Nociceptor spontaneous activity is responsible for fragmenting non-rapid eye movement sleep in mouse models of neuropathic pain.

Spontaneous pain, a major complaint of patients with neuropathic pain, has eluded study because there is no reliable marker in either preclinical models or clinical studies. Here, we performed a comprehensive electroencephalogram/electromyogram analysis of sleep in several mouse models of chronic pain: neuropathic (spared nerve injury and chronic constriction injury), inflammatory (Freund's complete adjuvant and carrageenan, plantar incision) and chemical pain (capsaicin). We find that peripheral axonal injury drives fragmentation of sleep by increasing brief arousals from non-rapid eye movement sleep (NREMS) without changing total sleep amount. In contrast to neuropathic pain, inflammatory or chemical pain did not increase brief arousals. NREMS fragmentation was reduced by the analgesics gabapentin and carbamazepine, and it resolved when pain sensitivity returned to normal in a transient neuropathic pain model (sciatic nerve crush). Genetic silencing of peripheral sensory neurons or ablation of CGRP+ neurons in the parabrachial nucleus prevented sleep fragmentation, whereas pharmacological blockade of skin sensory fibers was ineffective, indicating that the neural activity driving the arousals originates ectopically in primary nociceptor neurons and is relayed through the lateral parabrachial nucleus. These findings identify NREMS fragmentation by brief arousals as an effective proxy to measure spontaneous neuropathic pain in mice.

Optimal reaching subject to computational and physical constraints reveals structure of the sensorimotor control system.

Optimal feedback control provides an abstract framework describing the architecture of the sensorimotor system without prescribing implementation details such as what coordinate system to use, how feedback is incorporated, or how to accommodate changing task complexity. We investigate how such details are determined by computational and physical constraints by creating a model of the upper limb sensorimotor system in which all connection weights between neurons, feedback, and muscles are unknown. By optimizing these parameters with respect to an objective function, we find that the model exhibits a preference for an intrinsic (joint angle) coordinate representation of inputs and feedback and learns to calculate a weighted feedforward and feedback error. We further show that complex reaches around obstacles can be achieved by augmenting our model with a path-planner based on via points. The path-planner revealed "avoidance" neurons that encode directions to reach around obstacles and "placement" neurons that make fine-tuned adjustments to via point placement. Our results demonstrate the surprising capability of computationally constrained systems and highlight interesting characteristics of the sensorimotor system.

Internal states as a source of subject-dependent movement variability are represented by large-scale brain networks.

Humans' ability to adapt and learn relies on reflecting on past performance. These experiences form latent representations called internal states that induce movement variability that improves how we interact with our environment. Our study uncovered temporal dynamics and neural substrates of two states from ten subjects implanted with intracranial depth electrodes while they performed a goal-directed motor task with physical perturbations. We identified two internal states using state-space models: one tracking past errors and the other past perturbations. These states influenced reaction times and speed errors, revealing how subjects strategize from trial history. Using local field potentials from over 100 brain regions, we found large-scale brain networks such as the dorsal attention and default mode network modulate visuospatial attention based on recent performance and environmental feedback. Notably, these networks were more prominent in higher-performing subjects, emphasizing their role in improving motor performance by regulating movement variability through internal states.

Towards optimizing single pulse electrical stimulation: High current intensity, short pulse width stimulation most effectively elicits evoked potentials.

BACKGROUND: While single pulse electrical stimulation (SPES) is increasingly used to study effective connectivity, the effects of varying stimulation parameters on the resulting cortico-cortical evoked potentials (CCEPs) have not been systematically explored. OBJECTIVE: We sought to understand the interacting effects of stimulation pulse width, current intensity, and charge on CCEPs through an extensive testing of this parameter space and analysis of several response metrics. METHODS: We conducted SPES in 11 patients undergoing intracranial EEG monitoring using five combinations of current intensity (1.5, 2.0, 3.0, 5.0, and 7.5 mA) and pulse width at each of three charges (0.750, 1.125, and 1.500 µC/phase) to study how CCEP amplitude, distribution, latency, morphology, and stimulus artifact amplitude vary with each parameter. RESULTS: Stimulations with a greater charge or a greater current intensity and shorter pulse width at a given charge generally resulted in greater CCEP amplitudes and spatial distributions, shorter latencies, and increased waveform correlation. These effects interacted such that stimulations with the lowest charge and highest current intensities resulted in greater response amplitudes and spatial distributions than stimulations with the highest charge and lowest current intensities. Stimulus artifact amplitude increased with charge, but this could be mitigated by using shorter pulse widths. CONCLUSIONS: Our results indicate that individual combinations of current intensity and pulse width, in addition to charge, are important determinants of CCEP magnitude, morphology, and spatial extent. Together, these findings suggest that high current intensity, short pulse width stimulations are optimal SPES settings for eliciting strong and consistent responses while minimizing charge.

Steering Toward Normative Wide-Dynamic-Range Neuron Activity in Nerve-Injured Rats With Closed-Loop Peripheral Nerve Stimulation.

OBJECTIVES: Chronic pain is primarily treated with pharmaceuticals, but the effects remain unsatisfactory. A promising alternative therapy is peripheral nerve stimulation (PNS), but it has been associated with suboptimal efficacy because its modulation mechanisms are not clear and the current therapies are primarily open loop (ie, manually adjusting the stimulation parameters). In this study, we developed a proof-of-concept computational modeling as the first step toward implementing closed-loop PNS in future biological studies. When developing new pain therapies, a useful pain biomarker is the wide-dynamic-range (WDR) neuron activity in the dorsal horn. In healthy animals, the WDR neuron activity occurs in a stereotyped manner; however, this response profile can vary widely after nerve injury to create a chronic pain condition. We hypothesized that if injury-induced changes of neuronal response can be normalized to resemble those of a healthy condition, the pathological aspects of pain may be treated while maintaining protective physiological nociception. MATERIALS AND METHODS: Using an in vivo electrophysiology data set of WDR neuron recordings obtained in nerve-injured rats and naïve rats, we constructed sets of linear phenomenologic models of WDR firing rate during windup stimulation for both conditions. Then, we applied robust control systems techniques to identify a closed-loop PNS controller, which can drive the dynamics of WDR neuron response in neuropathic pain model into ranges associated with normal physiological pain. RESULTS: The sets of identified linear models can accurately predict, in silico, nonlinear neural responses to electrical stimulation of the peripheral nerve. In addition, we showed that continuous closed-loop control of PNS can be used to normalize WDR neuron firing responses in three injured cases. CONCLUSIONS: In this proof-of-concept study, we show how tractable, linear mathematical models of pain-related neurotransmission can be used to inform the development of closed-loop PNS. This new application of robust control to neurotechnology may also be expanded and applied across other neuromodulation applications.

Cortico-cortical evoked potentials in response to varying stimulation intensity improves seizure localization.

OBJECTIVE: As single pulse electrical stimulation (SPES) is increasingly utilized to help localize the seizure onset zone (SOZ), it is important to understand how stimulation intensity can affect the ability to use cortico-cortical evoked potentials (CCEPs) to delineate epileptogenic regions. METHODS: We studied 15 drug-resistant epilepsy patients undergoing intracranial EEG monitoring and SPES with titrations of stimulation intensity. The N1 amplitude and distribution of CCEPs elicited in the SOZ and non-seizure onset zone (nSOZ) were quantified at each intensity. The separability of the SOZ and nSOZ using N1 amplitudes was compared between models using responses to titrations, responses to one maximal intensity, or both. RESULTS: At 2 mA and above, the increase in N1 amplitude with current intensity was greater for responses within the SOZ, and SOZ response distribution was maximized by 4-6 mA. Models incorporating titrations achieved better separability of SOZ and nSOZ compared to those using one maximal intensity. CONCLUSIONS: We demonstrated that differences in CCEP amplitude over a range of current intensities can improve discriminability of SOZ regions. SIGNIFICANCE: This study provides insight into the underlying excitability of the SOZ and how differences in current-dependent amplitudes of CCEPs may be used to help localize epileptogenic sites.

Cortico-cortical drive in a coupled premotor-primary motor cortex dynamical system.

In the conventional view of sensorimotor control, the premotor cortex (PM) plans actions that are executed by the primary motor cortex (M1). This notion arises in part from many experiments that have imposed a preparatory "planning" period, during which PM becomes active without M1. But during many natural movements, PM and M1 are co-activated, making it difficult to distinguish their functional roles. We leverage coupled dynamical systems models (cDSMs) to uncover interactions between PM and M1 during movements performed with no preparatory period. We build cDSMs using neural and behavioral data recorded from two non-human primates as they performed a reach-grasp-manipulate task. PM and M1 interact dynamically throughout these movements. Whereas PM drives the M1 in some situations, in other situations, M1 drives PM activity, contrary to the conventional assumption. Our DSM framework provides additional predictions differentiating the roles of PM and M1 in controlling movement.

Source-sink connectivity: a novel interictal EEG marker for seizure localization.

Over 15 million epilepsy patients worldwide have drug-resistant epilepsy. Successful surgery is a standard of care treatment but can only be achieved through complete resection or disconnection of the epileptogenic zone, the brain region(s) where seizures originate. Surgical success rates vary between 20% and 80%, because no clinically validated biological markers of the epileptogenic zone exist. Localizing the epileptogenic zone is a costly and time-consuming process, which often requires days to weeks of intracranial EEG (iEEG) monitoring. Clinicians visually inspect iEEG data to identify abnormal activity on individual channels occurring immediately before seizures or spikes that occur interictally (i.e. between seizures). In the end, the clinical standard mainly relies on a small proportion of the iEEG data captured to assist in epileptogenic zone localization (minutes of seizure data versus days of recordings), missing opportunities to leverage these largely ignored interictal data to better diagnose and treat patients. IEEG offers a unique opportunity to observe epileptic cortical network dynamics but waiting for seizures increases patient risks associated with invasive monitoring. In this study, we aimed to leverage interictal iEEG data by developing a new network-based interictal iEEG marker of the epileptogenic zone. We hypothesized that when a patient is not clinically seizing, it is because the epileptogenic zone is inhibited by other regions. We developed an algorithm that identifies two groups of nodes from the interictal iEEG network: those that are continuously inhibiting a set of neighbouring nodes ('sources') and the inhibited nodes themselves ('sinks'). Specifically, patient-specific dynamical network models were estimated from minutes of iEEG and their connectivity properties revealed top sources and sinks in the network, with each node being quantified by source-sink metrics. We validated the algorithm in a retrospective analysis of 65 patients. The source-sink metrics identified epileptogenic regions with 73% accuracy and clinicians agreed with the algorithm in 93% of seizure-free patients. The algorithm was further validated by using the metrics of the annotated epileptogenic zone to predict surgical outcomes. The source-sink metrics predicted outcomes with an accuracy of 79% compared to an accuracy of 43% for clinicians' predictions (surgical success rate of this dataset). In failed outcomes, we identified brain regions with high metrics that were untreated. When compared with high frequency oscillations, the most commonly proposed interictal iEEG feature for epileptogenic zone localization, source-sink metrics outperformed in predictive power (by a factor of 1.2), suggesting they may be an interictal iEEG fingerprint of the epileptogenic zone.

Temporal and morphological characteristics of high-frequency oscillations in an acute in vivo model of epilepsy.

Approximately 30% of patients with epilepsy do not respond to anti-epileptogenic drugs. Surgical removal of the epileptogenic zone (EZ), the brain regions where the seizures originate and spread, can be a possible therapy for these patients, but localizing the EZ is challenging due to a variety of clinical factors. High-frequency oscillations (HFOs) in intracranial electroencephalography (EEG) are a promising biomarker of the EZ, but it is currently unknown whether HFO rates and HFO morphology modulate as pathological brain networks evolve in a way that gives rise to seizures. To address this question, we assessed the temporal evolution of the duration of HFO events, amplitude of HFO events, and rates of HFOs per minute. HFO events were quantified using the 4AP in vivo rodent model of epilepsy, inducing seizures in two different brain areas. We found that the duration and amplitude of HFO events were significantly increased for the cortex model when compared to the hippocampus model. Additionally, the duration and amplitude increased significantly between baseline and pre-ictal HFOs in both models. On the other hand, the two models did not display a consistent increasing or decreasing trend in amplitude, duration or rate when comparing ictal and postictal intervals. Clinical Relevance- We assessed the amplitude, duration, and rate of HFOs in two acute in vivo rodent models of epilepsy. The significant modulation of HFO morphology from baseline to pre-ictal periods suggests that these features may be a robust biomarker for pathological tissue involved in epileptogenesis. Moreover, the differences in HFO morphology observed between cortex and hippocampus animal models possibly indicate that different structural network characteristics of the EZ cause this modulation. In all, we found that HFO features modulate significantly with the onset of seizures, further highlighting the need to consider of HFO morphology in EZ-localizing studies.

A computational perspective on coordinate systems for motor control.

It is currently unknown what coordinate system or systems the primate motor cortex uses to represent movement, although experimental evidence has suggested several candidates. In order to understand how the physical geometry of the arm combines with computational constraints to influence the optimal choice of coordinate system, we construct a two-dimensional, physics-based arm model and couple it to a linear model of the motor cortex. The cortical model is provided with target positions and real time feedback of the current hand position in two different coordinate systems: cartesian and joint angle. We then optimize the parameters of the model subject to penalties on neural connectivity and muscle and neural energy use. We find that the optimized model strongly prefers to work in the joint angle coordinate system, suggesting that for neurons whose activity is closely tied to muscle activation, this is computationally the most efficient coordinate system in which to represent movement.

Decomposing Executive Function into Distinct Processes Underlying Human Decision Making.

Executive function (EF) consists of higher level cognitive processes including working memory, cognitive flexibility, and inhibition which together enable goal-directed behaviors. Many neurological disorders are associated with EF dysfunctions which can lead to suboptimal behavior. To assess the roles of these processes, we introduce a novel behavioral task and modeling approach. The gamble-like task, with sub-tasks targeting different EF capabilities, allows for quantitative assessment of the main components of EF. We demonstrate that human participants exhibit dissociable variability in the component processes of EF. These results will allow us to map behavioral outcomes to EEG recordings in future work in order to map brain networks associated with EF deficits. Clinical relevance- This work will allow us to quantify EF deficits and corresponding brain activity in patient populations in future work.

Stimulating native seizures with neural resonance: a new approach to localize the seizure onset zone.

Successful outcomes in epilepsy surgery rely on the accurate localization of the seizure onset zone. Localizing the seizure onset zone is often a costly and time-consuming process wherein a patient undergoes intracranial EEG monitoring, and a team of clinicians wait for seizures to occur. Clinicians then analyse the intracranial EEG before each seizure onset to identify the seizure onset zone and localization accuracy increases when more seizures are captured. In this study, we develop a new approach to guide clinicians to actively elicit seizures with electrical stimulation. We propose that a brain region belongs to the seizure onset zone if a periodic stimulation at a particular frequency produces large amplitude oscillations in the intracranial EEG network that propagate seizure activity. Such responses occur when there is 'resonance' in the intracranial EEG network, and the resonant frequency can be detected by observing a sharp peak in the magnitude versus frequency response curve, called a Bode plot. To test our hypothesis, we analysed single-pulse electrical stimulation response data in 32 epilepsy patients undergoing intracranial EEG monitoring. For each patient and each stimulated brain region, we constructed a Bode plot by estimating a transfer function model from the intracranial EEG 'impulse' or single-pulse electrical stimulation response. The Bode plots were then analysed for evidence of resonance. First, we showed that when Bode plot features were used as a marker of the seizure onset zone, it distinguished successful from failed surgical outcomes with an area under the curve of 0.83, an accuracy that surpassed current methods of analysis with cortico-cortical evoked potential amplitude and cortico-cortical spectral responses. Then, we retrospectively showed that three out of five native seizures accidentally triggered in four patients during routine periodic stimulation at a given frequency corresponded to a resonant peak in the Bode plot. Last, we prospectively stimulated peak resonant frequencies gleaned from the Bode plots to elicit seizures in six patients, and this resulted in an induction of three seizures and three auras in these patients. These findings suggest neural resonance as a new biomarker of the seizure onset zone that can guide clinicians in eliciting native seizures to more quickly and accurately localize the seizure onset zone.

Cyclic, Condition-Independent Activity in Primary Motor Cortex Predicts Corrective Movement Behavior.

Reaching movements are known to have large condition-independent (CI) neural activity and cyclic neural dynamics. A new precision center-out task was performed by rhesus macaques to test the hypothesis that cyclic, CI neural activity in the primary motor cortex (M1) occurs not only during initial reaching movements but also during subsequent corrective movements. Corrective movements were observed to be discrete with time courses and bell-shaped speed profiles similar to the initial movements. CI cyclic neural trajectories were similar and repeated for initial and each additional corrective submovement. The phase of the cyclic CI neural activity predicted the time of peak movement speed more accurately than regression of instantaneous firing rate, even when the subject made multiple corrective movements. Rather than being controlled as continuations of the initial reach, a discrete cycle of motor cortex activity encodes each corrective submovement.

Towards Robust Control of PNS for Chronic Pain: Modeling Spinal Cord Wide-Dynamic Range Neurons with Structured Uncertainty.

Pain is a protective physiological system essential for survival. However, it can malfunction and create a debilitating disease known as chronic pain (CP), which is primarily treated with drugs that can produce negative side effects (e.g., opioid addiction). Peripheral nerve stimulation (PNS) is a promising alternative therapy; it has fewer negative side effects but has been associated with suboptimal efficacy since its mechanisms are unclear, and the current therapies are primarily open-loop (i.e. manual adjustment). To adapt to the needs of the user, the next step in advancing PNS therapies is to "close the loop" by using feedback to adjust the stimulation in real-time. A critical step in developing closed-loop PNS treatment is a deeper understanding of pain processing in the dorsal horn (DH) of the spinal cord, which is the first central relay station on the pain pathway. Mechanistic models of the DH have been developed to investigate modulation mechanisms but are non-linear, high-dimensional, and thus difficult to analyze. In this paper, we propose a novel application of structured uncertainty to model and analyze the nonlinear dynamical nature of the DH, and provide the foundation for developing robust PNS controllers using µ-synthesis. Using electrophysiological DH recordings from both naive and nerve-injured rats during windup stimulation, we build two separate models, which contains a linear time-invariant nominal (average) model, and structured uncertainty to quantify the nonlinear deviations in response from the nominal model. Using the structured uncertainty, we analyze the naive and injured models to discover underlying DH dynamics not identifiable using traditional methods, such as spike counting.

Predicting Wide-Dynamic Range Neuron Activity from Peripheral Nerve Stimulation using Linear Parameter Varying Models.

Neuromodulation treatments for chronic pain are programmed with limited knowledge of how electrical stimulation of nerve fibers affects the dynamic response of pain-processing neurons in the spinal cord and the brain. By modeling these effects with tractable representations, we may be able to improve efficacy of stimulation therapy. However, pain transmitting neurons in the dorsal horn of the spinal cord, the first pain relay station in the nervous system, have complex responses to peripheral nerve stimulation (PNS) with nonlinearities and history effects. Wide-dynamic range (WDR) neurons are well studied in pain models and respond to peripheral noxious and non-noxious stimuli. We propose to use linear parameter varying (LPV) models to capture PNS responses of WDR neurons of the deep lamina in the dorsal horn in the spinal cord. Here we show that LPV models perform better than a single linear time-invariant (LTI) model in representing the responses of the WDR neurons to widely varying amplitudes of PNS current. In the future, we can use these models alongside LPV control techniques to design closed-loop PNS stimulation that may accomplish optimal pain treatment goals.Clinical Relevance- Electrical nerve stimulation as a therapy for chronic pain is in need of a more informed approach to programming. By describing the effects of stimulation on the pain system with tractable mathematical models, we may be able to titrate the stimulation to more effectively treat chronic pain.

Transfer Entropy between Intracranial EEG Nodes Highlights Network Dynamics that Cause and Stop Epileptic Seizures.

Transfer entropy (TE) is used to examine the connectivity between nodes and the roles of nodes in epileptic neural networks during rest, moments before seizure, during seizure, and moments after seizure. There is a set of nodes that dominate information flow to epileptogenic zone (EZ) nodes, regions that trigger seizure, and non-EZ nodes during rest. The TE from the dominant to the EZ nodes decreases shortly before a seizure event and reaches a minimum during seizure. During the seizure, the dominant nodes cease or only weakly interact with the EZ nodes. This supports the hypothesis that seizure occurs when some nodes stop inhibiting the EZ nodes. The TE from the dominant to the EZ nodes peaks immediately after seizure, suggesting that seizure may stop when the brain exerts the highest level of information flow/activation/communication to the EZ nodes. The information flow from the dominant to EZ nodes is different from that to non-EZ nodes. This TE dynamics entering and exiting seizures may identify more accurately the EZ nodes, which may improve surgical planning.

Sources and Sinks in Interictal iEEG Networks: An iEEG Marker of the Epileptogenic Zone.

Around 30% of epilepsy patients have seizures that cannot be controlled with medication. The most effective treatments for medically resistant epilepsy are interventions that surgically remove the epileptogenic zone (EZ), the regions of the brain that initiate seizure activity. A precise identification of the EZ is essential for surgical success but unfortunately, current success rates range from 20-80%. Localization of the EZ requires visual inspection of intracranial EEG (iEEG) recordings during seizure events. The need for seizure occurrence makes the process both costly and time-consuming and in the end, less than 1% of the data captured is used to assist in EZ localization. In this study, we aim to leverage interictal (between seizures) data to localize the EZ. We develop and test the source-sink index as an interictal iEEG marker by identifying two groups of network nodes from a patient's interictal iEEG network: those that inhibit a set of their neighboring nodes ("sources") and the inhibited nodes themselves ("sinks"). Specifically, we i) estimate patient-specific dynamical network models from interictal iEEG data and ii) compute a source-sink index for every network node (iEEG channel) to identify pathological nodes that correspond to the EZ. Our results suggest that in patients with successful surgical outcomes, the source-sink index clearly separates the clinically identified EZ (CA-EZ) channels from other channels whereas in patients with failed outcomes CA-EZ channels cannot be distinguished from the rest of the network.

Alpha Power in the Cingulate Cortex reflects Accumulated Winnings During Gambling in Humans.

When making bets one's level of attention determines how much they may win. The cingulate cortex is a brain region associated with attention and may influence behaviors during gambling. With data gathered from the cingulate cortex in humans implanted with depth electrodes for clinical purposes while performing a gambling task of high card, we determine a relationship between neural correlates of attention and accumulated winnings. Specifically, we analyze how changes in alpha power (8-12 Hz) in the CC relate to accumulated winnings. We compared three subjects with different betting strategies: Reflexive (betting low on cards 2, 4, and 6), Logical (varying how they bet on card 6), and Illogical (betting randomly on all cards). We found that alpha power encodes attention in the cingulate cortex and relates to their accumulated winnings, especially in the illogical subject who had the least winning.

Neural Encoding of Reaches in a Linear Cortical Model.

To effectively control the arm, motor cortical neurons must produce complex patterns of activation that vary with the position and orientation of the arm and reach direction. In order to better understand how such a finely tuned dynamical system could arise and what its basic organizing principles are, we develop a model of the motor cortex as a linear dynamical system with feedback coupled to a two-joint model of the macaque arm. By optimizing the connections between neural populations with respect to an objective function that penalizes error between hand and target, as well as neural and muscular energy use, we show that certain properties of the motor cortex, such as muscle synergies, can naturally be obtained. We also demonstrate that the optimization process produces a stable neural system in which targets in the physical space are mapped to attracting fixed points in the neural state space. Finally, we show that this optimization process produces neural units with complex spatial and temporal activation patterns.