Maciej Lazarewicz

Ih channels are uniquely positioned to act as neuromodulatory control points for tuning hippocampal theta (4-12 Hz) and gamma (> 25 Hz) oscillations, oscillations which are thought to have importance for organization of information flow. Ih contributes to neuronal membrane resonance and resting membrane potential, and is modulated by second messengers. We investigated Ih oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and… Show more

Ih channels are uniquely positioned to act as neuromodulatory control points for tuning hippocampal theta (4-12 Hz) and gamma (> 25 Hz) oscillations, oscillations which are thought to have importance for organization of information flow. Ih contributes to neuronal membrane resonance and resting membrane potential, and is modulated by second messengers. We investigated Ih oscillatory control using a multiscale computer model of hippocampal CA3, where each cell class (pyramidal, basket, and oriens-lacunosum moleculare cells), contained type-appropriate isoforms of Ih. Our model demonstrated that modulation of pyramidal and basket Ih allows tuning theta and gamma oscillation frequency and amplitude. Pyramidal Ih also controlled cross-frequency coupling (CFC) and allowed shifting gamma generation towards particular phases of the theta cycle, effected via Ih's ability to set pyramidal excitability. Our model predicts that in vivo neuromodulatory control of Ih allows exibly controlling CFC and the timing of gamma discharges at particular theta phases. Show less

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson’s disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal… Show more

While modulating neural activity through stimulation is an effective treatment for neurological diseases such as Parkinson’s disease and essential tremor, an opportunity for improving neuromodulation therapy remains in automatically adjusting therapy to continuously optimize patient outcomes. Practical issues associated with achieving this include the paucity of human data related to disease states, poorly validated estimators of patient state, and unknown dynamic mappings of optimal stimulation parameters based on estimated states. To overcome these challenges, we present an investigational platform including: an implanted sensing and stimulation device to collect data and run automated closed-loop algorithms; an external tool to prototype classifier and control-policy algorithms; and real-time telemetry to update the implanted device firmware and monitor its state. The prototyping system was demonstrated in a chronic large animal model studying hippocampal dynamics. We used the platform to find biomarkers of the observed states and transfer functions of different stimulation amplitudes. Data showed that moderate levels of stimulation suppress hippocampal beta activity, while high levels of stimulation produce seizure-like after-discharge activity. The biomarker and transfer function observations were mapped into classifier and control-policy algorithms, which were downloaded to the implanted device to continuously titrate stimulation amplitude for the desired network effect. The platform is designed to be a flexible prototyping tool and could be used to develop improved mechanistic models and automated closed-loop systems for a variety of neurological disorders. Show less

The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system:… Show more

The current state of neuromodulation can be cast in a classical dynamic control framework such that the nervous system is the classical "plant", the neural stimulator is the controller, tools to collect clinical data are the sensors, and the physician's judgment is the state estimator. This framework characterizes the types of opportunities available to advance neuromodulation. In particular, technology can potentially address two dominant factors limiting the performance of the control system: "observability," the ability to observe the state of the system from output measurements, and "controllability," the ability to drive the system to a desired state using control actuation. Improving sensors and actuation methods are necessary to address these factors. Equally important is improving state estimation by understanding the neural processes underlying diseases. Development of enabling technology to utilize control theory principles facilitates investigations into improving intervention as well as research into the dynamic properties of the nervous system and mechanisms of action of therapies. In this paper, we provide an overview of the control system framework for neuromodulation, its practical challenges, and investigational devices applying this framework for limited applications. To help motivate future efforts, we describe our chronically implantable, low-power neural stimulation system, which integrates sensing, actuation, and state estimation. This research system has been implanted and used in an ovine to address novel research questions. Show less

Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and… Show more

Abnormalities in oscillations have been suggested to play a role in schizophrenia. We studied theta-modulated gamma oscillations in a computer model of hippocampal CA3 in vivo with and without simulated application of ketamine, an NMDA receptor antagonist and psychotomimetic. Networks of 1200 multi-compartment neurons (pyramidal, basket and oriens-lacunosum moleculare, OLM, cells) generated theta and gamma oscillations from intrinsic network dynamics: basket cells primarily generated gamma and amplified theta, while OLM cells strongly contributed to theta. Extrinsic medial septal inputs paced theta and amplified both theta and gamma oscillations. Exploration of NMDA receptor reduction across all location combinations demonstrated that the experimentally-observed ketamine effect occurred only with isolated reduction of NMDA receptors on OLMs. In the ketamine simulations, lower OLM activity reduced theta power and disinhibited pyramidal cells, resulting in increased basket cell activation and gamma power. Our simulations predict:
• ketamine increases firing rates; • oscillations can be generated by intrinsic hippocampal circuits; • medial septum inputs pace and augment oscillations; • pyramidal cells lead basket cells at the gamma peak but lag at trough; • basket cells amplify theta rhythms; • ketamine alters oscillations due to primary blockade at OLM NMDA receptors; • ketamine alters phase relationships of cell firing; • ketamine reduces network responsivity to the environment • ketamine effect could be reversed by providing a continuous inward current to OLM cells.
We suggest that this last prediction has implications for a possible novel treatment for cognitive deficits of schizophrenia by targeting OLM cells. Show less

Ketamine, an N-methyl-D-aspartate (NMDA) receptor glutamatergic antagonist, has been studied as a model of schizophrenia when applied in subanesthetic doses. In EEG studies, ketamine affects sensory gating and alters the oscillatory characteristics of neuronal signals in a complex manner. We investigated the effects of ketamine on in vivo recordings from the CA3 region of mouse hippocampus referenced to the ipsilateral frontal sinus using a paired-click auditory gating paradigm. One issue of… Show more

Ketamine, an N-methyl-D-aspartate (NMDA) receptor glutamatergic antagonist, has been studied as a model of schizophrenia when applied in subanesthetic doses. In EEG studies, ketamine affects sensory gating and alters the oscillatory characteristics of neuronal signals in a complex manner. We investigated the effects of ketamine on in vivo recordings from the CA3 region of mouse hippocampus referenced to the ipsilateral frontal sinus using a paired-click auditory gating paradigm. One issue of particular interest was elucidating the effect of ketamine on background network activity, poststimulus evoked and induced activity. We find that ketamine attenuates the theta frequency band in both background activity and in poststimulus evoked activity. Ketamine also disrupts a late, poststimulus theta power reduction seen in control recordings. In the gamma frequency range, ketamine enhances both background and evoked power, but decreases relative induced power. These findings support a role for NMDA receptors in mediating the balance between theta and gamma responses to sensory stimuli, with possible implications for dysfunction in schizophrenia. Show less

We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron.
The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from
simulated spike trains via NMDA, AMPA, and GABAA receptors. After tuning the model by adjusting maximal current conductances in
each compartment, the model cell closely matched whole-cell recordings from an adult rat NAcb slice… Show more

We describe a computational model of the principal cell in the nucleus accumbens (NAcb), the medium spiny projection (MSP) neuron.
The model neuron, constructed in NEURON, includes all of the known ionic currents in these cells and receives synaptic input from
simulated spike trains via NMDA, AMPA, and GABAA receptors. After tuning the model by adjusting maximal current conductances in
each compartment, the model cell closely matched whole-cell recordings from an adult rat NAcb slice preparation. Synaptic inputs in the
range of 1000 –1300 Hz are required to maintain an “up” state in the model. Cell firing in the model required concurrent depolarization
of several dendritic branches, which responded independently to afferent input. Depolarization from action potentials traveled to the tips
of the dendritic branches and increased Ca2? influx through voltage-gated Ca2? channels. As NMDA/AMPA current ratios were increased,
the membrane showed an increase in hysteresis of “up” and “down” state dwell times, but intrinsic bistability was not observed.
The number of oscillatory inputs required to entrain the model cell was determined to be ?20% of the “up” state inputs. Altering the
NMDA/AMPA ratio had a profound effect on processing of afferent input, including the ability to entrain to oscillations in afferent input
in the theta range (4 –12 Hz). These results suggest that afferent information integration by the NAcb MSP cell may be compromised by
pathology in which the NMDA current is altered or modulated, as has been proposed in both schizophrenia and addiction. Show less

We introduce a novel computational model of hippocampal pyramidal cells physiology based on an up-to-date, detailed description of passive and active biophysical properties and real dendritic morphology. This model constitutes a modification of a previous (1995) model which included complex calcium dynamics and Na( ), K( ), and Ca(2 ) currents. Changes reflect recently acquired experimental knowledge regarding the types and spatial distributions of these currents. The updated model responds to… Show more

We introduce a novel computational model of hippocampal pyramidal cells physiology based on an up-to-date, detailed description of passive and active biophysical properties and real dendritic morphology. This model constitutes a modification of a previous (1995) model which included complex calcium dynamics and Na( ), K( ), and Ca(2 ) currents. Changes reflect recently acquired experimental knowledge regarding the types and spatial distributions of these currents. The updated model responds to simulated somatic current clamp stimulation with a train of spikes (burst). The shape of the burst reproduces the characteristic behavior observed experimentally, similarly to the previous model. However, an analysis of dendritic membrane voltage distribution during the burst shows that the mechanisms underlying this somatic behavior are dramatically different in the two models. In the previous model, all spikes were generated in the soma and backpropagated in the dendrites. In the updated model, in contrast, only the first spike is initiated somatically. The second somatic spike is preceded by a dendritic spike (triggered by the first spike backpropagation), which propagates both backward and forward, reaching the soma just before the rise of the second somatic spike. The third and fourth spikes are similarly caused by a complex spatio-temporal interplay between somatic and dendritic depolarization. These results suggest that the distribution of ionic currents recently characterized in hippocampal pyramidal cells can support both somatic and dendritic spike initiation. In addition, these simulations demonstrate that models with considerably different distributions of active conductances can reproduce the same experimental bursting behavior with distinct biophysical mechanisms. Show less