticated. In consequence, this practical laboratory helps students to make connections with physiological concepts acquired in theoretical classes and to introduce them to electrophysiological research.

Texture discrimination and multi-unit recording in the rat vibrissal nerve.

BACKGROUND: Rats distinguish objects differing in surface texture by actively moving their vibrissae. In this paper we characterized some aspects of texture sensing in anesthetized rats during active touch. We analyzed the multifiber discharge from a deep vibrissal nerve when the vibrissa sweeps materials (wood, metal, acrylic, sandpaper) having different textures. We polished these surfaces with sandpaper (P1000) to obtain close degrees of roughness and we induced vibrissal movement with two-branch facial nerve stimulation. We also consider the change in pressure against the vibrissa as a way to improve the tactile information acquisition. The signals were compared with a reference signal (control)--vibrissa sweeping the air--and were analyzed with the Root Mean Square (RMS) and the Power Spectrum Density (PSD). RESULTS: We extracted the information about texture discrimination hidden in the population activity of one vibrissa innervation, using the RMS values and the PSD. The pressure level 3 produced the best differentiation for RMS values and it could represent the "optimum" vibrissal pressure for texture discrimination. The frequency analysis (PSD) provided information only at low-pressure levels and showed that the differences are not related to the roughness of the materials but could be related to other texture parameters. CONCLUSION: Our results suggest that the physical properties of different materials could be transduced by the trigeminal sensory system of rats, as are shown by amplitude and frequency changes. Likewise, varying the pressure could represent a behavioral strategy that improves the information acquisition for texture discrimination.