Primary sensorimotor cortex exhibits complex dependencies of spike-field coherence on neuronal firing rates, field power, and behavior

Biomechanical and Cortical Control of Tongue Movements During Chewing and Swallowing

Tongue function is vital for chewing and swallowing and lingual dysfunction is often associated with dysphagia. Better treatment of dysphagia depends on a better understanding of hyolingual morphology, biomechanics, and neural control in humans and animal models. Recent research has revealed significant variation among animal models in morphology of the hyoid chain and suprahyoid muscles which may be associated with variation in swallowing mechanisms. The recent deployment of XROMM (X-ray Reconstruction of Moving Morphology) to quantify 3D hyolingual kinematics has revealed new details on flexion and roll of the tongue during chewing in animal models, movements similar to those used by humans. XROMM-based studies of swallowing in macaques have falsified traditional hypotheses of mechanisms of tongue base retraction during swallowing, and literature review suggests that other animal models may employ a diversity of mechanisms of tongue base retraction. There is variation among animal models in distribution of hyolingual proprioceptors but how that might be related to lingual mechanics is unknown. In macaque monkeys, tongue kinematics-shape and movement-are strongly encoded in neural activity in orofacial primary motor cortex, giving optimism for development of brain-machine interfaces for assisting recovery of lingual function after stroke. However, more research on hyolingual biomechanics and control is needed for technologies interfacing the nervous system with the hyolingual apparatus to become a reality.

Impact of oral motor task training on corticomotor pathways and diadochokinetic rates in young healthy participants

Background

Studies addressing the training‐induced neuroplasticity and interrelationships of the lip, masseter, and tongue motor representations in the human motor cortex using single syllable repetition are lacking.

Objective

This study investigated the impact of a repeated training in a novel PaTaKa diadochokinetic (DDK) orofacial motor task (OMT) on corticomotor control of the lips, masseter, and tongue muscles in young healthy participants.

Methods

A total of 22 young healthy volunteers performed 3 consecutive days of training in an OMT. Transcranial magnetic stimulation was applied to elicit motor evoked potentials (MEPs) from the lip, masseter, tongue, and first dorsal interosseous (FDI, internal control) muscles. MEPs were assessed by stimulus–response curves and corticomotor mapping at baseline and after OMT. The DDK rate from PaTaKa single syllable repetition and numeric rating scale (NRS) scores were also obtained at baseline and immediately after each OMT. Repeated‐measures analysis of variance was used to detect differences at a significance level of 5%.

Results

There was a significant effect of OMT and stimulus intensity on the lips, masseter, and tongue MEPs compared to baseline (p < .001), but not FDI MEPs (p > .05). OMT increased corticomotor topographic maps area (p < .001), and DDK rates (p < .01).

Conclusion

Our findings suggest that 3 consecutive days of a repeated PaTaKa training in an OMT can induce neuroplastic changes in the corticomotor pathways of orofacial muscles, and it may be related to mechanisms underlying the improvement of orofacial fine motor skills due to short‐term training. The clinical utility should now be investigated.

A Synaptic Pruning-Based Spiking Neural Network for Hand-Written Digits Classification

A spiking neural network model inspired by synaptic pruning is developed and trained to extract features of hand-written digits. The network is composed of three spiking neural layers and one output neuron whose firing rate is used for classification. The model detects and collects the geometric features of the images from the Modified National Institute of Standards and Technology database (MNIST). In this work, a novel learning rule is developed to train the network to detect features of different digit classes. For this purpose, randomly initialized synaptic weights between the first and second layers are updated using average firing rates of pre- and postsynaptic neurons. Then, using a neuroscience-inspired mechanism named, “synaptic pruning” and its predefined threshold values, some of the synapses are deleted. Hence, these sparse matrices named, “information channels” are constructed so that they show highly specific patterns for each digit class as connection matrices between the first and second layers. The “information channels” are used in the test phase to assign a digit class to each test image. In addition, the role of feed-back inhibition as well as the connectivity rates of the second and third neural layers are studied. Similar to the abilities of the humans to learn from small training trials, the developed spiking neural network needs a very small dataset for training, compared to the conventional deep learning methods that have shown a very good performance on the MNIST dataset. This work introduces a new class of brain-inspired spiking neural networks to extract the features of complex data images.

Integrated open-source software for multiscale electrophysiology

The methods for electrophysiology in neuroscience have evolved tremendously over the recent years with a growing emphasis on dense-array signal recordings. Such increased complexity and augmented wealth in the volume of data recorded, have not been accompanied by efforts to streamline and facilitate access to processing methods, which too are susceptible to grow in sophistication. Moreover, unsuccessful attempts to reproduce peer-reviewed publications indicate a problem of transparency in science. This growing problem could be tackled by unrestricted access to methods that promote research transparency and data sharing, ensuring the reproducibility of published results. Here, we provide a free, extensive, open-source software that provides data-analysis, data-management and multi-modality integration solutions for invasive neurophysiology. Users can perform their entire analysis through a user-friendly environment without the need of programming skills, in a tractable (logged) way. This work contributes to open-science, analysis standardization, transparency and reproducibility in invasive neurophysiology.