Such an impact might reveal the vital role of coupling strength on phonon scattering and on the thermal transport property of supported monolayer graphene. To further interpret the thermal annealing mechanism, the compressive stress in supported monolayer graphene, which is closely related to coupling strength and is studied through the temperature-dependent Raman spectra. It is found that the variation tendency for compressive stress induced by thermal annealing is the same as that for temperature coefficient, implying the intense connection between compressive stress and thermal transport. Actually, 773 K thermal annealing can result in 2.02 GPa compressive stress on supported monolayer graphene due to the lattice mismatch of graphene and substrate. This study proposes thermal annealing as a feasible path to modulate the thermal transport in supported graphene and to design future graphene-based devices.The coupling strength between two-dimensional (2D) materials and substrate plays a vital role on thermal transport properties of 2D materials. Here we systematically investigate the influence of vacuum thermal annealing on the temperature-dependence of in-plane Raman phonon modes in monolayer graphene supported on silicon dioxide substrate via Raman spectroscopy. Intriguingly, raising the thermal annealing temperature can significantly enlarge the temperature coefficient of supported monolayer graphene. The derived temperature coefficient of G band remains mostly unchanged with thermal annealing temperature below 473 K, while it increases from −0.030 cm As an important member in the family of two-dimensional 2D) atomic layer materials, graphene owns unique physical and chemical properties, which make it a promising candidate for various applications such as optoelectronic and high-frequency electronic devices D atomic ,4,5,6. H2 bonds in graphitic carbon can endure extremely high mechanical strains and present interesting electromechanical properties [2O molecules at the graphene–substrate interface. The phonon dispersion can be modified by altering the coupling strength between graphene and substrate, especially for flexural acoustic (ZA) phonons, which are the main carriers of interfacial thermal transport (out-of-plane) in supported graphene [2/Si substrate with an ultra-thin sild oxide layer (8 nm and 10 nm) has been experimentally characterized via an optothermal Raman technique [2 substrate (300 nm) so far.Theoretical and experimental studies suggest that much effort has been put into power dissipation by designing a special pattern, introducing surface encapsulation and enhancing the coupling strength through current annealing ,14. Amonoperties . Additiooperties ,17. In ggraphene . In addiechnique . As the attering ,20,21. Tattering ,23,24, t2 substrate (300 nm) after different thermal annealing processes via Raman thermometry. Specifically, the Raman peak position of supported monolayer graphene with no thermal annealing is redshifted, as the ambient temperature ranges from 193 K to 303 K, deriving the corresponding temperature coefficient of −0.030 cm−1/K. This may be attributed to the phonon softening during the ambient temperature increase. However, a discrepancy of temperature coefficient can be observed among supported monolayer graphene samples after vacuum thermal annealing at various annealing temperatures. For the lower annealing temperatures , the temperature coefficients of annealed sample are comparable to that of pristine monolayer graphene. When the annealing temperature increases from 473 K to 773 K, a large discrepancy takes place in the temperature coefficients ranging from −0.030 cm−1/K to −0.0602 cm−1/K. The discrepancy could be attributed to the enhancement of strain induced by coupling strength and phonon–interface scattering. Moreover, the compressive stress on supported monolayer graphene through various thermal annealing processes are characterized by Raman spectroscopy. The compressive stress induced by thermal annealing exhibits a similar tendency, with the temperature coefficient under these thermal annealing processes, indicating the evident regulation from thermal annealing on thermal transport of supported monolayer graphene. After annealing at 773 K, the compressive stress runs up to 2.02 GPa, implying an enormous lattice mismatch between graphene and substrate. Our finding provides an insight into the thermal annealing of supported graphene and paves a new way to solve the thermal challenge in graphene-based devices.In this work, the temperature-dependent Raman phonon modes were investigated for monolayer graphene supported on Si/SiO2/Si wafer with p-type-doping by traditional micromechanical exfoliated methods from the bulk graphite . Characterized by optical microscopy , the monolayer graphene was collected due to the discrepancy in optical contrast for graphene with different layers. The thickness of graphene was ulteriorly identified by Raman spectra (WITEC 300R Raman spectrophotometer). The temperature-dependent Raman spectra were also conducted  with laser power of 1 mW and 50−1 Pa) by quartz tube furnace (GSL-1500X-50). The temperature ramp was 2 °C/min in the beginning and the sample naturally cooled down to the room temperature, which can minimize the damage.The monolayer graphene was firstly prepared on SiO2 substrate. After approximate identification of flake thickness by optical microscopy, Atomic force microscopy (AFM)and Raman spectroscopy to the red marked region in −1 and 2673 cm−1 can be observed in the Raman spectrum . The Raman spectra in −1/K) can be observed for 773 K thermal annealing temperature, which is twice than that of the unannealed pristine monolayer graphene (−0.03 cm−1/K). The reason for this tendency will be discussed in the following sections. The observed enhancement in Raman temperature coefficients may be contributed to by the anharmonicity scattering effect of phonon and the thermal expansion during ambient temperature increase [The Raman peak shifts are closely related to thermal transport in graphene. Former experimental studies show that the measurement of thermal transport, including in-plane thermal conductivity and interface thermal conductivity, is based on either 2D peak shift or G peak shift via Raman thermometry ,23,24. Tincrease . The sim 2D band .2 substrate due to the removal of H2O molecules. According to a previous report, the coupling strength is associated with strain [2O molecules between graphene and substrate for the initially mechanical exfoliated graphene flake, as depicted in the schematic image  is comparable to that on Si/SiO2 substrate (0.030 cm−1/K), indicating that the temperature coefficient shows no obvious reliance on the type of substrate. Additionally, previous literature shows that the ratio of Raman 2D and G peak shift induced by thermal annealing can serve as an effective probe to reveal the roles of p-type charge carrier density and strain [To pinpoint the root reason for the observed enhancement in temperature coefficient and to interpret the discrepancy induced by thermal annealing, we then attempt to investigate the thermal annealing mechanism from coupling strength of supported monolayer graphene and substrate. It is known that the thermal annealing can significantly enhance the degree of coupling strength for monolayer graphene supported on Si/SiOh strain . Little h strain ,33, the ic image a. Considd strain . Thus, wd strain ,35. Tabl density .Additionally, the rising of temperature coefficient is believed to be ascribed to strain instead defects upon 673 K and 773 K annealing, because there exists little or no D peak in the Raman spectrum . As repo−1, which is smaller than the bandwidth of 10 cm−1 induced by charge doping [−1 in In order to identify the strain upon different thermal annealing processes, we have systematically analyzed the G band of supported monolayer graphene flakes after thermal annealing. The evolution of G band in different thermal annealing processes is shown in e doping ,38. Thuse doping  It was fThe compressive stress is originated from the lattice mismatch at graphene and substrate interface induced by thermal annealing. Therefore, the generated stress should be biaxial. By analyzing the blue shift of Raman G band, the value of biaxial compressive stress can be estimated. In hexagonal system, the strain equation ,39,40,(x and y axes in graphene plane and the z axis perpendicular to the plane, we get Considering the −1/GPa. Thus,The relationship between compressive stress and annealing temperature can be fitted as:−1/K, which is twofold than that of pristine monolayer sample without any thermal annealing. The influence from thermal annealing on the temperature coefficient of supported monolayer graphene might be attributed to the increased coupling strength and enhanced compressive stress. As an evidence, the corresponding Raman analysis also shows that the compressive stress in annealed monolayer graphene can reach up to 2.02 GPa. Our finding proposes thermal annealing as an alternative route to regulate the thermal transport and heat dissipation of supported graphene and other 2D material devices.In summary, we systematically investigated the thermal annealing temperature-dependent phonon modes of monolayer graphene supported on substrate by Raman spectroscopy with ambient temperature ranging from 193 K to 303 K. As the ambient temperature rises up, the Raman peak position of G mode is redshifted due to phonon softening. Moreover, such a redshift is observed commonly for monolayer graphene samples upon different thermal annealing treatment. Especially for 773 K annealing, the temperature coefficient suddenly surges to −0.0602 cm


The subthalamic nucleus (STN) is theorized to globally suppress movement through connections with downstream basal ganglia structures. Current theories are supported by increased STN activity when subjects withhold an uninitiated action plan, but a critical test of these theories requires studying STN responses when an ongoing action is replaced with an alternative. We perform this test in subjects with Parkinson’s disease using an extended reaching task where the movement trajectory changes mid-action. We show that STN activity decreases during action switches, contrary to prevalent theories. Furthermore, beta oscillations in the STN local field potential, which are associated with movement inhibition, do not show increased power or spiking entrainment during switches. We report an inhomogeneous population neural code in STN, with one sub-population encoding movement kinematics and direction and another encoding unexpected action switches. We suggest an elaborate neural code in STN that contributes to planning actions and changing the plans. The ability to change an ongoing plan of action is essential to success in a dynamic world. How do we switch from executing one action to another? The gating of movements is thought to differentially engage cortico-striatal-thalamic pathways. Executing a planned movement involves activation of the direct pathway , while pHowever, it is possible that STN plays a more elaborate role in governing actions than just cancelling existing plans. This possibility is supported by past studies that showed STN neurons increase their activity during movement initiation  and distTo investigate STN neural responses during action switches, we developed a novel behavioral task in which human subjects with Parkinson’s disease undergoing implantation of deep brain stimulation (DBS) electrodes moved their hands on a straight or turning trajectory from a home position to target regions on either side of the home position . On a random subset of trials, intermixed with others, subjects were cued to initiate a straight reach but then cued to switch to a turning trajectory mid-movement . We recorded spiking activity of STN units and LFPs from the same sites during the task. Our new task contrasts with prior studies of STN which focused largely on the starting or withholding of simple movements, such as button presses or joystick movements in humans , saccadeWe found that interruption of a planned movement with a cue to switch movement trajectory was associated with decreased firing rates in STN, contrary to predictions of prevailing theories. Careful investigation of the neural responses revealed an elaborate code, in which the population of neurons encoded the movement plan, hand kinematics during the movement, and changes in the movement plan. Unsupervised clustering of the population response dynamics revealed functional clusters of neurons, one specialized for encoding movement kinematics and the other for encoding changes in movement plan. The neurons also exhibited a wide range of response lags, some predicting upcoming changes in kinematics and some reflecting recently executed changes, creating a time reservoir for planning and tracking of actions. Finally, we show that switching the movement trajectory was associated with constant beta power in the LFP and similar entrainment of action potentials with beta frequency oscillations, further supporting the idea that switching ongoing actions has a distinct neural mechanism from withholding uninitiated actions. Together, our results suggest an elaborate population code in STN and an intricate role in planning and execution of complex actions, beyond what is predicted by existing theories.We recorded single and multiunit spiking activity from the STN while human subjects with Parkinson’s disease performed single-step or multistep reaching movements during surgeries to implant DBS electrodes. Subjects controlled a cursor on a screen with free hand movements in 3D space as recorded by a stereoscopic camera . They usWe recorded from 39 units in 9 STNs of 8 subjects (one subject completed separate recording sessions from STNs of both hemispheres). Subject demographics and the number of units recorded in each subject are shown in Although response patterns varied considerably across units, many units elicited similar motifs across trials. We, therefore, hypothesized that the STN neural population consists of functional clusters with a mixture of selectivities . To bettAll subjects had recordings of Movement and/or Turn Units. Our dataset includes both single and multi-units, in which most spikes originate from a single unit . Single –4, binomial test). However, only 19% of Movement Units showed decreased firing on Planned Turn compared to Reach trials . The difference of the firing rates at the population level afforded accurate classification of trials. A decoder trained to classify trials based on population responses achieves a cross-validated accuracy of 67% and 71% for decoding Reach from Planned Turn trials and Planned Turn from Impromptu Turn trials, respectively.The firing rates of Movement Units were modulated by task events and trial types. They increased at movement onset and decreased after feedback. Firing rates were significantly lower on Planned Turn trials beginning 140 ms after movement onset and lasting until 130 ms before turn onset  and explained the firing rate dynamics . However, the best-fitting models had lags that varied considerably in the population of Movement Units  across all units was chosen . The best model was identical to the main model in the paragraphs above and contained two predictors: the total speed and total acceleration. The alternative models underperformed in predicting the held-out data. But among them, models that included total speed or total acceleration tended to explain response modulations better than the models that included only directional speed and acceleration , and a population response decoder trained on population responses can separate medial from lateral trials with an accuracy of 80%.The models above demonstrate that the modulation of Movement Unit responses with movement speed and acceleration is not directionally tuned. However, that does not necessarily imply that these units lack other forms of directional tuning. In particular, Movement Units have higher mean firing rates for the medial movement direction but similar kinematic-dependent response modulations around the mean for different movement directions. In our task, the instructed movement direction was either medial (towards the recorded STN) or lateral (away from the recorded STN) to the recorded STN. For successful completion of trials, subjects had to select not only the correct movement type (Reach vs. Turn) but also the correct direction . The mean firing rates across trial types from 10 ms before to 270 ms after movement onset were higher on trials where the movement direction was medial, as shown in Since the firing rate of these units is also associated with movement kinematics, we tested whether the mean firing rate difference could be ascribed to different speeds and kinematics on medial compared to lateral movements. However, the speed and acceleration were comparable on medial and lateral trials . FurtherIf movement direction affected firing rate, why did models that incorporated the x- and y- components of speed and acceleration perform poorly predicting the firing rate? Those models could accommodate differential modulation of firing rates with the kinematics of medial and lateral movements. For example, if firing rates changed by different amounts with changes of speed on medial compared to lateral movements, models with directional components would outperform. This did not occur in the data; firing rate modulated positively and by the same amounts with the magnitude of speed and acceleration on both medial and lateral trials, but the mean firing rate was overall lower on lateral trials. These differences between medial and lateral trials were only present after movement onset and disappeared prior to Turn Onset. Thus, there appears to be a firing rate offset determined by movement direction that occurs after movement onset.Movement Unit PSTHs exhibited their greatest modulation at movement onset and at feedback , which c2, Turn Units 0.13, 95% CI: [0.085 0.18], Movements Units 0.48, 95% CI: [0.45 0.51]; in-sample R2, Turn Units, 0.47, 95% CI: [0.44 0.49], Movement Units 0.59, 95% CI [0.57 0.61]). Furthermore, movement kinematics did not accurately predict the PSTHs of these units . This corresponded to a firing rate peak-to-trough modulation of 30% and 35%, respectively, for Movement and Turn Units.Both Movement and Turn Units spiked in synchrony with beta oscillations as quantified by the Pairwise-Phase Consistency (PPC), shown in –4, IQR: , Wilcoxon signed-rank test p=0.88). Notably, the uncoupling occurred even earlier for Turn Units, around the time of movement onset . Also, our signed rank test could detect median action potential couplings with beta oscillations as small as 0.004 for Movement Units and 0.003 for Turn Units, well within the range observed in other task epochs . Furthermore, the action potentials of Movement Units were uncoupled from the beta oscillation around the time of the turn onset  and underwent a CT scan for frame localization, which was then aligned with a preoperative structural MRI with gadolinium.Microelectrodes were targeted to the ventral border of the STN based on structural MRI and the AC-PC coordinates . The trajectory to the target was chosen such that the skull entry point was in the vicinity of the coronal suture and modified to avoid structures including vessels, ventricles, and the caudate nucleus. Sedation was typically discontinued after making the incision, and subjects were awake on minimal or no sedation for the electrophysiological recordings. Recordings were performed from a single microelectrode or concurrently from two microelectrodes spaced 2 mm apart at their insertion. For each electrode, an outer cannula was advanced to 15 mm above the target. A microelectrode in an inner cannula  was advanced to the end of the outer cannula and further advanced with a microdrive . When two microelectrodes were used, they were driven concurrently on the same microdrive. The microelectrode tip protruded 3 mm distal to the end of the inner cannula. Recordings were referenced to the outer cannula. Online analysis of spiking activity was performed with the Alpha Omega recording system.The recorded units spanned the space between the dorsal and ventral borders of STN. Entrance to the STN was noted based on two criteria: (1) real-time alignment of a CT scan performed at the time of surgery with a high-resolution pre-surgical structural MRI, and (2) an increase in background spiking  and idenWe used the pre-operative high resolution structural MRI with gadolinium, pre-operative high resolution CT scan with the Leksell frame in place, planned trajectory, and intra-operative or post-operative CT scan with DBS lead in place to localize our recordings. Our procedure is as follows. The pre-operatively planned target in AC-PC coordinates was identified on MRI. This MRI was merged with the pre-operative frame-localizing CT scan. Using the frame localization, the target was converted into frame coordinates. The pre-operatively planned trajectory was identified using this target and the ring and arc angle of the Leksell frame. Based on the alignment of the post-operative CT scan, which shows the DBS lead, with the intra-operative CT scan, the recording trajectory was confirmed and corrected whenever necessary . The distance of the microelectrode from the ventral STN target was measured using our microdrive. The location of each recording was identified as the point along the trajectory with the measured distance from target. In the case of multiple simultaneously used microelectrodes, the additional trajectories were offset by the distance of electrodes (2 mm) in the appropriate direction. These calculations were performed using the Brainlab Elements stereotaxy software.After identification of a stable single or multiunit, we proceeded with our behavioral task. A monitor on a jointed arm was positioned in front of the patient. A stereoscopic infrared camera  was positioned under the subject’s contralateral hand with respect to the recorded STN. The subject was instructed to rest his or her elbow on the armrest and make forearm movements to control the cursor. The subject’s hand, therefore, was assumed to move on the surface of a sphere with elbow at its center. The built-in software of the Leap Motion Controller identified the absolute position of the subject’s palm relative to the controller (sampling rate ~100 Hz). The device latency was ~10 ms. The palm position was mapped to a spherical surface with center located at approximately the subject’s elbow and radius equal to the forearm length. The horizontal and vertical angles of this position were scaled to generate the x- and y-coordinates of the cursor on the screen.A central white circle (fixation point) appeared on the screen at the beginning of each trial. The subject moved the cursor into the fixation point to indicate readiness, beginning the fixation period. After a variable duration , an instruction cue appeared on the right or left of the screen, which indicated both the type and the direction of the trial. A green circle indicated a Reach trial , instructing subjects to move the cursor to a target area defined by the location of the cue. A blue arrow indicated a Planned Turn trial , instructing subjects to move toward the cue and then upward to a target area at the top of the screen. A minimum horizontal distance from the fixation point had to be attained before the turn . Subjects kept their hands still to maintain the cursor in the fixation area until the fixation point disappeared . They were instructed to initiate their movements as soon as they detected the go cue. Trials on which subjects left the fixation area before the go cue were halted and treated as fixation breaks. On a random half of the trials that were initially cued as Reach trials, the cue changed to a blue arrow during the movement toward the target . The cue changed when subjects entered a rectangular window whose center was chosen from a uniform distribution that spanned 35–65% of distance between the fixation and target points. Subjects had to change their movement paths, making a vertical movement to a target region at the top of the screen. These were designated as Impromptu Turn trials. After completion of each trial, distinct auditory feedback indicated correct responses, incorrect responses (trajectories that did not meet the criteria above), or fixation breaks. The inter-trial interval was 1.5 s.A session was terminated when the subject showed signs of fatigue or after approximately 10 min. If subjects wished to perform a second block of the task, the microelectrode was moved to a new location with stable units and a new block started. Surgical implantation of DBS electrodes then proceeded per our usual protocol.Spike detection and sorting were performed using an offline sorter  on raw electrophysiological recordings sampled at 44 kHz. Spike-sorting was performed manually using principal components of spike waveforms. Single units, multi-units, and unsorted background spiking (hash) were considered as separate units. The characteristics of all units are shown in All analyses were performed using Matlab R2017a. Spikes were aligned to behavioral events. In addition to events defined by the stimuli on the screen, we also identified two events based on hand movements on each trial. Movement onset was defined as the period of maximum acceleration that most closely preceded the peak velocity of cursor. Turn onset was found as the time of maximum angular acceleration of the cursor. The calculated movement and turn onsets were examined visually relative to each movement trajectory and manually corrected as needed by author D.L. Manual corrections of these events, if needed, were small and blinded to the electrophysiological data and the trial type.To compare spiking activity around the turn cue on Impromptu Turn trials with spiking activity on Reach and Planned Turn trials, we sampled randomly from the distribution of turn cue onsets on Impromptu Turn trials to define corresponding analysis windows on Reach and Planned Turn trials. On Reach trials, subjects did not make a turn movement, and therefore, there was no turn onset event. To compare responses around the onset of the turn movement on Planned and Impromptu Turn trials with Reach trials, we sampled randomly from turn onset times on Planned Turn and Impromptu Turn trials to define corresponding analysis windows on Reach trials.−1). PSTH error bars were calculated as the standard error over trials. Units were excluded from analyses if there were fewer than four correct trials of each type or if the subject was unable to perform the task . There was a mean ± s.d. of 18.8 ± 11.9 trials per subject split across three conditions that did not meet the accuracy criteria. Adding these trials to the analyses did not critically change our results. The number of correct trials of each type for each unit is shown in We sought to determine spiking activity associated with the variety of task events. Inter-event intervals were variable because they were selected from random distributions or depended on the subject’s responses. In calculating PSTHs aligned to an event, we censored spikes that occurred before the preceding event or after the following event. Spikes occurring within 500 ms of the end of the preceding trial were also censored. For smoothing the PSTHs, we used a causal kernel . These large windows were adopted to maximize our precision for estimation of firing rates, but similar results were obtained with shorter windows too. Since movement and turn onsets had short latencies relative to the Go and Turn cues, analysis windows aligned to these events had to be narrower .To explore the similarity of responses across recorded units, we created a response profile for each unit by concatenating the unit’s PSTHs for each trial type in the analysis windows listed in the previous paragraph. To account for different ranges of firing rates across units, the response profiles of each unit were z-scored based on the mean and standard deviations obtained across all trials. Then, we performed PCA on the response profiles across recorded units to find the key patterns that shaped the profiles. As there was no turn onset event on Reach trials, the principal components were determined based on Planned and Impromptu Turn trials only. Reach trial response profiles were then projected onto this space. The top four principal components explained 61% of the total variance on Planned and Impromptu Turn trials, indicating that a small number of patterns captured the diversity of neural responses. The coefficients of the top four components were used to perform a hierarchical clustering analysis on the recorded units using the Chebyshev distance metric and a complete linkage function. The analysis revealed two main clusters that accounted for 87% of units. The remaining units were part of a third cluster with strong responses around planned turns and activity profiles that resembled those of the movement-selective cluster of units. However, we could not extensively explore this third cluster due to low sample size.Significant differences between firing rates across different trial types were assessed by averaging firing rates within time intervals of interest and making pairwise comparisons using a linear mixed effects model with a fixed effect for the difference of firing rates and random effects for subjects and for units nested within subjects. When reporting pairwise comparisons in cases where there are more than two groups, the p-values were corrected using the Holm–Bonferroni method.https://github.com/dlondon12/InterruptedReachClustMass); copy archived at SWH swh:1:rev:c597f70832fa3bc86e53aaa6030cba643e60f92b ;Choosing analysis intervals in time series data is susceptible to Type I error as one can often define a large number of potential analysis intervals within an epoch of interest. It would therefore be ideal to have a systematic approach that guards against such errors while also maintaining adequate temporal resolution to capture transient effects in the data. We developed such an approach , we extended this method. The cluster mass test consists of two basic operations: selecting the time clusters and testing their significance. In the original method, two groups are compared at each point in time to generate a F-statistic was calculated as follows. For each unit In our analyses, we consider units to be random effects and trial types to be fixed effects . The Considering each unit as a repeated measure, the residual sum of squares isand the statistic for the difference across trial types isF-distribution with parameters within each unit, calculating the This statistic was thresholded at the 90th percentile of the Because our framework is analogous to a repeated measures ANOVA with a single factor  per unit, it can also be generalized seamlessly to a repeated measures ANOVA with two factors . For this extension, consider the additional factor For factors where F-statistics are thresholded to generate clusters. The cluster-level statistic is the sum of the sum of squares in the denominator of the respective These e units’ . For tesnot repeated measures; they represent orthogonal components of the data. Therefore, instead of considering one-dimensional repeated measures data of the form We further extended this framework to the case of multidimensional data, as in the case of principal components analysis. Up until this point we have considered the case of F-statistics, but we can use the sum of squares directly. Again, we analyze each factor separately: for each trial type m, we consider Our goal is to find the times at which the trajectories in the PC space are significantly different. As there are no repeated measures, we cannot calculate where th percentile as was done with the F-statistics. To determine the value of the 90th percentile, we generate a permutation distribution as follows. For each unit, we randomly permute the trials across the appropriate factor without permutation of the other factors. The mean firing rates are calculated for each unit across the permuted trials, and the population activity patterns projected on the same principal components as the original dataset. These projections are then used to calculate the sums of squares. After creation of the clusters, the cluster-level statistics are calculated by adding the sum of squares values within each cluster. The maximum cluster-level statistic on each permutation is used to generate the distribution of cluster level statistics across permutations. The p-values for clusters of the original dataset are calculated as the probability of values in the permutation distribution being higher than the cluster level statistics.We threshold these sums of squares above the 90Once significant clusters are located with this multidimensional test, we perform pairwise comparisons of the relevant conditions to determine if the distances in state space between conditions in these time intervals are larger than those expected by chance. At each time point, we calculate the mean squared Euclidean distance between the trajectories of levels 1 and 2 of factor distance . A suffiFinally, we used this same framework to analyze firing rate differences between trial types within individual units. Here, we focus on the firing rate on each individual trial instead of the PSTH across all trials for the unit. Each trial contains only a single condition . The single-trial firing rate at time where F-distribution is used to select clusters whose cluster-level statistic is the sum of where We used this method to quantify the number of units that reflect the firing rate differences identified on the population level. If a time interval contained a significant difference between a pair of conditions across the population of units, we determined if this time interval contained a significant difference between that pair of trial types within each unit. A significant difference was considered to be a cluster with p<0.1. Under this criterion, we would expect 10% of units to contain significant differences by chance alone; this is the null hypothesis. We calculated a p-value for this null hypothesis as the probability of obtaining at least as many significantly different units under the binomial distribution with success rate of 10% .We tested if the firing rate of the recorded units changes as a function of the movement speed and acceleration. During trial , the hand position was used to update the cursor position where where To evaluate the relationship between the firing rate of each unit and movement kinematics, we used a lagged regression analysis:2 as the best fitting model. Across units and across 2 were calculated using bootstrapping .where Overall, we compared 14 alternative models that contained a combination of speed, acceleration, and their interaction –4. In soTo determine whether population firing rate differences were sufficient for decoding of behavior, we used a cross-validated ideal observer analysis to determine if the firing rate changes across the population of units were sufficient to correctly classify the trial type. Our procedure is as follows. On each iteration, we randomly held out one trial of the same type from each unit (test set). Next, we calculated the mean response of each unit on the rest of the trials (training set) marginalized by the trial type. We then calculated the mean Euclidean distance of the firing rate patterns of the held out trials across the population from each of the patterns of marginalized mean responses for the training trials. The held out trials were classified according to the smallest Euclidean distance. We performed 10,000 iterations, randomly dividing the trials for each unit into training and test sets. The decoding accuracy was the fraction of iterations correctly classified.For LFP analyses, the raw 44 kHz recorded voltage signals from each electrode were downsampled to 2 kHz. We then locally detrended and removed line noise using the Chronux toolbox , augmentEvoked and induced power calculations are sensitive to varying trial counts. In our study, each condition had 4–47 trials depending on the session. Furthermore, because of variability in the timing of events in our task, the number of trials available for analysis varied by time. To control for potential noise induced by this variability, we used the time period between 500 ms to 250 ms before the fixation onset to calculate a baseline for the average evoked and induced power for each trial. We then used a permutation procedure where we sampled without replacement from trials in each session, without regard to trial type, in order to estimate the average powers for a variety of trial counts. Repeating this process one hundred times generated a baseline distribution of evoked and induced power for any trial count at each time point. These baselines were separately calculated for each recording session. We normalized our calculated evoked and induced power values at each point in time by dividing by the mean of the appropriate baseline distribution for the trial count of that particular trial type at that particular point in time in each session.We also calculated evoked and induced power of the beta frequency range. To do this we applied a notch filter between 13 and 30 Hz to the downsampled, detrended voltage data. We then used the Hilbert transform to calculate the analytic signal. Evoked and induced power were calculated from this signal and normalized by the baseline for the trial counts in the session.The coupling of spiking activity to the LFP was quantified using the pairwise-phase consistency metric (PPC). We use the We examined the data for differences between spectrograms using the cluster mass test in the same fashion as explained above for firing rates. No significant clusters were located. As explained in the section on Statistical Analysis, we avoided analyzing arbitrarily selected intervals to control for Type I error.Because the shortest of our event-aligned analysis intervals was approximately 500 ms, we limited our frequency-based analysis to above 4 Hz.To confirm that subjects performed the task as instructed, we generated average movement trajectories across trial types. Since movements were self-paced and could take different times, averaging the hand position across trials at a particular time would not accurately reflect the variability of trajectories across space. To calculate the mean and standard deviation of movement trajectories in a particular trial type, we devised the following analysis using pairs of trajectories. The mean of an arbitrary vector which can be rewritten as the mean of the mean of independently-sampled pairs of data points, To calculate the mean of an arbitrary number of trajectories, we used dynamic time warping  to alignThe variance can be calculated from pairs of trajectories in a similar way:The method above was used only to calculate the mean and standard deviation of movement trajectories shown in To classify single-trial movement trajectories, we performed hierarchical clustering of the trajectories using the discrete Frechet distance  as a dis In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.Acceptance summary:This manuscript reports on a unique data set of human subthalamic nucleus electrophysiological recordings during a behavioral task. The results  point to the complex nature of the neural code in this structure and challenge some existing models related to the role of the subthalamic nucleus.Decision letter after peer review:[Editors’ note: the authors submitted for reconsideration following the decision after peer review. What follows is the decision letter after the first round of review.]eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and a Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Andrew Sharott (Reviewer #1); Harrison Walker (Reviewer #3).Thank you for submitting your work entitled "Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus" for consideration by eLife.Our decision has been reached after consultation between the reviewers. Based on these discussions and the individual reviews below, we regret to inform you that your work will not be considered further for publication in While the reviewers were overall positive about many aspects of the manuscript, there were a number of points they brought up. Upon discussion, we felt that the changes required were substantial enough that we were not confident they could be fully addressed in a revision. In particular, the reviewers noted missing information about the type of patient(s) studied and noted that this could have a substantial impact on the interpretations of the results. Additionally, we felt that important information about number of units contributed by each patient (and how those may have influenced different STN sub-populations was not clear). Finally, the reviewers felt additional statistical analyses and justification were necessary. While the reviewers agreed that it might be possible for all these points to be addressed in a revision, they also noted it may substantially change the interpretation (depending on the updated results). The full reviewers are included below.Reviewer #1:1. There are important details missing regarding the data and data acquisition. What type of patients were these recordings made in? (a) Are they from different patient populations? This is an important consideration for several aspects of the study including the clustering of neurons into different response types and the analysis of β oscillations. (b) What is the baseline firing rates of the STN neurons and how many were considered single units? The homogeneity of STN unit spiking means that single units should be in consistently firing at a relatively high rate (10-35 spikes/s). Defining the entrance, the STN alone is not sufficient to establish that the units in the STN . Further evidence that the units were in the STN and were single units is important to substantiate the authors claims as to the relevance of their results to STN function.2. On a related issue, do the authors have information as to the spatial parameters of the STN recordings? If so, do these parameters correlate with the function clustering of the response types?3. The authors use custom use a range of custom designed data analyses to tease out STN neurons with tendencies to respond to differently to parts of the task in the different conditions. In some cases, this is a strength of the study and many of the analyses elegantly pull out features of the data that may not be immediately clear . However, in it's current form it is difficult to establish the effect sizes for other analyses – particularly in the case of the "Turn Units." While the authors go to impressive lengths to show that the differences at the Turn are different, from Figure 3C they seem to be very small and a fraction of those seen in response to kinematic parameters. Currently, many of the novel claims are dependent on this difference . Can the authors provide evidence of the effect size of the responses to task switching as well as being able to say they are significantly different? How many individual units are able to distinguish between specific aspects of the task .4. Given the size of the sample – the authors should provide some evidence that their main findings are scene across the majority of patients. For example, how may units in each cluster are derived from different patients?5. The analysis of β oscillations/synchrony is potentially very interesting, but I cannot interpret it's relevance without knowing the disease-status of the patients.Reviewer #2:The authors report on subthalamic nucleus neural population dynamics and its role in movement planning and execution, especially as it relates to changes in the motor plans . They used principal component analysis of firing rates and unsupervised clustering to identify two types of cells – movement and turning units, each contributing specific information regarding movement dynamics encoded in the STN . The manuscript is generally well written and the complex analysis is logically presented although sometimes conclusions are difficult to appreciate from the figures. This type of analysis is increasingly necessary to try and understand the complex dynamics in the nervous system, but the challenge will be to relate to behavior in a meaningful way. The authors should relate their findings to (presumably) parkinsonian pathology especially when discussing synchronization of units with β band oscillations which is the hallmark of the disease.– The analysis is presented only for move and turn onsets. Was there a difference in firing rates at Go and Impromptu Turn cues? Also, when was the impromptu turn cue given and did this differ by movement speed (within and across subjects)?– "This result was not due to lack of statistical power of the cluster mass test, which showed great sensitivity to systematic trends in our datasets." – It is difficult to assess such statements which occur several times. Could you explain what trends?– "Neuronal spiking in STN is typically phase-locked to β oscillations " – is this because of parkinsonian state? The authors need to acknowledge that subjects had Parkinson's disease  so findings may be influenced by pathological parkinsonian state, especially in regards to single cell firing being phase locked to β oscillations which is known to be exaggerated in PD.– "Interestingly, these sub-populations are partially spatially segregated within STN, with Movement Units located more laterally…" – provide a depiction of unit locations if commenting on this in the Discussion.– "Further, stimulation of glutamatergic cortical inputs to STN  worsens bradykinesia " – that paper showed quite the opposite – stimulation of hyperdirect pathway improved bradykinesia. Please address in your conclusions.– "To compare spiking activity around the turn on the Impromptu Turn trials with spiking activity on Reach and Planned Turn trials, we sampled randomly from the distribution of turn cue onsets on Impromptu Turn Trials to define corresponding analysis windows on Reach and Planned Turn trials."– I understand why you had to do this for Reach trials, but why did you not use the actual turn on Planned turn trials as the analysis window?– Methods: "Subjects were patients undergoing implantation of deep brain stimulation…" Please specific that these were presumably patients with Parkinson's disease, and were they off dopaminergic medications?– Authors report "13.5{plus minus}3.3 trials per trial type per subject" – How may trials of each type were done for each unit recorded? How many units were recorded per patient?Reviewer #3:Conceptually, even if 'impromptu' units are an actual subpopulation of neurons, a decrease in STN activity just before the execution of the unexpected turn seems consistent, not inconsistent, with previous hypotheses on STN as a "brake" or "global stop" nucleus. I think they could clarify their thoughts on this more in their writing.Would like to see more specific examples of sorted units and examples that clearly demonstrate the phenomena they are claiming.All group level plots and panels should include the number of unique participants, the number of individual units, and the number of trials. For example, in Figure 2e PC1 appears to just be the PTH from the unit in Figure 2C. This unit appears to overwhelmingly dominate the population variance. Was PCA performed across all subjects/trials/units?Given 39 units / 9 STNs = 4 units per STN. How many subjects had multiple recording sites, what was the average number of units per recording site?Plots should generally be aligned to impromptu turn cue onset. Without this time alignment, it’s unclear whether the observed changes are related to the cue, the prior movement, or the subsequent movement. Even with this alignment there are some questions about prior events spilling into the 'impromptu' turn condition, depending on the latency of the second command and the nature of the kinematic response of the units in a given experiment.Figure 2B/D: How do these trajectories differ? Following feedback, the traces look identical. Were stats performed across all principal components?Figure 3C: Turn versus impromptu turn changes are < 0.2 Z-scores – these are not convincing changes.Use of terms ipsilateral and contralateral is confusing. I think they mean medial/lateral or flexor/extensor as these tasks appear to be occurring in the same hand.Figure 7E-F – description says that turn trials show a decreased spike synchronization, but this is because β disappears. Also it appears that this happens regardless of trial type (not just during turns). They might consider also including high frequency LFP activity for comparison with β.The authors should explicitly state what disease the diagnoses of the patients .Supplemental Video with 3D PCA visualization, why are the plotted dimensions PC1, PC3, and PC4? Where is PC2?eLife. Your revised article has been evaluated by Richard Ivry (Senior Editor) and a Reviewing Editor.Thank you for resubmitting your work entitled "Distinct population code for movement kinematics and changes of ongoing movements in human subthalamic nucleus" for further consideration by The manuscript has been improved but there are some remaining issues that need to be addressed, as outlined below:Primarily we had some concerns about the interpretation of the model especially with response to the "Turn units" and what they may actually be encoding. We have further elaborated on this concern, as well as highlighting a few additional concerns below.Essential revisions:1) Please comment more on what the 'turning' units are actually encoding, and if they could actually be encoding the acceleration and velocity in the vertical axis? Further, do the results still support the statement that 'one sub-population encoding movement kinematics and direction and another encoding unexpected action switches'. Specifically, Figure 4 indicates that both 'Movement Unit' and 'Turn Unit' firing rate can reflect kinematic parameters with different time lags. The authors argued that (Line 276-278): '… the strength of this relationship (for Turn Units) was considerably weaker than for Movement Units (mean R2 = 0.45 compared to 0.61 for Movement Units). Furthermore, movement kinematics did not accurately predict the PSTHs of these units .' However, it is quite subjective to say the R2 values are 'considerably weaker'. Comparing the plots of A, C and E against B, D, and F in Figure 4 —figure supplement 2, can the author's confidently say that movement kinematics can predict Movement Units , but cannot predict Turn Units ? Furthermore, even if the model fitting is a bit worse for Turn Units compare to Movement Units, did the authors used the same model that fits best for movement units? Or to be more fair, did the authors do model comparison for best fitting models for the two types of Units separately?2) The authors mentioned that 'Turn Units decreased their firing rates before turn onset on Impromptu Turn trials but increased them before turn onset on Planned Turn trials.' (Line 289-291). However, we don't see this statement being supported by data/figure. Can the authors provide evidence/data for this? Or did we miss anything?3) Reviewer 3 from the previous round of review raised that 'Conceptually, even if 'impromptu' units are an actual subpopulation of neurons, a decrease in STNactivity just before the execution of the unexpected turn seems consistent, not inconsistent, with the previous hypotheses on STN as a "brake" or "global stop" nucleus. We think the authors should clarify their thoughts on this more in their writing.' We feel this was not sufficiently addressed.4) For the statistical analysis Equation. 1 – Equation. 9, it is not clear whether the authors have used self-programmed scripts for these tests or existing toolbox/functions in Matlab to do this. As far as I understand, Equation. 1 – Equation. 9 are similar to what is implemented in the linear mixed effects models available in Matlab (https://www.mathworks.com/help/stats/linear-mixed-effects-models). This can be applied to each time point, and then using cluster mass test to identify the time window with significant effect. Or alternatively, can the authors specify what is difference between the method used in this study and the linear mixed effects model? The authors mentioned that 'For testing factor ;, the levels of ; are permuted within each unit and without permutation of the other factor. The remainder of the calculation continues as for the one factor case, resulting in separate p-values for each main effect, as in a multi-way ANOVA.' Is it equivalent to repeating the multilevel modeling permuting the levels of m? If the method is based on the framework of multi-level modeling, it might not be necessary to present all the Equations.5) The accurate rate of different conditions was around 0.7. Did the authors only focus on the 'accurate trials' which satisfy the 'accuracy criteria' or considered all trials? Is there any difference between the neural activities for 'accurate trials' vs 'inaccurate trials'?7) Can the authors compare the baseline spiking rate of the 'movement related' and 'turning related' units identified by clustering analysis applied on the PSTHs. [Editors’ note: The authors appealed the original decision. What follows is the authors’ response to the first round of review.]Reviewer #1:1. There are important details missing regarding the data and data acquisition. What type of patients were these recordings made in? (a) Are they from different patient populations? This is an important consideration for several aspects of the study including the clustering of neurons into different response types and the analysis of β oscillations. (b) What is the baseline firing rates of the STN neurons and how many were considered single units? The homogeneity of STN unit spiking means that single units should be in consistently firing at a relatively high rate (10-35 spikes/s). Defining the entrance, the STN alone is not sufficient to establish that the units in the STN . Further evidence that the units were in the STN and were single units is important to substantiate the authors claims as to the relevance of their results to STN function.Many thanks for your thoughtful comments, which helped us close the gaps in the paper and improve it in the process. All recordings are from a homogeneous population – patients with medically refractory Parkinson’s disease undergoing surgical implantation of DBS electrodes in the STN. They did not take their anti-Parkinsonian medications on the day of surgery. We regret omitting the description of the disease status. The disease status has now been explicitly noted in the abstract, introduction, results, methods, and discussion. Patient age, pre-operative UPDRS motor score, side of recording, and number of single and multi-units recorded per patient are listed in a new table (Supplementary File 1) which we reference in the results. Our conclusions are not changed if any of subjects 1, 2, or 7, who contributed relatively more units than others, are removed from the data (page 43).The characteristics of all units are now listed in a new table (Supplementary File 2). We were conservative in classifying single-units and chose only units with clearest isolation from background and consistency of action potential waveforms (8/39). The single units had a baseline median firing rate of 32.9 Hz (IQR: 18.3-53.9), consistent with single units identified in past studies . Furthermore, the median firing rate of multi-units was 40.4 Hz (IQR: 20.7-82.6), consistent with the results of Weinberger et al., suggesting that most multi-units are largely contributed to by a single neuron. The baseline firing rate was calculated from spikes in the intervals between trials. We discuss this on page 20.We analyzed both single- and multi-units as is commonly done , and our analyses do not depend on the units being well-isolated. We have largely consistent tuning across single and multi-units, and because all analyses were performed on Z-scored firing rates, they did not depend on differences in the range of firing rates. Critically, single units individually display characteristics seen in the population of all units. We now show in the revised Figure 3 which neural responses are from single units . Units in this figure are sorted by proximity in the hierarchical clustering algorithm. If our results do not depend on whether a unit is well-isolated we would expect single units to not be clustered together by the algorithm, as is the case in the figure (discussed on page 6).Microelectrodes were targeted to the ventral border of the STN based on structural MRI and the AC-PC coordinates . However, the recorded units spanned the space between the dorsal and ventral borders of STN. Entrance to the STN was noted based on two criteria: (1) real-time alignment of a CT scan performed at the time of surgery with a high-resolution pre-surgical structural MRI , and (2) an increase in background spiking and identification of rhythmically firing cells at high firing rates. A decrease in background spiking marked the ventral boundary of STN. In many of our surgeries deeper cells along this trajectory fired rapidly with a relatively constant firing rate, characteristic of SNr cells. This procedure has been previously described in detail by our group . We have added these points to the Methods (page 19).2. On a related issue, do the authors have information as to the spatial parameters of the STN recordings? If so, do these parameters correlate with the function clustering of the response types?We used the pre-operative high resolution structural MRI with gadolinium, pre-operative high resolution CT scan with the Leksell frame in place, planned trajectory, and intra-operative or post-operative CT scan with DBS lead in place to localize our recordings. Our procedure is as follows. The pre-operatively planned target in AC-PC coordinates was identified on MRI. This MRI was merged with the pre-operative frame-localizing CT scan. Using the frame localization, the target was converted into frame coordinates. The pre-operatively planned trajectory was identified using this target and the ring and arc angle used. Based on the alignment of the post-operative CT scan with the DBS lead with the intra-operative CT scan, the trajectory was confirmed and corrected whenever necessary . The distance of the microelectrode from the ventral STN target was measured using our microdrive. The location of each recording was identified as the point along the trajectory with the measured distance from target. In the case of multiple simultaneously used microelectrodes, the additional trajectories were offset by the distance of electrodes (2 mm) in the appropriate direction. These calculations were performed using the Brainlab Elements stereotaxy software. This description has been added to the Methods (page 19).We have added Figure 2 —figure supplement 2 to show the locations of all recorded units in AC-PC coordinates (A) and in coordinates normalized to the electrophysiological entry point of STN . There was a nonsignificant trend toward movement units being located more dorsolaterally in raw AC-PC coordinates (A) but this was not evident when examining the normalized coordinates. We state on page 6 that there was no clear association with unit classification and recording location.3. The authors use custom use a range of custom designed data analyses to tease out STN neurons with tendencies to respond to differently to parts of the task in the different conditions. In some cases, this is a strength of the study and many of the analyses elegantly pull out features of the data that may not be immediately clear . However, in it's current form it is difficult to establish the effect sizes for other analyses – particularly in the case of the "Turn Units." While the authors go to impressive lengths to show that the differences at the Turn are different, from Figure 3C they seem to be very small and a fraction of those seen in response to kinematic parameters. Currently, many of the novel claims are dependent on this difference . Can the authors provide evidence of the effect size of the responses to task switching as well as being able to say they are significantly different? How many individual units are able to distinguish between specific aspects of the task .4. Given the size of the sample – the authors should provide some evidence that their main findings are scene across the majority of patients. For example, how may units in each cluster are derived from different patients?While we focused our manuscript on population-level results, these results are indeed evident when examining individual units. While we acknowledge that our sample size of patients was small, the results were consistent across them. The table of unit properties (Supplementary File 2) shows the number of units contributed by each patient and the classification of the units. Movement units were seen in 75% of patients (6/8), and turn units were seen in 75% (6/8), as well. All patients had recordings of Movement and/or Turn Units . This is reported on pages 4-5 of the revised manuscript.5. The analysis of β oscillations/synchrony is potentially very interesting, but I cannot interpret it's relevance without knowing the disease-status of the patients.All patients had Parkinson’s disease. Their age and pre-operative UPDRS scores are now reported in Supplementary File 1 of the revised manuscript. We now discuss these results in the context of Parkinson’s disease (page 16) , suggesting that our conclusions are more broadly applicable given concordant results between studies of STN in humans with Parkinson’s disease or OCD, and non-pathologic recordings in non-human primates and rodents.Reviewer #2:The authors report on subthalamic nucleus neural population dynamics and its role in movement planning and execution, especially as it relates to changes in the motor plans . They used principal component analysis of firing rates and unsupervised clustering to identify two types of cells – movement and turning units, each contributing specific information regarding movement dynamics encoded in the STN . The manuscript is generally well written and the complex analysis is logically presented although sometimes conclusions are difficult to appreciate from the figures. This type of analysis is increasingly necessary to try and understand the complex dynamics in the nervous system, but the challenge will be to relate to behavior in a meaningful way. The authors should relate their findings to (presumably) parkinsonian pathology especially when discussing synchronization of units with β band oscillations which is the hallmark of the disease.We very much appreciate the reviewer’s comments and have used them as an opportunity to improve the paper, as we explain below.– The analysis is presented only for move and turn onsets. Was there a difference in firing rates at Go and Impromptu Turn cues? Also, when was the impromptu turn cue given and did this differ by movement speed (within and across subjects)?Figure 3 —figure supplement 1 shows average neural activity around the go and turn cues (A and B) compared to the movement and turn onset (C and D) for both unit types. There was no significant difference between trial types around the go cue. However, a clear increase in firing rate is visible beginning about 200 ms before movement onset and about 300 ms after the go cue, suggesting that this response is likely related to movement or movement planning, not the processing of the go cue itself.The turn cue was presented after the movement onset and before the hand reached the target position, prompting subjects to alter their movement trajectory mid-movement. Turn Units responded similarly in different trial types around the time when the turn cue appeared (B). Movement Units had significantly smaller firing rates on Planned Turn trials than Reach trials between 50 ms before and 170 ms after the expected time of the turn cue (A) because subjects had already begun their turn on Planned Turn trials and movement units track hand kinematics .Because our goal in this study was to determine neural responses in STN to planned and unplanned movements, our key comparisons are with respect to turn onset not the turn cue. Past human studies in STN frequently used go/no-go or stop-signal tasks with button presses or joystick movements as the operant response. Reporting cue-aligned responses is reasonable in those cases. However, our task has the advantage of recording a full, extended movement, which could also be multi-segmented . We can thus parse when exactly movement onset and turn onset occur. Indeed, this is where we also found significant modulation of firing rates.The turn cue on Impromptu Turn trials was presented when subjects were within a specific rectangular window on their path to the reach target. The center of this window was randomly chosen from a uniform distribution between 35% and 65% of the distance between the fixation and target points. Since the time the turn cue was presented was determined by the subject’s position, faster movements were more likely to result in a sooner presentation of the turn cue. However, as explained in the paper, Turn Units were minimally sensitive to changes in movement in kinematics, including speed and acceleration. The turn cue was presented 206 ± 71 ms after movement onset and 504 ± 154 ms after the go cue (mean ± SD across sessions).To clarify these points, we have added the figure above to the manuscript  and explain the findings and the conceptual advance on pages 7 and 11.– "This result was not due to lack of statistical power of the cluster mass test, which showed great sensitivity to systematic trends in our datasets." – It is difficult to assess such statements which occur several times. Could you explain what trends?We apologize for being vague and have now clarified our approach and results in the revised manuscript. A decoder trained to classify population neural responses performs with ∼75% accuracy  for differences of 0.2 z-scores and performs above chance for differences as low as 0.05 z-scores. These analyses give us confidence that absence of significance is not simply due to low power. We have now included the accuracy with which a decoder classifies population neural responses for individual results in the manuscript.– "Neuronal spiking in STN is typically phase-locked to β oscillations " – is this because of parkinsonian state? The authors need to acknowledge that subjects had Parkinson's disease  so findings may be influenced by pathological parkinsonian state, especially in regards to single cell firing being phase locked to β oscillations which is known to be exaggerated in PD.Thanks for bringing up this important point. We regret the omission of the fact that all of our patients had medically refractory Parkinson’s disease and were off medication on the day of surgery. While our study took place in these patients that are known to have underlying hypersynchronous β activity , β oscillatory activity in STN is not a unique phenomenon in Parkinson’s disease; β activity in the STN has been found in patients with OCD . Furthermore, β band activity increases during stopping in both Parkinson’s  and OCD , suggesting that the functional role of β oscillations in action inhibition is not unique to Parkinson’s disease. It is, therefore, notable that we did not find an increase in β induced power or spike-field locking prior to subjects interrupting their planned behaviors. However, our results may still be influenced by a pathological Parkinsonsian state consisting of elevated β activity and generalizing them would require recordings in non-Parkinsonian patients or in animal models without underlying disease. We discuss these points on page 16 of the discussion.– "Interestingly, these sub-populations are partially spatially segregated within STN, with Movement Units located more laterally…" – provide a depiction of unit locations if commenting on this in the Discussion.We now provide our methodology for locating recorded units on page 19 of the revision, and we have included Figure 2 —figure supplement 2, which depicts unit locations. There was a nonsignificant trend toward movement units being located more dorsolaterally in raw AC-PC coordinates but this was not evident when examining the normalized coordinates . While there may be some segregation of unit types, this is not a strong trend and we have removed this claim from our results and discussion.– "Further, stimulation of glutamatergic cortical inputs to STN  worsens bradykinesia " – that paper showed quite the opposite – stimulation of hyperdirect pathway improved bradykinesia. Please address in your conclusions.Gradinaru et al.’s findings regarding the effects on bradykinesia caused by stimulation of the hyperdirect pathway were complex. Gradinaru et al. explain that stimulation of the hyperdirect pathway from motor cortex to STN improved bradykinesia, but stimulation of glutamatergic inputs in STN resulted in differing effects depending on the frequency of stimulation. We have now corrected our statement in the revised manuscript (page 18)– "To compare spiking activity around the turn on the Impromptu Turn trials with spiking activity on Reach and Planned Turn trials, we sampled randomly from the distribution of turn cue onsets on Impromptu Turn Trials to define corresponding analysis windows on Reach and Planned Turn trials." – I understand why you had to do this for Reach trials, but why did you not use the actual turn on Planned turn trials as the analysis window?cue and to the turn movement itself. The turn cue is not present on Reach or Planned Turn trials so we sampled from the turn cues on Impromptu Turn trials to generate analysis windows for other trial types. For analyses of the turn movement itself, we used the distribution of the times of onset of turn movement in planned turn and impromptu turn trials to define temporally comparable analysis windows for Reach trials on which there was no turn. We have corrected the confusion caused by our wording in the revision (page 20-21).Thank you for the great question. Our wording here was not clear enough in distinguishing analyses aligned to the turn – Methods: "Subjects were patients undergoing implantation of deep brain stimulation…" Please specific that these were presumably patients with Parkinson's disease, and were they off dopaminergic medications?All recordings are from a homogeneous population – patients with medically refractory Parkinson’s disease undergoing surgical implantation of DBS electrodes in the STN. They did not take their anti-Parkinsonian medications on the day of surgery. The description of the disease status is now specified in the abstract, introduction, results, discussion, and methods. Patient age, pre-operative UPDRS motor score, side of recording, and number of single and multi-units recorded per patient are now listed in Supplementary File 1. We also confirm that our conclusions do not critically depend on subjects 1, 2, or 7, who contributed relatively more units than others (page 43).– Authors report "13.5{plus minus}3.3 trials per trial type per subject" – How may trials of each type were done for each unit recorded? How many units were recorded per patient?The number of units recorded per patient is shown in Supplementary File 1. The number of trials of each type per unit is shown in Supplementary File 2. Please also see our response to the first comment of Reviewer #1, which includes copies of the tables in Supplementary Files 1 and 2.– Could not play the second movieReviewer #3:Conceptually, even if 'impromptu' units are an actual subpopulation of neurons, a decrease in STN activity just before the execution of the unexpected turn seems consistent, not inconsistent, with previous hypotheses on STN as a "brake" or "global stop" nucleus. I think they could clarify their thoughts on this more in their writing.Many thanks for bringing up this key point, which we now explain more extensively in the revised manuscript. Briefly, theories positing STN as mediating stop processes predict that stopping is accompanied by increased firing rates. Schmidt et al. 2013 is an example of this, where STN spiking immediately follows a stop cue. In our task, movement speed decreases before the turn on both Planned and Impromptu Turn trials, though more so for the latter . If the Turn Units contribute to stopping, we would expect them to have increased firing rates on Impromptu Turn and Planned Turn trials compared to Reach trials and also higher firing rates on Impromptu Turn than Planned Turn trials. Our data indicate the opposite result; Turn Unit firing rates decrease before impromptu turns and increase before planned turns.Would like to see more specific examples of sorted units and examples that clearly demonstrate the phenomena they are claiming.t indicates trial counts. Asterisks above the time axis indicate periods with statistically significant differences between Planned and Impromptu Turn trials. Although the dynamics of responses vary across units, our main findings are apparent in single units. The Movement Units show increased firing beginning around the movement onset and returning to baseline after the feedback. Meanwhile, the Turn Units show decreased firing in the period around the onset of turn movement on Impromptu Turn trials. The units in panels B and D reach statistical significance, and the unit in F shows a trend in the same direction as the majority of the population .Absolutely. In our revision, we now provide Figure 2 —figure supplement 1, which shows 3 example Turn Units and 3 example Movement Units. S and U indicate the subject and unit identity, and NAll group level plots and panels should include the number of unique participants, the number of individual units, and the number of trials. For example, in Figure 2e PC1 appears to just be the PTH from the unit in Figure 2C. This unit appears to overwhelmingly dominate the population variance. Was PCA performed across all subjects/trials/units?We have now added the information to the figures. Regarding the specific example of Figure 2, Figure 2C shows an individual unit while 2E-H show the firing rates of all units projected on the principal components. The first principal component shown in Figure 2E was recapitulated in many individual units, including the example unit shown in 2C, but the PCs were not based on any individual unit or overwhelmingly shaped by a single unit. The example unit does not dominate the variance; it is representative of it. In fact, removing this unit for the calculation of PCs would make little difference in the outcome. PCA was done on PSTHs of correct trials on all units across all subjects. The PCA procedure is described on page 21 of the manuscript.Given 39 units / 9 STNs = 4 units per STN. How many subjects had multiple recording sites, what was the average number of units per recording site?We obtained spiking data from 20 recording sites (on average 1.95 units per site). 62.5% of subjects (5/8) had multiple recording sites. This has been added to the Methods (page 21).Plots should generally be aligned to impromptu turn cue onset. Without this time alignment, it’s unclear whether the observed changes are related to the cue, the prior movement, or the subsequent movement. Even with this alignment there are some questions about prior events spilling into the 'impromptu' turn condition, depending on the latency of the second command and the nature of the kinematic response of the units in a given experiment.We have added a new supplementary figure  in the revision to show firing rates aligned to the turn cue (panels A-B) and the onset of turn movement (panels C-D) for the two groups of units. When aligned to the turn cue, there is no difference in firing rates of Turn Units on Impromptu and Planned Turn trials (page 11). However, when aligned to the turn movement itself there is a decrease in the firing rate of Turn Units on Impromptu Turn trials compared to Planned Turn trials. This difference precedes the turn movement. Therefore, this finding cannot be related to the turn cue but is instead related to the turn movement.In addition, in calculating the PSTHs displayed relative to the onset of the turn movement, all spikes before movement onset (including those around the instruction and go cues) and after feedback are removed prior to calculating the firing rate (discussed on page 21). Furthermore, the turn onset occurs 541 ± 263 ms and 731 ± 217 ms after the movement onset on Planned Turn and Impromptu Turn trials, respectively. It is unlikely that the movement onset would cause firing rate responses in STN cells >500 ms later. Indeed, responses to the movement onset peak <200 ms after this event. The comparison of firing rates aligned to the turn cue and the onset of the turn movement, the time course of firing rate differences, and our methodology in calculating the PSTHs boosts confidence that our results are not contaminated by the effects of prior events.Because our goal in this study was to determine neural responses in STN to planned and unplanned movements, our key comparisons are with respect to turn onset not the turn cue. Past human studies in STN frequently used go/no-go or stop-signal tasks with button presses or joystick movements as the operant response. Reporting cue-aligned responses is reasonable in those cases. However, our task has the advantage of recording a full, extended movement, which could also be multi-segmented . We can thus parse when exactly movement onset and turn onset occur. Indeed, this is where we also found significant modulation of firing rates. We now include and discuss the cue-aligned results for Movement Units (page 7) and Turn Units (page 11) in the revised manuscriptFigure 1D: When is the go or the cue signal. Unclear how the kinematic data are arranged in time.Figure 1F: What is the purpose of clustering trajectories? Is this used in the subsequent analyses?Figure 1E-1F: Single subject or across all subjects?Figure 2B/D: How do these trajectories differ? Following feedback, the traces look identical. Were stats performed across all principal components?Figure 2B/D shows an individual unit classified as a Turn Unit using hierarchical clustering. A key characteristic of this type of unit  is a relatively flat response profile except around the turn where Impromptu Turn trials have the lowest firing rate and Planned Turn trials the highest. Other Turn Units have similar response profiles . Averaged across all Turn Units, the firing rate is significantly smaller on Impromptu Turn trials than Planned Turn trials between 240 ms before to 80 ms after the turn. Indeed, for this example unit, the mean firing rates on Impromptu and Planned turn trials over this interval are significantly different . The similarity of responses following feedback strengthens our classification. Statistics in Figure 2E-H were performed jointly over all principal components, as described on pages 24-25.Figure 3C: Turn versus impromptu turn changes are < 0.2 Z-scores – these are not convincing changes.While the difference in normalized firing rates between Planned and Impromptu Turn trials may seem small to the reviewer, it is a substantial range of the overall modulation of firing rates, which varies from -0.16 to +0.18 z-scores . Further, the difference is highly significant , and quite sufficient for decoding. In fact, a decoder trained to separate trial types based on population responses achieves an accuracy of 74%. The decoder analysis and clarifications above are now included in the revision (page 11).Use of terms ipsilateral and contralateral is confusing. I think they mean medial/lateral or flexor/extensor as these tasks appear to be occurring in the same hand.Ipsilateral and contralateral trials and responses correspond to medial and lateral, respectively, since patients always used the hand contralateral from which recordings are made. We have now adopted the medial/lateral terminology suggested by the reviewer.Figure 7E-F – description says that turn trials show a decreased spike synchronization, but this is because β disappears. Also it appears that this happens regardless of trial type (not just during turns). They might consider also including high frequency LFP activity for comparison with β.That’s an excellent question. We do not disagree that part of the effect of decreased spike synchronization to β oscillations is because β disappears. However, while β synchronization disappears during the movement phases of the task, firing rates increase. If STN spiking were driven only by β oscillations we would have expected firing rates to decrease at this point. Because the data go against this prediction, STN neurons appear to switch from entrainment to β to alternative firing patterns during the movement phases of the trial including around the turn onset. We also agree that the β desynchronization happens regardless of trial type, and this in itself is noteworthy as, based on existing theories, we would have expected to see an increase in β before impromptu turns. We have clarified these points on page 15 of the revised manuscript.We had limited our analysis to the lower frequency bands of the spectrum because of significant contamination of the LFP spectrum at higher frequencies by spiking itself. We have now implemented the spike-removal algorithm of Boroujeni et al. (2020) to attempt to analyze this spectrum. The results are shown in Figure 7. We do not detect any difference with the cluster mass test.Page 24 – Bootstrap is defined as sampling WITH replacement, please justify/clarify.We had meant to state “permutation procedure”, as we did sample without replacement. We apologize for the error and have now corrected it in the revision (page 27).The authors should explicitly state what disease the diagnoses of the patients .All patients had medically refractory Parkinson’s disease. Their ages and UPDRS scores are listed Supplementary File 1. We regret the omission and have now stated patients’ underlying disease status in various sections of the manuscript, including the abstract.Supplemental Video with 3D PCA visualization, why are the plotted dimensions PC1, PC3, and PC4? Where is PC2?In Video 1, our goal was to show the separation of the trajectories in a clearer fashion than Figure 2E-H. For the 3D plot, it was necessary to omit one dimension. Since PC2 distinguishes the different trial types the least, we chose to omit it from this plot. It was still included in all statistical analyses of principal component data. We now clarify this point in the caption for Video 1 in the revised manuscript (page 43) and will be happy to generate additional videos that show PC2.[Editors’ note: what follows is the authors’ response to the second round of review.]Essential revisions:1) Please comment more on what the 'turning' units are actually encoding, and if they could actually be encoding the acceleration and velocity in the vertical axis? Further, do the results still support the statement that 'one sub-population encoding movement kinematics and direction and another encoding unexpected action switches'. Specifically, Figure 4 indicates that both 'Movement Unit' and 'Turn Unit' firing rate can reflect kinematic parameters with different time lags. The authors argued that (Line 276-278): '… the strength of this relationship (for Turn Units) was considerably weaker than for Movement Units (mean R2 = 0.45 compared to 0.61 for Movement Units). Furthermore, movement kinematics did not accurately predict the PSTHs of these units .' However, it is quite subjective to say the R2 values are 'considerably weaker'. Comparing the plots of A, C and E against B, D, and F in Figure 4 —figure supplement 2, can the author's confidently say that movement kinematics can predict Movement Units , but cannot predict Turn Units ? Furthermore, even if the model fitting is a bit worse for Turn Units compare to Movement Units, did the authors used the same model that fits best for movement units? Or to be more fair, did the authors do model comparison for best fitting models for the two types of Units separately?2 of 0.089. Equivalent models for velocity were even worse. Therefore, we do not see evidence that Turn Units preferentially encode vertical acceleration or velocity. These analyses are now explained on page 7 of the revised manuscript.Thank you for bringing up these important points. The comment raises three key questions, which we answer in the same order asked by the reviewers. First, we explored the possibility of Turn Units encoding vertical acceleration and velocity, using models containing separate predictors for horizontal and vertical movement, as shown in a new figure, Figure 4 – supplement 4. These models did not indicate preferential encoding of vertical movements and performed poorly compared to the models that used the total acceleration and velocity. For example, linear models with separate terms for vertical and horizontal acceleration achieved an out-of-sample R2 of the kinematic model with total speed and acceleration was [0.45 0.51] for the Movement Units and [0.085 0.18] for the Turn Units. The 95% confidence interval for in-sample R2 was [0.57 0.61] for the Movement Units and [0.44 0.49] for the Turn Units. There is no overlap between these confidence intervals, supporting our conclusion. However, please note that our key point in the manuscript is not that Turn Units are void of information about movement kinematics. Rather, we show that:Regarding the reviewers’ second question about the superior encoding of movement kinematics by the Movement Units, the firing rates of these units were significantly better explained by the movement kinematics compared to the Turn Units. The 95% confidence interval for the out-of-sample R1. Movement Units represent movement kinematics better while they lack information about unexpected changes of movement plans (unplanned turns).2. In contrast, Turn Units have distinct firing rates for unplanned and planned turns but weaker representation of movement kinematics.2 of the kinematic model in Figures 4 – supplements 3-4.This relative double dissociation between the Movement and Turn Units is the basis for the names we have chosen for these two groups of neurons. Finally, it is worth clarifying that we arrived at these two groups of neurons not by searching for the neurons that do or don’t represent movement kinematics and unplanned turns. The groups emerged from an unsupervised clustering of the neurons based on the overall dynamics of their PSTHs. We prefer this natural clustering to an engineered search, even though by implementing such a search to identify neurons with specific response properties we could have improved the double dissociation mentioned above. The emergence of the two natural clusters of neurons with different response dynamics and distinct functional properties bolsters the hypothesis that the STN population is inhomogeneous and has the capacity to contribute to complex mental functions. We now clarify the relative double dissociation on pages 4, 11 and 12, and report confidence intervals of the R2 for both Movement Units and Turn Units. Figure 4 – supplement 4 shows the model fits for Turn Units. The out-of-sample R2 for Turn Units in the model with total speed and acceleration is 0.13, and the best fitting model, which has total acceleration as its only predictor, has an out-of-sample R2 of 0.23. The same two models produce out-of-sample R2 of 0.48 and 0.43, respectively, for the Movement Units. We now report 95% confidence intervals on the in-sample and out-of-sample R2 of all competing models in Figure 4 – supplements 3-4. The differential ability of the two groups of neurons for encoding kinematics is clear. Further, for any of the top kinematic models, the residual firing rates of Turn Units showed a significant difference for planned and unplanned turns, 240 ms before to 80 ms after turn onset . Thus, our conclusions are robust to the exact choice of kinematic models. We now clarify this robustness on page 21.Regarding the reviewers’ third question, we selected the kinematic model with the best out-of-sample R2) The authors mentioned that 'Turn Units decreased their firing rates before turn onset on Impromptu Turn trials but increased them before turn onset on Planned Turn trials.' (Line 289-291). However, we don't see this statement being supported by data/figure. Can the authors provide evidence/data for this? Or did we miss anything?Thank you for another great question. In retrospect, we see that our phrasing has been confusing as it could have implied that the firing rates on planned and impromptu turn trials are increased and decreased, respectively, compared to some baseline period. We simply meant that firing rates on impromptu turn trials are lower than on planned turn trials 240 ms before to 80 ms after turn onset . The firing rate for the equivalent times on simple reach trials is in between the other two trial types, but it is not a baseline for the statistical tests. We now clarify this point in the paper and have rephrased this sentence (page 7) and a similar statement in our Discussion (page 9-10) to avoid confusion.3) Reviewer 3 from the previous round of review raised that 'Conceptually, even if 'impromptu' units are an actual subpopulation of neurons, a decrease in STNactivity just before the execution of the unexpected turn seems consistent, not inconsistent, with the previous hypotheses on STN as a "brake" or "global stop" nucleus. We think the authors should clarify their thoughts on this more in their writing.' We feel this was not sufficiently addressed.Existing theories about the role of STN are based on studies in which subjects were instructed to withhold uninitiated action plans. A common observation in those studies has been that STN responses increase when subjects halt their planned (but uninitiated) movements, leading to the hypothesis that increased activity is associated with stopping (explained in the first paragraph of the Introduction and page 16 in our Discussion). In general, the reviewer is correct that it is possible for any change in STN activity, including a decrease in firing, to be responsible for stopping. However, to our best knowledge, this has not been observed experimentally in past studies. Our finding of decreased STN firing before changes in ongoing movements does not preclude STN acting to stop or change movements, but they do point to the incompleteness of existing models. We now clarify this point to our discussion (page 10).4) For the statistical analysis Equation. 1 – Equation. 9, it is not clear whether the authors have used self-programmed scripts for these tests or existing toolbox/functions in Matlab to do this. As far as I understand, Equation. 1 – Equation. 9 are similar to what is implemented in the linear mixed effects models available in Matlab (https://www.mathworks.com/help/stats/linear-mixed-effects-models). This can be applied to each time point, and then using cluster mass test to identify the time window with significant effect. Or alternatively, can the authors specify what is difference between the method used in this study and the linear mixed effects model? The authors mentioned that 'For testing factor ;, the levels of ; are permuted within each unit and without permutation of the other factor. The remainder of the calculation continues as for the one factor case, resulting in separate p-values for each main effect, as in a multi-way ANOVA.' Is it equivalent to repeating the multilevel modeling permuting the levels of m? If the method is based on the framework of multi-level modeling, it might not be necessary to present all the Equations.a priori at which points in time to expect such differences. Furthermore, simple testing of firing rate differences in different windows chosen by examining the data would have resulted in an uncontrolled multiple comparisons problem. Our newly-developed analyses explicitly deal with these multiple comparisons while preserving statistical power. The analysis framework shares many components with the mixed effect models, as astutely observed by the reviewers. Our scripts are available in a public github repositoryOur statistical analyses heavily rely on the math of mixed effects modeling, but we could not have readily used pre-existing packages in Matlab because they do not offer a systematic method to determine the time windows for our analyses. To address this shortcoming, we developed the necessary analyses and their associated code by extending the standard cluster mass test. As a reminder, we sought to determine if different conditions  were associated with changes in firing rates, but we did not know (https://github.com/dlondon12/InterruptedReachClustMass) and we are working on a methods manuscript that explains the analysis approach in depth.5) The accurate rate of different conditions was around 0.7. Did the authors only focus on the 'accurate trials' which satisfy the 'accuracy criteria' or considered all trials? Is there any difference between the neural activities for 'accurate trials' vs 'inaccurate trials'?We did focus all of our analyses on trials which satisfied the accuracy criteria. Those that did not can be split into fixation breaks (which should obviously be excluded as these represent lapses in task engagement) and trials in which the subject performed a movement that did not meet the accuracy criteria. Since our goal was to determine the neural responses to a cue to change movement, we had to ensure that the movement actually changed and in a way that allowed planned and impromptu turns trials to be compared. The median number of inaccurate trials per unit that were not fixation breaks was 19 , unfortunately too few to draw conclusions about the neural responses accompanying the inaccurate movements. As expected, adding these trials to our analyses (skipping the exclusion step) does not change our conclusions . We have now added these points to the Methods section (page 15) and changed the caption for Figure 1B to clarify that it shows the fraction of trials that met the accuracy criteria.6) What is 'level s' in Equation 9 and Line 748? The ; trial represented trial types, and N represent the different units. What is level s?Level s refers to the movement direction (target side) of each trial . This is an orthogonal factor to the trial type . The two factor cluster mass test considers the separate effects of each factor. This is now described on page 17 just prior to Equation 4.7) Can the authors compare the baseline spiking rate of the 'movement related' and 'turning related' units identified by clustering analysis applied on the PSTHs.The median baseline spiking rate of Movement Units is 35.5 Hz (IQR: 15.5 – 88.0 Hz) compared to 36.6 Hz (IQR: 22.2 – 56.6 Hz) for Turn Units. These rates are not significantly different . This is now reported on page 4.8) Figure 2. It is mentioned in the figure legend that 'Shadings in C-H are SE.' The same for Figure 4, 'Shading indicates SE in A-F'. But I didn't see the shadings in these plots? The same for Figure 3 plot B-C, it will be informative to show the SE in the plots.