Neuronal activation sequences in lateral prefrontal cortex encode visuospatial working memory during virtual navigation.

Working memory (WM) is the ability to maintain and manipulate information 'in mind'. The neural codes underlying WM have been a matter of debate. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of male macaque monkeys during a visuospatial WM task that required navigation in a virtual 3D environment. Here, we demonstrate distinct neuronal activation sequences (NASs) that encode remembered target locations in the virtual environment. This NAS code outperformed the persistent firing code for remembered locations during the virtual reality task, but not during a classical WM task using stationary stimuli and constraining eye movements. Finally, blocking NMDA receptors using low doses of ketamine deteriorated the NAS code and behavioral performance selectively during the WM task. These results reveal the versatility and adaptability of neural codes supporting working memory function in the primate lateral prefrontal cortex.

A Systematic Review of Neurophysiology-Based Localization Techniques Used in Deep Brain Stimulation Surgery of the Subthalamic Nucleus.

OBJECTIVE: This systematic review is conducted to identify, compare, and analyze neurophysiological feature selection, extraction, and classification to provide a comprehensive reference on neurophysiology-based subthalamic nucleus (STN) localization. MATERIALS AND METHODS: The review was carried out using the methods and guidelines of the Kitchenham systematic review and provides an in-depth analysis on methods proposed on STN localization discussed in the literature between 2000 and 2021. Three research questions were formulated, and 115 publications were identified to answer the questions. RESULTS: The three research questions formulated are answered using the literature found on the respective topics. This review discussed the technologies used in past research, and the performance of the state-of-the-art techniques is also reviewed. CONCLUSION: This systematic review provides a comprehensive reference on neurophysiology-based STN localization by reviewing the research questions other new researchers may also have.

Intracortical brain-computer interfaces in primates: a review and outlook.

Brain-computer interfaces (BCI) translate brain signals into artificial output to restore or replace natural central nervous system (CNS) functions. Multiple processes, including sensorimotor integration, decision-making, motor planning, execution, and updating, are involved in any movement. For example, a BCI may be better able to restore naturalistic motor behaviors if it uses signals from multiple brain areas and decodes natural behaviors' cognitive and motor aspects. This review provides an overview of the preliminary information necessary to plan a BCI project focusing on intracortical implants in primates. Since the brain structure and areas of non-human primates (NHP) are similar to humans, exploring the result of NHP studies will eventually benefit human BCI studies. The different types of BCI systems based on the target cortical area, types of signals, and decoding methods will be discussed. In addition, various successful state-of-the-art cases will be reviewed in more detail, focusing on the general algorithm followed in the real-time system. Finally, an outlook for improving the current BCI research studies will be debated.

Volitional control of beta activities in Parkinson's disease patients.

Patients diagnosed with Parkinson's disease (PD) have difficulty initiating and executing movements due to an acquired imbalance of the basal ganglia thalamocortical circuit secondary to loss of dopaminergic input into the striatum. The unbalanced circuit is hyper-synchronized, presenting as larger and longer bursts of beta-band (13-30 Hz) oscillations in the subthalamic nucleus (STN). As a first step toward a novel PD therapy that aims to improve symptoms through beta desynchronization, we sought to determine if individuals with PD could acquire volitional control of STN beta power in a neurofeedback task. We found a significant difference in STN beta power between task conditions, and relevant brain signal features could be detected and decoded in real time. This demonstration of volitional control of STN beta motivates development of a neurofeedback therapy to modulate PD symptom severity.

View cells in the hippocampus and prefrontal cortex of macaques during virtual navigation.

Cells selectively activated by a particular view of an environment have been found in the primate hippocampus (HPC). Whether view cells are present in other brain areas, and how view selectivity interacts with other variables such as object features and place remain unclear. Here, we explore these issues by recording the responses of neurons in the HPC and the lateral prefrontal cortex (LPFC) of rhesus macaques performing a task in which they learn new context-object associations while navigating a virtual environment using a joystick. We measured neuronal responses at different locations in a virtual maze where animals freely directed gaze to different regions of the visual scenes. We show that specific views containing task relevant objects selectively activated a proportion of HPC units, and an even higher proportion of LPFC units. Place selectivity was scarce and generally dependent on view. Many view cells were not affected by changing the object color or the context cue, two task relevant features. However, a small proportion of view cells showed selectivity for these two features. Our results show that during navigation in a virtual environment with complex and dynamic visual stimuli, view cells are found in both the HPC and the LPFC. View cells may have developed as a multiarea specialization in diurnal primates to encode the complexities and layouts of the environment through gaze exploration which ultimately enables building cognitive maps of space that guide navigation.

Ensembles code for associative learning in the primate lateral prefrontal cortex.

The lateral prefrontal cortex (LPFC) of primates is thought to play a role in associative learning. However, it remains unclear how LPFC neuronal ensembles dynamically encode and store memories for arbitrary stimulus-response associations. We recorded the activity of neurons in LPFC of two macaques during an associative learning task using multielectrode arrays. During task trials, the color of a symbolic cue indicated the location of one of two possible targets for a saccade. During a trial block, multiple randomly chosen associations were learned by the subjects. A state-space analysis indicated that LPFC neuronal ensembles rapidly learn new stimulus-response associations mirroring the animals' learning. Multiple associations acquired during training are stored in a neuronal subspace and can be retrieved hours after learning. Finally, knowledge of old associations facilitates learning new, similar associations. These results indicate that neuronal ensembles in the primate LPFC provide a flexible and dynamic substrate for associative learning.

Stable Working Memory and Perceptual Representations in Macaque Lateral Prefrontal Cortex during Naturalistic Vision.

Primates use perceptual and mnemonic visuospatial representations to perform everyday functions. Neurons in the lateral prefrontal cortex (LPFC) have been shown to encode both of these representations during tasks where eye movements are strictly controlled and visual stimuli are reduced in complexity. This raises the question of whether perceptual and mnemonic representations encoded by LPFC neurons remain robust during naturalistic vision-in the presence of a rich visual scenery and during eye movements. Here we investigate this issue by training macaque monkeys to perform working memory and perception tasks in a visually complex virtual environment that requires navigation using a joystick and allows for free visual exploration of the scene. We recorded the activity of 3950 neurons in the LPFC (areas 8a and 9/46) of two male rhesus macaques using multielectrode arrays, and measured eye movements using video tracking. We found that navigation trajectories to target locations and eye movement behavior differed between the perception and working memory tasks, suggesting that animals used different behavioral strategies. Single neurons were tuned to target location during cue encoding and working memory delay, and neural ensemble activity was predictive of the behavior of the animals. Neural decoding of the target location was stable throughout the working memory delay epoch. However, neural representations of similar target locations differed between the working memory and perception tasks. These findings indicate that during naturalistic vision, LPFC neurons maintain robust and distinct neural codes for mnemonic and perceptual visuospatial representations.SIGNIFICANCE STATEMENT We show that lateral prefrontal cortex neurons encode working memory and perceptual representations during a naturalistic task set in a virtual environment. We show that despite eye movement and complex visual input, neurons maintain robust working memory representations of space, which are distinct from neuronal representations for perception. We further provide novel insight into the use of virtual environments to construct behavioral tasks for electrophysiological experiments.

Distinct neural codes in primate hippocampus and lateral prefrontal cortex during associative learning in virtual environments.

The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here, we hypothesized that the codes mediating neuronal communication in the HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals selected one of the two targets in the arms of the maze, according to a learned context-color rule. Our results show that during associative learning, HPC principal cells concentrate spikes in bursts, enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layer II/III LPFC pyramidal cells fire spikes more sparsely distributed over time. The latter would facilitate broadcasting of signals loaded in short-term memory across neuronal populations without necessarily triggering fast synaptic plasticity.

Ketamine disrupts naturalistic coding of working memory in primate lateral prefrontal cortex networks.

Ketamine is a dissociative anesthetic drug, which has more recently emerged as a rapid-acting antidepressant. When acutely administered at subanesthetic doses, ketamine causes cognitive deficits like those observed in patients with schizophrenia, including impaired working memory. Although these effects have been linked to ketamine's action as an N-methyl-D-aspartate receptor antagonist, it is unclear how synaptic alterations translate into changes in brain microcircuit function that ultimately influence cognition. Here, we administered ketamine to rhesus monkeys during a spatial working memory task set in a naturalistic virtual environment. Ketamine induced transient working memory deficits while sparing perceptual and motor skills. Working memory deficits were accompanied by decreased responses of fast spiking inhibitory interneurons and increased responses of broad spiking excitatory neurons in the lateral prefrontal cortex. This translated into a decrease in neuronal tuning and information encoded by neuronal populations about remembered locations. Our results demonstrate that ketamine differentially affects neuronal types in the neocortex; thus, it perturbs the excitation inhibition balance within prefrontal microcircuits and ultimately leads to selective working memory deficits.

Realtime phase-amplitude coupling analysis of micro electrode recorded brain signals.

OBJECTIVE: To demonstrate a method to calculate phase amplitude coupling (PAC) quickly and robustly for realtime applications. METHODS: We designed and implemented a multirate PAC algorithm with efficient filter bank processing and efficient computation of PAC for many frequency-pair combinations. We tested the developed algorithm for computing PAC on simulated data and on intraoperative neural recording data obtained during deep brain stimulation (DBS) electrode implantation surgery. RESULTS: A combination of parallelized frequency-domain filtering and modulation index for PAC estimation provided robust results that could be calculated in real time on modest computing hardware. CONCLUSION: The standard methods for calculating PAC can be optimized for quick and robust performance. SIGNIFICANCE: These results demonstrated that PAC can be extracted in real time and is suitable for neurofeedback applications.

A Quadrantic Bias in Prefrontal Representation of Visual-Mnemonic Space.

Single neurons in primate dorsolateral prefrontal cortex (dLPFC) are known to encode working memory (WM) representations of visual space. Psychophysical studies have shown that the horizontal and vertical meridians of the visual field can bias spatial information maintained in WM. However, most studies and models have tacitly assumed that dLPFC neurons represent mnemonic space homogenously. The anatomical organization of these representations has also eluded clear parametric description. We investigated these issues by recording from neuronal ensembles in macaque dLPFC with microelectrode arrays while subjects performed an oculomotor delayed-response task. We found that spatial WM representations in macaque dLPFC are biased by the vertical and horizontal meridians of the visual field, dividing mnemonic space into quadrants. This bias is reflected in single neuron firing rates, neuronal ensemble representations, the spike count correlation structure, and eye movement patterns. We also found that dLPFC representations of mnemonic space cluster anatomically in a nonretinotopic manner that partially reflects the organization of visual space. These results provide an explanation for known WM biases, and reveal novel principles of WM representation in prefrontal neuronal ensembles and across the cortical surface, as well as the need to reconceptualize models of WM to accommodate the observed representational biases.

Visual and presaccadic activity in area 8Ar of the macaque monkey lateral prefrontal cortex.

Common trends observed in many visual and oculomotor-related cortical areas include retinotopically organized receptive and movement fields exhibiting a Gaussian shape and increasing size with eccentricity. These trends are demonstrated in the frontal eye fields, many visual areas, and the superior colliculus but have not been thoroughly characterized in prearcuate area 8Ar of the prefrontal cortex. This is important since area 8Ar, located anterior to the frontal eye fields, is more cytoarchitectonically similar to prefrontal areas than premotor areas. Here we recorded the responses of 166 neurons in area 8Ar of two male macaques while the animals made visually guided saccades to a peripheral sine-wave grating stimulus positioned at 1 of 40 possible locations (8 angles along 5 eccentricities). To characterize the neurons' receptive and movement fields, we fit a bivariate Gaussian model to the baseline-subtracted average firing rate during stimulus presentation (early and late visual epochs) and before saccade onset (presaccadic epoch). One hundred twenty-one of one hundred sixty-six neurons showed spatially selective visual and presaccadic responses. Of the visually selective neurons, 76% preferred the contralateral visual hemifield, whereas 24% preferred the ipsilateral hemifield. The angular width of visual and movement-related fields scaled positively with increasing eccentricity. Moreover, responses of neurons with visual receptive fields were modulated by target contrast, exhibiting sigmoid tuning curves that resemble those of visual neurons in upstream areas such as MT and V4. Finally, we found that neurons with receptive fields at similar spatial locations were clustered within the area; however, this organization did not appear retinotopic.NEW & NOTEWORTHY We recorded the responses of neurons in lateral prefrontal area 8Ar of macaques during a visually guided saccade task using multielectrode arrays. Neurons have Gaussian-shaped visual and movement fields in both visual hemifields, with a bias toward the contralateral hemifield. Visual neurons show contrast response functions with sigmoid shapes. Visual neurons tend to cluster at similar locations within the cortical surface; however, this organization does not appear retinotopic.

Correlated variability modifies working memory fidelity in primate prefrontal neuronal ensembles.

Neurons in the primate lateral prefrontal cortex (LPFC) encode working memory (WM) representations via sustained firing, a phenomenon hypothesized to arise from recurrent dynamics within ensembles of interconnected neurons. Here, we tested this hypothesis by using microelectrode arrays to examine spike count correlations (rsc ) in LPFC neuronal ensembles during a spatial WM task. We found a pattern of pairwise rsc during WM maintenance indicative of stronger coupling between similarly tuned neurons and increased inhibition between dissimilarly tuned neurons. We then used a linear decoder to quantify the effects of the high-dimensional rsc structure on information coding in the neuronal ensembles. We found that the rsc structure could facilitate or impair coding, depending on the size of the ensemble and tuning properties of its constituent neurons. A simple optimization procedure demonstrated that near-maximum decoding performance could be achieved using a relatively small number of neurons. These WM-optimized subensembles were more signal correlation (rsignal )-diverse and anatomically dispersed than predicted by the statistics of the full recorded population of neurons, and they often contained neurons that were poorly WM-selective, yet enhanced coding fidelity by shaping the ensemble's rsc structure. We observed a pattern of rsc between LPFC neurons indicative of recurrent dynamics as a mechanism for WM-related activity and that the rsc structure can increase the fidelity of WM representations. Thus, WM coding in LPFC neuronal ensembles arises from a complex synergy between single neuron coding properties and multidimensional, ensemble-level phenomena.

Single-trial decoding of intended eye movement goals from lateral prefrontal cortex neural ensembles.

Neurons in the lateral prefrontal cortex (LPFC) encode sensory and cognitive signals, as well as commands for goal-directed actions. Therefore, the LPFC might be a good signal source for a goal-selection brain-computer interface (BCI) that decodes the intended goal of a motor action previous to its execution. As a first step in the development of a goal-selection BCI, we set out to determine if we could decode simple behavioral intentions to direct gaze to eight different locations in space from single-trial LPFC neural activity. We recorded neuronal spiking activity from microelectrode arrays implanted in area 8A of the LPFC of two adult macaques while they made visually guided saccades to one of eight targets in a center-out task. Neuronal activity encoded target location immediately after target presentation, during a delay epoch, during the execution of the saccade, and every combination thereof. Many (40%) of the neurons that encoded target location during multiple epochs preferred different locations during different epochs. Despite heterogeneous and dynamic responses, the neuronal feature set that best predicted target location was the averaged firing rates from the entire trial and it was best classified using linear discriminant analysis (63.6-96.9% in 12 sessions, mean 80.3%; information transfer rate: 21-59, mean 32.8 bits/min). Our results demonstrate that it is possible to decode intended saccade target location from single-trial LPFC activity and suggest that the LPFC is a suitable signal source for a goal-selection cognitive BCI.

Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market. Here, we address both of these issues by investigating the use of signal power in a single narrow frequency band as a decoding feature for extracting information from electrocorticographic (ECoG), electromyographic (EMG), and intracortical neural data. We have designed and tested the Multi-modal Implantable Neural Interface (MINI), a wireless recording system which extracts and transmits signal power in a single, configurable frequency band. In prerecorded datasets, we used the MINI to explore low frequency signal features and any resulting tradeoff between power savings and decoding performance losses. When processing intracortical data, the MINI achieved a power consumption 89.7% less than a more typical system designed to extract action potential waveforms. When processing ECoG and EMG data, the MINI achieved similar power reductions of 62.7% and 78.8%. At the same time, using the single signal feature extracted by the MINI, we were able to decode all three modalities with less than a 9% drop in accuracy relative to using high-bandwidth, modality-specific signal features. We believe this system architecture can be used to produce a viable, cost-effective, clinical BMI.

Lack of efficacy of motor cortex stimulation for the treatment of neuropathic pain in 14 patients.

OBJECTIVES: Motor cortex stimulation has been reported as an effective treatment for medically resistant neuropathic pain. The goal of this study is to review the efficacy of this treatment in a series of 14 patients. MATERIALS AND METHODS: The records of a consecutive series of 14 patients undergoing MCS for neuropathic pain at Stanford University Hospital and Clinics between 2002 and 2010 were retrospectively analyzed. The primary outcome measure was a visual analogue scale, which patients completed prior to surgery and following each programming session. The motor cortex was localized using 1) MR image guidance, 2) intraoperative somatosensory evoked potentials and motor response to stimulation, and 3) postoperative imaging. All patients underwent extensive stimulator programming. RESULTS: Five patients exhibited a transient improvement of >50%. Of these, only two patients maintained >50% improvement to their last clinic visit. One of these patients died of unrelated causes, and the other complained of variable response at home. The median time from best to final VAS was 50 days. Average postoperative follow-up was 55.5 weeks. Postoperative imaging demonstrated appropriate lead placement in 12 patients. The other two patients did not undergo postoperative imaging. CONCLUSIONS: In our cohort of 14 patients with neuropathic pain, motor cortex stimulation failed to produce acceptable long-term benefit. Possible reasons for this failure are discussed in the context of a small retrospective study.

A metric-based analysis of the contribution of spike timing to contrast and motion direction coding by single neurons in macaque area MT.

Spike timing is thought to contribute to the coding of motion direction information by neurons in macaque area MT. Here, we examined whether spike timing also contributes to the coding of stimulus contrast. We applied a metric-based approach to spike trains fired by MT neurons in response to stimuli that varied in contrast, or direction. We assessed the performance of three metrics, D(spike) and D(product) (containing spike count and timing information), and the spike count metric D(count). We analyzed responses elicited during the first 200 msec of stimulus presentation from 205 neurons. For both contrast and direction, the large majority of neurons showed the highest mutual information using D(spike), followed by D(product), and D(count). This was corroborated by the performance of a theoretical observer model at discriminating contrast and direction using the three metrics. Our results demonstrate that spike timing can contribute to contrast coding in MT neurons, and support previous reports of its potential contribution to direction coding. Furthermore, they suggest that a combination of spike count with periodic and non-periodic spike timing information (contained in D(spike), but not in D(product) and D(count) which are insensitive to spike counts and timing respectively) provides the largest coding advantage in spike trains fired by MT neurons during contrast and direction discrimination.

Psychophysical receptive fields of edge detection mechanisms.

Theories of edge detection generally assume a front-end linear stage involving some population of neural filters. Here we study these early mechanisms using psychophysical techniques, and evaluate a number of models for edge detection. We measured psychophysical efficiency for detection of noisy luminance edge stimuli over a range of stimulus sizes and shapes. The data suggest a diversity in receptive field shape and orientation bandwidth, consistent with physiological evidence, but inconsistent with standard multi-channel models of visual processing.