Excitatory/Inhibitory Responses Shape Coherent Neuronal Dynamics Driven by Optogenetic Stimulation in the Primate Brain.

Coherent neuronal dynamics play an important role in complex cognitive functions. Optogenetic stimulation promises to provide new ways to test the functional significance of coherent neural activity. However, the mechanisms by which optogenetic stimulation drives coherent dynamics remain unclear, especially in the nonhuman primate brain. Here, we perform computational modeling and experiments to study the mechanisms of optogenetic-stimulation-driven coherent neuronal dynamics in three male nonhuman primates. Neural responses arise from stimulation-evoked, temporally dynamic excitatory (E) and inhibitory (I) activity. Spiking activity is more likely to occur during E/I imbalances. Thus the relative difference in the driven E and I responses precisely controls spike timing by forming a brief time interval of increased spiking likelihood. Experimental results agree with parameter-dependent predictions from the computational models. These results demonstrate that optogenetic stimulation driven coherent neuronal dynamics are governed by the temporal properties of E/I activity. Transient imbalances in excitatory and inhibitory activity may provide a general mechanism for generating coherent neuronal dynamics without the need for an oscillatory generator.SIGNIFICANCE STATEMENT We examine how coherent neuronal dynamics arise from optogenetic stimulation in the primate brain. Using computational models and experiments, we demonstrate that coherent spiking and local field potential activity is generated by stimulation-evoked responses of excitatory and inhibitory activity in networks, extending the growing literature on neuronal dynamics. These responses create brief time intervals of increased spiking tendency and are consistent with previous observations in the literature that balanced excitation and inhibition controls spike timing, suggesting that optogenetic-stimulation-driven coherence may arise from intrinsic E/I balance. Most importantly, our results are obtained in nonhuman primates and thus will play a leading role in driving the use of causal manipulations with optogenetic tools to study higher cognitive functions in the primate brain.

Temporal coding of reward-guided choice in the posterior parietal cortex.

Making a decision involves computations across distributed cortical and subcortical networks. How such distributed processing is performed remains unclear. We test how the encoding of choice in a key decision-making node, the posterior parietal cortex (PPC), depends on the temporal structure of the surrounding population activity. We recorded spiking and local field potential (LFP) activity in the PPC while two rhesus macaques performed a decision-making task. We quantified the mutual information that neurons carried about an upcoming choice and its dependence on LFP activity. The spiking of PPC neurons was correlated with LFP phases at three distinct time scales in the theta, beta, and gamma frequency bands. Importantly, activity at these time scales encoded upcoming decisions differently. Choice information contained in neural firing varied with the phase of beta and gamma activity. For gamma activity, maximum choice information occurred at the same phase as the maximum spike count. However, for beta activity, choice information and spike count were greatest at different phases. In contrast, theta activity did not modulate the encoding properties of PPC units directly but was correlated with beta and gamma activity through cross-frequency coupling. We propose that the relative timing of local spiking and choice information reveals temporal reference frames for computations in either local or large-scale decision networks. Differences between the timing of task information and activity patterns may be a general signature of distributed processing across large-scale networks.

7T-fMRI: Faster temporal resolution yields optimal BOLD sensitivity for functional network imaging specifically at high spatial resolution.

Recent developments in accelerated imaging methods allow faster acquisition of high spatial resolution images. This could improve the applications of functional magnetic resonance imaging at 7 Tesla (7T-fMRI), such as neurosurgical planning and Brain Computer Interfaces (BCIs). However, increasing the spatial and temporal resolution will both lead to signal-to-noise ratio (SNR) losses due to decreased net magnetization per voxel and T1-relaxation effect, respectively. This could potentially offset the SNR efficiency gains made with increasing temporal resolution. We investigated the effects of varying spatial and temporal resolution on fMRI sensitivity measures and their implications on fMRI-based BCI simulations. We compared temporal signal-to-noise ratio (tSNR), observed percent signal change (%∆S), volumes of significant activation, Z-scores and decoding performance of linear classifiers commonly used in BCIs across a range of spatial and temporal resolution images acquired during an ankle-tapping task. Our results revealed an average increase of 22% in %∆S (p=0.006) and 9% in decoding performance (p=0.015) with temporal resolution only at the highest spatial resolution of 1.5×1.5×1.5mm3, despite a 29% decrease in tSNR (p<0.001) and plateaued Z-scores. Further, the volume of significant activation was indifferent (p>0.05) across spatial resolution specifically at the highest temporal resolution of 500ms. These results demonstrate that the overall BOLD sensitivity can be increased significantly with temporal resolution, granted an adequately high spatial resolution with minimal physiological noise level. This shows the feasibility of diffuse motor-network imaging at high spatial and temporal resolution with robust BOLD sensitivity with 7T-fMRI. Importantly, we show that this sensitivity improvement could be extended to an fMRI application such as BCIs.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization.

Connections between spatially distant primary language regions strengthen with age during infancy, as revealed by resting-state fNIRS.

Objective.Hearing is an important sensory function that plays a key role in how children learn to speak and develop language skills. Although previous neuroimaging studies have established that much of brain network maturation happens in early childhood, our understanding of the developmental trajectory of language areas is still very limited. We hypothesized that typical development trajectory of language areas in early childhood could be established by analyzing the changes of functional connectivity in normal hearing infants at different ages using functional near-infrared spectroscopy.Approach.Resting-state data were recorded from two bilateral temporal and prefrontal regions associated with language processing by measuring the relative changes of oxy-hemoglobin (HbO) and deoxy-hemoglobin (HbR) concentrations. Connectivity was calculated using magnitude-squared coherence of channel pairs located in (a) inter-hemispheric homologous and (b) intra-hemispheric brain regions to assess connectivity between homologous regions across hemispheres and two regions of interest in the same hemisphere, respectively.Main results.A linear regression model fitted to the age vs coherence of inter-hemispheric homologous test group revealed a significant coefficient of determination for both HbO (R2= 0.216,p= 0.0169) and HbR (R2= 0.206,p= 0.0198). A significant coefficient of determination was also found for intra-hemispheric test group for HbO (R2= 0.237,p= 0.0117) but not for HbR (R2= 0.111,p= 0.0956).Significance.The findings from HbO data suggest that both inter-hemispheric homologous and intra-hemispheric connectivity between primary language regions significantly strengthen with age in the first year of life. Mapping out the developmental trajectory of primary language areas of normal hearing infants as measured by functional connectivity could potentially allow us to better understand the altered connectivity and its effects on language delays in infants with hearing impairments.

Investigating the Effect of Data Length on the Performance of Frequency-Domain fNIRS Functional Connectivity Measures.

Resting-state functional connectivity is a promising tool for understanding and characterizing brain network architecture. However, obtaining uninterrupted long recording of resting-state data is challenging in many clinically relevant populations. Moreover, the interpretation of connectivity results may heavily depend on the data length and functional connectivity measure used. We compared the performance of three frequency-domain connectivity measures: magnitude-squared, wavelet and multitaper coherence; and the effect of data length ranging from 3 to 9 minutes. Performance was characterized by distinguishing two groups of channel pairs with known different connectivity strengths. While all methods considered improved the ability to distinguish the two groups with increasing data lengths, wavelet coherence performed best for the shortest time window of 3 minutes. Knowledge of which measure is more reliably used when shorter fNIRS recordings are available could make the utility of functional connectivity biomarkers more feasible in clinical populations of interest.

Cortical layering disrupts multi-electrode current steering.

Objective.Blindness affects approximately 40 million people worldwide and has inspired the development of cortical visual prostheses for restoring sight. Cortical visual prostheses electrically stimulate neurons of the visual cortex to artificially evoke visual percepts. Of the 6 layers of the visual cortex, layer 4 contains neurons that are likely to evoke a visual percept. Intracortical prostheses therefore aim to target layer 4; however, this can be difficult due to cortical curvature, inter-subject cortical variability, blindness-induced anatomical changes in cortex, and electrode placement variations. We investigated the feasibility of using current steering to stimulate specific cortical layers between electrodes in the laminar column.Approach.We explored whether the multiunit neural activity peak can be manipulated between two simultaneously stimulating electrodes in different layers of the cortical column. A 64-channel, 4-shank electrode array was implanted into the visual cortex of Sprague-Dawley rats (n= 7) orthogonal to the cortical surface. A remote return electrode was positioned over the frontal cortex in the same hemisphere. Charge was supplied to two stimulating electrodes along a single shank. Differing ratios of charge (100:0, 75:25, 50:50) and separation distances (300-500µm) were tested.Results.Current steering across the cortical layers did not result in a consistent shift of the neural activity peak. Both single-electrode and dual-electrode stimulation induced activity throughout the cortical column. This contrasts observations that current steering evoked a controllable peak of neural activity between electrodes implanted at similar cortical depths. However, dual-electrode stimulation across the layers did reduce the stimulation threshold at each site compared to single-electrode stimulation.Significance.Multi-electrode stimulation is not suitable for targeted activation of layers using current steering. However, it can be used to reduce activation thresholds at adjacent electrodes within a given cortical layer. This may be applied to reduce the stimulation side effects of neural prostheses, such as seizures.

Putative cell-type-specific multiregional mode in posterior parietal cortex during coordinated visual behavior.

In the reach and saccade regions of the posterior parietal cortex (PPC), multiregional communication depends on the timing of neuronal activity with respect to beta-frequency (10-30 Hz) local field potential (LFP) activity, termed dual coherence. Neural coherence is believed to reflect neural excitability, whereby spiking tends to occur at a particular phase of LFP activity, but the mechanisms of multiregional dual coherence remain unknown. Here, we investigate dual coherence in the PPC of non-human primates performing eye-hand movements. We computationally model dual coherence in terms of multiregional neural excitability and show that one latent component, a multiregional mode, reflects shared excitability across distributed PPC populations. Analyzing the power in the multiregional mode with respect to different putative cell types reveals significant modulations with the spiking of putative pyramidal neurons and not inhibitory interneurons. These results suggest a specific role for pyramidal neurons in dual coherence supporting multiregional communication in PPC.

Competition for visual selection in the oculomotor system.

During behavior, the oculomotor system is tasked with selecting objects from an ever-changing visual field and guiding eye movements to these locations. The attentional priority given to visual targets during selection can be strongly influenced by external stimulus properties or internal goals based on previous experience. Although these exogenous and endogenous drivers of selection are known to operate across partially overlapping timescales, the form of their interaction over time remains poorly understood. Using a novel choice task that simultaneously manipulates stimulus- and goal-driven attention, we demonstrate that exogenous and endogenous attentional biases change linearly as a function of time after stimulus onset and have an additive influence on the visual selection process in rhesus macaques (Macaca mulatta). We present a family of computational models that quantify this interaction over time and detail the history dependence of both processes. The computational models reveal the existence of a critical 140-180 ms attentional "switching" time, when stimulus- and goal-driven processes simultaneously favor competing visual targets. These results suggest that the brain uses a linear sum of attentional biases to guide visual selection.

Optimizing the decoding of movement goals from local field potentials in macaque cortex.

The successful development of motor neuroprosthetic devices hinges on the ability to accurately and reliably decode signals from the brain. Motor neuroprostheses are widely investigated in behaving non-human primates, but technical constraints have limited progress in optimizing performance. In particular, the organization of movement-related neuronal activity across cortical layers remains poorly understood due, in part, to the widespread use of fixed-geometry multielectrode arrays. In this study, we use chronically implanted multielectrode arrays with individually movable electrodes to examine how the encoding of movement goals depends on cortical depth. In a series of recordings spanning several months, we varied the depth of each electrode in the prearcuate gyrus of frontal cortex in two monkeys as they performed memory-guided eye movements. We decode eye movement goals from local field potentials (LFPs) and multiunit spiking activity recorded across a range of depths up to 3 mm from the cortical surface. We show that both LFP and multiunit signals yield the highest decoding performance at superficial sites, within 0.5 mm of the cortical surface, while performance degrades substantially at sites deeper than 1 mm. We also analyze performance by varying bandpass filtering characteristics and simulating changes in microelectrode array channel count and density. The results indicate that the performance of LFP-based neuroprostheses strongly depends on recording configuration and that recording depth is a critical parameter limiting system performance.

Neurophysiological considerations for visual implants.

Neural implants have the potential to restore visual capabilities in blind individuals by electrically stimulating the neurons of the visual system. This stimulation can produce visual percepts known as phosphenes. The ideal location of electrical stimulation for achieving vision restoration is widely debated and dependent on the physiological properties of the targeted tissue. Here, the neurophysiology of several potential target structures within the visual system will be explored regarding their benefits and downfalls in producing phosphenes. These regions will include the lateral geniculate nucleus, primary visual cortex, visual area 2, visual area 3, visual area 4 and the middle temporal area. Based on the existing engineering limitations of neural prostheses, we anticipate that electrical stimulation of any singular brain region will be incapable of achieving high-resolution naturalistic perception including color, texture, shape and motion. As improvements in visual acuity facilitate improvements in quality of life, emulating naturalistic vision should be one of the ultimate goals of visual prostheses. To achieve this goal, we propose that multiple brain areas will need to be targeted in unison enabling different aspects of vision to be recreated.

Reducing false discoveries in resting-state functional connectivity using short channel correction: an fNIRS study.

Significance: Functional near-infrared spectroscopy (fNIRS) is a neuroimaging tool that can measure resting-state functional connectivity; however, non-neuronal components present in fNIRS signals introduce false discoveries in connectivity, which can impact interpretation of functional networks. Aim: We investigated the effect of short channel correction on resting-state connectivity by removing non-neuronal signals from fNIRS long channel data. We hypothesized that false discoveries in connectivity can be reduced, hence improving the discriminability of functional networks of known, different connectivity strengths. Approach: A principal component analysis-based short channel correction technique was applied to resting-state data of 10 healthy adult subjects. Connectivity was analyzed using magnitude-squared coherence of channel pairs in connectivity groups of homologous and control brain regions, which are known to differ in connectivity. Results: By removing non-neuronal components using short channel correction, significant reduction of coherence was observed for oxy-hemoglobin concentration changes in frequency bands associated with resting-state connectivity that overlap with the Mayer wave frequencies. The results showed that short channel correction reduced spurious correlations in connectivity measures and improved the discriminability between homologous and control groups. Conclusions: Resting-state functional connectivity analysis with short channel correction performs better than without correction in its ability to distinguish functional networks with distinct connectivity characteristics.

A Model for Assessing the Electromagnetic Safety of an Inductively Coupled, Modular Brain-Machine Interface.

Brain-Machine Interfaces (BMI) offer the potential to modulate dysfunctional neurological networks by electrically stimulating the cerebral cortex via chronically-implanted microelectrodes. Wireless transmitters worn by BMI recipients must operate within electromagnetic emission and tissue heating limits, such as those prescribed by the IEEE and International Commission on Non-Ionizing Radiation Protection (ICNIRP), to ensure that radiofrequency emissions of BMI systems are safe. Here, we describe an approach to generating pre-compliance safety data by simulating the Specific Absorption Rate (SAR) and tissue heating of a multi-layered human head model containing a system of wireless, modular BMIs powered and controlled by an externally worn telemetry unit. We explore a number of system configurations such that our approach can be utilized for similar BMI systems, and our results provide a benchmark for the electromagnetic emissions of similar telemetry units. Our results show that the volume-averaged SAR per 10g of tissue exposed to our telemetry field complies with ICNIRP and IEEE reference levels, and that the maximum temperature increase in tissues was within permissible limits. These results were unaffected by the number of implants in the system model, and therefore we conclude that the electromagnetic emissions our BMI in any configuration are safe.

Intracortical current steering shifts the location of evoked neural activity.

Objective.Intracortical visual prostheses are being developed to restore sight in people who are blind. The resolution of artificial vision is dictated by the location, proximity and number of electrodes implanted in the brain. However, increasing electrode count and proximity is traded off against tissue damage. Hence, new stimulation methods are needed that can improve the resolution of artificial vision without increasing the number of electrodes. We investigated whether a technique known as current steering can improve the resolution of artificial vision provided by intracortical prostheses without increasing the number of physical electrodes in the brain.Approach.We explored how the locus of neuronal activation could be steered when low amplitude microstimulation was applied simultaneously to two intracortical electrodes. A 64-channel, four-shank electrode array was implanted into the visual cortex of rats (n= 7). The distribution of charge ranged from single-electrode stimulation (100%:0%) to an equal distribution between the two electrodes (50%:50%), thereby steering the current between the physical electrodes. The stimulating electrode separation varied between 300 and 500µm. The peak of the evoked activity was defined as the 'virtual electrode' location.Main results.Current steering systematically shifted the virtual electrode on average between the stimulating electrodes as the distribution of charge was moved from one stimulating electrode to another. This effect was unclear in single trials due to the limited sampling of neurons. A model that scales the cortical response to each physical electrode when stimulated in isolation predicts the evoked virtual electrode response. Virtual electrodes were found to elicit a neural response as effectively and predictably as physical electrodes within cortical tissue on average.Significance.Current steering could be used to increase the resolution of intracortical electrode arrays without altering the number of physical electrodes which will reduce neural tissue damage, power consumption and potential heat dispersion issues.

Multiscale low-dimensional motor cortical state dynamics predict naturalistic reach-and-grasp behavior.

Motor function depends on neural dynamics spanning multiple spatiotemporal scales of population activity, from spiking of neurons to larger-scale local field potentials (LFP). How multiple scales of low-dimensional population dynamics are related in control of movements remains unknown. Multiscale neural dynamics are especially important to study in naturalistic reach-and-grasp movements, which are relatively under-explored. We learn novel multiscale dynamical models for spike-LFP network activity in monkeys performing naturalistic reach-and-grasps. We show low-dimensional dynamics of spiking and LFP activity exhibited several principal modes, each with a unique decay-frequency characteristic. One principal mode dominantly predicted movements. Despite distinct principal modes existing at the two scales, this predictive mode was multiscale and shared between scales, and was shared across sessions and monkeys, yet did not simply replicate behavioral modes. Further, this multiscale mode's decay-frequency explained behavior. We propose that multiscale, low-dimensional motor cortical state dynamics reflect the neural control of naturalistic reach-and-grasp behaviors.

Microstimulation-evoked neural responses in visual cortex are depth dependent.

BACKGROUND: Cortical visual prostheses often use penetrating electrode arrays to deliver microstimulation to the visual cortex. To optimize electrode placement within the cortex, the neural responses to microstimulation at different cortical depths must first be understood. OBJECTIVE: We investigated how the neural responses evoked by microstimulation in cortex varied with cortical depth, of both stimulation and response. METHODS: A 32-channel single shank electrode array was inserted into the primary visual cortex of anaesthetized rats, such that it spanned all cortical layers. Microstimulation with currents up to 14 µA (single biphasic pulse, 200 µs per phase) was applied at depths spanning 1600 µm, while simultaneously recording neural activity on all channels within a response window 2.25-11 ms. RESULTS: Stimulation elicited elevated neuronal firing rates at all depths of cortex. Compared to deep sites, superficial stimulation sites responded with higher firing rates at a given current and had lower thresholds. The laminar spread of evoked activity across cortical depth depended on stimulation depth, in line with anatomical models. CONCLUSION: Stimulation in the superficial layers of visual cortex evokes local neural activity with the lowest thresholds, and stimulation in the deep layers evoked the most activity across the cortical column. In conjunction with perceptual reports, these data suggest that the optimal electrode placement for cortical microstimulation prostheses has electrodes positioned in layers 2/3, and at the top of layer 5.

Spectral features of cortical auditory evoked potentials inform hearing threshold and intensity percepts in acoustic and electric hearing.

Objective. Stimulus-elicited changes in electroencephalography (EEG) recordings can be represented using Fourier magnitude and phase features (Makeiget al(2004Trends Cogn. Sci.8204-10)). The present study aimed to quantify how much information about hearing responses are contained in the magnitude, quantified by event-related spectral perturbations (ERSPs); and the phase, quantified by inter-trial coherence (ITC). By testing if one feature contained more information and whether this information was mutually exclusive to the features, we aimed to relate specific EEG magnitude and phase features to hearing perception.Approach.EEG responses were recorded from 20 adults who were presented with acoustic stimuli, and 20 adult cochlear implant users with electrical stimuli. Both groups were presented with short, 50 ms stimuli at varying intensity levels relative to their hearing thresholds. Extracted ERSP and ITC features were inputs for a linear discriminant analysis classifier (Wonget al(2016J. Neural. Eng.13036003)). The classifier then predicted whether the EEG signal contained information about the sound stimuli based on the input features. Classifier decoding accuracy was quantified with the mutual information measure (Cottaris and Elfar (2009J. Neural. Eng.6026007), Hawelleket al(2016Proc. Natl Acad. Sci.11313492-7)), and compared across the two feature sets, and to when both feature sets were combined.Main results. We found that classifiers using either ITC or ERSP feature sets were both able to decode hearing perception, but ITC-feature classifiers were able to decode responses to a lower but still audible stimulation intensity, making ITC more useful than ERSP for hearing threshold estimation. We also found that combining the information from both feature sets did not improve decoding significantly, implying that ERSP brain dynamics has a limited contribution to the EEG response, possibly due to the stimuli used in this study.Significance.We successfully related hearing perception to an EEG measure, which does not require behavioral feedback from the listener; an objective measure is important in both neuroscience research and clinical audiology.

Modeling behaviorally relevant neural dynamics enabled by preferential subspace identification.

Neural activity exhibits complex dynamics related to various brain functions, internal states and behaviors. Understanding how neural dynamics explain specific measured behaviors requires dissociating behaviorally relevant and irrelevant dynamics, which is not achieved with current neural dynamic models as they are learned without considering behavior. We develop preferential subspace identification (PSID), which is an algorithm that models neural activity while dissociating and prioritizing its behaviorally relevant dynamics. Modeling data in two monkeys performing three-dimensional reach and grasp tasks, PSID revealed that the behaviorally relevant dynamics are significantly lower-dimensional than otherwise implied. Moreover, PSID discovered distinct rotational dynamics that were more predictive of behavior. Furthermore, PSID more accurately learned behaviorally relevant dynamics for each joint and recording channel. Finally, modeling data in two monkeys performing saccades demonstrated the generalization of PSID across behaviors, brain regions and neural signal types. PSID provides a general new tool to reveal behaviorally relevant neural dynamics that can otherwise go unnoticed.

Feasibility of using discrete Brain Computer Interface for people with Multiple Sclerosis.

AIM: Brain-Computer Interfaces (BCIs) hold promise to provide people with partial or complete paralysis, the ability to control assistive technology. This study reports offline classification of imagined and executed movements of the upper and lower limb in one participant with multiple sclerosis and people with no limb function deficits. METHODS: We collected neural signals using electroencephalography (EEG) while participants performed executed and imagined motor tasks as directed by prompts shown on a screen. RESULTS: Participants with no limb function attained >70% decoding accuracy on their best-imagined task compared to rest and on at-least one task comparison. The participant with multiple sclerosis also achieved accuracies within the range of participants with no limb function loss.Clinical Relevance - While only one case study is provided it was promising that the participant with MS was able to achieve comparable classification to that of the seven healthy controls. Further studies are needed to assess whether people suffering from MS may be able to use a BCI to improve their quality of life.

Neuron-specific responses to acetylcholine within the spinal dorsal horn circuits of rodent and primate.

Excitatory and inhibitory neurotransmission within the spinal dorsal horn is tightly controlled to regulate transmission of nociceptive signals to the brain. One aspect of this control is modulation of neuronal activity through cholinergic signaling. Nociceptive neurons in the dorsal horn express both nicotinic and muscarinic cholinergic receptors and activation of these receptors reduces pain in humans, while inhibition leads to nociceptive hypersensitivity. At a cellular level, acetylcholine (ACh) has diverse effects on excitability which is dependent on the receptor and neuronal subtypes involved. In the present study we sought to characterize the electrophysiological responses of specific subsets of lamina II interneurons from rat and marmoset spinal cord. Neurons were grouped by morphology and by action potential firing properties. Whole-cell voltage-clamp recordings from lamina II dorsal horn neurons of adult rats showed that bath applied acetylcholine increased, decreased or had no effect on spontaneous synaptic current activity in a cell-type specific manner. ACh modulated inhibitory synaptic activity in 80% of neurons, whereas excitatory synaptic activity was affected in less than 50% of neurons. In whole-cell current clamp recordings, brief somatic application of ACh induced cell-type specific responses in 79% of rat lamina II neurons, which included: depolarization and action potential firing, subthreshold membrane depolarization, biphasic responses characterized by transient depolarization followed by hyperpolarization and membrane hyperpolarization alone. Similar responses were seen in marmoset lamina II neurons and the properties of each neuron group were consistent across species. ACh-induced hyperpolarization was blocked by the muscarinic antagonist atropine and all forms of acetylcholine-induced depolarization were blocked by the nicotinic antagonist mecamylamine. The cholinergic system plays an important role in regulating nociception and this study contributes to our understanding of how circuit activity is controlled by ACh at a cellular level in primate and rodent spinal cord.