Proceedings of the Third Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies.

The proceedings of the 3rd Annual Deep Brain Stimulation Think Tank summarize the most contemporary clinical, electrophysiological, imaging, and computational work on DBS for the treatment of neurological and neuropsychiatric disease. Significant innovations of the past year are emphasized. The Think Tank's contributors represent a unique multidisciplinary ensemble of expert neurologists, neurosurgeons, neuropsychologists, psychiatrists, scientists, engineers, and members of industry. Presentations and discussions covered a broad range of topics, including policy and advocacy considerations for the future of DBS, connectomic approaches to DBS targeting, developments in electrophysiology and related strides toward responsive DBS systems, and recent developments in sensor and device technologies.

Neuroplasticity subserving the operation of brain-machine interfaces.

Neuroplasticity is key to the operation of brain machine interfaces (BMIs)-a direct communication pathway between the brain and a man-made computing device. Whereas exogenous BMIs that associate volitional control of brain activity with neurofeedback have been shown to induce long lasting plasticity, endogenous BMIs that use prolonged activity-dependent stimulation--and thus may curtail the time scale that governs natural sensorimotor integration loops--have been shown to induce short lasting plasticity. Here we summarize recent findings from studies using both categories of BMIs, and discuss the fundamental principles that may underlie their operation and the longevity of the plasticity they induce. We draw comparison to plasticity mechanisms known to mediate natural sensorimotor skill learning and discuss principles of homeostatic regulation that may constrain endogenous BMI effects in the adult mammalian brain. We propose that BMIs could be designed to facilitate structural and functional plasticity for the purpose of re-organization of target brain regions and directed augmentation of sensorimotor maps, and suggest possible avenues for future work to maximize their efficacy and viability in clinical applications.

Temporal precision in population-but not individual neuron-dynamics reveals rapid experience-dependent plasticity in the rat barrel cortex.

Cortical reorganization following sensory deprivation is characterized by alterations in the connectivity between neurons encoding spared and deprived cortical inputs. The extent to which this alteration depends on Spike Timing Dependent Plasticity (STDP), however, is largely unknown. We quantified changes in the functional connectivity between layer V neurons in the vibrissal primary somatosensory cortex (vSI) (barrel cortex) of rats following sensory deprivation. One week after chronic implantation of a microelectrode array in vSI, sensory-evoked activity resulting from mechanical deflections of individual whiskers was recorded (control data) after which two whiskers on the contralateral side were paired by sparing them while trimming all other whiskers on the rat's mystacial pad. The rats' environment was then enriched by placing novel objects in the cages to encourage exploratory behavior with the spared whiskers. Sensory-evoked activity in response to individual stimulation of spared whiskers and adjacent re-grown whiskers was then recorded under anesthesia 1-2 days and 6-7 days post-trimming (plasticity data). We analyzed spike trains within 100 ms of stimulus onset and confirmed previously published reports documenting changes in receptive field sizes in the spared whisker barrels. We analyzed the same data using Dynamic Bayesian Networks (DBNs) to infer the functional connectivity between the recorded neurons. We found that DBNs inferred from population responses to stimulation of each of the spared whiskers exhibited graded increase in similarity that was proportional to the pairing duration. A significant early increase in network similarity in the spared-whisker barrels was detected 1-2 days post pairing, but not when single neuron responses were examined during the same period. These results suggest that rapid reorganization of cortical neurons following sensory deprivation may be mediated by an STDP mechanism.

A fully automated rodent conditioning protocol for sensorimotor integration and cognitive control experiments.

Rodents have been traditionally used as a standard animal model in laboratory experiments involving a myriad of sensory, cognitive, and motor tasks. Higher cognitive functions that require precise control over sensorimotor responses such as decision-making and attentional modulation, however, are typically assessed in nonhuman primates. Despite the richness of primate behavior that allows multiple variants of these functions to be studied, the rodent model remains an attractive, cost-effective alternative to primate models. Furthermore, the ability to fully automate operant conditioning in rodents adds unique advantages over the labor intensive training of nonhuman primates while studying a broad range of these complex functions. Here, we introduce a protocol for operantly conditioning rats on performing working memory tasks. During critical epochs of the task, the protocol ensures that the animal's overt movement is minimized by requiring the animal to 'fixate' until a Go cue is delivered, akin to nonhuman primate experimental design. A simple two alternative forced choice task is implemented to demonstrate the performance. We discuss the application of this paradigm to other tasks.

Sorting and tracking neuronal spikes via simple thresholding.

A fundamental goal in systems neuroscience is to assess the individual as well as the synergistic roles of single neurons in a recorded ensemble as they relate to an observed behavior. A mandatory step to achieve this goal is to sort spikes in an extracellularly recorded mixture that belong to individual neurons through feature extraction and clustering techniques. Here, we propose an approach for approximating the often nonlinear and time varying decision boundaries between spike-derived feature classes based on a simple, yet optimal thresholding mechanism. Because thresholding is a binary classifier, we show that the complex nonlinear decision boundaries required for spike class discrimination can be achieved by adequately fusing a set of weak binary classifiers. The thresholds for these binary classifiers are adaptively estimated through a learning algorithm that maximizes the separability between the sparsely represented classes. Based on our previous work, the approach substantially reduces the computational complexity of extracting, aligning and sorting multiple single unit activity early in the data stream. Here, we also show its ability to track changes in spike features over extended periods of time, making it highly suitable for basic neuroscience studies as well as for implementation in miniaturized, fully implantable electronics in brain-machine interface applications.

Neural feedback for instantaneous spatiotemporal modulation of afferent pathways in bi-directional brain-machine interfaces.

In bi-directional brain-machine interfaces (BMIs), precisely controlling the delivery of microstimulation, both in space and in time, is critical to continuously modulate the neural activity patterns that carry information about the state of the brain-actuated device to sensory areas in the brain. In this paper, we investigate the use of neural feedback to control the spatiotemporal firing patterns of neural ensembles in a model of the thalamocortical pathway. Control of pyramidal (PY) cells in the primary somatosensory cortex (S1) is achieved based on microstimulation of thalamic relay cells through multiple-input multiple-output (MIMO) feedback controllers. This closed loop feedback control mechanism is achieved by simultaneously varying the stimulation parameters across multiple stimulation electrodes in the thalamic circuit based on continuous monitoring of the difference between reference patterns and the evoked responses of the cortical PY cells. We demonstrate that it is feasible to achieve a desired level of performance by controlling the firing activity pattern of a few "key" neural elements in the network. Our results suggest that neural feedback could be an effective method to facilitate the delivery of information to the cortex to substitute lost sensory inputs in cortically controlled BMIs.

Millisecond-timescale local network coding in the rat primary somatosensory cortex.

Correlation among neocortical neurons is thought to play an indispensable role in mediating sensory processing of external stimuli. The role of temporal precision in this correlation has been hypothesized to enhance information flow along sensory pathways. Its role in mediating the integration of information at the output of these pathways, however, remains poorly understood. Here, we examined spike timing correlation between simultaneously recorded layer V neurons within and across columns of the primary somatosensory cortex of anesthetized rats during unilateral whisker stimulation. We used bayesian statistics and information theory to quantify the causal influence between the recorded cells with millisecond precision. For each stimulated whisker, we inferred stable, whisker-specific, dynamic bayesian networks over many repeated trials, with network similarity of 83.3±6% within whisker, compared to only 50.3±18% across whiskers. These networks further provided information about whisker identity that was approximately 6 times higher than what was provided by the latency to first spike and 13 times higher than what was provided by the spike count of individual neurons examined separately. Furthermore, prediction of individual neurons' precise firing conditioned on knowledge of putative pre-synaptic cell firing was 3 times higher than predictions conditioned on stimulus onset alone. Taken together, these results suggest the presence of a temporally precise network coding mechanism that integrates information across neighboring columns within layer V about vibrissa position and whisking kinetics to mediate whisker movement by motor areas innervated by layer V.

Model-based analysis and control of a network of basal ganglia spiking neurons in the normal and parkinsonian states.

Controlling the spatiotemporal firing pattern of an intricately connected network of neurons through microstimulation is highly desirable in many applications. We investigated in this paper the feasibility of using a model-based approach to the analysis and control of a basal ganglia (BG) network model of Hodgkin-Huxley (HH) spiking neurons through microstimulation. Detailed analysis of this network model suggests that it can reproduce the experimentally observed characteristics of BG neurons under a normal and a pathological Parkinsonian state. A simplified neuronal firing rate model, identified from the detailed HH network model, is shown to capture the essential network dynamics. Mathematical analysis of the simplified model reveals the presence of a systematic relationship between the network's structure and its dynamic response to spatiotemporally patterned microstimulation. We show that both the network synaptic organization and the local mechanism of microstimulation can impose tight constraints on the possible spatiotemporal firing patterns that can be generated by the microstimulated network, which may hinder the effectiveness of microstimulation to achieve a desired objective under certain conditions. Finally, we demonstrate that the feedback control design aided by the mathematical analysis of the simplified model is indeed effective in driving the BG network in the normal and Parskinsonian states to follow a prescribed spatiotemporal firing pattern. We further show that the rhythmic/oscillatory patterns that characterize a dopamine-depleted BG network can be suppressed as a direct consequence of controlling the spatiotemporal pattern of a subpopulation of the output Globus Pallidus internalis (GPi) neurons in the network. This work may provide plausible explanations for the mechanisms underlying the therapeutic effects of deep brain stimulation (DBS) in Parkinson's disease and pave the way towards a model-based, network level analysis and closed-loop control and optimization of DBS parameters, among many other applications.

On the use of dynamic Bayesian networks in reconstructing functional neuronal networks from spike train ensembles.

Coordination among cortical neurons is believed to be a key element in mediating many high-level cortical processes such as perception, attention, learning, and memory formation. Inferring the structure of the neural circuitry underlying this coordination is important to characterize the highly nonlinear, time-varying interactions between cortical neurons in the presence of complex stimuli. In this work, we investigate the applicability of dynamic Bayesian networks (DBNs) in inferring the effective connectivity between spiking cortical neurons from their observed spike trains. We demonstrate that DBNs can infer the underlying nonlinear and time-varying causal interactions between these neurons and can discriminate between mono- and polysynaptic links between them under certain constraints governing their putative connectivity. We analyzed conditionally Poisson spike train data mimicking spiking activity of cortical networks of small and moderately large size. The performance was assessed and compared to other methods under systematic variations of the network structure to mimic a wide range of responses typically observed in the cortex. Results demonstrate the utility of DBN in inferring the effective connectivity in cortical networks.

Identifying functional connectivity in large-scale neural ensemble recordings: a multiscale data mining approach.

Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

Impact of compressed sensing of motor cortical activity on spike train decoding in Brain Machine Interfaces.

Decoding spike trains is an essential step to translate multiple single unit activity to useful control commands in cortically controlled Brain Machine Interface (BMI) systems. Extracting the spike trains of individual neurons from the recorded mixtures requires spike sorting, a computationally prohibitive step that precludes the development of fully implantable, small size and low power electronics. Previously, we reported on the ability to extract the critical information in these spike trains such as precise spike timing and firing rate of individual neurons using a compressed sensing strategy that overcomes the computational burden of the spike sorting step. Herein, we assess the decoding performance using this method and compare it to the case where classical spike sorting takes place prior to decoding. We use the local average of the sparsely represented data as discriminative features to 'informally' detect and classify spikes in the data stream. We demonstrate that there is a substantial gain in performance assessed under different decoding strategies, while much less computations are needed compared to spike sorting in the traditional sense.

Clustering synaptically-coupled neuronal populations under systematic variations in temporal dependence.

Identifying clusters of neurons that exhibit functional interdependency in a recorded population has recently emerged as a direct result of the ability to simultaneously record multiple single unit activity with high-density microelectrode arrays. We demonstrated in a previous study that a graph theoretic approach can identify functional interdependency over multiple time scales between models of neuronal firing in response to a common input or synaptically-coupled in a multi-cluster population. In this paper, we investigate the performance of the technique in the case of neuronal interaction arising at various latencies and interval lengths. We report the capability of the approach to track these variable degrees of interactions. This feature can be very useful in decoding variable motor cortical response patterns during sensorimotor integration in Brain Machine Interface applications.

Impact of lossy compression on neural response characteristics extracted from high-density intra-cortical implant data.

In this paper we examine the impact of lossy wavelet compression on the information contained within high-density microelectrode array neural recordings. We have previously reported on the ability of our hardware architecture to perform under the constraints imposed by implantable hardware, as well as on its performance from a compression and signal distortion standpoint. Here we extend that work by examining the amount of information that is lost from the recorded data as a result of the finite precision integer arithmetic and thresholding operations inherent in our system. One method commonly used for the classification and sorting of recorded extracellular action potentials is principal component analysis. This technique is used to statistically obtain the most significant attributes of the spikes, thereby allowing for more accurate classification. We use the separability of the resultant clusters as a measure of the information content within the data, and present the results of simulations demonstrating the impact of various hardware design parameters on this separability.

A systems approach for data compression and latency reduction in cortically controlled brain machine interfaces.

This paper suggests a new approach for data compression during extracutaneous transmission of neural signals recorded by high-density microelectrode array in the cortex. The approach is based on exploiting the temporal and spatial characteristics of the neural recordings in order to strip the redundancy and infer the useful information early in the data stream. The proposed signal processing algorithms augment current filtering and amplification capability and may be a viable replacement to on chip spike detection and sorting currently employed to remedy the bandwidth limitations. Temporal processing is devised by exploiting the sparseness capabilities of the discrete wavelet transform, while spatial processing exploits the reduction in the number of physical channels through quasi-periodic eigendecomposition of the data covariance matrix. Our results demonstrate that substantial improvements are obtained in terms of lower transmission bandwidth, reduced latency and optimized processor utilization. We also demonstrate the improvements qualitatively in terms of superior denoising capabilities and higher fidelity of the obtained signals.

Assessing temporal and spatial evolution of clusters of functionally interdependent neurons using graph partitioning techniques.

This paper suggests a new approach for identifying clusters of neurons with correlated spiking activity in large-size neuronal ensembles recorded with high-density microelectrode arrays. The nonparametric approach relies on mapping the neuronal spike trains to a 'scale space' using a nested multiresolution projection. Similarity measures can be arbitrarily defined in the scale space independent of the fixed bin width classically used to assess neuronal correlation. This representation allows efficient graph partitioning techniques to be used to identify clusters of correlated firing within distinct behavioral contexts. We use a new probabilistic spectral clustering algorithm that simultaneously maximizes cluster aggregation based on similarity measures. The technique is able to efficiently identify functionally interdependent neurons regardless of the temporal scale from which rate functions are typically estimated. We report the clustering performance of the algorithm applied to a synthesized neurophysiological data set and compare it to known clustering techniques to illustrate the substantial gain in the performance.

An adaptive multiple-input multiple-output analog-to-digital converter for high density neuroprosthetic electrode arrays.

On chip signal compression is one of the key technologies driving development of energy efficient biotelemetry devices. In this paper, we describe a novel architecture for analog-to-digital (A/D) conversion that combines sigma delta conversion with the spatial data compression in a single module. The architecture called multiple-input multiple-output (MIMO) sigma-delta is based on a min-max gradient descent optimization of a regularized cost function that naturally leads to an A/D formulation. Experimental results with simulated and recorded multichannel data demonstrate the effectiveness of the proposed architecture to eliminate cross-channel redundancy in high density microelectrode data, thus superceding the performance of parallel independent data converters in terms of its energy efficiency.

A MIMO channel approach for characterizing electrode-tissue interface in long-term chronic microelectrode array recordings.

Characterizing the encapsulation layer caused by glial scar formation surrounding microelectrode arrays in chronic implants has been the subject of extensive research. Typically, an equivalent circuit model is used to characterize the reactive tissue response by nonlinearly fitting the electrical impedance spectroscopy (EIS) data. This model assumes a time invariant adjacent layer of encapsulation tissue to have the same structure on every electrode site. In this paper, an alternative approach is proposed based on modeling the encapsulation layer as a time varying communication channel. The channel is characterized by a multi-input multi-output (MIMO) transfer function with time varying coefficients. This model circumvents spatial resolution limitations of existing EIS equivalent circuit models. It further allows capturing the observed changes in neural signal quality over time. We show that "equalizing" the channel using this model can yield a substantial improvement in signal quality. With tendency towards high-density electrode arrays for cortical implantation, the proposed model is better suited to equalize the fading channel and interpret the recorded signals with higher accuracy. We also show conceptually how patterned waveforms can periodically be used to probe the channel if adverse effects can be avoided. This can potentially improve the channel estimator performance, particularly when cell migration occurs.

Hardware considerations of a spatial filter for decorrelating high-density multielectrode neural recordings.

High density implantable microelectrode arrays record large amounts of highly correlated data, which causes large strains on limited bandwidth telemetry systems. Previous work has shown that the use of a spatial filter can significantly reduce the number of channels that must be transmitted to adequately represent the data. However, the limitations on power and size for an implantable neuroprosthetic device impose significant limitations on the computational complexity of the spatial filter. We assess the performance of the floating point operations of spatial filtering and show that it can be approximated to integers with negligible losses to signal fidelity, thus reducing the computational complexity.