Multiple regions of sensorimotor cortex encode bite force and gape.

The precise control of bite force and gape is vital for safe and effective breakdown and manipulation of food inside the oral cavity during feeding. Yet, the role of the orofacial sensorimotor cortex (OSMcx) in the control of bite force and gape is still largely unknown. The aim of this study was to elucidate how individual neurons and populations of neurons in multiple regions of OSMcx differentially encode bite force and static gape when subjects (Macaca mulatta) generated different levels of bite force at varying gapes. We examined neuronal activity recorded simultaneously from three microelectrode arrays implanted chronically in the primary motor (MIo), primary somatosensory (SIo), and cortical masticatory (CMA) areas of OSMcx. We used generalized linear models to evaluate encoding properties of individual neurons and utilized dimensionality reduction techniques to decompose population activity into components related to specific task parameters. Individual neurons encoded bite force more strongly than gape in all three OSMCx areas although bite force was a better predictor of spiking activity in MIo vs. SIo. Population activity differentiated between levels of bite force and gape while preserving task-independent temporal modulation across the behavioral trial. While activation patterns of neuronal populations were comparable across OSMCx areas, the total variance explained by task parameters was context-dependent and differed across areas. These findings suggest that the cortical control of static gape during biting may rely on computations at the population level whereas the strong encoding of bite force at the individual neuron level allows for the precise and rapid control of bite force.

Robust cortical encoding of 3D tongue shape during feeding in macaques.

Dexterous tongue deformation underlies eating, drinking, and speaking. The orofacial sensorimotor cortex has been implicated in the control of coordinated tongue kinematics, but little is known about how the brain encodes-and ultimately drives-the tongue's 3D, soft-body deformation. Here we combine a biplanar x-ray video technology, multi-electrode cortical recordings, and machine-learning-based decoding to explore the cortical representation of lingual deformation. We trained long short-term memory (LSTM) neural networks to decode various aspects of intraoral tongue deformation from cortical activity during feeding in male Rhesus monkeys. We show that both lingual movements and complex lingual shapes across a range of feeding behaviors could be decoded with high accuracy, and that the distribution of deformation-related information across cortical regions was consistent with previous studies of the arm and hand.

Propagating motor cortical patterns of excitability are ubiquitous across human and non-human primate movement initiation.

A spatiotemporal pattern of excitability propagates across the primary motor cortex prior to the onset of a reaching movement in non-human primates. If this pattern is a necessary component of voluntary movement initiation, it should be present across a variety of motor tasks, end-effectors, and even species. Here, we show that propagating patterns of excitability occur during the initiation of precision grip force and tongue protrusion in non-human primates, and even isometric wrist extension in a human participant. In all tasks, the directions of propagation across the cortical sheet were bimodally distributed across trials with modes oriented roughly opposite to one another. Propagation speed was unimodally distributed with similar mean speeds across tasks and species. Additionally, propagation direction and speed did not vary systematically with any behavioral measures except response times indicating that this propagating pattern is invariant to kinematic or kinetic details and may be a generic movement initiation signal.

Loss of oral sensation impairs feeding performance and consistency of tongue-jaw coordination.

BACKGROUND: Individuals with impaired oral sensation report difficulty chewing, but little is known about the underlying changes to tongue and jaw kinematics. Methodological challenges impede the measurement of 3D tongue movement and its relationship to the gape cycle. OBJECTIVE: The aim of this study was to quantify the impact of loss of oral somatosensation on feeding performance, 3D tongue kinematics and tongue-jaw coordination. METHODOLOGY: XROMM (X-ray Reconstruction of Moving Morphology) was used to quantify 3D tongue and jaw kinematics during feeding in three rhesus macaques (Macaca mulatta) before and after an oral tactile nerve block. Feeding performance was measured using feeding sequence duration, number of manipulation cycles and swallow frequency. Coordination was measured using event- and correlation-based metrics of jaw pitch, anterior tongue length, width and roll. RESULTS: In the absence of tactile sensation to the tongue and other oral structures, feeding performance decreased, and the fast open phase of the gape cycle became significantly longer, relative to the other phases (p < .05). The tongue made similar shapes in both the control and nerve block conditions, but the pattern of tongue-jaw coordination became significantly more variable after the block (p < .05). CONCLUSION: Disruption of oral somatosensation impacts feeding performance by introducing variability into the typically tight pattern of tongue-jaw coordination.

The Association between Age-Related Changes in Oral Neuromechanics and Alzheimer's Disease.

The global population of 80 years and older is predicted to reach 437 million by 2050. As overall brain structure and function progressively degrades, older and younger adults show differences in sensorimotor performance and brain activity in the sensorimotor regions. Oral sensorimotor functions are an important area of focus in natural aging and Alzheimer's Disease (AD) because oral health issues are commonly found in both elderly and AD populations. While human behavioral studies on changes in oral sensorimotor functions abound, very little is known about their neuronal correlates in normal and pathological aging.

Integrating XMALab and DeepLabCut for high-throughput XROMM.

Marker tracking is a major bottleneck in studies involving X-ray reconstruction of moving morphology (XROMM). Here, we tested whether DeepLabCut, a new deep learning package built for markerless tracking, could be applied to videoradiographic data to improve data processing throughput. Our novel workflow integrates XMALab, the existing XROMM marker tracking software, and DeepLabCut while retaining each program's utility. XMALab is used for generating training datasets, error correction and 3D reconstruction, whereas the majority of marker tracking is transferred to DeepLabCut for automatic batch processing. In the two case studies that involved an in vivo behavior, our workflow achieved a 6 to 13-fold increase in data throughput. In the third case study, which involved an acyclic, post-mortem manipulation, DeepLabCut struggled to generalize to the range of novel poses and did not surpass the throughput of XMALab alone. Deployed in the proper context, this new workflow facilitates large scale XROMM studies that were previously precluded by software constraints.

Similarity in Neuronal Firing Regimes across Mammalian Species.

UNLABELLED: The architectonic subdivisions of the brain are believed to be functional modules, each processing parts of global functions. Previously, we showed that neurons in different regions operate in different firing regimes in monkeys. It is possible that firing regimes reflect differences in underlying information processing, and consequently the firing regimes in homologous regions across animal species might be similar. We analyzed neuronal spike trains recorded from behaving mice, rats, cats, and monkeys. The firing regularity differed systematically, with differences across regions in one species being greater than the differences in similar areas across species. Neuronal firing was consistently most regular in motor areas, nearly random in visual and prefrontal/medial prefrontal cortical areas, and bursting in the hippocampus in all animals examined. This suggests that firing regularity (or irregularity) plays a key role in neural computation in each functional subdivision, depending on the types of information being carried. SIGNIFICANCE STATEMENT: By analyzing neuronal spike trains recorded from mice, rats, cats, and monkeys, we found that different brain regions have intrinsically different firing regimes that are more similar in homologous areas across species than across areas in one species. Because different regions in the brain are specialized for different functions, the present finding suggests that the different activity regimes of neurons are important for supporting different functions, so that appropriate neuronal codes can be used for different modalities.

Primary motor and sensory cortical areas communicate via spatiotemporally coordinated networks at multiple frequencies.

Skilled movements rely on sensory information to shape optimal motor responses, for which the sensory and motor cortical areas are critical. How these areas interact to mediate sensorimotor integration is largely unknown. Here, we measure intercortical coherence between the orofacial motor (MIo) and somatosensory (SIo) areas of cortex as monkeys learn to generate tongue-protrusive force. We report that coherence between MIo and SIo is reciprocal and that neuroplastic changes in coherence gradually emerge over a few days. These functional networks of coherent spiking and local field potentials exhibit frequency-specific spatiotemporal properties. During force generation, theta coherence (2-6 Hz) is prominent and exhibited by numerous paired signals; before or after force generation, coherence is evident in alpha (6-13 Hz), beta (15-30 Hz), and gamma (30-50 Hz) bands, but the functional networks are smaller and weaker. Unlike coherence in the higher frequency bands, the distribution of the phase at peak theta coherence is bimodal with peaks near 0° and ±180°, suggesting that communication between somatosensory and motor areas is coordinated temporally by the phase of theta coherence. Time-sensitive sensorimotor integration and plasticity may rely on coherence of local and large-scale functional networks for cortical processes to operate at multiple temporal and spatial scales.

Modulation dynamics in the orofacial sensorimotor cortex during motor skill acquisition.

The orofacial sensorimotor cortex is known to play a role in motor learning. However, how motor learning changes the dynamics of neuronal activity and whether these changes differ between orofacial primary motor (MIo) and somatosensory (SIo) cortices remain unknown. To address these questions, we used chronically implanted microelectrode arrays to track learning-induced changes in the activity of simultaneously recorded neurons in MIo and SIo as two naive monkeys (Macaca mulatta) were trained in a novel tongue-protrusion task. Over a period of 8-12 d, the monkeys showed behavioral improvements in task performance that were accompanied by rapid and long-lasting changes in neuronal responses in MIo and SIo occurring in parallel: (1) increases in the proportion of task-modulated neurons, (2) increases in the mutual information between tongue-protrusive force and spiking activity, (3) reductions in the across-trial firing rate variability, and (4) transient increases in coherent firing of neuronal pairs. More importantly, the time-resolved mutual information in MIo and SIo exhibited temporal alignment. While showing parallel changes, MIo neurons exhibited a bimodal distribution of peak correlation lag times between spiking activity and force, whereas SIo neurons showed a unimodal distribution. Moreover, coherent activity between pairs of MIo neurons was higher and centered around force onset compared with pairwise coherence of SIo neurons. Overall, the results suggest that the neuroplasticity in MIo and SIo occurring in parallel serves as a substrate for linking sensation and movement during sensorimotor learning, whereas the differing dynamic organizations reflect specific ways to control movement parameters as learning progresses.