Successful choice behavior is associated with distinct and coherent network states in anterior cingulate cortex

Parvalbumin interneuron deficiency in the prefrontal and motor cortices of spontaneously hypertensive rats: an attention-deficit hyperactivity disorder animal model insight

Background

Attention deficit hyperactivity disorder (ADHD) is characterized by impairments in developmental-behavioral inhibition, resulting in impulsivity and hyperactivity. Recent research has underscored cortical inhibition deficiencies in ADHD via the gamma-aminobutyric acid (GABA)ergic system, which is crucial for maintaining excitatory-inhibitory balance in the brain. This study explored postnatal changes in parvalbumin (PV) immunoreactivity, indicating GABAergic interneuron types, in the prefrontal (PFC) and motor (MC) cortices of spontaneously hypertensive rats (SHRs), an ADHD animal model.

Methods

Examining PV- positive (PV+) cells associated with dopamine D2 receptors (D2) and the impact of dopamine on GABA synthesis, we also investigated changes in the immunoreactivity of D2 and tyrosine hydroxylase (TH). Brain sections from 4- to 10-week-old SHRs and Wistar Kyoto rats (WKYs) were immunohistochemically analyzed, comparing PV+, D2+ cells, and TH+ fiber densities across age-matched SHRs and WKYs in specific PFC/MC regions.

Results

The results revealed significantly reduced PV+ cell density in SHRs: prelimbic (~20% less), anterior cingulate (~15% less), primary (~15% less), and secondary motor (~17% less) cortices. PV+ deficits coincided with the upregulation of D2 in prepubertal SHRs and the downregulation of TH predominantly in pubertal/postpubertal SHRs.

Conclusion

Reduced PV+ cells in various PFC regions could contribute to inattention/behavioral alterations in ADHD, while MC deficits could manifest as motor hyperactivity. D2 upregulation and TH deficits may impact GABA synthesis, exacerbating behavioral deficits in ADHD. These findings not only shed new light on ADHD pathophysiology but also pave the way for future research endeavors.

Anterior cingulate cortex regulates pain catastrophizing-like behaviors in rats

Negative pain expectation including pain catastrophizing is a well-known clinical phenomenon whereby patients amplify the aversive value of a painful or oftentimes even a similar, non-painful stimulus. Mechanisms of pain catastrophizing, however, remain elusive. Here, we modeled pain catastrophizing behavior in rats, and found that rats subjected to repeated noxious pin pricks on one paw demonstrated an aversive response to similar but non-noxious mechanical stimuli delivered to the contralateral paw. Optogenetic inhibition of pyramidal neuron activity in the anterior cingulate cortex (ACC) during the application of repetitive noxious pin pricks eliminated this catastrophizing behavior. Time-lapse calcium (Ca2+) imaging in the ACC further revealed an increase in spontaneous neural activity after the delivery of noxious stimuli. Together these results suggest that the experience of repeated noxious stimuli may drive hyperactivity in the ACC, causing increased avoidance of subthreshold stimuli, and that reducing this hyperactivity may play a role in treating pain catastrophizing.

Flexible coding schemes in dorsomedial prefrontal cortex underlie decision-making during delay discounting

Determining how an agent decides between a small, immediate versus a larger, delayed reward has provided insight into the psychological and neural basis of decision-making. The tendency to excessively discount the value of delayed rewards is thought to reflect deficits in brain regions critical for impulse control such as the prefrontal cortex (PFC). This study tested the hypothesis that dorsomedial PFC (dmPFC) is critically involved in flexibly managing neural representations of strategies that limit impulsive choices. Optogenetic silencing of neurons in the rat dmPFC increased impulsive choices at an 8 sec, but not 4 sec, delay. Neural recordings from dmPFC ensembles revealed that, at the 8-sec delay, the encoding landscape transitions to reflect a deliberative-like process rather than the schema-like processes observed at the 4-sec delay. These findings show that changes in the encoding landscape reflect changes in task demands and that dmPFC is uniquely involved in decisions requiring deliberation.

Differential processing of decision information in subregions of rodent medial prefrontal cortex

Decision-making involves multiple cognitive processes requiring different aspects of information about the situation at hand. The rodent medial prefrontal cortex (mPFC) has been hypothesized to be central to these abilities. Functional studies have sought to link specific processes to specific anatomical subregions, but past studies of mPFC have yielded controversial results, leaving the precise nature of mPFC function unclear. To settle this debate, we recorded from the full dorso-ventral extent of mPFC in each of 8 rats, as they performed a complex economic decision task. These data revealed four distinct functional domains within mPFC that closely mirrored anatomically identified subregions, including novel evidence to divide prelimbic cortex into dorsal and ventral components. We found that dorsal aspects of mPFC (ACC, dPL) were more involved in processing information about active decisions, while ventral aspects (vPL, IL) were more engaged in motivational factors.

Unsupervised discovery of behaviorally relevant brain states in rats playing hide-and-seek

In classical neuroscience experiments, neural activity is measured across many identical trials of animals performing simple tasks and is then analyzed, associating neural responses to pre-defined experimental parameters. This type of analysis is not suitable for patterns of behavior that unfold freely, such as play behavior. Here, we attempt an alternative approach for exploratory data analysis on a single-trial level, applicable in more complex and naturalistic behavioral settings in which no two trials are identical. We analyze neural population activity in the prefrontal cortex (PFC) of rats playing hide-and-seek and show that it is possible to discover what aspects of the task are reflected in the recorded activity with a limited number of simultaneously recorded cells (≤ 31). Using hidden Markov models, we cluster population activity in the PFC into a set of neural states, each associated with a pattern of neural activity. Despite high variability in behavior, relating the inferred states to the events of the hide-and-seek game reveals neural states that consistently appear at the same phases of the game. Furthermore, we show that by applying the segmentation inferred from neural data to the animals’ behavior, we can explore and discover novel correlations between neural activity and behavior. Finally, we replicate the results in a second dataset and show that population activity in the PFC displays distinct sets of states during playing hide-and-seek and observing others play the game. Overall, our results reveal robust, state-like representations in the rat PFC during unrestrained playful behavior and showcase the applicability of population analyses in naturalistic neuroscience.

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We used hidden Markov models to cluster neural activity of rats playing hide-and-seek

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Inferred latent states follow the phases of the hide-and-seek game

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Reverse physiology of latent states allows discovery of neurally relevant behaviors

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Rat prefrontal cortex is in distinct sets of states while playing and observing play

Event-based control of autonomic and emotional states by the anterior cingulate cortex

Despite being an intensive area of research, the function of the anterior cingulate cortex (ACC) remains somewhat of a mystery. Human imaging studies implicate the ACC in various cognitive functions, yet surgical ACC lesions used to treat emotional disorders have minimal lasting effects on cognition. An alternative view is that ACC regulates autonomic states, consistent with its interconnectivity with autonomic control regions and that stimulation evokes changes in autonomic/emotional states. At the cellular level, ACC neurons are highly multi-modal and promiscuous, and can represent a staggering array of task events. These neurons nevertheless combine to produce highly event-specific ensemble patterns that likely alter activity in downstream regions controlling emotional and autonomic tone. Since neuromodulators regulate the strength of the ensemble activity patterns, they would regulate the impact these patterns have on downstream targets. Through these mechanisms, the ACC may determine how strongly to react to the very events its ensembles represent. Pathologies arise when specific event-related representations gain excessive control over autonomic/emotional states.

Impaired cognitive flexibility and heightened urgency are associated with increased alcohol consumption in rodent models of excessive drinking

Alcohol use disorder (AUD) is characterized by impairments in decision-making that can exist as stable traits or transient states. Cognitive inflexibility reflects an inability to update information that guides decision-making and is thought to contribute to the inability to abstain from drinking. While several studies have reported evidence of impaired cognitive flexibility following chronic alcohol exposure, evidence that a pre-existing impairment in cognitive flexibility is a heritable risk factor for AUD is scarce. Here, we found that cognitive flexibility was impaired in rodents selectively bred for excessive alcohol consumption (alcohol preferring (P) rats), on the attentional set-shifting task (ASST). Further, the degree of impairment is predictive of future ethanol consumption, thus suggesting that cognitive inflexibility is a stable trait capable of predisposing one for drinking. In a second set of experiments, we observed an impairment in the ability of P rats to use a previously learned rule to guide foraging in a simple discrimination task. Convergence across several behavioral measures suggested that this impairment reflected a state of heightened urgency that interfered with decision-making. A similar impairment on a simple discrimination task was observed in Wistar rats with a history of alcohol consumption. These findings indicate how trait and state variables-in this case, impaired cognitive flexibility and heightened urgency, respectively-may influence the risk for excessive drinking. Furthermore, our results suggest that cognitive inflexibility and urgency can exist as both risk factors for and the result of alcohol exposure.

Dorsomedial prefrontal neural ensembles reflect changes in task utility that culminate in task quitting

When performing a physically demanding behavior, sometimes the optimal choice is to quit the behavior rather than persist to minimize energy expenditure for the benefits gained. The dorsomedial prefrontal cortex (dmPFC), consisting of the anterior cingulate cortex and secondary motor area, likely contributes toward such utility assessments. Here, we examined how male rat dmPFC single unit and ensemble-level activity corresponded to changes in task utility and quitting in an effortful weight lifting task. Rats carried out two task paradigms: one that became progressively more physically demanding over time and a second fixed effort version. Rats could quit the task at any time. Dorsomedial PFC neurons were highly responsive to each behavioral stage of the task, consisting of rope pulling, reward retrieval, and reward area leaving. Activity was highest early in sessions, commensurate with the highest relative task utility, then decreased until the point of quitting. Neural ensembles consistently represented the sequential behavioral phases of the task. However, these representations were modified over time and became more distinct over the course of the session. These results suggest that dmPFC neurons represent behavioral states that are dynamically modified as behaviors lose their utility, culminating in task quitting.NEW & NOTEWORTHY When carrying out a physically demanding task, animals must continually assess whether to persist or quit. In this study, we recorded neurons in the dorsomedial prefrontal cortex (dmPFC) of rats as they carried out a challenging weightlifting task, up to the point of quitting. We demonstrate that dmPFC neurons form a representation of the task that is modified, via a decrease in firing rate, by the decreasing the utility of the task that may signal quitting.

Focused Representation of Successive Task Episodes in Frontal and Parietal Cortex

Complex cognition is dynamic, with each stage of a task requiring new cognitive processes appropriately linked to stimulus or other content. To investigate control over successive task stages, we recorded neural activity in lateral frontal and parietal cortex as monkeys carried out a complex object selection task, with each trial separated into phases of visual selection and learning from feedback. To study capacity limitation, complexity was manipulated by varying the number of object targets to be learned in each problem. Different task phases were associated with quasi-independent patterns of activity and information coding, with no suggestion of sustained activity linked to a current target. Object and location coding were largely parallel in frontal and inferior parietal cortex, though frontal cortex showed somewhat stronger object representation at feedback, and more sustained location coding at choice. At both feedback and choice, coding precision diminished as task complexity increased, matching a decline in performance. We suggest that, across successive task steps, there is radical but capacity-limited reorganization of frontoparietal activity, selecting different cognitive operations linked to their current targets.

The anterior cingulate cortex and event-based modulation of autonomic states

In spite of being an intensive area of research focus, the anterior cingulate cortex (ACC) remains somewhat of an enigma. Many theories have focused on its role in various aspects of cognition yet surgically precise lesions of the ACC, used to treat severe emotional disorders in human patients, typically have no lasting effects on cognition. An alternative view is that the ACC has a prominent role in regulating autonomic states. This view is consistent with anatomical data showing that a main target of the ACC are regions involved in autonomic control and with the observation that stimulation of the ACC evokes changes in autonomic states in both animals and humans. From an electrophysiological perspective, ACC neurons appear able to represent virtually any event or internal state, even though there is not always a strong link between these representations and behavior. Ensembles of neurons form robust contextual representations that strongly influence how specific events are encoded. The activity patterns associated with these contextually-based event representations presumably impact activity in downstream regions that control autonomic state. As a result, the ACC may regulate the autonomic and perhaps emotional reactions to events it is representing. This event-based control of autonomic tone by the ACC would likely arise during all types of cognitive and affective processes, without necessarily being critical for any of them.

Potential roles of the rodent medial prefrontal cortex in conflict resolution between multiple decision-making systems

Mammalian decision-making is mediated by the interaction of multiple, neurally and computationally separable decision systems. Having multiple systems requires a mechanism to manage conflict and converge onto the selection of singular actions. A long history of evidence has pointed to the prefrontal cortex as a central component in processing the interactions between distinct decision systems and resolving conflicts among them. In this chapter we review four theories of how that interaction might occur and identify how the medial prefrontal cortex in the rodent may be involved in each theory. We then present experimental predictions implied by the neurobiological data in the context of each theory as a starting point for future investigation of medial prefrontal cortex and decision-making.

Amphetamine reduces reward encoding and stabilizes neural dynamics in rat anterior cingulate cortex

Psychostimulants such as d-amphetamine (AMPH) often have behavioral effects that appear paradoxical within the framework of optimal choice theory. AMPH typically increases task engagement and the effort animals exert for reward, despite decreasing reward valuation. We investigated neural correlates of this phenomenon in the anterior cingulate cortex (ACC), a brain structure implicated in signaling cost-benefit utility. AMPH decreased signaling of reward, but not effort, in the ACC of freely-moving rats. Ensembles of simultaneously recorded neurons generated task-specific trajectories of neural activity encoding past, present, and future events. Low-dose AMPH contracted these trajectories and reduced their variance, whereas high-dose AMPH expanded both. We propose that under low-dose AMPH, increased network stability balances moderately increased excitability, which promotes accelerated unfolding of a neural 'script' for task execution, despite reduced reward valuation. Noise from excessive excitability at high doses overcomes stability enhancement to drive frequent deviation from the script, impairing task execution.

Chemogenetic Modulation and Single-Photon Calcium Imaging in Anterior Cingulate Cortex Reveal a Mechanism for Effort-Based Decisions

The ACC is implicated in effort exertion and choices based on effort cost, but it is still unclear how it mediates this cost-benefit evaluation. Here, male rats were trained to exert effort for a high-value reward (sucrose pellets) in a progressive ratio lever-pressing task. Trained rats were then tested in two conditions: a no-choice condition where lever-pressing for sucrose was the only available food option, and a choice condition where a low-value reward (lab chow) was freely available as an alternative to pressing for sucrose. Disruption of ACC, via either chemogenetic inhibition or excitation, reduced lever-pressing in the choice, but not in the no-choice, condition. We next looked for value coding cells in ACC during effortful behavior and reward consumption phases during choice and no-choice conditions. For this, we used in vivo miniaturized fluorescence microscopy to reliably track responses of the same cells and compare how ACC neurons respond during the same effortful behavior where there was a choice versus when there was no-choice. We found that lever-press and sucrose-evoked responses were significantly weaker during choice compared with no-choice sessions, which may have rendered them more susceptible to chemogenetic disruption. Together, findings from our interference experiments and neural recordings suggest that a mechanism by which ACC mediates effortful decisions is in the discrimination of the utility of available options. ACC regulates these choices by providing a stable population code for the relative value of different options.

SIGNIFICANCE STATEMENT The ACC is implicated in effort-based decision-making. Here, we used chemogenetics and in vivo calcium imaging to explore its mechanism. Rats were trained to lever press for a high-value reward and tested in two conditions: a no-choice condition where lever-pressing for the high-value reward was the only option, and a choice condition where a low-value reward was also available. Inhibition or excitation of ACC reduced effort toward the high-value option, but only in the choice condition. Neural responses in ACC were weaker in the choice compared with the no-choice condition. A mechanism by which ACC regulates effortful decisions is in providing a stable population code for the discrimination of the utility of available options.

Central network changes in patients with advanced monocular blindness: A voxel-based morphometric study

OBJECTIVE

To study the changes in gray matter volume (GMV) in patients with advanced monocular blindness (MB) using voxel-based morphometry (VBM).

METHODS

Thirty-one patients with advanced MB (25 males and six females) and 31 normal controls (25 males and six females) were enrolled. The t test was applied to determine the differences in GMV, white matter volume (WMV), and volume of cerebrospinal fluid in different regions of the brain. The local characteristics of spontaneous concentrations of brain tissue were evaluated by the VBM method. The effects of blindness duration on differences in the GMV were evaluated by correlation and regression analyses.

RESULTS

Compared with the control group, the GMV was decreased in the upper right margin, bilateral insular cortex, right cingulate gyrus, left occipital gyrus, and right suboccipital lobe, and negatively correlated with blindness duration in the upper right posterior margin, bilateral insular cortex, and right cingulate cortex.

CONCLUSIONS

We found that patients with MB showed abnormal WMV and GMV, as evidenced by local changes in the brain. In addition, reduced GMV in specific parts of the brain was associated with the duration of blindness, which may indicate neuropathological mechanisms of visual loss in patients with MB.

Enhanced Population Coding for Rewarded Choices in the Medial Frontal Cortex of the Mouse

Instrumental behavior is characterized by the selection of actions based on the degree to which they lead to a desired outcome. However, we lack a detailed understanding of how rewarded actions are reinforced and preferentially implemented. In rodents, the medial frontal cortex is hypothesized to play an important role in this process, based in part on its capacity to encode chosen actions and their outcomes. We therefore asked how neural representations of choice and outcome might interact to facilitate instrumental behavior. To investigate this question, we imaged neural ensemble activity in layer 2/3 of the secondary motor region (M2) while mice engaged in a two-choice auditory discrimination task with probabilistic outcomes. Correct choices could result in one of three reward amounts (single, double or omitted reward), which allowed us to measure neural and behavioral effects of reward magnitude, as well as its categorical presence or absence. Single-unit and population decoding analyses revealed a consistent influence of outcome on choice signals in M2. Specifically, rewarded choices were more robustly encoded relative to unrewarded choices, with little dependence on the exact magnitude of reinforcement. Our results provide insight into the integration of past choices and outcomes in the rodent brain during instrumental behavior.

Conserved features of anterior cingulate networks support observational learning across species

The ability to observe, interpret, and learn behaviors and emotions from conspecifics is crucial for survival, as it bypasses direct experience to avoid potential dangers and maximize rewards and benefits. The anterior cingulate cortex (ACC) and its extended neural connections are emerging as important networks for the detection, encoding, and interpretation of social signals during observational learning. Evidence from rodents and primates (including humans) suggests that the social interactions that occur while individuals are exposed to important information in their environment lead to transfer of information across individuals that promotes adaptive behaviors in the form of either social affiliation, alertness, or avoidance. In this review, we first showcase anatomical and functional connections of the ACC in primates and rodents that contribute to the perception of social signals. We then discuss species-specific cognitive and social functions of the ACC and differentiate between neural activity related to 'self' and 'other', extending into the difference between social signals received and processed by the self, versus observing social interactions among others. We next describe behavioral and neural events that contribute to social learning via observation. Finally, we discuss some of the neural mechanisms underlying observational learning within the ACC and its extended network.

Probabilistic associative learning suffices for learning the temporal structure of multiple sequences

From memorizing a musical tune to navigating a well known route, many of our underlying behaviors have a strong temporal component. While the mechanisms behind the sequential nature of the underlying brain activity are likely multifarious and multi-scale, in this work we attempt to characterize to what degree some of this properties can be explained as a consequence of simple associative learning. To this end, we employ a parsimonious firing-rate attractor network equipped with the Hebbian-like Bayesian Confidence Propagating Neural Network (BCPNN) learning rule relying on synaptic traces with asymmetric temporal characteristics. The proposed network model is able to encode and reproduce temporal aspects of the input, and offers internal control of the recall dynamics by gain modulation. We provide an analytical characterisation of the relationship between the structure of the weight matrix, the dynamical network parameters and the temporal aspects of sequence recall. We also present a computational study of the performance of the system under the effects of noise for an extensive region of the parameter space. Finally, we show how the inclusion of modularity in our network structure facilitates the learning and recall of multiple overlapping sequences even in a noisy regime.

The Rat Medial Prefrontal Cortex Exhibits Flexible Neural Activity States during the Performance of an Odor Span Task

Medial prefrontal cortex (mPFC) activity is fundamental for working memory (WM), attention, and behavioral inhibition; however, a comprehensive understanding of the neural computations underlying these processes is still forthcoming. Toward this goal, neural recordings were obtained from the mPFC of awake, behaving rats performing an odor span task of WM capacity. Neural populations were observed to encode distinct task epochs and the transitions between epochs were accompanied by abrupt shifts in neural activity patterns. Putative pyramidal neuron activity increased earlier in the delay for sessions where rats achieved higher spans. Furthermore, increased putative interneuron activity was only observed at the termination of the delay thus indicating that local processing in inhibitory networks was a unique feature to initiate foraging. During foraging, changes in neural activity patterns associated with the approach to a novel odor, but not familiar odors, were robust. Collectively, these data suggest that distinct mPFC activity states underlie the delay, foraging, and reward epochs of the odor span task. Transitions between these states likely enables adaptive behavior in dynamic environments that place strong demands on the substrates of working memory.

Anterior cingulate cortex encoding of effortful behavior

An animal’s ability to assess the value of their behaviors to minimize energy use while maximizing goal achievement is critical to its survival. The anterior cingulate cortex (ACC) has been previously shown to play a critical role in this behavioral optimization process, especially when animals are faced with effortful behaviors. In the present study, we designed a novel task to investigate the role of the ACC in evaluating behaviors that varied in effort but all resulted in the same outcome. We recorded single unit activity from the ACC as rats ran back and forth in a shuttle box that could be tilted to different tilt angles (0, 15, and 25°) to manipulate effort. Overall, a majority of ACC neurons showed selective firing to specific effort conditions. During effort expenditure, ACC units showed a consistent firing rate bias toward the downhill route compared with the more difficult uphill route, regardless of the tilt angle of the apparatus. Once rats completed a run and received their fixed reward, ACC units also showed a clear firing rate preference for the single condition with the highest relative value (25° downhill). To assess effort preferences, we used a choice version of our task and confirmed that rats prefer downhill routes to uphill routes when given the choice. Overall, these results help to elucidate the functional role of the ACC in monitoring and evaluating effortful behaviors that may then bias decision-making toward behaviors with the highest utility.

NEW & NOTEWORTHY We developed a novel effort paradigm to investigate how the anterior cingulate cortex (ACC) responds to behaviors with varied degrees of physical effort and how changes in effort influence the ACC’s evaluation of behavioral outcomes. Our results provide evidence for a wider role of the ACC in its ability to motivate effortful behaviors and evaluate the outcome of multiple behaviors within an environment.

Thalamic afferents to prefrontal cortices from ventral motor nuclei in decision-making

The focus of this literature review is on the three interacting brain areas that participate in decision‐making: basal ganglia, ventral motor thalamic nuclei, and medial prefrontal cortex, with an emphasis on the participation of the ventromedial and ventral anterior motor thalamic nuclei in prefrontal cortical function. Apart from a defining input from the mediodorsal thalamus, the prefrontal cortex receives inputs from ventral motor thalamic nuclei that combine to mediate typical prefrontal functions such as associative learning, action selection, and decision‐making. Motor, somatosensory and medial prefrontal cortices are mainly contacted in layer 1 by the ventral motor thalamic nuclei and in layer 3 by thalamocortical input from mediodorsal thalamus. We will review anatomical, electrophysiological, and behavioral evidence for the proposed participation of ventral motor thalamic nuclei and medial prefrontal cortex in rat and mouse motor decision‐making.

What, If Anything, Is Rodent Prefrontal Cortex?

Prefrontal cortex (PFC) means different things to different people. In recent years, there has been a major increase in publications on the PFC, especially using mice. However, inconsistencies in the nomenclature and anatomical boundaries of PFC areas has made it difficult for researchers to compare data and interpret findings across species. We conducted a meta-analysis of publications on the PFC of humans and rodents and found dramatic differences in the focus of research on these species. In addition, we compared anatomical terms and criteria across several common rodent brain atlases and found inconsistencies among, and even within, leading atlases. To assess the impact of these issues on the research community, we conducted a survey of established PFC researchers on their use of anatomical terms and found little consensus. We report on the results of the survey and propose an alternative scheme for interpreting data from rodent studies, based on structural analysis of the corpus callosum and nomenclature used in research on the anterior cingulate cortex (ACC) of primates.

Persistent Valence Representations by Ensembles of Anterior Cingulate Cortex Neurons

The anterior cingulate cortex (ACC) responds to outcomes of a positive or negative valence, but past studies typically focus on one valence or the other, making it difficult to know how opposing valences are disambiguated. We recorded from ACC neurons as rats received tones followed by aversive, appetitive or null outcomes. The responses to the different tones/outcomes were highly inter-mixed at the single neuron level but combined to produce robust valence-specific representations at the ensemble level. The valence-specific patterns far outlasted the tones and outcomes, persisting throughout the long inter-trial intervals (ITIs) and even throughout trial blocks. When the trials were interleaved, the valence-specific patterns abruptly shifted at the start of each new trial. Overall the aversive trials had the greatest impact on the neurons. Thus within the ACC, valence-specificity is largely an emergent property of ensembles and valence-specific representations can appear quickly and persist long after the initiating event.

Learning the meaning of new stimuli increases the cross-correlated activity of prefrontal neurons

The prefrontal cortex (PF) has a key role in learning rules and generating associations between stimuli and responses also called conditional motor learning. Previous studies in PF have examined conditional motor learning at the single cell level but not the correlation of discharges between neurons at the ensemble level. In the present study, we recorded from two rhesus monkeys in the dorsolateral and the mediolateral parts of the prefrontal cortex to address the role of correlated firing of simultaneously recorded pairs during conditional motor learning. We trained two rhesus monkeys to associate three stimuli with three response targets, such that each stimulus was mapped to only one response. We recorded the neuronal activity of the same neuron pairs during learning of new associations and with already learned associations. In these tasks after a period of fixation, a visual instruction stimulus appeared centrally and three potential response targets appeared in three positions: right, left, and up from center. We found a higher number of neuron pairs significantly correlated and higher cross-correlation coefficients during stimulus presentation in the new than in the familiar mapping task. These results demonstrate that learning affects the PF neural correlation structure.

Temporal Dynamics of Hippocampal and Medial Prefrontal Cortex Interactions During the Delay Period of a Working Memory-Guided Foraging Task

Connections between the hippocampus (HC) and medial prefrontal cortex (mPFC) are critical for working memory; however, the precise contribution of this pathway is a matter of debate. One suggestion is that it may stabilize retrospective memories of recently encountered task-relevant information. Alternatively, it may be involved in encoding prospective memories, or the internal representation of future goals. To explore these possibilities, simultaneous extracellular recordings were made from mPFC and HC of rats performing the delayed spatial win-shift on a radial maze. Each trial consisted of a training-phase (when 4 randomly chosen arms were open) and test phase (all 8 arms were open but only previously blocked arms contained food) separated by a 60-s delay. Theta power was highest during the delay, and mPFC units were more likely to become entrained to hippocampal theta as the delay progressed. Training and test phase performance were accurately predicted by a linear classifier, and there was a transition in classification for training-phase to test-phase activity patterns throughout the delay on trials where the rats performed well. These data suggest that the HC and mPFC become more strongly synchronized as mPFC circuits preferentially shift from encoding retrospective to prospective information.

Human midcingulate cortex encodes distributed representations of task progress

The function of midcingulate cortex (MCC) remains elusive despite decades of investigation and debate. Complicating matters, individual MCC neurons respond to highly diverse task-related events, and MCC activation is reported in most human neuroimaging studies employing a wide variety of task manipulations. Here we investigate this issue by applying a model-based cognitive neuroscience approach involving neural network simulations, functional magnetic resonance imaging, and representational similarity analysis. We demonstrate that human MCC encodes distributed, dynamically evolving representations of extended, goal-directed action sequences. These representations are uniquely sensitive to the stage and identity of each sequence, indicating that MCC sustains contextual information necessary for discriminating between task states. These results suggest that standard univariate approaches for analyzing MCC function overlook the major portion of task-related information encoded by this brain area and point to promising new avenues for investigation.

Rate and Temporal Coding Mechanisms in the Anterior Cingulate Cortex for Pain Anticipation

Pain is a complex sensory and affective experience. Through its anticipation, animals can learn to avoid pain. Much is known about passive avoidance during a painful event; however, less is known about active pain avoidance. The anterior cingulate cortex (ACC) is a critical hub for affective pain processing. However, there is currently no mechanism that links ACC activities at the cellular level with behavioral anticipation or avoidance. Here we asked whether distinct populations of neurons in the ACC can encode information for pain anticipation. We used tetrodes to record from ACC neurons during a conditioning assay to train rats to avoid pain. We found that in rats that successfully avoid acute pain episodes, neurons that responded to pain shifted their firing rates to an earlier time, whereas neurons that responded to the anticipation of pain increased their firing rates prior to noxious stimulation. Furthermore, we found a selected group of neurons that shifted their firing from a pain-tuned response to an anticipatory response. Unsupervised learning analysis of ensemble spike activity indicates that temporal spiking patterns of ACC neurons can indeed predict the onset of pain avoidance. These results suggest rate and temporal coding schemes in the ACC for pain avoidance.

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.

Rat anterior cingulate cortex recalls features of remote reward locations after disfavoured reinforcements

The anterior cingulate cortex (ACC) encodes information supporting mnemonic and cognitive processes. We show here that a rat’s position can be decoded with high spatiotemporal resolution from ACC activity. ACC neurons encoded the current state of the animal and task, except for brief excursions that sometimes occurred at target feeders. During excursions, the decoded position became more similar to a remote target feeder than the rat’s physical position. Excursions recruited activation of neurons encoding choice and reward, and the likelihood of excursions at a feeder was inversely correlated with feeder preference. These data suggest that the excursion phenomenon was related to evaluating real or fictive choice outcomes, particularly after disfavoured reinforcements. We propose that the multiplexing of position with choice-related information forms a mental model isomorphic with the task space, which can be mentally navigated via excursions to recall multimodal information about the utility of remote locations.

Functional connectivity of task context representations in prefrontal nodes of the multiple demand network

A subset of regions in the lateral and medial prefrontal cortex and the anterior insula increase their activity level whenever a cognitive task becomes more demanding, regardless of the specific nature of this demand. During execution of a task, these areas and the surrounding cortex temporally encode aspects of the task context in spatially distributed patterns of activity. It is not clear whether these patterns reflect underlying anatomical subnetworks that still exist when task execution has finished. We use fMRI in 12 participants performing alternating blocks of three cognitive tasks to address this question. A first data set is used to define multiple demand regions in each participant. A second dataset from the same participants is used to determine multiple demand voxel assemblies with a preference for one task over the others. We then show that these voxels remain functionally coupled during execution of non-preferred tasks and that they exhibit stronger functional connectivity during rest. This indicates that the assemblies of task preference sharing voxels reflect patterns of underlying anatomical connections. Moreover, we show that voxels preferring the same task have more similar whole brain functional connectivity profiles that are consistent across participants. This suggests that voxel assemblies differ in patterns of input-output connections, most likely reflecting task demand-specific information exchange.

Habitual Behavior Is Mediated by a Shift in Response-Outcome Encoding by Infralimbic Cortex

The ability to flexibly switch between goal-directed actions and habits is critical for adaptive behavior. The infralimbic prefrontal cortex (IfL-C) has been consistently identified as a crucial structure for the regulation of response strategies. To investigate the role of the IfL-C, the present study employed two validated reinforcement schedules that either promote habits or goal-directed actions in mice. The results reveal that information about action-outcome relationships is differentially encoded in the IfL-C during actions and habits as evidenced by encoding of behavioral outcomes during goal-directed actions that is lost during habits. Optogenetic inhibition of the IfL-C selectively at press during habitual behavior (when firing rates are reduced during unreinforced goal-directed actions) resulted in restoration of sensitivity to change of action-outcome contingency. These results reveal a novel functional mechanism by which IfL-C promotes habitual behavior, and provide insight into strategies for the treatment and prevention of pathological, inflexible behavior common in neuropsychiatric illness.

Interactions between medial prefrontal cortex and dorsomedial striatum are necessary for odor span capacity in rats: role of GluN2B-containing NMDA receptors

Working memory is involved in the maintenance and manipulation of information essential for complex cognition. While the neural substrates underlying working memory capacity have been studied in humans, considerably less is known about the circuitry mediating working memory capacity in rodents. Therefore, the present experiments tested the involvement of medial prefrontal cortex (mPFC) and dorsal striatum (STR) in the odor span task (OST), a task proposed to assay working memory capacity in rodents. Initially, Long Evans rats were trained to dig in scented sand for food following a serial delayed nonmatching-to-sample rule. Temporary inactivation of dorsomedial (dm) STR significantly reduced span in well trained rats. Inactivation of mPFC or contralateral disconnection of the mPFC and dmSTR also reduced span. Infusing the GluN2B-containing NMDA receptor antagonist Ro 25-6981 into mPFC did not affect span; however, span was significantly reduced following bilateral Ro 25-6981 infusions into dmSTR or contralateral disconnection of mPFC (inactivation) and dmSTR (Ro 25-6981). These results suggest that span capacity in rats depends on GluN2B-containing NMDA receptor-dependent interactions between the mPFC and the dmSTR. Therefore, interventions targeting this circuit may improve the working memory capacity impairments in patients with schizophrenia, Alzheimer's disease, and Parkinson's disease.

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.

Fronto-parietal Cortical Circuits Encode Accumulated Evidence with a Diversity of Timescales

Decision-making in dynamic environments often involves accumulation of evidence, in which new information is used to update beliefs and select future actions. Using in vivo cellular resolution imaging in voluntarily head-restrained rats, we examined the responses of neurons in frontal and parietal cortices during a pulse-based accumulation of evidence task. Neurons exhibited activity that predicted the animal's upcoming choice, previous choice, and graded responses that reflected the strength of the accumulated evidence. The pulsatile nature of the stimuli enabled characterization of the responses of neurons to a single quantum (pulse) of evidence. Across the population, individual neurons displayed extensive heterogeneity in the dynamics of responses to pulses. The diversity of responses was sufficiently rich to form a temporal basis for accumulated evidence estimated from a latent variable model. These results suggest that heterogeneous, often transient sensory responses distributed across the fronto-parietal cortex may support working memory on behavioral timescales. VIDEO ABSTRACT.

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.

Cell assemblies at multiple time scales with arbitrary lag constellations

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

DOI:http://dx.doi.org/10.7554/eLife.19428.001

Neural representation of cost-benefit selections in rat anterior cingulate cortex in self-paced decision making

The anterior cingulate cortex (ACC) is crucial for decision making which involves the processing of cost-benefit information. Our previous study has shown that ACC is essential for self-paced decision making. However, it is unclear how ACC neurons represent cost-benefit selections during the decision-making process. In the present study, we trained rats on the same "Do More Get More" (DMGM) task as in our previous work. In each trial, the animals stand upright and perform a sustained nosepoke of their own will to earn a water reward, with the amount of reward positively correlated to the duration of the nosepoke (i.e., longer nosepokes earn larger rewards). We then recorded ACC neuronal activity on well-trained rats while they were performing the DMGM task. Our results show that (1) approximately 3/5 ACC neurons (296/496, 59.7%) exhibited changes in firing frequency that were temporally locked with the main events of the DMGM task; (2) about 1/5 ACC neurons (101/496, 20.4%) or 1/3 of the event-modulated neurons (101/296, 34.1%) showed differential firing rate changes for different cost-benefit selections; and (3) many ACC neurons exhibited linear encoding of the cost-benefit selections in the DMGM task events. These results suggest that ACC neurons are engaged in encoding cost-benefit information, thus represent the selections in self-paced decision making.

A Dynamic Bayesian Model for Characterizing Cross-Neuronal Interactions During Decision-Making

The goal of this paper is to develop a novel statistical model for studying cross-neuronal spike train interactions during decision making. For an individual to successfully complete the task of decision-making, a number of temporally-organized events must occur: stimuli must be detected, potential outcomes must be evaluated, behaviors must be executed or inhibited, and outcomes (such as reward or no-reward) must be experienced. Due to the complexity of this process, it is likely the case that decision-making is encoded by the temporally-precise interactions between large populations of neurons. Most existing statistical models, however, are inadequate for analyzing such a phenomenon because they provide only an aggregated measure of interactions over time. To address this considerable limitation, we propose a dynamic Bayesian model which captures the time-varying nature of neuronal activity (such as the time-varying strength of the interactions between neurons). The proposed method yielded results that reveal new insight into the dynamic nature of population coding in the prefrontal cortex during decision making. In our analysis, we note that while some neurons in the prefrontal cortex do not synchronize their firing activity until the presence of a reward, a different set of neurons synchronize their activity shortly after stimulus onset. These differentially synchronizing sub-populations of neurons suggests a continuum of population representation of the reward-seeking task. Secondly, our analyses also suggest that the degree of synchronization differs between the rewarded and non-rewarded conditions. Moreover, the proposed model is scalable to handle data on many simultaneously-recorded neurons and is applicable to analyzing other types of multivariate time series data with latent structure. Supplementary materials (including computer codes) for our paper are available online.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.

A Detailed Data-Driven Network Model of Prefrontal Cortex Reproduces Key Features of In Vivo Activity

The prefrontal cortex is centrally involved in a wide range of cognitive functions and their impairment in psychiatric disorders. Yet, the computational principles that govern the dynamics of prefrontal neural networks, and link their physiological, biochemical and anatomical properties to cognitive functions, are not well understood. Computational models can help to bridge the gap between these different levels of description, provided they are sufficiently constrained by experimental data and capable of predicting key properties of the intact cortex. Here, we present a detailed network model of the prefrontal cortex, based on a simple computationally efficient single neuron model (simpAdEx), with all parameters derived from in vitro electrophysiological and anatomical data. Without additional tuning, this model could be shown to quantitatively reproduce a wide range of measures from in vivo electrophysiological recordings, to a degree where simulated and experimentally observed activities were statistically indistinguishable. These measures include spike train statistics, membrane potential fluctuations, local field potentials, and the transmission of transient stimulus information across layers. We further demonstrate that model predictions are robust against moderate changes in key parameters, and that synaptic heterogeneity is a crucial ingredient to the quantitative reproduction of in vivo-like electrophysiological behavior. Thus, we have produced a physiologically highly valid, in a quantitative sense, yet computationally efficient PFC network model, which helped to identify key properties underlying spike time dynamics as observed in vivo, and can be harvested for in-depth investigation of the links between physiology and cognition.

Computational network models are an important tool for linking physiological and neuro-dynamical processes to cognition. However, harvesting network models for this purpose may less depend on how much biophysical detail is included, but more on how well the model can capture the functional network physiology. Here, we present the first network model of the prefrontal cortex which has not only its single neuron properties and anatomical layout tightly constrained by experimental data, but is also able to quantitatively reproduce a large range of spiking, field potential, and membrane voltage statistics obtained from in vivo data, without need of specific parameter tuning. It thus represents a novel computational tool for addressing questions about the neuro-dynamics of cognition in health and disease.

Dorsal Anterior Cingulate Cortex: A Bottom-Up View

The dorsal anterior cingulate cortex (dACC) has attracted great interest from neuroscientists because it is associated with so many important cognitive functions. Despite, or perhaps because of, its rich functional repertoire, we lack a single comprehensive view of its function. Most research has approached this puzzle from the top down, using aggregate measures such as neuroimaging. We provide a view from the bottom up, with a focus on single-unit responses and anatomy. We summarize the strengths and weaknesses of the three major approaches to characterizing the dACC: as a monitor, as a controller, and as an economic structure. We argue that neurons in the dACC are specialized for representing contexts, or task-state variables relevant for behavior, and strategies, or aspects of future plans. We propose that dACC neurons link contexts with strategies by integrating diverse task-relevant information to create a rich representation of task space and exert high-level and abstract control over decision and action.

Alcohol-induced alterations in dopamine modulation of prefrontal activity

Long-term alcohol use leads to persistent cognitive deficits that may be associated with maladaptive changes in the neurocircuitry that mediates executive functions. Impairments caused by these changes can persist well into abstinence and have a negative impact on quality of life and job performance, and can increase the probability of relapse. Many of the changes that affect cognitive function appear to involve dysregulation of the mesocortical dopamine system. This includes changes in dopamine release and alterations in dopamine receptor expression and function in the medial prefrontal cortex (PFC). This review summarizes the cellular effects of acute and chronic ethanol exposure on dopamine release and dopamine receptor function in the PFC with the goal of providing greater understanding of the effects of alcohol-use disorders on the dopamine system and how this relates to deficits in the executive function of the PFC.

Inactivation of the prelimbic or infralimbic cortex impairs decision-making in the rat gambling task

RATIONALE

Studies employing the Iowa Gambling Task (IGT) demonstrated that areas of the frontal cortex, including the ventromedial prefrontal cortex, orbitofrontal cortex (OFC), dorsolateral prefrontal cortex, and anterior cingulate cortex (ACC), are involved in the decision-making process. However, the precise role of these regions in maintaining optimal choice is not clear.

OBJECTIVES

We used the rat gambling task (rGT), a rodent analogue of the IGT, to determine whether inactivation of or altered dopamine signalling within discrete cortical sub-regions disrupts decision-making.

METHODS

Following training on the rGT, animals were implanted with guide cannulae aimed at the prelimbic (PrL) or infralimbic (IL) cortices, the OFC, or the ACC. Prior to testing, rats received an infusion of saline or a combination of baclofen and muscimol (0.125 μg of each/side) to inactivate the region and an infusion of a dopamine D2 receptor antagonist (0, 0.1, 0.3, and 1.0 μg/side).

RESULTS

Rats tended to increase their choice of a disadvantageous option and decrease their choice of the optimal option following inactivation of either the IL or PrL cortex. In contrast, OFC or ACC inactivation did not affect decision-making. Infusion of a dopamine D2 receptor antagonist into any sub-region did not alter choice preference.

CONCLUSIONS

Online activity of the IL or PrL cortex is important for maintaining an optimal decision-making strategy, but optimal performance on the rGT does not require frontal cortex dopamine D2 receptor activation. Additionally, these results demonstrate that the roles of different cortical regions in cost-benefit decision-making may be dissociated using the rGT.

A statistical approach for segregating cognitive task stages from multivariate fMRI BOLD time series

Multivariate pattern analysis can reveal new information from neuroimaging data to illuminate human cognition and its disturbances. Here, we develop a methodological approach, based on multivariate statistical/machine learning and time series analysis, to discern cognitive processing stages from functional magnetic resonance imaging (fMRI) blood oxygenation level dependent (BOLD) time series. We apply this method to data recorded from a group of healthy adults whilst performing a virtual reality version of the delayed win-shift radial arm maze (RAM) task. This task has been frequently used to study working memory and decision making in rodents. Using linear classifiers and multivariate test statistics in conjunction with time series bootstraps, we show that different cognitive stages of the task, as defined by the experimenter, namely, the encoding/retrieval, choice, reward and delay stages, can be statistically discriminated from the BOLD time series in brain areas relevant for decision making and working memory. Discrimination of these task stages was significantly reduced during poor behavioral performance in dorsolateral prefrontal cortex (DLPFC), but not in the primary visual cortex (V1). Experimenter-defined dissection of time series into class labels based on task structure was confirmed by an unsupervised, bottom-up approach based on Hidden Markov Models. Furthermore, we show that different groupings of recorded time points into cognitive event classes can be used to test hypotheses about the specific cognitive role of a given brain region during task execution. We found that whilst the DLPFC strongly differentiated between task stages associated with different memory loads, but not between different visual-spatial aspects, the reverse was true for V1. Our methodology illustrates how different aspects of cognitive information processing during one and the same task can be separated and attributed to specific brain regions based on information contained in multivariate patterns of voxel activity.

Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory

Modulation of neural activity by monoamine neurotransmitters is thought to play an essential role in shaping computational neurodynamics in the neocortex, especially in prefrontal regions. Computational theories propose that monoamines may exert bidirectional (concentration-dependent) effects on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped function. To date, this hypothesis has not been addressed directly, in part because of the absence of appropriate statistical methods required to assess attractor-like behavior in vivo. The present study used a combination of advanced multivariate statistical, time series analysis, and machine learning methods to assess dynamic changes in network activity from multiple single-unit recordings from the medial prefrontal cortex (mPFC) of rats while the animals performed a foraging task guided by working memory after pretreatment with different doses of d-amphetamine (AMPH), which increases monoamine efflux in the mPFC. A dose-dependent, bidirectional effect of AMPH on neural dynamics in the mPFC was observed. Specifically, a 1.0 mg/kg dose of AMPH accentuated separation between task-epoch-specific population states and convergence toward these states. In contrast, a 3.3 mg/kg dose diminished separation and convergence toward task-epoch-specific population states, which was paralleled by deficits in cognitive performance. These results support the computationally derived hypothesis that moderate increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine levels would severely diminish it. Furthermore, they are consistent with the proposed inverted U-shaped and concentration-dependent modulation of cortical efficiency by monoamines.