Differences in the emergent coding properties of cortical and striatal ensembles

Reconfiguration of Behavioral Signals in the Anterior Cingulate Cortex Based on Emotional State

Behaviors and their execution depend on the context and emotional state in which they are performed. The contextual modulation of behavior likely relies on regions such as the anterior cingulate cortex (ACC) that multiplex information about emotional/autonomic states and behaviors. The objective of the present study was to understand how the representations of behaviors by ACC neurons become modified when performed in different emotional states. A pipeline of machine learning techniques was developed to categorize and classify complex, spontaneous behaviors in male rats from the video. This pipeline, termed Hierarchical Unsupervised Behavioural Discovery Tool (HUB-DT), discovered a range of statistically separable behaviors during a task in which motivationally significant outcomes were delivered in blocks of trials that created three unique "emotional contexts." HUB-DT was capable of detecting behaviors specific to each emotional context and was able to identify and segregate the portions of a neural signal related to a behavior and to emotional context. Overall, ∼10× as many neurons responded to behaviors in a contextually dependent versus a fixed manner, highlighting the extreme impact of emotional state on representations of behaviors that were precisely defined based on detailed analyses of limb kinematics. This type of modulation may be a key mechanism that allows the ACC to modify the behavioral output based on emotional states and contextual demands.

The role of elasticity on adhesion and clustering of neurons on soft surfaces

The question of whether material stiffness enhances cell adhesion and clustering is still open to debate. Results from the literature are seemingly contradictory, with some reports illustrating that adhesion increases with surface stiffness and others suggesting that the performance of a system of cells is curbed by high values of elasticity. To address the role of elasticity as a regulator in neuronal cell adhesion and clustering, we investigated the topological characteristics of networks of neurons on polydimethylsiloxane (PDMS) surfaces - with values of elasticity (E) varying in the 0.55-2.65 MPa range. Results illustrate that, as elasticity increases, the number of neurons adhering on the surface decreases. Notably, the small-world coefficient - a topological measure of networks - also decreases. Numerical simulations and functional multi-calcium imaging experiments further indicated that the activity of neuronal cells on soft surfaces improves for decreasing E. Experimental findings are supported by a mathematical model, that explains adhesion and clustering of cells on soft materials as a function of few parameters - including the Young's modulus and roughness of the material. Overall, results indicate that - in the considered elasticity interval - increasing the compliance of a material improves adhesion, improves clustering, and enhances communication of neurons.

The effective enhancement of information in 3D small-world networks of biological neuronal cells

The cardiovascular system, the kidney, or the brain, are examples of complex systems - where the properties of the systems arise because of the layout of cells in those systems. One way to characterize systems is using networks, where elements and interactions of the systems are represented as nodes and links of a graph. Network's topology can be, in turn, measured by the small-world coefficient. Small world networks feature increased clustering and shorter paths compared to random or periodic networks of the same size. This suggests that systems with small world attributes can also efficiently transport signals, nutrients, or information within their body. While several reports in literature have illustrated that real biological systems are small-world, yet little is known about how information varies as a function of the small-world-ness (sw) of three dimensional graphs. Here, we used a model of the brain to estimate quantitatively the information processed in 3D networks. In the model, nodes of the graph are neuronal units capable to receive, integrate and transmit signals to other neurons of the system in parallel. The information encoded in the signals was then extracted using the techniques of information theory. In simulations where the topology of networks of400nodes was varied over large intervals, we found that in the0-9swrange information scales linearly with the small world coefficient, with a five-fold increase. Results of the paper and review of the existing literature on model organisms suggest that a small-world architecture may offer an evolutionary advantage to biological systems.

Dorsomedial Striatal Activity Tracks Completion of Behavioral Sequences in Rats

For proper execution of goal-directed behaviors, individuals require both a general representation of the goal and an ability to monitor their own progress toward that goal. Here, we examine how dorsomedial striatum (DMS), a region pivotal for forming associations among stimuli, actions, and outcomes, encodes the execution of goal-directed action sequences that require self-monitoring of behavior. We trained rats to complete a sequence of at least five consecutive lever presses (without visiting the reward port) to obtain a reward and recorded the activity of individual cells in DMS while rats performed the task. We found that the pattern of DMS activity gradually changed during the execution of the sequence, permitting accurate decoding of sequence progress from neural activity at a population level. Moreover, this sequence-related activity was blunted on trials where rats did not complete a sufficient number of presses. Overall, these data suggest a link between DMS activity and the execution of behavioral sequences that require monitoring of ongoing behavior.

Concomitant Processing of Choice and Outcome in Frontal Corticostriatal Ensembles Correlates with Performance of Rats

The frontal cortex-basal ganglia network plays a pivotal role in adaptive goal-directed behaviors. Medial frontal cortex (MFC) encodes information about choices and outcomes into sequential activation of neural population, or neural trajectory. While MFC projects to the dorsal striatum (DS), whether DS also displays temporally coordinated activity remains unknown. We studied this question by simultaneously recording neural ensembles in the MFC and DS of rodents performing an outcome-based alternative choice task. We found that the two regions exhibited highly parallel evolution of neural trajectories, transforming choice information into outcome-related information. When the two trajectories were highly correlated, spike synchrony was task-dependently modulated in some MFC-DS neuron pairs. Our results suggest that neural trajectories concomitantly process decision-relevant information in MFC and DS with increased spike synchrony between these regions.

Different Multidimensional Representations across the Amygdalo-Prefrontal Network during an Approach-Avoidance Task

The prelimbic (PL) area and basolateral amygdala (lateral [LA] and basolateral [BL] nuclei) have closely related functions and similar extrinsic connectivity. Reasoning that the computational advantage of such redundancy should be reflected in differences in how these structures represent information, we compared the coding properties of PL and amygdala neurons during a task that requires rats to produce different conditioned defensive or appetitive behaviors. Rather than unambiguous regional differences in the identities of the variables encoded, we found gradients in how the same variables are represented. Whereas PL and BL neurons represented many different parameters through minor variations in firing rates, LA cells coded fewer task features with stronger changes in activity. At the population level, whereas valence could be easily distinguished from amygdala activity, PL neurons could distinguish both valence and trial identity as well as or better than amygdala neurons. Thus, PL has greater representational capacity.

Communication between the Anterior Cingulate Cortex and Ventral Tegmental Area during a Cost-Benefit Reversal Task

The anterior cingulate cortex (ACC) is implicated in value-based decision making, anticipation, and adaptation; however, how ACC activity modulates these behaviors is unclear. One possibility is via the ACC's connections with the ventral tegmental area (VTA), a dopaminergic region implicated in motivation and feedback processing. We tested this by monitoring ACC and VTA local field potentials in rats performing a cost-benefit reversal task that elicited both value-based and anticipatory choices. Partial directed coherence analyses revealed that elevated 4-Hz ACC-to-VTA signaling accompanied decisions that appeared to be anticipatory. ACC-to-VTA signaling also occurred post-reversal, consistent with it being involved in the initiation of non-default behavior. An analysis of 4-Hz signals in the other direction (VTA-to-ACC) revealed that it was elevated when the rats committed errors and that this signal was followed by behavioral adaptation. Together, these findings suggest that bidirectional communication between the ACC and VTA supports behavioral flexibility.

Conflict and adaptation signals in the anterior cingulate cortex and ventral tegmental area

The integration and utilization of feedback in order to determine which decision strategy to use in different contexts is the core of executive function. The anterior cingulate cortex (ACC) is central to these processes but how feedback is made available to the ACC is unclear. To address this question, we trained rats with implants in the ACC and the ventral tegmental area (VTA), a dopaminergic brain region implicated in feedback processing, in a spatial decision reversal task with rule switching occurring approximately every 12 trials. Following a rule switch, the rats had to shift and sustain responses to the alternative side in order to obtain reward. Partial directed coherence (PDC) models of signal directionality between the ACC and VTA indicated that VTA → ACC communication (near 4 Hz) increased immediately prior to incorrect choices and during post-error decisions. This increase did not occur during correct choices. These data indicate that the VTA provides a feedback-driven, bottom-up modulating signal to the ACC which may be involved in assessing, and correcting for, decision conflict.

How Much Does Movement and Location Encoding Impact Prefrontal Cortex Activity? An Algorithmic Decoding Approach in Freely Moving Rats

Specialized brain structures encode spatial locations and movements, yet there is growing evidence that this information is also represented in the rodent medial prefrontal cortex (mPFC). Disambiguating such information from the encoding of other types of task-relevant information has proven challenging. To determine the extent to which movement and location information is relevant to mPFC neurons, tetrodes were used to record neuronal activity while limb positions, poses (i.e., recurring constellations of limb positions), velocity, and spatial locations were simultaneously recorded with two cameras every 200 ms as rats freely roamed in an experimental enclosure. Regression analyses using generalized linear models revealed that more than half of the individual mPFC neurons were significantly responsive to at least one of the factors, and many were responsive to more than one. On the other hand, each factor accounted for only a very small portion of the total spike count variance of any given neuron (<20% and typically <1%). Machine learning methods were used to analyze ensemble activity and revealed that ensembles were usually superior to the sum of the best neurons in encoding movements and spatial locations. Because movement and location encoding by individual neurons was so weak, it may not be such a concern for single-neuron analyses. Yet because these weak signals were so widely distributed across the population, this information was strongly represented at the ensemble level and should be considered in population analyses.

Mixed selectivity encoding and action selection in the prefrontal cortex during threat assessment

The medial prefrontal cortex (mPFC) regulates expression of emotional behavior. The mPFC combines multivariate information from its inputs, and depending on the imminence of threat, activates downstream networks that either increase or decrease the expression of anxiety-related motor behavior and autonomic activation. Here, we selectively highlight how subcortical input to the mPFC from two example structures, the amygdala and ventral hippocampus, help shape mixed selectivity encoding and action selection during emotional processing. We outline a model where prefrontal subregions modulate behavior along orthogonal motor dimensions, and exhibit connectivity that selects for expression of one behavioral strategy while inhibiting the other.

Fluid network dynamics in the prefrontal cortex during multiple strategy switching

Coordinated shifts of neuronal activity in the prefrontal cortex are associated with strategy adaptations in behavioural tasks, when animals switch from following one rule to another. However, network dynamics related to multiple-rule changes are scarcely known. We show how firing rates of individual neurons in the prelimbic and cingulate cortex correlate with the performance of rats trained to change their navigation multiple times according to allocentric and egocentric strategies. The concerted population activity exhibits a stable firing during the performance of one rule but shifted to another neuronal firing state when a new rule is learnt. Interestingly, when the same rule is presented a second time within the same session, neuronal firing does not revert back to the original neuronal firing state, but a new activity-state is formed. Our data indicate that neuronal firing of prefrontal cortical neurons represents changes in strategy and task-performance rather than specific strategies or rules.

Population activity of neurons in the prefrontal cortex has been shown to represent cognitive strategy and rules. Here the authors report that when the same rule is repeated on multiple occasions in the task, it is accompanied each time by a new prefrontal firing rate state.

Rodent Medial Frontal Control of Temporal Processing in the Dorsomedial Striatum

Although frontostriatal circuits are critical for the temporal control of action, how time is encoded in frontostriatal circuits is unknown. We recorded from frontal and striatal neurons while rats engaged in interval timing, an elementary cognitive function that engages both areas. We report four main results. First, "ramping" activity, a monotonic change in neuronal firing rate across time, is observed throughout frontostriatal ensembles. Second, frontostriatal activity scales across multiple intervals. Third, striatal ramping neurons are correlated with activity of the medial frontal cortex. Finally, interval timing and striatal ramping activity are disrupted when the medial frontal cortex is inactivated. Our results support the view that striatal neurons integrate medial frontal activity and are consistent with drift-diffusion models of interval timing. This principle elucidates temporal processing in frontostriatal circuits and provides insight into how the medial frontal cortex exerts top-down control of cognitive processing in the striatum.SIGNIFICANCE STATEMENT The ability to guide actions in time is essential to mammalian behavior from rodents to humans. The prefrontal cortex and striatum are critically involved in temporal processing and share extensive neuronal connections, yet it remains unclear how these structures represent time. We studied these two brain areas in rodents performing interval-timing tasks and found that time-dependent "ramping" activity, a monotonic increase or decrease in neuronal activity, was a key temporal signal. Furthermore, we found that striatal ramping activity was correlated with and dependent upon medial frontal activity. These results provide insight into information-processing principles in frontostriatal circuits.

A Quantitative Analysis of Context-Dependent Remapping of Medial Frontal Cortex Neurons and Ensembles

The frontal cortex has been implicated in a number of cognitive and motivational processes, but understanding how individual neurons contribute to these processes is particularly challenging as they respond to a broad array of events (multiplexing) in a manner that can be dynamically modulated by the task context, i.e., adaptive coding (Duncan, 2001). Fundamental questions remain, such as how the flexibility gained through these mechanisms is balanced by the need for consistency and how the ensembles of neurons are coherently shaped by task demands. In the present study, ensembles of medial frontal cortex neurons were recorded from rats trained to perform three different operant actions either in two different sequences or two different physical environments. Single neurons exhibited diverse mixtures of responsivity to each of the three actions and these mixtures were abruptly altered by context/sequence switches. Remarkably, the overall responsivity of the population remained highly consistent both within and between context/sequences because the gains versus losses were tightly balanced across neurons and across the three actions. These data are consistent with a reallocation mixture model in which individual neurons express unique mixtures of selectivity for different actions that become reallocated as task conditions change. However, because the allocations and reallocations are so well balanced across neurons, the population maintains a low but highly consistent response to all actions. The frontal cortex may therefore balance consistency with flexibility by having ensembles respond in a fixed way to task-relevant actions while abruptly reconfiguring single neurons to encode "actions in context."

SIGNIFICANCE STATEMENT

Flexible modes of behavior involve performance of similar actions in contextually relevant ways. The present study quantified the changes in how rat medial frontal cortex neurons respond to the same actions when performed in different task contexts (sequences or environments). Most neurons altered the mixture of actions they were responsive to in different contexts or sequences. Nevertheless, the responsivity profile of the ensemble remained fixed as did the ability of the ensemble to differentiate between the three actions. These mechanisms may help to contextualize the manner in which common events are represented across different situations.

Computational Models of Anterior Cingulate Cortex: At the Crossroads between Prediction and Effort

In the last two decades the anterior cingulate cortex (ACC) has become one of the most investigated areas of the brain. Extensive neuroimaging evidence suggests countless functions for this region, ranging from conflict and error coding, to social cognition, pain and effortful control. In response to this burgeoning amount of data, a proliferation of computational models has tried to characterize the neurocognitive architecture of ACC. Early seminal models provided a computational explanation for a relatively circumscribed set of empirical findings, mainly accounting for EEG and fMRI evidence. More recent models have focused on ACC's contribution to effortful control. In parallel to these developments, several proposals attempted to explain within a single computational framework a wider variety of empirical findings that span different cognitive processes and experimental modalities. Here we critically evaluate these modeling attempts, highlighting the continued need to reconcile the array of disparate ACC observations within a coherent, unifying framework.

Integrating Spatial Working Memory and Remote Memory: Interactions between the Medial Prefrontal Cortex and Hippocampus

In recent years, two separate research streams have focused on information sharing between the medial prefrontal cortex (mPFC) and hippocampus (HC). Research into spatial working memory has shown that successful execution of many types of behaviors requires synchronous activity in the theta range between the mPFC and HC, whereas studies of memory consolidation have shown that shifts in area dependency may be temporally modulated. While the nature of information that is being communicated is still unclear, spatial working memory and remote memory recall is reliant on interactions between these two areas. This review will present recent evidence that shows that these two processes are not as separate as they first appeared. We will also present a novel conceptualization of the nature of the medial prefrontal representation and how this might help explain this area’s role in spatial working memory and remote memory recall.

Hetereogeneity in Neuronal Intrinsic Properties: A Possible Mechanism for Hub-Like Properties of the Rat Anterior Cingulate Cortex during Network Activity

The anterior cingulate cortex (ACC) is vital for a range of brain functions requiring cognitive control and has highly divergent inputs and outputs, thus manifesting as a hub in connectomic analyses. Studies show diverse functional interactions within the ACC are associated with network oscillations in the β (20–30 Hz) and γ (30-80 Hz) frequency range. Oscillations permit dynamic routing of information within cortex, a function that depends on bandpass filter–like behavior to selectively respond to specific inputs. However, a putative hub region such as ACC needs to be able to combine inputs from multiple sources rather than select a single input at the expense of others. To address this potential functional dichotomy, we modeled local ACC network dynamics in the rat in vitro. Modal peak oscillation frequencies in the β- and γ-frequency band corresponded to GABA_Aergic synaptic kinetics as seen in other regions; however, the intrinsic properties of ACC principal neurons were highly diverse. Computational modeling predicted that this neuronal response diversity broadened the bandwidth for filtering rhythmic inputs and supported combination—rather than selection—of different frequencies within the canonical γ and β electroencephalograph bands. These findings suggest that oscillating neuronal populations can support either response selection (routing) or combination, depending on the interplay between the kinetics of synaptic inhibition and the degree of heterogeneity of principal cell intrinsic conductances.

Impact of anxiety on prefrontal cortex encoding of cognitive flexibility

Anxiety often is studied as a stand-alone construct in laboratory models. But in the context of coping with real-life anxiety, its negative impacts extend beyond aversive feelings and involve disruptions in ongoing goal-directed behaviors and cognitive functioning. Critical examples of cognitive constructs affected by anxiety are cognitive flexibility and decision making. In particular, anxiety impedes the ability to shift flexibly between strategies in response to changes in task demands, as well as the ability to maintain a strategy in the presence of distractors. The brain region most critically involved in behavioral flexibility is the prefrontal cortex (PFC), but little is known about how anxiety impacts PFC encoding of internal and external events that are critical for flexible behavior. Here we review animal and human neurophysiological and neuroimaging studies implicating PFC neural processing in anxiety-induced deficits in cognitive flexibility. We then suggest experimental and analytical approaches for future studies to gain a better mechanistic understanding of impaired cognitive inflexibility in anxiety and related disorders.