{"paper_id":"39d4f1c1-6919-4d09-bec9-cc06e92570e1","body_text":"3D direc Ɵ onal tuning in the orofacial sensorimotor cortex during \nnatural feeding and drinking \nVictoria B. Hosack1 and Fritzie I. Arce-McShane1,2,3  \n1Department of Oral Health Sciences, School of DenƟstry, University of Washington, SeaƩ le, WA \n2Division of Neuroscience, Washington Na Ɵonal Primate Research Center, University of \nWashington, SeaƩ le, WA \n3Graduate Program in Neuroscience, University of Washington, SeaƩ le, WA \nAbstract \nDirecƟonal tongue movements are crucial for feeding and speech, ensuring proper food \nposiƟoning for chewing and swallowing, as well as accurate sound producƟon. While direcƟonal \ntuning in the arm region of the sensorimotor cortex during reaching tasks is well-studied, liƩ le is \nknown about how 3D tongue direc Ɵon is encoded in the orofacial sensorimotor cortex (OSMCx) \nduring natural behaviors. Understanding this neural representa Ɵon has important implica Ɵons \nfor rehabilitaƟng individuals with orolingual dysfunc Ɵons. This study examines the direcƟonal \ntuning and populaƟon dynami cs in OSMCx during naturalisƟc feeding and drinking, and how \nthese are affected by sensory loss. Using biplanar videoradiography, we tracked implanted tongue \nmarkers in behaving rhesus macaques ( Macaca mula Ʃ a) and simultaneously recorded 3D \nposiƟonal data  with spiking acƟvity from chronically implanted microelectrode arrays in primary \nmotor (MIo) and somatosensory (SIo) areas of the orofacial cortex. In some sessions, tasks were \npreceded by bilateral nerve block injec Ɵons to the sensory branches of the trigeminal nerve. \nModulaƟon to 3D tongue direcƟon during feeding and drinking was found in most MIo and SIo \nneurons. DirecƟonal informaƟon in both individual- and populaƟon-level was higher in feeding \nand was more robust in MIo. Following sensory loss, alteraƟons in tongue kinema Ɵcs were \naccompanied by changes in direcƟonal informaƟon in MIo and SIo, manifesƟng as modificaƟons \nin both individual neuron tuning characteris Ɵcs and the broader dynamics of popula Ɵon-level \nneural ac Ɵvity. Overall, this study advances our understanding of how OSMCx contributes to \ncomplex, coordinated control of naturalis Ɵc tongue movements. It expands our current  \nknowledge of orofacial control to three dimensions and demonstrates the specificity and \nadaptability of populaƟon acƟvity in MIo and SIo in response to different behavioral contexts. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nIntroducƟ on \nMotor and somatosensory cor Ɵcal neurons modulate their spiking ac Ɵvity based on movement \ndirecƟon as seen in  arm reaching tasks (Georgopoulos et al., 1988; Schwartz et al., 1988a; \nPrud’homme and Kalaska, 1994) and orofacial behaviors. In the primary motor (MIo) and primary \nsomatosensory (SIo) areas of the orofacial sensorimotor cortex (OSMCx), neurons encode the \ndirecƟon of voluntary tongue protrusion  (Murray and Sessle, 1992; Lin et al., 1994a) and semi-\nautomaƟc tongue movements in chewing and swallowing (Sessle et al., 2005b). Extensive \nresearch has explored how the arm region of the sensorimotor cortex encodes movement \ndirecƟon (Ajemian et al., 2000; Georgopoulos et al., 2007; Churchland et al., 2012; Lillicrap and \nScoƩ , 2013). Since the tongue is enclosed within the oral cavity and thus hidden from view, it has \nproved difficult to study the neuromechanical processes underlying direc Ɵonal tongue \nmovements that are essenƟal for these behaviors (Hiiemae and Palmer, 2003). Thus, considerably \nless is known about how 3D tongue direc Ɵon is encoded in the OSMCx and the role of tac Ɵle \nsensaƟon (Bach-y-Rita et al., 1998; Lamm et al., 2005; Lozano et al., 2009) during natural feeding \nand drinking. This knowledge has important implicaƟons for improving evaluaƟon and treatment \nstrategies for individuals with sensorimotor dysfuncƟons (Takizawa et al., 2016; Avivi-Arber and \nSessle, 2017). \nThe OSMCx plays an important role in the coordinaƟon of complex tongue movements. Seminal \nstudies on the direc Ɵonal tuning proper Ɵes of OSM Cx neurons by Sessle and colleagues \nemployed varying locaƟons of spouts that delivered a juice reward to elicit direc Ɵonal tongue \nprotrusions without tracking tongue movements. A later study incorporated tracking of 2D \ntongue movements using videofluoroscopy during voluntary direcƟonal protrusions (Arce et al., \n2013), but the tongue trajectories were not used to study d irecƟonal tuning. In all these prior \nstudies, primates have been trained to interact with a computer display to elicit a tongue \nprotrusion to a specific direcƟon on cue. There is a knowledge gap on how spiking acƟvity in the \nOSMCx relates to tongue movements during natural behaviors. With the development of biplanar \nvideo-radiography (Brainerd et al., 2010), it is now possible to track these 3D tongue movements \nwithin the oral cavity at a high temporal and spaƟal resoluƟon (Montuelle et al., 2020; Feilich et \nal., 2021). By simultaneous recording of precise tongue movements and spiking acƟvity, we have \nshown recently that tongue posiƟon and shape c an be accurately decoded from OSMCx during \nfeeding (Laurence-Chasen et al., 2023). \nOur study invesƟgated how OSMCx encodes and decodes tongue direc Ɵon during untrained \nfeeding and drinking, comparing acƟvity across mul Ɵple corƟcal regions. Given the importance \nof oral somatosensaƟon in tongue posiƟoning and bolus control during chewing and swallowing \n(Smith and Cutrer, 2011), we also examined its role in sensorimotor control by selecƟvely blocking \noral tacƟle sensaƟon. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nMethods \nSubjects. Experiments were performed on two adult male rhesus macaques (Macaca mulaƩ a, 9–\n10 kg, ages 8 and 9 years) in the University of Chicago XROMM Facility. This sample size was \nchosen based on precedent in the field of non-human primate motor neuroscience. All protocols \nwere approved by the University of Chicago Animal Care and Use Commi Ʃ ee and complied with \nthe NaƟonal InsƟtutes of Health Guide for the Care and Use of Laboratory Animals.  \nThe subjects were seated in a standard primate chair and head-fixed to keep their head posiƟon \nconstant during feeding and drinking trials. Each trial lasted 10 seconds. In a feeding trial, a piece \nof food (grape, gummy bear, pasta) of roughly the same size was presented directly to the \nanimals’ mouth using a stylus. In a drinking trial, juice was delivered through one of three spouts \nposiƟoned in front of the subject (Fig. 1A). \nCranial Nerve V anesthesia.  For some sessions, these behavioral tasks were preceded by nerve \nblock injecƟons (0.25% Bupivacaine HCL and Epinephrine 1:200,000, 0.25 mL/injec Ɵon site) to \nthe sensory branches of bilateral trigeminal nerves (lingual, inferior alveolar, buccal, palaƟne) to \neliminate oral tacƟle sensaƟon locally and temporarily. The nerve block was administered while \nthe subjects were under sedaƟon, and all data were collected within 90 minutes of the nerve \nblock. Each monkey served as its own control, with nerve block feeding data collec Ɵon sessions \ntaking place either a day before or a day aŌ er the associated control session. Nerve block drinking \ndata collec Ɵon was performed immediately following the control drinking session. MulƟple \ndatasets (40-60 trials) were collected for both subjects across mulƟple days. However, due to the \ncomplex and Ɵme-consuming nature of processing integrated XROMM and neural data, one \nsession per subject, behavior, and condiƟon was used for this study. Thus, we analyzed a total of \n8 datasets. \nVideo-radiography. Prior to data collecƟon, the animals were implanted with spherical tantalum \nbeads (1-mm diameter) in the cranium, mandible, and the tongue, from the Ɵp to the region of \nthe circumvallate papillae. During feeding or drinking, the movement of these markers was \nrecorded using high- resoluƟon (200 Hz , <0.1 mm) biplanar video-radiography collected with \nXcitex ProCapture version 1.0.3.6. The 3D posi Ɵonal data was obtained following the previously \ndescribed X-ray ReconstrucƟon of Moving Morphology (XROMM) workflow (Laurence-Chasen et \nal., 2020) incorporaƟng the use of XMALab (Knörlein et al., 2016) and machine learning using \nDeepLabCut (Mathis et al., 2018) to reconstruct the kinema Ɵc data.  The 𝑥, 𝑦, 𝑧 values of the \nmarkers were then smoothed with a 30 Hz low-pass B uƩ erworth filter and transformed into a \ncranial coordinate space with the origin fixed at the posterior nasal spine. Gape cycles within each \nfeeding sequence were manually idenƟfied and categorized by cycle type (manipulaƟon, stage 1 \ntransport, chew, stage 2 transport, or swallow). \nElectrophysiology. Under general anesthesia, a microelectrode array was chronically implanted \nin four areas of the leŌ  hemisphere (Supplementary file 1, Fig. 1): rostral MIo (96-electrode Utah \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\narray; electrode length: 1.5 mm), caudal MIo (32-electrode FloaƟng microelectrode array (FMA), \nelectrode length: 3.0–4.5 mm), area 1/2 (96-electrode Utah array, electrode length: 1.0 mm), and \narea 3a/3b (32-electrode FMA, electrode length: 4.0–8.7 mm). The neural data was recorded \nusing Grapevine Neural Interface Processor (Ripple Neuro, Salt Lake City, UT). Signals were \namplified and bandpass filtered between 0.1 Hz and 7.5 kHz and recorded digitally (16-bit) at \n30 kHz per channel. Only waveforms (1.7 ms in dura Ɵon; 48 sample Ɵme points per waveform) \nthat crossed a threshold were stored and offline spike sorted (Offline Sorter, Plexon, Dallas, TX) \nto remove noise and to isolate individual neurons. Neurons recorded during control feeding \nsessions are the same as those previously reported on in Laurence-Chasen et al., 2023. The \nchannel name assigned to each recorded neuron was kept consistent between control and nerve \nblock data for comparison. \nData analysis \n3D kinemaƟ cs. 3D tongue kinemaƟcs were recorded simultaneously with the neural data in all \nbehavioral sessions. All data analyses were performed in MATLAB 2022b (MathWorks , NaƟck, \nMA). For feeding, the instantaneous 3D direc Ɵon of the tongue Ɵp marker for every 100 ms \nthroughout each gape cycle was calculated as: \n3D angle, ϑ = tan-1(‖v1×v2‖/v1⋅v2) (1) \nWhere 𝑣ଵ  is the 𝑥, 𝑦, 𝑧 posiƟon at the start of each 100-ms interval and 𝑣ଶ  is the posiƟon at the \nend (Fig. 1B) . These direc Ɵons were then categorized based on whether the movement was \nnegaƟve or posi Ɵve rela Ɵve to the horizontal plane (LeŌ /Right), the sagi Ʃ al plane  \n(Inferior/Superior), and the 𝑥 axis (Posterior/Anterior). This resulted in eight direcƟons: AntSupL, \nAntSupR, AntInfL, AntInfR, PostSupL, PostSupR, PostInfL, and PostInfR. An equal number of 100-\nms intervals from each of these direcƟons was sampled  to eliminate the possible effect of \ndifferent distribuƟons of kinemaƟcs across datasets, and spike data during each interval was used \nfor neural analysis. For comparison with the drinking task, the sign was determined relaƟve to \nthe horizontal plane, with rightward tongue movement being posi Ɵve. This is also the plane of \nmoƟon which has been the least studied. These leŌ -right direcƟons were categorized into six 10-\nbins with a total range of -30  to 30 , which encapsulated most of the observed distribuƟon of \ndirecƟons in each subject. Lingual yaw (transverse rotaƟon) and pitch (elevaƟon/depression) \nwere also calculated to compare tuning across the lateral and verƟcal components of tongue \ndirecƟon (Supplementary file 2, EquaƟon 2). For drinking, the direcƟon was determined by which \nof the three spouts juice was dispensed from during each lick. The spiking acƟvity used for neural \nanalysis of the drinking task was from intervals of ±250 ms around each minimum protrusion of \nthe tongue. As 100 ms was not sufficient to capture the full range of tongue moƟon during each \ndrinking cycle, the length of Ɵme used was increased to allow a clear dis ƟncƟon between the \nthree direcƟons. The period of 250 ms spans about 75% of the average lick length from minimum \nto maximum protrusion of the tongue. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nKinemaƟc performance for feeding was determined by the spread of tongue direcƟons observed \nacross trials. For drinking trials, performance was determined by the variance of endpoint \nposiƟons as well as by the propor Ɵon of “failed” cycles, where the mon key missed the correct \nspout loca Ɵon with their tongue Ɵp. The difference between control and nerve block \nperformance was evaluated using a two-tailed t-test and f-test. \nDirecƟ onal tuning of single neurons.  Tongue direcƟons were subsequently compared with the \nfiring rates of individual neurons across corƟcal areas. To determine if neurons were direcƟonally \nmodulated, we used a bootstrap procedure (Arce et al., 2010): we resampled the firing rates from \nan equal number of trials in each direc Ɵon with replacement 1000 Ɵmes and computed 95% \nconfidence intervals from the resulƟng distribuƟon to test whether the mean ranks are the same \nacross direcƟons. The proporƟons of neurons that were found to be direc Ɵonally tuned were \ncompared across groups using a chi-square test. Due to limited neuron counts in some cor Ɵcal \nregions, we combined rM1 and cM1 recordings as MIo, and areas 1/2 and 3a/3b as SIo for \nsubsequent analyses (Supplementary file 1, Table 1). Then, mulƟple linear regression was used to \ndetermine if the firing of each neuron fit the cosine tuning func Ɵon that has been previously \ndescribed for the arm area of the motor cortex (Schwartz et al., 1988b). To accomplish this, the \ndirecƟonal components of a unit vector represen Ɵng each group of direc Ɵons were calculated. \nFor neurons that fit the tuning func Ɵon, a preferred direcƟon (PD) in 3D space was es Ɵmated. \nThese PDs are distributed around a unit sphere , with the origin represen Ɵng the start of the \nmovement. The direcƟonal index was calculated as a measure of the depth of direcƟonal tuning. \nTo determine PDs for the drinking task, we resampled the original distribuƟon of firing rates with \nreplacement for each direc Ɵon and calculated the direc Ɵon for which  a neuron exhibited its \nmaximal firing rate over 1000 bootstrap samples. Similarly, a PD across the le Ō -right feeding \ndirecƟons was determined for comparison. Circular concentra Ɵon (k -test) to compare \ndistribuƟons of PDs during feeding and polar plot genera Ɵon were performed using the CircStat \nMATLAB toolbox (Berens, 2009). For drinking, distribu Ɵons of PDs were compared using a chi-\nsquare test. We analyzed the trial-by-trial variability of neuronal ac Ɵvity using the Fano factor, \nwhich was computed as the spike-count variance divided by spike-count mean within each \nsession. The Fano Factor was calculated separately for each subject, task, and corƟcal region. For \nanalysis across sessions, we used the mean-matched Fano factor (Churchland et al., 2010).  \nFactor Analysis of popula Ɵ on ac Ɵ vity. We used Factor Analysis (FA), a linear dimensionality \nreducƟon method, to obtain latent trajectories of spiking ac Ɵvity and compare popula Ɵon \nresponses to different direcƟons across trials. FA defined as: \ny ∼ N (μ, CC’ + R) (2) \nwhere y is the spike counts from n neurons, μ is the mean spike counts from n neurons, C (m × n) \nis the loading matrix mapping m latent factors to the spike counts of n neurons, and R (n × n) \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nrepresents the unexplained variance of independent neurons(Santhanam et al., 2009; Horrocks \net al., 2024). The parameters μ, C and R were esƟmated with expecta Ɵon-maximizaƟon using \nDataHigh Matlab toolbox (Cowley et al., 2013). The latent factor space obtained using FA \nrepresents the shared popula Ɵon variance of neurons. To obtain latent factors of shared \npopulaƟon acƟvity, we binned the spike Ɵmes of each neuron into 10 ms bins (10 bins for feeding; \n50 bins for drinking). Neurons with a mean firing rate < 1.0 spike/s were excluded and the \nresultant vectors were smoothed using a Gaussian kernel with a 10 ms standard devia Ɵon (SD). \nTo determine the dimensionality of the latent variable, we used 3-fold cross-validaƟon to find the \nvalue of m which maximized the likelihood of the data. We then obtained an FA model by fiƫ ng \nan m-dimensional latent factor model. FA was performed using trial-averaged data to examine \nthe direcƟon-relevant latent factor responses (i.e., neural populaƟon trajectories).  \nWe quanƟfied direcƟonal differences in populaƟon acƟvity by calculaƟng the Euclidean distance \nover m latent factors) between trial-averaged neural popula Ɵon trajectories for each unique \ndirecƟon pair (drinking = 3 pairs; feeding = 28 pairs). This analysis was performed for every 10 ms \nbin throughout each trial. To assess differences between experimental condiƟons (control vs. \nnerve block) or corƟcal region  (MIo vs. SIo), we applied two-sample t-test to the mean inter-\ntrajectory distances across all direcƟon pairs. We further characterized the neural space spanned \nby populaƟon acƟvity by measuring the cumulaƟve Euclidean distance travelled by  trajectories \nfrom start to end of a trial. \nTo control for potenƟal sampling biases, we implemented two criƟcal validaƟon procedures. First, \nwe addressed the varying trial counts across direc Ɵons in the feeding task by performing Factor \nAnalysis with standardized samples (N = 80 trials per direc Ɵon) through random subsampling \nrepeated 10 Ɵmes. We then compared the cumulaƟve explained variance between the full and \nsubsampled datasets. Second, we controlled for populaƟon size differences by subsampling MIo \nand SIo neurons to equivalent counts, enabling unbiased comparison of Factor Analysis results \nbetween corƟcal regions. \nTo compare behaviors, we performed FA on only caudal MIo neurons that remained stable across \nboth feeding and drinking recording sessions (N = 20). These neurons were determined through \na stability test (Dickey et al., 2009), which compared the average waveform and interspike interval \nfor both datasets. We analyzed two subsets of kinema Ɵc data : data collected within 200 ms \nsurrounding minimum gape, and data from trials where the 3D angle measured 100 ms a Ō er \nminimum tongue protrusion was between -5 and +5 degrees. \nDecoding tongue direc Ɵ on. The ability to predict tongue direc Ɵon from spiking ac Ɵvity of MIo \nand SIo neurons was evaluated using a K-nearest neighbor (KNN) classifier. The Euclidean distance \nwas used to idenƟfy nearest neighbors, and the number of nearest neighbors used was K = 7. This \nK value was determined aŌ er tesƟng different Ks which yielded comparable results. The feature \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nwas the firing rate of each neuron over each trial: every 100 ms throughout feeding sequence, or \n100 ms centered at minimum tongue protrusion during drinking. As a more direct comparison to \nthe drinking, feeding direcƟons were split into three groups represenƟng leŌ , middle, and right  \nmovement direcƟons. The decoder was trained on 80% of trials and tested on the remaining 20%, \nthen decoder performance was determined by the percentage of test trials where the direc Ɵon \nof movement was correctly decoded from the neural data. We ran 100 iteraƟons of the classifier \nusing a different set of randomly selected training and test trials then calculated the average \nperformance. The same sets of training and test trials were used for decoding from \nsimultaneously recorded MIo and SIo data. However, our recorded populaƟons were of variable \nsizes, and decoding performance was found to be related to the number of neurons in the \nensemble. Because the smallest popula Ɵon of neurons we recorded was 28, we selected 28 \nrandom neurons from the larger populaƟons for each iteraƟon. Based on the posiƟve relaƟonship \nbetween populaƟon size and decoding accuracy, we expect that performance would increase with \nmore neurons. T hese results will show whether tongue direc Ɵon can be decoded from a very \nsmall number of neurons. We fit a linear regression model with interacƟons to compare decoding \nperformance across the other variables in the experiment. To determine if a mixed populaƟon of \nMIo and SIo neurons performs be Ʃ er than the pure popula Ɵons, we started with the full MIo \npopulaƟon and systemaƟcally replaced 25 MIo neurons with an equal number of SIo neurons. We \nrepeated this replacement over 100 iteraƟons, each with a different random selecƟon of neurons, \nand decoded tongue direc Ɵon using the KNN classifier. We compared the average decoding \nperformance of these slightly different mixed populaƟons to the baseline of running the decoder \nwith the full MIo populaƟon. \nWe also decoded tongue direcƟon using a long short-term memory (LSTM) network (Hochreiter \nand Schmidhuber, 1997; Glaser et al., 2020; Laurence-Chasen et al., 2023; Hahn and Arce-\nMcShane, 2024)  implemented in MATLAB's Deep Learning Toolbox. For the feeding task, we \nanalyzed 3D tongue movement direcƟon (relaƟve to the sagi Ʃ al plane) at 100 ms intervals. For \nthe drinking task, we categorized tongue direcƟon every 500 ms as either leŌ  (-45°), middle (0°), \nor right (45°). For each neural populaƟon, we created a 2D array containing spike counts for these \nƟme intervals (neurons × intervals). We randomly selected five groups of 28 neurons with \nreplacement from each popula Ɵon. Using 5 -fold cross-validaƟon, we trained an LSTM network \n(400 hidden units, 50 training epochs) on 85% of the intervals. The network was then tested on \nthe remaining 15% of neural data in a stepwise manner, producing a sequence of predicted \ntongue direcƟons. We used mean R² to measure predicƟon accuracy.  \nResults \nPrevious research has invesƟgated direcƟonal tuning of OSMCx  neurons during trained tongue-\nprotrusion tasks. Our study extends this work by inves ƟgaƟng two natural, untrained behaviors: \nfeeding and licking (\"drinking\") from a spout. These behaviors provide dis Ɵnct contexts for \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nstudying tongue coordina Ɵon - feeding allows unrestricted tongue movement, while drinking \nconstrains movement direcƟon based on spout locaƟon. This comparison enables us to examine \nhow OSMCx coordinates tongue movements under different behavioral contexts. \nNeuronal modulaƟ on paƩ erns differ between MIo and SIo.  Many neurons exhibited significant \nmodulaƟon of spiking acƟvity to tongue direcƟon (bootstrap, p < 0.05), though there were diverse \npaƩ erns. Figure 2 shows peri-event Ɵme histograms (PETHs) of two example neurons related to \ntongue movements during feeding and drinking. In feeding, both neurons showed complex \noscillatory firing, with notable peaks between -0.05s to 0.1s for upward movements to the right \n(MIo, Fig. 2A-leŌ ) and posterior movements to the leŌ  (SIo, Fig. 2A-right). For drinking there was \nclear separaƟon between different direcƟons in the MIo neuron , with the leŌ  (green) exhibiƟng \nthe highest ac Ɵvity (Fig. 2B- leŌ ), while SIo showed oscillatory pa Ʃ erns with less dis Ɵnct \nseparaƟon between spout locaƟons, but higher overall acƟvity (Fig. 2B-right). Like the arm region, \nthe tuning curves of direc Ɵonally modulated MIo and SIo neurons fit the cosine tuning funcƟon \n(F-test, p < 0.05, feeding: MIo = 86%, SIo = 75%).  Figure 3A maps a neuron’s firing rate for tongue \nmovements in leŌ -right, inferior-superior, and posterior-anterior axes. Here, a MIo neuron is \nstrongly tuned to posterior-anterior and inferior-superior direcƟons, while remaining \nunresponsive to leŌ -right movements during natural feeding. Many of the recorded neurons in \neach populaƟon behaved in a similar fashion, with peaks most frequently observed toward the \nanterior and superior direcƟons. This observaƟon was consistent with the tongue movements \nbeing most frequent in the Anterior Superior direcƟons, followed by the Posterior Inferior (Figure \n3 – figure supplement 1. The varying neuronal responses to tongue direc Ɵon c ould not be \naƩ ributed to variability in their firing , as the distribuƟon of the Fano factor was similar across \ndirecƟons (Kruskal-Wallis, p > 0.1 for all except SIo control drinking p = 0.06).  \nThe proporƟon of direc Ɵonally tuned neurons was higher in the feeding vs. drinking task (Chi-\nsquare, p < 0.05, feeding: 72%, drinking: 66%) and differed significantly between MIo and SIo \nduring the feeding task in both subjects (Chi-square, p < 0.001). In rostral and caudal MIo, 80% of \nneurons were modulated to 3D direcƟon (bootstrap, p < 0.05, Fig. 3B , leŌ ), compared to 52% in \nareas 1/2 and 3a/3b. Notably, fewer MIo neurons showed direcƟonal tuning during swallows \ncompared to chewing, while SIo neurons maintained consistent proporƟons (Figure 3 – figure \nsupplement 2; Chi-square, MIo: p < 0.05, SIo: p > 0.1). During drinking, the propor Ɵon of \ndirecƟonally modulated neurons was more similar between regions (69% in MIo vs. 60% in SIo: \nChi-square, p > 0.05, Fig. 3B right). Mean-matched Fano factor was significantly lower in MIo than \nSIo in both tasks (Wilcoxon rank sum test, p < 0.001). We considered that the difference in the \ndirecƟonal tuning between the two behaviors  could be due to the different Ɵme intervals used \nfor each task since the period around minimum tongue protrusion in the drinking may contain \nmore of the sensory inputs from the previous lick. However, when sampling spiking acƟvity from \nan earlier period in feeding , the percentage of direc Ɵonally tuned SIo neurons was s Ɵll \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nsignificantly lower than MIo (Chi-square, p < 0.001, data not shown). Shi Ō ing the period of the \ndrinking trials to be aŌ er minimum protrusion also did not lead to a significant difference between \nMIo and SIo (p > 0.1).  \nFurther analysis of the tongue’s lateral (yaw) and ver Ɵcal (pitch) components during feeding \nrevealed addiƟonal insights. Figure 4A shows peak acƟvity of a neuron in MIo and in SIo at varying \ndegrees of pitch and yaw. Overall, more neurons responded to pitch than yaw (Fig. 4B), with MIo \nshowing a higher proporƟon of neurons tuned to both components compared to SIo (Chi-square, \nyaw: p < 0.08, pitch: p < 0.001), consistent with our 3D direc Ɵon results. MIo neurons exhibited \nsharper and narrower tuning curves than the broader tuning curves observed in SIo (Fig. 4A, see \nSupplementary file 2).  \nThe peak of the PD distribuƟ on coincides with leŌ ward tongue movements. The distribuƟon of \npreferred direcƟons provides insight into how tongue muscles are coordinated during movement. \nIntrinsic and extrinsic tongue muscles are involved in shaping the tongue (e.g., elongaƟon, \nbroadening) and posiƟoning the tongue (e.g., protrusion/retracƟon, eleva Ɵon/depression), \nrespecƟvely. These muscles receive bilateral motor innervaƟon except for genioglossus. Straight \ntongue protrusion requires the balanced acƟon of the right and leŌ  genioglossi while the lateral \nprotrusion involves primarily the contralateral genioglossus. Given this unilateral innerva Ɵon \npaƩ ern, we hypothesized that le Ō  MIo/SIo neurons would preferen Ɵally respond to le Ō ward \ntongue movements, corresponding to right genioglossus acƟvaƟon.  \nDuring feeding, MIo and SIo showed non-uniform distribuƟon of preferred direc Ɵons across a \nunit sphere (Fig. 5; Rayleigh test, p < 0.001) and similar direcƟonal indices (t-test, p > 0.1, mean ± \n1 SD: MIo: 0.533 ± 0.3, SIo: 0.604 ± 0.5). As hypothesized, m ost neuronal popula Ɵons showed \npeaks in PD distribuƟons toward leŌ ward tongue movements, except in Monkey R's SIo (Fig. 6A). \nSimilar results were found with the distribuƟons of preferred yaw during feeding (Supplementary \nfile 2, Fig. 4). While feeding showed comparable PD distribu Ɵons between MIo and SIo in both \nsubjects (circular k-test, p > 0.1), drinking revealed significant differences between regions in \nMonkey R (Chi-square, p < 0.001) but not Monkey Y (p > 0.09). Monkey Y maintained \npredominantly leŌ -directed PDs across both tasks, while Monkey R showed more balanced le Ō -\nright PDs during drinking, sugges Ɵng poten Ɵal involvement of addi Ɵonal muscles beyond the \nright genioglossus. \nNeural populaƟ on trajectories differed based on task and corƟ cal regions . We analyzed \ndirecƟonal tuning at the popula Ɵon level using Factor Analysis (FA) on simultaneously recorded \nneurons to extract neural trajectories and idenƟfy paƩ erns in their shared acƟvity. In both tasks, \nneural trajectories of popula Ɵon ac Ɵvity in both MIo and SIo exhibited robust direc Ɵonal \ninformaƟon; inter-trajectory distances of all unique direcƟon pairs were significantly higher than \nzero (Fig. 7, t-test, p < 0.001, for all comparisons in both subjects and region). Notably in the \nfeeding task, MIo and SIo showed smaller inter-trajectory distances between Anterior-Posterior \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\npaired trajectories during feeding (e.g., AntSupL-brown and PostSupL-green) compared to a \ngreater separaƟon between other direcƟonal pairs (Fig. 7-top, t-test, p < 0.05 for both monkeys). \nPaired Superior-Inferior direcƟons, e.g., PostSupL-green and PostInfL-cyan, showed the largest \nseparaƟon, though this difference was not significant (p > 0.1). CumulaƟve explained variance for \nthe first three factors was higher in feeding (MIo: 82%, SIo: 81%) than in drinking (MIo: 74%, SIo: \n63%) when all neurons were used for the factor analysis (Fig. 7). Similar results were obtained \nwhen equal number of neurons were used (Fig. 7- figure supplement 1). To control for factors \nsuch as different neurons and kinemaƟcs that might influence the results, we performed factor \nanalysis on stable neurons across both tasks using all trials (Fig. 7- figure supplement 2A) and \nusing trials with similar kinemaƟcs (Fig. 7- figure supplement 2B). While the general shape of the \npopulaƟon trajectory was preserved across tasks, the inter-trajectory distance between them was \nsignificant (t-test, p < 0.001, mean ± 1 SD: 0.6786 ± 0.0424). Moreover, the MIo trajectory length \nwas longer in feeding (feeding: 0.8954, drinking: 0.6038) despite similar firing rates across tasks \n(t-test, p > 0.05) and longer tongue displacement in drinking ( p < 0.001, mean ± 1 SD: feeding: \n27.5 ± 9.8, drinking: 47.6 ± 11). When trials were limited to those with similar direc Ɵonal angles \n(±5 degrees), the difference in trajectory length was no longer observed but the inter-trajectory \ndistance between tasks remained significantly different (t-test, p < 0.001, mean ± 1 SD: 0.6967 ± \n0.0793).  \nMIo populaƟon trajectories followed a circular path and exhibited consistent paƩ erns based on \ndirecƟonal components. For example (Fig. 7 top), trajectories with upward (Sup) components \n(AntSupL/R, PostSupL/R) rotated opposite from trajectories with downward (Inf) components \n(AntInfL/R, PostInfL/R). In feeding, Factors 1 and 2 captured superior-inferior and right- leŌ  \ndirecƟons, respec Ɵvely. In drinking, MIo trajectories exhibited consistent rotaƟonal direc Ɵon \nregardless of spout locaƟon (Fig. 7 boƩ om leŌ ), while exhibiƟng disƟnct separaƟon of trajectories \nfor leŌ , center, and right spout-directed tongue movements clustering at approximately -0.5, 0, \nand 0.5 posiƟons along the Factor 2 axis, respecƟvely. Indeed, inter-trajectory distances in Factor \n1 were significantly higher in feeding (t-test, p < 0.001, mean ± 1 SD: 0.4628 ± 0.0246) than in \ndrinking (mean ± 1 SD: 0.1286 ± 0.0610 ), indica Ɵng that Factor 1 resembled direc Ɵonal \ninformaƟon in feeding but a condiƟon-independent feature of populaƟon acƟvity in drinking. The \nlatent factors revealed a clear organizaƟonal principle: Factor 1 predominantly captured superior-\ninferior direc Ɵonal components  in feeding , while Factor 2 primarily represented le Ō -right \ndirecƟonal components of tongue movement in both tasks.   \nSimilar to previous findings (Russo et al., 2018), SIo trajectories in both feeding and drinking \nshowed stark differences from MIo as they were more tangled and exhibited less direct (i.e., sharp \nturns) paths (Fig. 7 right). Unlike MIo trajectories, SIo trajectories spanned a smaller neural space, \nhad variable distances between trajectories, and showed inconsistent paƩ erns based on \ndirecƟonal components . Q uanƟtaƟve analysis revealed greater separa Ɵon between trial-\naveraged populaƟon trajectories in MIo compared to SIo  (Fig. 7 inset, t-test, p < 0.01, mean ± 1 \nSD: MIo: 0.34 ± 0.03; SIo: 0.12 ± 0.02). These results were consistent with significantly longer \ndistance travelled by MIo populaƟon trajectories compared to SIo in both tasks for Monkey R only \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n(t-test, p < 0.001, mean ± 1 SD: feeding: MIo: 0.43 ± 0.07; SIo: 0.26 ± 0.09; drinking: MIo: 2.85 ± \n0.42; SIo: 1.36 ± 0.52). T he regional differences cannot be a Ʃ ributed to fewer SIo neurons used \n(Figure 7 – figure supplement 3).  \nEffects of nerve block \nSensaƟon plays a key role in tongue posi Ɵoning and movements for natural behaviors. During \ningesƟon, tacƟle feedback is necessary for loca Ɵng the bolus, preven Ɵng tongue bites, feeling \nwhere the drinking spout is, and idenƟfying when it is safe to swallow. To evaluate the role of oral \nsensaƟon, we used a bilateral oral nerve block to temporarily eliminate tacƟle sensaƟon in the \noral cavity and observe how the control of tongue movement was impacted. Below, we show how \nthe loss of sensa Ɵon affected both tongue kinemaƟcs and direc Ɵonal tuning of neurons during \nfeeding and drinking. To verify that differenc es between the control and nerve block condi Ɵons \nwere due to the loss of sensory feedback and not because of other factors such as sedaƟon and \ninjecƟon, a sham experiment was conducted where saline was administered to the injecƟon sites \ninstead of nerve block. No significant changes to tongue kinemaƟcs were observed following the \nsham experiments (Fig. 8). \nChanges to t ongue kinema Ɵ cs. In feeding, the mean and overall spread of direc Ɵons w ere \nsignificantly different between the control and nerve block condiƟons (t-test, p < 0.01 and f-test, \np < 0.001). There was a shiŌ  towards a smaller range of 3D direcƟons in Monkey R, whereas there \nwas a shiŌ  towards a broader distribuƟon in Monkey Y under the nerve block condiƟon (Fig. 8A). \nThe posiƟons of maximum protrusion of the tongue during drinking, i.e., the endpoints, were also \naffected by the loss of sensa Ɵon. These endpoints represent the planned target posi Ɵon of the \ntongue to receive the juice reward from a specific spout. In the control drinking task, the \nendpoints for each spout loca Ɵon were very dis Ɵnct. In contrast, the endpoints of tongue \nmovements in nerve block exhibited a greater overlap across loca Ɵons and more variance in all \nthree axes of moƟon, i.e., Posterior-Anterior, Inferior-Superior, and LeŌ -Right (Fig. 8B).  \nCompared to the control, the trajectories of the tongue Ɵp in the nerve block condi Ɵon during \ndrinking had a smaller range of Le Ō -Right values. Visually, the tongue trajectories toward the \ndifferent spout loca Ɵons were messier and less dis Ɵnct in failed cycles where the tongue Ɵp \nmissed the loca Ɵon of the correct spout  by more than 2 SD from the mean (Fig. 9). In both \nmonkeys there was a significant increase in the average distance from the mean endpoint \nposiƟon, though this difference was much greater in Monkey R ( Fig. 8C). We noted a difference \nbetween subjects in the frequency of failed cycles and the range of leŌ -right tongue movements \nunder nerve block. This may reflect a possible compensatory strategy of reaching the drinking \nspouts with an adjacent region of the tongue, instead of contacƟng the right or leŌ  spout with \nthe ipsilateral tongue in Monkey R. We observed a decrease in the average speed (t-test, Monkey \nR: p < 0.001, Monkey Y: p > 0.1) and an increase in the variance of the speed (F-test p < 0.001) of \ntongue movement during drinking with nerve block. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nDecreased direcƟ onal tuning of MIo and SIo neurons . Loss of oral sensa Ɵon also affected the \nproporƟon of direcƟonally tuned neurons and the overall distribuƟon of PDs, though the paƩ ern \nof changes differed between subjects. Following nerve block, MIo and SIo showed significant \ndecreases in the proporƟon of direcƟonally modulated neurons across both tasks (Fig. 10A; Chi-\nsquare, MIo:  p  < 0.001, SIo: p < 0.05). To confirm this effect was not merely due to altered \nkinemaƟcs, we conducted parallel analyses using carefully subsampled trials with matched \nkinemaƟc profiles from both control and nerve -blocked condi Ɵons. This controlled analysis \nconfirmed the persist ent decrease in direc Ɵonal tuning during nerve block  (Figure 10 – figure \nsupplement 1). \nWe further invesƟgated whether neurons gaining or losing direc Ɵonal selecƟvity differed across \nregions. During feeding, MIo and SIo exhibited similar propor Ɵons of neurons gaining or losing \ndirecƟonal tuning  (Fig. 10B-top row, Chi-square, p > 0.1). The drinking task revealed subject-\nspecific differences (Fig. 10B- boƩ om row) : in Monkey R, significantly more neurons lost \ndirecƟonal tuning in SIo compared to MIo (p < 0.01), while in Monkey Y , SIo showed a higher \nproporƟon of neurons gaining direcƟonal tuning than MIo (p < 0.05). Comparing across behaviors, \nMonkey R demonstrated consistent paƩ erns of direcƟonal tuning changes (Chi-square, p > 0.1), \nwhereas Monkey Y showed significantly higher percentages of neurons gaining direc Ɵonality \nduring drinking than feeding in both MIo and SIo ( p < 0.05). InteresƟngly, there was a large \nproporƟon (40%) of SIo neurons in Monkey Y that gained direcƟonal tuning following sensory loss \ncompared to Monkey R (8%) during drinking. \nNerve block significantly altered PD distribuƟons during both tasks. During feeding, MIo neurons \nin both subjects exhibited a significant clockwise shiŌ  in mean PD toward the center (0°), resulƟng \nin more uniform distribuƟons (Fig. 11A; circular k-test, p < 0.01). In contrast, SIo neurons showed \nsubject-specific responses, with only Monkey R demonstraƟng a significant counterclockwise shiŌ  \n(p < 0.05). During drinking under nerve block, MIo neurons displayed subject-dependent \ndirecƟonal shi Ō s. In Monkey R, the proporƟon of  neurons with rightward PDs decreased and \nincreased in all other direcƟons whereas Monkey Y showed the opposite with increased neurons \nwith rightward PDs (Fig. 11B; Chi-square, Y: p = 0.04). Meanwhile, SIo neurons consistently shiŌ ed \nrightward in both animals (Chi-square, R: p = 0.02), sugges Ɵng differenƟal regional responses to \nperipheral deafferentaƟon. \nSeparaƟ on of neural popula Ɵ on trajectories was reduced in MIo.  DisrupƟon of tac Ɵle inputs \nduring feeding had opposite effects on MIo and SIo. Neural populaƟon trajectories in MIo showed \nreduced inter-trajectory distances during nerve block compared to control condi Ɵons (Fig. 12, \nleŌ , t-test, p < 0.05 in >89% of pairs), whereas SIo exhibited increased inter-trajectory distances \n(Fig. 12 top right, t-test, p < 0.05 in >75% of pairs). In drinking, inter-trajectory distances in both \nMIo and SIo were significantly reduced across all pairs (two-tailed t-test, p < 0.01) except middle-\nright in Monkey R's SIo neurons (p>0.1). \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nThe effects of nerve block on the distance travelled by populaƟon trajectories were inconsistent \nacross subjects and behavior. Following nerve block, the total distance travelled by SIo trajectories \nbecame longer in different behaviors for a specific subject (t-test, Monkey R feeding: p < 0.001; \nMonkey Y drinking: p < 0.05). In contrast, MIo trajectories became shorter in drinking (Monkey Y , \np < 0.01). \nPopulaƟ on decoding of tongue direc Ɵ on. To assess direc Ɵonal informaƟon within popula Ɵon \nacƟvity further, we implemented two decoding approaches to predict tongue movement \ndirecƟon from neuronal spiking paƩ erns: k-nearest neighbor (KNN) classifier and long short-term \nmemory (LSTM) neural network. Consistent with previous study on a cued tongue protrusion task \n(Arce et al., 2013), we found that the 3D direcƟon of tongue movements in naturalisƟc behaviors \ncould be decoded from simultaneously recorded MIo and SIo popula Ɵons. The KNN classifier \nsuccessfully decoded 3D tongue movement direc Ɵon above chance level across behaviors and \nexperimental condiƟons (Fig. 13A). Results of analyses using m ulƟple linear regression model \nwith interacƟons revealed several key factors affecƟng decoder performance: behavior type (p < \n0.001, drinking outperformed feeding by 11 %), corƟcal region ( p < 0.001, MIo exceeded SIo by \n13%), and inter-subject variability with behavior ( p < 0.001, 12% higher for Monkey R during \ndrinking, comparable accuracy during feeding). Notably, disrupƟng tac Ɵle sensa Ɵon through \nnerve block did not significantly impair KNN classifier performance ( p > 0.1). The results using \nLSTM were different from that of KNN; decoding tongue direc Ɵon using LSTM showed \nsubstanƟally higher performance when using MIo neural ac Ɵvity (mean R2 ± 1 SD: control: 0.46-\n0.81 ± 0.1, nerve block: 0.26-0.7 ± 0.1) compared to SIo (mean R2 ± 1 SD: control: 0.12-0.43 ± 0.1, \nnerve block: 0.05-0.17 ± 0.07) across all experimental condiƟons (Fig. 13B, t-test, p < 0.001). This \nregional difference became par Ɵcularly pronounced during nerve block, where SIo decoding \naccuracy decreased substan Ɵally more than MIo, sugges Ɵng differen Ɵal reliance on tac Ɵle \nfeedback between these cor Ɵcal regions.  Combining MIo and SIo showed significantly be Ʃ er \ndecoder performance compared to performance using neuronal populaƟons separately (mean R2 \n± 1 SD: control: 0.78-0.92 ± 0.05, nerve block: 0.58-0.91 ± 0.05, p < 0.001) in feeding, but not \ndrinking. To address the potenƟal confound of varying populaƟon sizes between MIo and SIo, we \nstandardized comparisons by downsampling all popula Ɵons to match our smallest recorded \ngroup (N = 28 neurons). Decoding accuracy improved by up to 10% when using all neurons in MIo \nor SIo compared to using subsamples of neurons. Decoding using LSTM showed consistently \nhigher accuracies in feeding compared to drinking regardless of the length of intervals used (100 \nms, 500 ms), behavioral window (  500 ms relaƟve to minimum protrusion, between maximal \nprotrusions), and direcƟonal angles (actual vs. {-45, 0, 45}). These results suggest that conƟnuous \nand non-linear decoding is beƩ er suited for feeding than drinking behavior. \n  \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nDiscussion \nOur study invesƟgated the representaƟon of 3D direcƟonal informaƟon in MIo and SIo during \nnatural feeding and drinking behaviors, and how corƟcal representaƟons change when tac Ɵle \nsensaƟon is disrupted . By simultaneously recording large-scale corƟcal acƟvity and 3D tongue \nkinemaƟcs, we revealed a nuanced neural encoding of tongue movement direc Ɵon that varies \nsystemaƟcally across corƟcal regions, behaviors, and sensory feedback condiƟons. We found that \na substanƟal propor Ɵon of neurons  exhibit direcƟonal tuning characterized by diverse tuning \ncurve proper Ɵes (PD, tuning shape, modulaƟon depth ). N eural popula Ɵon trajectories  \ndemonstrated disƟnct paƩ erns across different movement direcƟons. DirecƟonal tuning in both \nindividual- and popula Ɵon-level is more robust in MIo. Following sensory loss, alteraƟons in \ntongue kinema Ɵcs were accompanied by changes in direcƟonal informa Ɵon in MIo and SIo, \nmanifesƟng as modifica Ɵons in both individual neuron tuning characteris Ɵcs and the broader \ndynamics of populaƟon-level neural acƟvity.  \nDifferences across behaviors.  In the present study, results from the more natural drinking are \nconsistent with previous findings that MIo and SIo encode tongue direcƟon during a trained \nprotrusion task (Murray and Sessle, 1992; Sessle et al., 2005a; Arce et al., 2013). Our study \nextends this knowledge by invesƟgaƟng the dynamics of direcƟonal tuning of individual and \npopulaƟon of neurons in OSMCx to 3D tongue direc Ɵon during naturalisƟc behaviors. Unlike \nprevious similar studies, the monkeys were not trained to reach specific targets and were instead \nallowed to eat and drink rela Ɵvely naturally. By comparing two natural isƟc behaviors, we found \nthat the direcƟonal informaƟon in OSMcx was higher in feeding than in the drinking task, as seen \nin higher proporƟon of direc Ɵonally tuned neurons, cumulaƟve variance explained by latent \nfactors, and decoding accuracies. That direcƟonal tuning is modifiable is consistent with previous \nfindings in primate motor cortex where direc Ɵonal tuning was modulated by movement \nparameters such as speed, posture, distance (Aflalo and Graziano, 2006) and by varying task \ncontexts such as availability of prior informa Ɵon (Rickert et al., 2009), individual vs. segmented \narm movements (Ben-Shaul et al., 2004) , one- vs two-target reaching (Ebina et al., 2024). A high \ndegree of similarity in neural modes have been reported across different wrist tasks in 1-D and \n2D (Gallego et al., 2018). This suggests that in our study, feeding and drinking may reflect more \ndisƟnct biomechanical constraints and sensor imotor requirements compared to the wrist tasks. \nIn feeding, the tongue moves in varied direc Ɵons to posi Ɵon the  food between le Ō -right \ntoothrows during chewing, whereas in the drinking task, the tongue moves to discrete and fixed \nspout locaƟons. AddiƟonally, feeding and drinking engage the jaw differently. During feeding, the \njaw moves more extensively and in mulƟple direcƟons, while jaw movements during drinking are \nsmaller and primarily verƟcal. Lastly, the tongue- jaw coordina Ɵon differs between tasks; \nmaximum tongue protrusion coincides with maximum gape in drinking but with minimum gape \nin the feeding behavior (Laurence-Chasen et al., 2022; Punacha et al., 2024). Indeed, MIo \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\ntrajectories’ predominant latent factor contained direc Ɵonal informa Ɵon in the feeding task, \nresembling superior- inferior direc Ɵonal components which rotated in opposite direc Ɵons. In \ncontrast, MIo trajectories in drinking exhibited a consistent rotaƟonal direc Ɵon regardless of \nspout loca Ɵon (Fig. 7). This may reflect a predominant non- direcƟonal informa Ɵon such as \ncondiƟon-independent Ɵme-varying spiking acƟvity during drinking (Kaufman et al., 2013; Kobak \net al., 2016; Arce-McShane et al., 2023). \nComparison between MIo and SIo. DirecƟonal tuning of neurons during feeding showed a notable \ndisparity between MIo and SIo, suggesƟng that MIo carries more robust direcƟonal informaƟon \nfor tongue movements in feeding tasks. The similar propor Ɵons of direc Ɵonally tuned MIo and \nSIo neurons in the drinking task studied here were consistent with previous findings (Arce et al., \n2013). At the populaƟon level, MIo trajectories showed more consistent rotaƟonal paƩ erns and \ngreater inter-trajectory separaƟon than those in SIo. Consistent with results from previous studies \n(Michaels et al., 2016; Seely et al., 2016; Russo et al., 2018), MIo trajectories exhibited low tanging \nand smoother dynamics than SIo trajectories. These may suggest that the low tangling in MIo \nconfers noise robustness while the higher tangling in SIo reflects variability in the tacƟle signals \nreceived by SIo during feeding. Consistent with our previous study (Laurence-Chasen et al., 2023), \ndecoding from MIo yielded higher accuracies than from SIo in both behaviors. These results \nsupport the well-established role of MIo in the control of movement parameters, especially \ndirecƟon. Varying tongue shape, tongue’s contact with varying bolus properƟes (size and texture) \nand other oral structures (palate, teeth) may weaken the direcƟonal signal contained in SIo \nacƟvity. Thus, small differences in tongue kinemaƟcs might create large differences in sensory \nsignals across trials. When looking at trial-averaged signals, this natural variability could make the \nneural response paƩ erns appear less precise or specific than they are. These are consistent with \nour findings that for both tasks, spiking variability was higher in SIo, and that the variance \naccounted for was lower in SIo populaƟon acƟvity compared to MIo. \nLaterality in OSMCx. Similar to previous results in arm motor cortex (Lillicrap and ScoƩ , 2013), we \nobserved non- uniform PD distribu Ɵons consistent with the frequency distribu Ɵon of tongue \nmovements, suggesƟng that neural populaƟons contain informaƟon that reflects the anatomical \nconstraints of the tongue . The highest frequency of both observed direc Ɵons and direc Ɵonal \ntuning peaks were in the anterior and superior direcƟons. We addiƟonally found that the peak of \nthe PD distribuƟon, especially in feeding, coincides with leŌ ward tongue movements, suggesƟng \nthe presence of laterality in the PDs of OSMCx neurons. Previous results in humans examined \nusing fMRI reported that hemispheric differences in sensorimotor ac Ɵvity during voluntary \ntongue movements are related to the preferred chew side (Shinagawa et al., 2003). This was not \nthe case in our study as the preferred chew side was the same for both monkeys (i.e., right side), \ndespite differences in the predominant PD. It is possible that the difference between the two \nsubjects is related to the difference in recording locaƟons, with Monkey Y’s being more lateral \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nand therefore closer to the swallow area of the cortex than Monkey R’s (Supplementary file 1, Fig. \n1). Monkey Y had a higher propor Ɵon of neurons that were tuned to tongue direc Ɵon during \nfeeding compared to Monkey R (Figure 3 – figure supplement 2), but fewer during drinking. An \navenue for further study could be a unilateral nerve block on the preferred side to measure how \nthe unaffected side of the tongue compensates for the lack of sensa Ɵon in the affected side. A \nprevious study found that unilateral lingual nerve transec Ɵon in pigs alters the coordina Ɵon of \nthe ipsilateral tongue side during chewing (Montuelle et al., 2020). The tongue is a complex group \nof muscles, with intrinsic muscles primarily contribuƟng to the shape of the tongue and extrinsic \nmuscles contribu Ɵng more to the posi Ɵoning of the tongue. Therefore, it is possible that the \nneurons which are strongly tun ed to tongue direc Ɵon have direct connec Ɵons to the extrinsic \nmuscles on the ipsilateral side. Looking at how each side of the tongue responds independently \nto unilateral nerve block, and how this interacts with direc Ɵonal preference may give us more \ninformaƟon about how the unique structure of the tongue is coordinated. \nRole of tacƟle feedback. Previously, we reported that the administraƟon of bilateral nerve block \nto the sensory branches of the trigeminal nerve impaired feeding performance and tongue jaw \ncoordinaƟon (Laurence-Chasen et al., 2022). The present study extends these findings by showing \nthat direcƟonal movement of the tongue ( kinemaƟcs) and the direcƟonal informaƟon in both \nMIo and SIo are also affected by the loss of tacƟle inputs from the tongue and other structures of \nthe oral cavity (e.g., palate, teeth, gingiva). These findings highlight the cri Ɵcal role of sensory \ninformaƟon in sensorimotor control in general (Dadarlat et al., 2015; Delhaye et al., 2018) and in \nthe representaƟon and computaƟon of direcƟonal signals for controlling tongue movements. MIo \nand SIo neurons, which respond to t acƟle and propriocepƟve inputs from the tongue (Huang et \nal., 1989; Lin et al., 1994b; Toda and Taoka, 2002, 2004, 2006; Arce et al., 2013; Cerkevich et al., \n2014), use sensory informaƟon to plan and adjusts tongue movements to achieve contact with \nthe spout or posiƟon the bolus appropriately at different stages of the feeding sequence. Without \ntacƟle feedback, subjects may rely on alternaƟve sensory cues like taste for locaƟng the spout or \nbolus (Todrank and Bartoshuk, 1991) . In a recent optogeneƟc inhibiƟon study on licking in mice, \nit was found that the tongue/jaw regions of the somatosensory cortex were necessary for proper \ntongue targeƟng but not for the core motor capabili Ɵes of the tongue (Xu et al., 2022). The \nreduced range of tongue mo Ɵon we observed likely stems from sensory loss rather than motor \nimpairment.  While our experimental setup did not eliminate visual feedback that monkeys might \nuse to readjust tongue posiƟon in the drinking task, oral sensory loss alone had a significant effect \non the monkeys’ performance in feeding, as the tongue was out of sight within the oral cavity. \nIndividual differences were notable in our study possibly due to differences in the electrode array \nplacement or compensatory strategies. This individual variability suggests that studying \naddiƟonal subjects would provide valuable insights into how OSMCx adapts following sensory \ndisrupƟon. In our study, head posi Ɵon and hand movement were restrained, to eliminate \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\ncontribuƟons of hand-to-mouth movements when handling food or drink. The hand and orofacial \ncorƟcal areas are anatomically adjacent and highly interconnected (Forrester and Rodriguez, \n2015), and researchers have found a neural region in mice that coordinates hand-to-mouth \nmovements during natural feeding (An et al., 2022). Truly natural feeding would involve holding \nfood up to the mouth, as well as free head movement, which would make tracking of the marker \nposiƟons difficult under this experimental setup. Advances in tracking tongue movements would \nbe necessary to study more complex feeding sequences. \nClinical implicaƟ ons \nThis study offers new informa Ɵon about the important role of sensorimotor integra Ɵon in \ncontrolling tongue direc Ɵon during natural behaviors. There is a high degree of direc Ɵonal \ninformaƟon contained in the spiking acƟvity of the orofacial cortex, especially in the motor areas. \nThe effect of the bilateral nerve block serves to enhance our understanding of the processes \naffected by oral sensorimotor dysfuncƟons such as trigeminal neuropathies. It demonstrates the \nimportance of oral sensa Ɵon for supporƟng the full range of direc Ɵonal moƟon but also shows \nthat significant direcƟonal informaƟon can be extracted even in the absence of tac Ɵle feedback. \nThis type of knowledge can inform the diagnosis and rehabilita Ɵon of orolingual dysfunc Ɵons, \nfollowing stroke or glossectomy. There have also been advancements in brain-computer interface \n(BCI) by decoding the real- Ɵme signals of arm region of the motor cortex to control prosthe Ɵc \narm movement (Collinger et al., 2013; Vilela and Hochberg, 2020) or muscle sƟmulaƟon (Ethier \nand Miller, 2015), as well as efforts to restore sensory feedback by s ƟmulaƟng correct areas of \nsomatosensory cortex in response to sensors on a prosthe Ɵc (Tabot et al., 2013; Flesher et al., \n2021). That the OSMCx, par Ɵcularly MIo, can rapidly decode tongue direc Ɵon during natural \nbehaviors is significant for developing neuroprostheƟc control or soŌ  prostheƟcs. \nEthics statement \nExperiments were performed in the University of Chicago XROMM Facility. All protocols were \napproved by the University of Chicago Animal Care and Use Commi Ʃ ee and complied with the \nNaƟonal InsƟtutes of Health Guide for the Care and Use of Laboratory Animals. \nFunding informaƟ on \nThis research was supported by NaƟonal InsƟtutes of Health grants from  the NaƟonal InsƟtute \non Aging, Grant Number: R01AG069227 (to F.I.A- M, PI) and the Na Ɵonal InsƟtute of Dental and \nCraniofacial Research, Grant Number: R01DE027236 (to F.I.A-M, PI). The content is solely the \nresponsibility of the authors and does not necessarily represent the official views of the NaƟonal \nInsƟtutes of Health. \nThe funders had no role in study design, data collec Ɵon and interpreta Ɵon, or the decision to \nsubmit the work for publicaƟon. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\nAuthor contribuƟ ons \nVictoria B. Hosack: data analysis, wri Ɵng – original dra Ō , wri Ɵng – review and edi Ɵng, \nvisualizaƟon. \nFritzie I. Arce- McShane: conceptualiza Ɵon, methodology, inves ƟgaƟon, wri Ɵng – review and \nediƟng, supervision, project administraƟon, funding acquisiƟon. \nAcknowledgements \nWe thank J.D. Laurence- Chasen for data collec Ɵon and so Ō ware, ChrisƟna Hahn for assistance \nwith LSTM analysis, as well as all members of the Arce-McShane Lab past and present, including \nRebecca Junod, Hernando Fereira, Derrick Tang, Emma Lesser, Jared Luckas, Tricia Nicholson, Eric \nHosack, for assistance with data collecƟon and processing.  \nReferences \nAflalo TN, Graziano MS (2006) ParƟal tuning of motor cortex neurons to final posture in a free-moving \nparadigm. 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Nature 603:464–469 Available at: \nhƩ ps://pubmed.ncbi.nlm.nih.gov/35264793. \n  \n \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n   \nFigure 1. DirecƟon of tongue mo Ɵon in each behavioral task. (A) SchemaƟc of the loca Ɵon of three \nspouts, leŌ  (L), middle (M), and right (R), for the drinking task. Tongue direcƟon was categorized based \non spout loca Ɵon. (B) CalculaƟon of 3D tongue direc Ɵon during feeding. θ is the instantaneous 3D \ndirecƟon of the tongue Ɵp over a 100 ms interval between its posiƟons at t1 and t2, where t1 = 0 and \nt2 = t1 + 100. The doƩ ed line shows the actual trajectory during this interval. \nFigure 2. Examples of single neuron activity in relation to tongue direction.  (A) Each peri-event time \nhistogram (PETH and ±1 SE, smoothed by a 25- ms Gaussian kernel) corresponds to spiking activity for a \nspecific range of tongue direction for feeding trials. Dashed lines indicate 100- ms interval used for \ncalculating the tongue direction. (B) PETHs for drinking trials with the same spout, centered at the point of \nminimum protrusion of the tongue (0-s). Percent tongue displacement along the anterior-posterior axis is \nshown in grey, with shaded area representing ±1 SD. Vertical lines indicate 500-ms interval used for tuning \nanalysis. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 3. Directional tuning of neurons during control tasks. (A) 3D firing rate map of a neuron in MIo \nduring feeding. Smaller inset plots are 1D tuning curves across each axis. (B) Percentage of neurons \ntuned to direction, combined for both subjects. Recordings were taken from four areas of the OSMCx: \nrMIo - rostral M1, cMIo - caudal M1, SIo(3a/3b) - area 3a/3b, and SIo(1/2) - area 1/2. Error bars \nrepresent ±1 SE. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 4. DirecƟonal tuning to yaw and pitch during feeding. (A) Firing rate maps of a neuron in MIo and \nin SIo across yaw and pitch angles. Firing rates were averaged across all 100 ms feeding intervals within a \n10° range. (B) Proportion of neurons tuned to yaw and pitch, combined for both subjects. Recordings were \ntaken from four areas of the OSMCx: rM1 - rostral M1, cM1 - caudal M1, SC(3a/3b) - area 3a/3b , and \nSC(1/2) - area 1/2. Error bars represent ±1 SE. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n \n \n \n \n \n  \nFigure 5. Cosine tuning of MIo and SIo neurons. (A) DistribuƟon of 3D preferred direcƟons in unit sphere \nfor neurons that fit the tuning funcƟon during feeding, combined for both subjects. The origin represents \nthe start of a movement. Color bar represents posterior-anterior axis. (B) DistribuƟon of the index for the \ndepth of direcƟonal tuning, combined for both subjects. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 6. DistribuƟon of PDs in MIo (yellow) and SIo (purple) neurons during control feeding (A) and \ndrinking (B). For the feeding task, polar plots are split into 10  bins with thick colored lines \nrepresenƟng the mean PD. For the drinking task, error bars represent ±1 SE. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n \n \n \nFigure 7. Neural populaƟon trajectories vary across direc Ɵons. Trial-averaged trajectories of MIo and SIo \npopulaƟon acƟvity along the first three latent factors for Monkey R, grouped by direc Ɵon. Axes for SIo are \n1/4 scale of MIo. Arrows indicate the end of the trajectory. Percentages denote the sum of the variance \nexplained by the first three factors. Inset plots show the difference between the average inter- trajectory \ndistances of MIo and SIo over Ɵme for both feeding and drinking. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 8. Effect of nerve block on direcƟon of tongue movement. (A) DistribuƟon of tongue direcƟons during \nfeeding. (B) Variance in 3D trajectory endpoints during drinking (Posterior-Anterior, Inferior-Superior, Le Ō -\nRight) for each direc Ɵon: leŌ  (L), middle (M), right (R). (C) VariaƟon in the distance of drinking endpoint \nposiƟons from the mean endpoint. Le Ō  halves of hemi-violins (black) are control and right halves (red) are \nnerve block for an individual. Horizontal black lines represent the mean and horizontal red lines the median. \nResults of two-tailed t-test and f-test are indicated by asterisks and crosses, respecƟvely: *,† p < 0.05; **,†† \np < 0.01; ***,††† p < 0.001. Smaller inset plots show that there was no effect in the sham nerve block \ncondiƟon, for reference. The sham procedure was iden Ɵcal to the nerve block, except the anesthe Ɵc was \nsubsƟtuted with saline soluƟon \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n \n  \nFigure 9. Effect of nerve block on drinking kinemaƟcs in Monkey R. (A) Tongue Ɵp trajectories \nfrom star Ɵng posi Ɵon to one of three drinking spouts in the control and nerve block \ncondiƟons. (B) Drinking trajectory endpoints, where the black dot represents the mean \nendpoint posiƟon. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 10. Effects of nerve block on direc Ɵonal tuning of OSMCx neurons during feeding and drinking \ntasks. (A) Percentage of direcƟonally tuned neurons in four areas: rMIo - rostral M1, cMIo - caudal M1, \nSIo(3a/3b) - area 3a/3b, and SIo(1/2) - area 1/2 . Filled in bars represent control while empty bars \nrepresent nerve block. Error bars represent ±1 SE. (B) Percentage of MIo and SIo neurons which gained \nor lost direcƟonality with the addiƟon of nerve block. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n  \nFigure 11. Effects of nerve block on the distribuƟon of PDs of MIo (yellow) and SIo (purple) neurons. \n(A) For the feeding task, polar plots are split into 10° bins with thick colored lines represenƟng the \nmean PD. Significant circular concentra Ɵon test (k- test) comparing control and nerve block are \nindicated by asterisks: *p < 0.05; **p < 0.01; ***p < 0.001.  (B) For the drinking task, error bars \nrepresent ±1 SE. Filled in bars represent control while empty bars represent nerve block. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n \n \n \n  \nFigure 12.  Effect of nerve block on popula Ɵon trajectories. Trial- averaged trajectories of MIo and SIo \npopulaƟon acƟvity for Monkey R’s feeding and drinking sessions, grouped by direcƟon. Axes represent the \nlatent factors from control data, with the x-axis chosen as the factor with the highest degree of separaƟon \nbetween direcƟons. Lighter, doƩ ed lines represent superimposed populaƟon trajectories in the nerve block \ncondiƟon. Insets show the difference between the average inter-trajectory distances for control and nerve \nblock condiƟons. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint \n\n \n \nFigure 13. Accuracies of two different decoding algorithms from MIo and SIo populations of equal size (N=28). \n(A) Comparison between average decoding accuracy of KNN classifier. Chance level is 33.33%. (B) Comparison\nbetween average decoding accuracy by LSTM network. Data shown separately for each subject, behavioral \ntask, and condition. The dashed line signifies equal decoding performance for MIo and SIo. Decoding \naccuracies from full populations are included in Figure 13 – figure supplement 2. \n.CC-BY 4.0 International licenseavailable under a \nwas not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made \nThe copyright holder for this preprint (whichthis version posted April 24, 2025. ; https://doi.org/10.1101/2024.07.02.601741doi: bioRxiv preprint","source_license":"CC-BY-4.0","license_restricted":false}