Coding of Self and Other's Future Choices in Dorsal Premotor Cortex during Social Interaction

Cross-sectional and longitudinal neuroanatomical profiles of distinct clinical (adaptive) outcomes in autism

Individuals with autism spectrum disorder (henceforth referred to as autism) display significant variation in clinical outcome. For instance, across age, some individuals' adaptive skills naturally improve or remain stable, while others' decrease. To pave the way for 'precision-medicine' approaches, it is crucial to identify the cross-sectional and, given the developmental nature of autism, longitudinal neurobiological (including neuroanatomical and linked genetic) correlates of this variation. We conducted a longitudinal follow-up study of 333 individuals (161 autistic and 172 neurotypical individuals, aged 6-30 years), with two assessment time points separated by ~12-24 months. We collected behavioural (Vineland Adaptive Behaviour Scale-II, VABS-II) and neuroanatomical (structural magnetic resonance imaging) data. Autistic participants were grouped into clinically meaningful "Increasers", "No-changers", and "Decreasers" in adaptive behaviour (based on VABS-II scores). We compared each clinical subgroup's neuroanatomy (surface area and cortical thickness at T1, ∆T (intra-individual change) and T2) to that of the neurotypicals. Next, we explored the neuroanatomical differences' potential genomic associates using the Allen Human Brain Atlas. Clinical subgroups had distinct neuroanatomical profiles in surface area and cortical thickness at baseline, neuroanatomical development, and follow-up. These profiles were enriched for genes previously associated with autism and for genes previously linked to neurobiological pathways implicated in autism (e.g. excitation-inhibition systems). Our findings suggest that distinct clinical outcomes (i.e. intra-individual change in clinical profiles) linked to autism core symptoms are associated with atypical cross-sectional and longitudinal, i.e. developmental, neurobiological profiles. If validated, our findings may advance the development of interventions, e.g. targeting mechanisms linked to relatively poorer outcomes.

F = ma. Is the macaque brain Newtonian?

Intuitive Physics, the ability to anticipate how the physical events involving mass objects unfold in time and space, is a central component of intelligent systems. Intuitive physics is a promising tool for gaining insight into mechanisms that generalize across species because both humans and non-human primates are subject to the same physical constraints when engaging with the environment. Physical reasoning abilities are widely present within the animal kingdom, but monkeys, with acute 3D vision and a high level of dexterity, appreciate and manipulate the physical world in much the same way humans do.

Social monitoring of actions in the macaque frontopolar cortex

The frontopolar cortex (FPC) of primates appeared as a main innovation in the evolution of anthropoid primates and it has been placed at the top of the prefrontal hierarchy. The only study to date that investigated the activity of FPC neurons in monkeys performing a cognitive task suggested that these cells were involved in the monitoring of self-generated actions. We recorded the activity of neurons in the FPCs of two rhesus monkeys while they performed a social variant of a nonmatch-to-goal task that required monitoring the actions of a human or computer agent. We discovered that the role of FPC neurons extends beyond self-generated actions to include monitoring others' actions. Their monitoring activity was very specific. First, neurons in the FPC encoded the spatial position of the target but not its object features. Second, a dedicated representation of the human agent actions was tied to the time of target acquisition, while it was reduced or absent in the successive epochs of the trial. Finally, this other-specific neural substrate did not emerge during the interaction with a virtual agent such as the computer. These results provide a new perspective on the functions of a uniquely primate brain area, suggesting that FPC might play an important role in social behaviors.

Prefrontal cortex interactions with the amygdala in primates

This review addresses functional interactions between the primate prefrontal cortex (PFC) and the amygdala, with emphasis on their contributions to behavior and cognition. The interplay between these two telencephalic structures contributes to adaptive behavior and to the evolutionary success of all primate species. In our species, dysfunction in this circuitry creates vulnerabilities to psychopathologies. Here, we describe amygdala-PFC contributions to behaviors that have direct relevance to Darwinian fitness: learned approach and avoidance, foraging, predator defense, and social signaling, which have in common the need for flexibility and sensitivity to specific and rapidly changing contexts. Examples include the prediction of positive outcomes, such as food availability, food desirability, and various social rewards, or of negative outcomes, such as threats of harm from predators or conspecifics. To promote fitness optimally, these stimulus-outcome associations need to be rapidly updated when an associative contingency changes or when the value of a predicted outcome changes. We review evidence from nonhuman primates implicating the PFC, the amygdala, and their functional interactions in these processes, with links to experimental work and clinical findings in humans where possible.

Neuronal Circuits for Social Decision-Making and Their Clinical Implications

Social living facilitates individual access to rewards, cognitive resources, and objects that would not be otherwise accessible. There are, however, some drawbacks to social living, particularly when competing for scarce resources. Furthermore, variability in our ability to make social decisions can be associated with neuropsychiatric disorders. The neuronal mechanisms underlying social decision-making are beginning to be understood. The momentum to study this phenomenon has been partially carried over by the study of economic decision-making. Yet, because of the similarities between these different types of decision-making, it is unclear what is a social decision. Here, we propose a definition of social decision-making as choices taken in a context where one or more conspecifics are involved in the decision or the consequences of it. Social decisions can be conceptualized as complex economic decisions since they are based on the subjective preferences between different goods. During social decisions, individuals choose based on their internal value estimate of the different alternatives. These are complex decisions given that conspecifics beliefs or actions could modify the subject's internal valuations at every choice. Here, we first review recent developments in our collective understanding of the neuronal mechanisms and circuits of social decision-making in primates. We then review literature characterizing populations with neuropsychiatric disorders showing deficits in social decision-making and the underlying neuronal circuitries associated with these deficits.

Dedicated Representation of Others in the Macaque Frontal Cortex: From Action Monitoring and Prediction to Outcome Evaluation

Social neurophysiology has increasingly addressed how several aspects of self and other are distinctly represented in the brain. In social interactions, the self–other distinction is fundamental for discriminating one’s own actions, intentions, and outcomes from those that originate in the external world. In this paper, we review neurophysiological experiments using nonhuman primates that shed light on the importance of the self–other distinction, focusing mainly on the frontal cortex. We start by examining how the findings are impacted by the experimental paradigms that are used, such as the type of social partner or whether a passive or active interaction is required. Next, we describe the 2 sociocognitive systems: mirror and mentalizing. Finally, we discuss how the self–other distinction can occur in different domains to process different aspects of social information: the observation and prediction of others’ actions and the monitoring of others’ rewards.

Macaque monkeys learn and perform a non-match-to-goal task using an automated home cage training procedure

In neurophysiology, nonhuman primates represent an important model for studying the brain. Typically, monkeys are moved from their home cage to an experimental room daily, where they sit in a primate chair and interact with electronic devices. Refining this procedure would make the researchers' work easier and improve the animals' welfare. To address this issue, we used home-cage training to train two macaque monkeys in a non-match-to-goal task, where each trial required a switch from the choice made in the previous trial to obtain a reward. The monkeys were tested in two versions of the task, one in which they acted as the agent in every trial and one in which some trials were completed by a "ghost agent". We evaluated their involvement in terms of their performance and their interaction with the apparatus. Both monkeys were able to maintain a constant involvement in the task with good, stable performance within sessions in both versions of the task. Our study confirms the feasibility of home-cage training and demonstrates that even with challenging tasks, monkeys can complete a large number of trials at a high performance level, which is a prerequisite for electrophysiological studies of monkey behavior.

Hidden Markov Models Predict the Future Choice Better Than a PSTH-Based Method

Beyond average firing rate, other measurable signals of neuronal activity are fundamental to an understanding of behavior. Recently, hidden Markov models (HMMs) have been applied to neural recordings and have described how neuronal ensembles process information by going through sequences of different states. Such collective dynamics are impossible to capture by just looking at the average firing rate. To estimate how well HMMs can decode information contained in single trials, we compared HMMs with a recently developed classification method based on the peristimulus time histogram (PSTH). The accuracy of the two methods was tested by using the activity of prefrontal neurons recorded while two monkeys were engaged in a strategy task. In this task, the monkeys had to select one of three spatial targets based on an instruction cue and on their previous choice. We show that by using the single trial's neural activity in a period preceding action execution, both models were able to classify the monkeys' choice with an accuracy higher than by chance. Moreover, the HMM was significantly more accurate than the PSTH-based method, even in cases in which the HMM performance was low, although always above chance. Furthermore, the accuracy of both methods was related to the number of neurons exhibiting spatial selectivity within an experimental session. Overall, our study shows that neural activity is better described when not only the mean activity of individual neurons is considered and that therefore, the study of other signals rather than only the average firing rate is fundamental to an understanding of the dynamics of neuronal ensembles.

Macaque monkeys learn by observation in the ghost display condition in the object-in-place task with differential reward to the observer

Observational learning has been investigated in monkeys mainly using conspecifics or humans as models to observe. Some studies attempted to clarify the social agent’s role and to test whether non-human primates could learn from observation of a non-social agent, usually mentioned as a ‘ghost display’ condition, but they reported conflicting results. To address this question, we trained three rhesus monkeys in an object-in-place task consisting of the presentation of five subsequent problems composed of two objects, one rewarded and one unrewarded, for six times, or runs. Three types of learning conditions were tested. In the individual learning condition, the monkeys performed the first run, learned from it and improved their performance in the following runs. In the social and non-social learning conditions, they observed respectively a human model and a computer performing the first run and learned by the observation of their successes or errors. In all three conditions, the monkeys themselves received the reward after correct choices only. One-trial learning occurred in all three conditions. The monkeys performed over chance in the second run in all conditions, providing evidence of non-social observational learning with differential reward in macaque monkeys using a “ghost display” condition in a cognitive task.

Neural Intrinsic Timescales in the Macaque Dorsal Premotor Cortex Predict the Strength of Spatial Response Coding

Our brain continuously receives information over multiple timescales that are differently processed across areas. In this study, we investigated the intrinsic timescale of neurons in the dorsal premotor cortex (PMd) of two rhesus macaques while performing a non-match-to-goal task. The task rule was to reject the previously chosen target and select the alternative one. We defined the intrinsic timescale as the decay constant of the autocorrelation structure computed during a baseline period of the task. We found that neurons with longer intrinsic timescale tended to maintain a stronger spatial response coding during a delay period. This result suggests that longer intrinsic timescales predict the functional role of PMd neurons in a cognitive task. Our estimate of the intrinsic timescale integrates an existing hierarchical model (Murray et al., 2014), by assigning to PMd a lower position than prefrontal cortex in the hierarchical ordering of the brain areas based on neurons' timescales.

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The spatial response encoding during a delay depends on neurons' timescales

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Longer intrinsic timescales foretell the role of PMd neurons in a cognitive task

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PMd occupies a lower level than PF in the hierarchical organization of brain areas