James M. Hyman

Working memory performance correlates with prefrontal-hippocampal theta interactions but not with prefrontal neuron firing rates

Performance of memory tasks is impaired by lesions to either the medial prefrontal cortex (mPFC) or the hippocampus (HPC); although how these two areas contribute to successful performance is not well understood. mPFC unit activity is temporally affected by hippocampal-theta oscillations, with almost half the mPFC population entrained to theta in behaving animals, pointing to theta interactions as the mechanism enabling collaborations between these two areas. mPFC neurons respond to sensory stimuli and responses in working memory tasks, though the function of these correlated firing rate changes remains unclear because similar responses are reported during mPFC dependent and independent tasks. Using a DNMS task we compared error trials vs. correct trials and found almost all mPFC cells fired at similar rates during both error and correct trials (92%), however theta-entrainment of mPFC neurons declined during error performance as only 17% of cells were theta-entrained (during correct trials 46% of the population was theta-entrained). Across the population, error and correct trials did not differ in firing rate, but theta-entrainment was impaired. Periods of theta-entrainment and firing rate changes appeared to be independent variables, and only theta-entrainment was correlated with successful performance, indicating mPFC-HPC theta-range interactions are the key to successful DNMS performance.

Contextual encoding by ensembles of medial prefrontal cortex neurons

Contextual representations serve to guide many aspects of behavior and influence the way stimuli or actions are encoded and interpreted. The medial prefrontal cortex (mPFC), including the anterior cingulate subregion, has been implicated in contextual encoding, yet the nature of contextual representations formed by the mPFC is unclear. Using multiple single-unit tetrode recordings in rats, we found that different activity patterns emerged in mPFC ensembles when animals moved between different environmental contexts. These differences in activity patterns were significantly larger than those observed for hippocampal ensembles. Whereas ≈11% of mPFC cells consistently preferred one environment over the other across multiple exposures to the same environments, optimal decoding (prediction) of the environmental setting occurred when the activity of up to ≈50% of all mPFC neurons was taken into account. On the other hand, population activity patterns were not identical upon repeated exposures to the very same environment. This was partly because the state of mPFC ensembles seemed to systematically shift with time, such that we could sometimes predict the change in ensemble state upon later reentry into one environment according to linear extrapolation from the time-dependent shifts observed during the first exposure. We also observed that many strongly action-selective mPFC neurons exhibited a significant degree of context-dependent modulation. These results highlight potential differences in contextual encoding schemes by the mPFC and hippocampus and suggest that the mPFC forms rich contextual representations that take into account not only sensory cues but also actions and time.

What is the Functional Relevance of Prefrontal Cortex Entrainment to Hippocampal Theta Rhythms

There has been considerable interest in the importance of oscillations in the brain and in how these oscillations relate to the firing of single neurons. Recently a number of studies have shown that the spiking of individual neurons in the medial prefrontal cortex (mPFC) become entrained to the hippocampal (HPC) theta rhythm. We recently showed that theta-entrained mPFC cells lost theta-entrainment specifically on error trials even though the firing rates of these cells did not change (Hyman et al., 2010). This implied that the level of HPC theta-entrainment of mPFC units was more predictive of trial outcome than differences in firing rates and that there is more information encoded by the mPFC on working memory tasks than can be accounted for by a simple rate code. Nevertheless, the functional meaning of mPFC entrainment to HPC theta remains a mystery. It is also unclear as to whether there are any differences in the nature of the information encoded by theta-entrained and non-entrained mPFC cells. In this review we discuss mPFC entrainment to HPC theta within the context of previous results as well as provide a more detailed analysis of the Hyman et al. (2010) data set. This re-analysis revealed that theta-entrained mPFC cells selectively encoded a variety of task-relevant behaviors and stimuli while never theta-entrained mPFC cells were most strongly attuned to errors or the lack of expected rewards. In fact, these error responsive neurons were responsible for the error representations exhibited by the entire ensemble of mPFC neurons. A theta reset was also detected in the post-error period. While it is becoming increasingly evident that mPFC neurons exhibit correlates to virtually all cues and behaviors, perhaps phase-locking directs attention to the task-relevant representations required to solve a spatially based working memory task while the loss of theta-entrainment at the start of error trials may represent a shift of attention away from these representations. The subsequent theta reset following error commission, when coupled with the robust responses of never theta-entrained cells, could produce a potent error-evoked signal used to alert the rat to changes in the relationship between task-relevant cues and reward expectations.

Action and outcome activity state patterns in the anterior cingulate cortex.

Although there are numerous theories regarding anterior cingulate cortex (ACC) function, most suggest that it is involved in some form of action or outcome processing. The present study characterized the dominant patterns of ACC activity on a task in which actions and outcomes could vary independently. Patterns of activity were detected using a modified form of principal component analysis (PCA), termed constrained PCA in which a regression procedure was applied prior to PCA to eliminate the contribution of nontask-related activity. When trials were grouped according to outcome, a PC was found in all subjects and sessions that had large fluctuations during actions but only differentiated correct versus error trials prior to the end of the delay and again at time of the outcome. Another PC was always present that separated right from left lever presses, but only around the time of the actual lever press. Individual neurons exhibited significant selectivities for trials involving different actions and/or outcomes. Of the ACC neurons that exhibited significant outcome selectivity, the majority fired more on error trials. The present study revealed separate as well as integrated action and outcome monitoring in the ACC, especially, although not exclusively, under conditions when an error is likely.

Feedback-related negativity observed in rodent anterior cingulate cortex

The feedback-related negativity (FRN) refers to a difference in the human event-related potential (ERP) elicited by feedback indicating success versus failure: the difference appears negative when subtracting the success ERP from the failure ERP (Miltner et al., 1997). Although source localization techniques (e.g., BESA) suggest that the FRN is produced in the ACC, the inverse problem (that any given scalp distribution can be produced by an infinite number of possible dipole configurations) limits the certainty of this conclusion. The inverse problem can be circumvented by directly recording from the ACC in animal models. Although a non-human primate homologue of the FRN has been observed in the macaque monkey (e.g. Emeric et al., 2008), a homologue of the FRN has yet to be identified in rodents. We recorded local field potentials (LFPs) directly from the ACC in 6 rodents in a task based on the FRN paradigm. The animals were trained to poke their nose into a lighted port and received a feedback smell indicating whether or not a reward pellet would drop 1.5s later. We observed a FRN-like effect time-locked to the feedback scent whereby the LFP to feedback predicting no-reward was significantly more negative than the LFP to feedback predicting reward. This deflection began on average 130ms before behavioral changes in response to the feedback. Thus, we provide the first evidence of the existence of a rodent homologue of the FRN.

Tracking Progress toward a Goal in Corticostriatal Ensembles

When performing sequences of actions, we constantly keep track of our current position in the sequence relative to the overall goal. The present study searched for neural representations of sequence progression in corticostriatal circuits. Neurons within the anterior cingulate cortex (ACC) and its target region in the dorsal striatum (DS) were recorded from simultaneously as rats performed different sequences of lever presses. We analyzed the responses of the neurons to presses occurring in the “first,” “second,” or “third” serial position regardless of the particular sequence or physical levers. Principal component analysis revealed that the main source of firing rate variance in the ACC was a smooth ramp-like change as the animal progressed through the sequence toward the reward. No such smooth-ramping activity was observed in DS ensembles as firing tended to be tightly linked to each action. In the ACC, the progression in firing was observed only for correct choices and not errors, whereas in the DS, firing associated with each action in a sequence was similar regardless of whether the action was correct or not. Therefore, different forms of a signal exist within corticostriatal circuits that evolve across a sequence of actions, with DS ensembles tracking every action and ACC ensembles tracking actual progress toward the goal.

Differences in the emergent coding properties of cortical and striatal ensembles

The function of a given brain region is often defined by the coding properties of its individual neurons, yet how this information is combined at the ensemble level is an equally important consideration. We recorded multiple neurons from the anterior cingulate cortex (ACC) and the dorsal striatum (DS) simultaneously as rats performed different sequences of the same three actions. Sequence and lever decoding was markedly similar on a per-neuron basis in the two regions. At the ensemble level, sequence-specific representations in the DS appeared synchronously, but transiently, along with the representation of lever location, whereas these two streams of information appeared independently and asynchronously in the ACC. As a result, the ACC achieved superior ensemble decoding accuracy overall. Thus, the manner in which information was combined across neurons in an ensemble determined the functional separation of the ACC and DS on this task.

Abrupt changes in the patterns and complexity of anterior cingulate cortex activity when food is introduced into an environment

Foraging typically involves two distinct phases, an exploration phase where an organism explores its local environment in search of needed resources and an exploitation phase where a discovered resource is consumed. The behavior and cognitive requirements of exploration and exploitation are quite different and yet organisms can quickly and efficiently switch between them many times during a foraging bout. The present study investigated neural activity state dynamics in the anterior cingulate sub-region of the rat medial prefrontal cortex (mPFC) when a reliable food source was introduced into an environment. Distinct and largely independent states were detected using a Hidden Markov Model (HMM) when food was present or absent in the environment. Measures of neural entropy or complexity decreased when rats went from exploring the environment to exploiting a reliable food source. Exploration in the absence of food was associated with many weak activity states, while bouts of food consumption were characterized by fewer stronger states. Widespread activity state changes in the mPFC may help to inform foraging decisions and focus behavior on what is currently most prominent or valuable in the environment.

Neuronal coding in the rodent prefrontal cortex

Apart from being associated with working memory, neurons in the rodent medial prefrontal cortex (mPFC) are known to be involved in encoding of spatial and temporal contexts [1], the deduction of rules [2], and decision making [3]. The context-dependent organization of neural assemblies encoding for different task events, stimuli or decisions [3,4], may account for the great flexibility required during the performance of higher cognitive tasks. The way in which single neurons and their interactions code for different entities may play a huge role in this flexibility [5], but has rarely been systematically investigated in the PFC. Here, we employ various multivariate statistical techniques and time series bootstraps to analyze the way in which neurons, neural interactions, and temporal patterns of activity within ensembles of simultaneously recorded rat PFC neurons contribute to the neural population code during the performance of different tasks comprised of multiple stimuli, task events, and responses. To examine the neural population representation of a given set of stimuli and task events, in a first step kernel density stimates of spiking activity were obtained from all recorded neurons. Both multivariate/ multiple regression and classification approaches were then utilized to characterize neuronal coding properties. Using regression, the distributions of single neuron contributions to the explained variation in stimulus conditions were charted, both individually and after regressing out or taking into account the contribution of other neurons. The same was done including neuronal interaction terms of various orders as well as time-lagged versions of the neuronal activities (based on the idea of delay-embedding, thus taking temporal patterns into account). Significant contributions of single terms or sets of terms were identified by construction of null hypothesis distributions through block-permutation bootstraps. In a complementary decoding-type of analysis, a linear discriminant analysis (LDA) classifier was run on sets of single neuron activities, timelagged versions of these, and their interaction terms, with performance evaluated through leave-one-out cross-validation. Results show that 1) contributions to explained variation in stimulus conditions follow monotonically falling, potentially power-law-like, distributions, and 2) both including temporal pattern information as well as neural interaction terms significantly improves prediction performance and strongly reduces the misclassification rate. These findings indicate that a) there appears to be no highly specialized subpopulation of neurons encoding for specific events, and b) that precise temporal patterns, and to a lesser degree correlations among units, have a major contribution to the neural representation of specific stimuli and internal task stages in the rat mPFC.