Validation of a suite of ERP and QEEG biomarkers in a pre-competitive, industry-led study in subjects with schizophrenia and healthy volunteers.

OBJECTIVE: Complexity and lack of standardization have mostly limited the use of event-related potentials (ERPs) and quantitative EEG (QEEG) biomarkers in drug development to small early phase trials. We present results from a clinical study on healthy volunteers (HV) and patients with schizophrenia (SZ) that assessed test-retest, group differences, variance, and correlation with functional assessments for ERP and QEEG measures collected at clinical and commercial trial sites with standardized instrumentation and methods, and analyzed through an automated data analysis pipeline. METHODS: 81 HV and 80 SZ were tested at one of four study sites. Subjects were administered two ERP/EEG testing sessions on separate visits. Sessions included a mismatch negativity paradigm, a 40 Hz auditory steady-state response paradigm, an eyes-closed resting state EEG, and an active auditory oddball paradigm. SZ subjects were also tested on the Brief Assessment of Cognition (BAC), Positive and Negative Syndrome Scale (PANSS), and Virtual Reality Functional Capacity Assessment Tool (VRFCAT). RESULTS: Standardized ERP/EEG instrumentation and methods ensured few test failures. The automated data analysis pipeline allowed for near real-time analysis with no human intervention. Test-retest reliability was fair-to-excellent for most of the outcome measures. SZ subjects showed significant deficits in ERP and QEEG measures consistent with published academic literature. A subset of ERP and QEEG measures correlated with functional assessments administered to the SZ subjects. CONCLUSIONS: With standardized instrumentation and methods, complex ERP/EEG testing sessions can be reliably performed at clinical and commercial trial sites to produce high-quality data in near real-time.

Lanicemine: a low-trapping NMDA channel blocker produces sustained antidepressant efficacy with minimal psychotomimetic adverse effects.

Ketamine, an N-methyl-D-aspartate receptor (NMDAR) channel blocker, has been found to induce rapid and robust antidepressant-like effects in rodent models and in treatment-refractory depressed patients. However, the marked acute psychological side effects of ketamine complicate the interpretation of both preclinical and clinical data. Moreover, the lack of controlled data demonstrating the ability of ketamine to sustain the antidepressant response with repeated administration leaves the potential clinical utility of this class of drugs in question. Using quantitative electroencephalography (qEEG) to objectively align doses of a low-trapping NMDA channel blocker, AZD6765 (lanicemine), to that of ketamine, we demonstrate the potential for NMDA channel blockers to produce antidepressant efficacy without psychotomimetic and dissociative side effects. Furthermore, using placebo-controlled data, we show that the antidepressant response to NMDA channel blockers can be maintained with repeated and intermittent drug administration. Together, these data provide a path for the development of novel glutamatergic-based therapeutics for treatment-refractory mood disorders.

Differential behavioral state-dependence in the burst properties of CA3 and CA1 neurons.

Brief bursts of fast high-frequency action potentials are a signature characteristic of CA3 and CA1 pyramidal neurons. Understanding the factors determining burst and single spiking is potentially significant for sensory representation, synaptic plasticity and epileptogenesis. A variety of models suggest distinct functional roles for burst discharge, and for specific characteristics of the burst in neural coding. However, little in vivo data demonstrate how often and under what conditions CA3 and CA1 actually exhibit burst and single spike discharges. The present study examined burst discharge and single spiking of CA3 and CA1 neurons across distinct behavioral states (awake-immobility and maze-running) in rats. In both CA3 and CA1 spike bursts accounted for less than 20% of all spike events. CA3 neurons exhibited more spikes per burst, greater spike frequency, larger amplitude spikes and more spike amplitude attenuation than CA1 neurons. A major finding of the present study is that the propensity of CA1 neurons to burst was affected by behavioral state, while the propensity of CA3 to burst was not. CA1 neurons exhibited fewer bursts during maze running compared with awake-immobility. In contrast, there were no differences in burst discharge of CA3 neurons. Neurons in both subregions exhibited smaller spike amplitude, fewer spikes per burst, longer inter-spike intervals and greater spike amplitude attenuation within a burst during awake-immobility compared with maze running. These findings demonstrate that the CA1 network is under greater behavioral state-dependent regulation than CA3. The present findings should inform both theoretic and computational models of CA3 and CA1 function.

A Bayesian decoding algorithm for analysis of information encoding in neural ensembles.

Developing optimal strategies for constructing and testing decoding algorithms is an important question in computational neuroscience, In this field, decoding algorithms are mathematical methods that model ensemble neural spiking activity as they dynamically represent a biological signal. We present a recursive decoding algorithm based on a Bayesian point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probability of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by analyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from a rat foraging in an open circular environment The median decoding error during 10 minutes of open foraging was 5.5 cm, and the true coverage probability for 0.95 confidence regions was 0.75 using 32 neurons. These findings improve significantly on our previous results and suggest an approach to reading dynamically information represented in ensemble neural spiking activity.

Construction and analysis of non-Poisson stimulus-response models of neural spiking activity.

A paradigm for constructing and analyzing non-Poisson stimulus-response models of neural spike train activity is presented. Inhomogeneous gamma (IG) and inverse Gaussian (IIG) probability models are constructed by generalizing the derivation of the inhomogeneous Poisson (IP) model from the exponential probability density. The resultant spike train models have Markov dependence. Quantile-quantile (Q-Q) plots and Kolmogorov-Smirnov (K-S) plots are developed based on the rate-rescaling theorem to assess model goodness-of-fit. The analysis also expresses the spike rate function of the neuron directly in terms of its interspike interval (ISI) distribution. The methods are illustrated with an analysis of 34 spike trains from rat CA1 hippocampal pyramidal neurons recorded while the animal executed a behavioral task. The stimulus in these experiments is the animal's position in its environment and the response is the neural spiking activity. For all 34 pyramidal cells, the IG and IIG models gave better fits to the spike trains than the IP. The IG model more accurately described the frequency of longer ISIs, whereas the IIG model gave the best description of the burst frequency, i.e. ISIs < or = 20 ms. The findings suggest that bursts are a significant component of place cell spiking activity even when position and the background variable, theta phase, are taken into account. Unlike the Poisson model, the spatial and temporal rate maps of the IG and IIG models depend directly on the spiking history of the neurons. These rate maps are more physiologically plausible since the interaction between space and time determines local spiking propensity. While this statistical paradigm is being developed to study information encoding by rat hippocampal neurons, the framework should be applicable to stimulus-response experiments performed in other neural systems.

Experience-dependent changes in extracellular spike amplitude may reflect regulation of dendritic action potential back-propagation in rat hippocampal pyramidal cells.

Activity-dependent attenuations in extracellular spike amplitude have been shown to correlate with a decrease in the effectiveness with which somatic action potentials back-propagate into the dendritic arbor of hippocampal pyramidal cells. In this paper we demonstrate that activity-dependent attenuations in amplitude occur during behavior and that the amount of attenuation is reduced with an animal's experience in an environment. The observed reductions are caused by an animal's experience within a specific environmental context, are dependent on functional NMDA receptors, and are accompanied by an increase in the effective coupling of pyramidal cells and interneurons. These results provide an important step in linking together in vivo studies with in vitro data and suggest that mechanisms of plasticity engaged during behavior may be sufficient to alter the biophysical and integrative properties of hippocampal pyramidal cells.

Experience-dependent asymmetric shape of hippocampal receptive fields.

We propose a novel parameter, namely, the skewness, or asymmetry, of the shape of a receptive field to characterize two properties of hippocampal place fields. First, a majority of hippocampal receptive fields on linear tracks are negatively skewed, such that during a single pass the firing rate is low as the rat enters the field but high as it exits. Second, while the place fields are symmetric at the beginning of a session, they become highly asymmetric with experience. Further experiments suggest that these results are likely to arise due to synaptic plasticity during behavior. Using a purely feed forward neural network model, we show that following repeated directional activation, NMDA-dependent long-term potentiation/long-term depotentiation (LTP/LTD) could result in an experience-dependent asymmetrization of receptive fields.

Interaction between spike waveform classification and temporal sequence detection.

In vivo extracellular recordings have allowed researchers to study the response properties of neurons to behaviorally relevant stimuli. In this paper we use multiple tetrode recordings from the hippocampus of the freely behaving rat to show that the action potential amplitude of a given cell can vary in a systematic and activity dependent manner over behaviorally relevant time scales. Since the discrimination algorithms used by experimenters to isolate cells from extracellular recordings are based on differences in waveforms, we show how these systematic changes in waveform shape can lead to non-random errors in single cell isolation. We further demonstrate that these non-random errors can lead to apparent temporal ordering effects between neurons in the absence of any specific temporal relationship. A firm understanding of these naturally occurring physiological changes is therefore critical for the evaluation of higher order phenomena such as the temporally correlated firing of ensembles of neurons.

A statistical paradigm for neural spike train decoding applied to position prediction from ensemble firing patterns of rat hippocampal place cells.

The problem of predicting the position of a freely foraging rat based on the ensemble firing patterns of place cells recorded from the CA1 region of its hippocampus is used to develop a two-stage statistical paradigm for neural spike train decoding. In the first, or encoding stage, place cell spiking activity is modeled as an inhomogeneous Poisson process whose instantaneous rate is a function of the animal's position in space and phase of its theta rhythm. The animal's path is modeled as a Gaussian random walk. In the second, or decoding stage, a Bayesian statistical paradigm is used to derive a nonlinear recursive causal filter algorithm for predicting the position of the animal from the place cell ensemble firing patterns. The algebra of the decoding algorithm defines an explicit map of the discrete spike trains into the position prediction. The confidence regions for the position predictions quantify spike train information in terms of the most probable locations of the animal given the ensemble firing pattern. Under our inhomogeneous Poisson model position was a three to five times stronger modulator of the place cell spiking activity than theta phase in an open circular environment. For animal 1 (2) the median decoding error based on 34 (33) place cells recorded during 10 min of foraging was 8.0 (7.7) cm. Our statistical paradigm provides a reliable approach for quantifying the spatial information in the ensemble place cell firing patterns and defines a generally applicable framework for studying information encoding in neural systems.