Anne C. Smith

Estimating a state-space model from point process observations

A widely used signal processing paradigm is the state-space model. The state-space model is defined by two equations: an observation equation that describes how the hidden state or latent process is observed and a state equation that defines the evolution of the process through time. Inspired by neurophysiology experiments in which neural spiking activity is induced by an implicit (latent) stimulus, we develop an algorithm to estimate a state-space model observed through point process measurements. We represent the latent process modulating the neural spiking activity as a gaussian autoregressive model driven by an external stimulus. Given the latent process, neural spiking activity is characterized as a general point process defined by its conditional intensity function. We develop an approximate expectation-maximization (EM) algorithm to estimate the unobservable state-space process, its parameters, and the parameters of the point process. The EM algorithm combines a point process recursive nonlinear filter algorithm, the fixed interval smoothing algorithm, and the state-space covariance algorithm to compute the complete data log likelihood efficiently. We use a Kolmogorov-Smirnov test based on the time-rescaling theorem to evaluate agreement between the model and point process data. We illustrate the model with two simulated data examples: an ensemble of Poisson neurons driven by a common stimulus and a single neuron whose conditional intensity function is approximated as a local Bernoulli process.

Dynamic Analysis of Learning in Behavioral Experiments

Understanding how an animals ability to learn relates to neural activity or is altered by lesions, different attentional states, pharmacological interventions, or genetic manipulations are central questions in neuroscience. Although learning is a dynamic process, current analyses do not use dynamic estimation methods, require many trials across many animals to establish the occurrence of learning, and provide no consensus as how best to identify when learning has occurred. We develop a state-space model paradigm to characterize learning as the probability of a correct response as a function of trial number (learning curve). We compute the learning curve and its confidence intervals using a state-space smoothing algorithm and define the learning trial as the first trial on which there is reasonable certainty (>0.95) that a subject performs better than chance for the balance of the experiment. For a range of simulated learning experiments, the smoothing algorithm estimated learning curves with smaller mean integrated squared error and identified the learning trials with greater reliability than commonly used methods. The smoothing algorithm tracked easily the rapid learning of a monkey during a single session of an association learning experiment and identified learning 2 to 4 d earlier than accepted criteria for a rat in a 47 d procedural learning experiment. Our state-space paradigm estimates learning curves for single animals, gives a precise definition of learning, and suggests a coherent statistical framework for the design and analysis of learning experiments that could reduce the number of animals and trials per animal that these studies require.

Functional Magnetic Resonance Imaging Activity during the Gradual Acquisition and Expression of Paired-Associate Memory

Recent neurophysiological findings from the monkey hippocampus showed dramatic changes in the firing rate of individual hippocampal cells as a function of learning new associations. To extend these findings to humans, we used blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) to examine the patterns of brain activity during learning of an analogous associative task. We observed bilateral, monotonic increases in activity during learning not only in the hippocampus but also in the parahippocampal and right perirhinal cortices. In addition, activity related to simple novelty signals was observed throughout the medial temporal lobe (MTL) memory system and in several frontal regions. A contrasting pattern was observed in a frontoparietal network in which a high level of activity was sustained until the association was well learned, at which point the activity decreased to baseline. Thus, we found that associative learning in humans is accompanied by striking increases in BOLD fMRI activity throughout the MTL as well as in the cingulate cortex and frontal lobe, consistent with neurophysiological findings in the monkey hippocampus. The finding that both the hippocampus and surrounding MTL cortex exhibited similar associative learning and novelty signals argues strongly against the view that there is a clear division of labor in the MTL in which the hippocampus is essential for forming associations and the cortex is involved in novelty detection. A second experiment addressed a striking aspect of the data from the first experiment by demonstrating a substantial effect of baseline task difficulty on MTL activity capable of rendering mnemonic activity as either “positive” or “negative.”

The Ageing Brain: Age-dependent changes in the electroencephalogram during propofol and sevoflurane general anaesthesia

BACKGROUND Anaesthetic drugs act at sites within the brain that undergo profound changes during typical ageing. We postulated that anaesthesia-induced brain dynamics observed in the EEG change with age. METHODS We analysed the EEG in 155 patients aged 18-90 yr who received propofol (n=60) or sevoflurane (n=95) as the primary anaesthetic. The EEG spectrum and coherence were estimated throughout a 2 min period of stable anaesthetic maintenance. Age-related effects were characterized by analysing power and coherence as a function of age using linear regression and by comparing the power spectrum and coherence in young (18- to 38-yr-old) and elderly (70- to 90-yr-old) patients. RESULTS Power across all frequency bands decreased significantly with age for both propofol and sevoflurane; elderly patients showed EEG oscillations ∼2- to 3-fold smaller in amplitude than younger adults. The qualitative form of the EEG appeared similar regardless of age, showing prominent alpha (8-12 Hz) and slow (0.1-1 Hz) oscillations. However, alpha band dynamics showed specific age-related changes. In elderly compared with young patients, alpha power decreased more than slow power, and alpha coherence and peak frequency were significantly lower. Older patients were more likely to experience burst suppression. CONCLUSIONS These profound age-related changes in the EEG are consistent with known neurobiological and neuroanatomical changes that occur during typical ageing. Commercial EEG-based depth-of-anaesthesia indices do not account for age and are therefore likely to be inaccurate in elderly patients. In contrast, monitoring the unprocessed EEG and its spectrogram can account for age and individual patient characteristics.

Probabilistic reinforcement learning in adults with autism spectrum disorders.

Background: Autism spectrum disorders (ASDs) can be conceptualized as disorders of learning, however there have been few experimental studies taking this perspective. Methods: We examined the probabilistic reinforcement learning performance of 28 adults with ASDs and 30 typically developing adults on a task requiring learning relationships between three stimulus pairs consisting of Japanese characters with feedback that was valid with different probabilities (80%, 70%, and 60%). Both univariate and Bayesian state–space data analytic methods were employed. Hypotheses were based on the extant literature as well as on neurobiological and computational models of reinforcement learning. Results: Both groups learned the task after training. However, there were group differences in early learning in the first task block where individuals with ASDs acquired the most frequently accurately reinforced stimulus pair (80%) comparably to typically developing individuals; exhibited poorer acquisition of the less frequently reinforced 70% pair as assessed by state–space learning curves; and outperformed typically developing individuals on the near chance (60%) pair. Individuals with ASDs also demonstrated deficits in using positive feedback to exploit rewarded choices. Conclusions: Results support the contention that individuals with ASDs are slower learners. Based on neurobiology and on the results of computational modeling, one interpretation of this pattern of findings is that impairments are related to deficits in flexible updating of reinforcement history as mediated by the orbito‐frontal cortex, with spared functioning of the basal ganglia. This hypothesis about the pathophysiology of learning in ASDs can be tested using functional magnetic resonance imaging.Autism Res 2011, 4: 109–120.

Stable Encoding of Task Structure Coexists With Flexible Coding of Task Events in Sensorimotor Striatum

The sensorimotor striatum, as part of the brains habit circuitry, has been suggested to store fixed action values as a result of stimulus-response learning and has been contrasted with a more flexible system that conditionally assigns values to behaviors. The stability of neural activity in the sensorimotor striatum is thought to underlie not only normal habits but also addiction and clinical syndromes characterized by behavioral fixity. By recording in the sensorimotor striatum of mice, we asked whether neuronal activity acquired during procedural learning would be stable even if the sensory stimuli triggering the habitual behavior were altered. Contrary to expectation, both fixed and flexible activity patterns appeared. One, representing the global structure of the acquired behavior, was stable across changes in task cuing. The second, a fine-grain representation of task events, adjusted rapidly. Such dual forms of representation may be critical to allow motor and cognitive flexibility despite habitual performance.

Comparison of Associative Learning-Related Signals in the Macaque Perirhinal Cortex and Hippocampus

Strong evidence suggests that the macaque monkey perirhinal cortex is involved in both the initial formation as well as the long-term storage of associative memory. To examine the neurophysiological basis of associative memory formation in this area, we recorded neural activity in this region as monkeys learned new conditional-motor associations. We report that a population of perirhinal neurons signal newly learned associations by changing their firing rate correlated with the animals behavioral learning curve. Individual perirhinal neurons signal learning of one or more associations concurrently and these neural changes could occur before, at the same time, or after behavioral learning was expressed. We also compared the associative learning signals in the perirhinal cortex to our previous findings in the hippocampus. We report global similarities in both the learning-related and task-related activity seen across these areas as well as clear differences in the within and across trial timing and relative proportion of different subtypes of learning-related signals. Taken together, these findings emphasize the important role of the perirhinal cortex in new associative learning and suggest that the perirhinal cortex together with the hippocampus contribute importantly to conditional-motor associative memory formation.

A Bayesian statistical analysis of behavioral facilitation associated with deep brain stimulation

Deep brain stimulation (DBS) is an established therapy for Parkinsons Disease and is being investigated as a treatment for chronic depression, obsessive compulsive disorder and for facilitating functional recovery of patients in minimally conscious states following brain injury. For all of these applications, quantitative assessments of the behavioral effects of DBS are crucial to determine whether the therapy is effective and, if so, how stimulation parameters can be optimized. Behavioral analyses for DBS are challenging because subject performance is typically assessed from only a small set of discrete measurements made on a discrete rating scale, the time course of DBS effects is unknown, and between-subject differences are often large. We demonstrate how Bayesian state-space methods can be used to characterize the relationship between DBS and behavior comparing our approach with logistic regression in two experiments: the effects of DBS on attention of a macaque monkey performing a reaction-time task, and the effects of DBS on motor behavior of a human patient in a minimally conscious state. The state-space analysis can assess the magnitude of DBS behavioral facilitation (positive or negative) at specific time points and has important implications for developing principled strategies to optimize DBS paradigms.

Effect of age on burst firing characteristics of rat hippocampal pyramidal cells.

During behavior, hippocampal pyramidal cells emit high frequency bursts, modulated by the animals location and the 7 Hz theta rhythm. During rest, CA1 EEG exhibits large irregular activity (LIA), containing sharp-wave/ripple complexes, during which pyramidal cells exhibit burst discharge. Aging results in altered intracellular calcium homeostasis, increased electrical coupling and reduced cholinergic modulation within CA1, all of which might affect burst discharge characteristics. During LIA, old rats exhibited more short (3–7 ms) inter-spike intervals, with no change in mean firing rate. During behavior induced theta rhythm, however, interval distributions were not affected by age. Thus, different mechanisms must underlie burst discharge in theta and LIA states. Moreover, age related changes in the cholinergic system appear not to play a major role in shaping the temporal discharge characteristics of CA1 pyramidal cells. The mechanism and significance of the higher frequency bursting in old rats during LIA remains to be determined.

State-space algorithms for estimating spike rate functions

The accurate characterization of spike firing rates including the determination of when changes in activity occur is a fundamental issue in the analysis of neurophysiological data. Here we describe a state-space model for estimating the spike rate function that provides a maximum likelihood estimate of the spike rate, model goodness-of-fit assessments, as well as confidence intervals for the spike rate function and any other associated quantities of interest. Using simulated spike data, we first compare the performance of the state-space approach with that of Bayesian adaptive regression splines (BARS) and a simple cubic spline smoothing algorithm. We show that the state-space model is computationally efficient and comparable with other spline approaches. Our results suggest both a theoretically sound and practical approach for estimating spike rate functions that is applicable to a wide range of neurophysiological data.