Multicentric tracking of multiple agents by anterior cingulate cortex during pursuit and evasion

Cognivue Clarity® characterizes amyloid status and preclinical Alzheimer's disease in biomarker confirmed cohorts in the Bio-Hermes Study

BackgroundCognivue Clarity® is an FDA-cleared computerized cognitive test to screen for cognitive impairment included in the Bio-Hermes Study to test blood-based and digital biomarkers' ability to screen for mild cognitive impairment (MCI) and Alzheimer's disease (AD). A subset of cognitively normal individuals have amyloid deposition (Preclinical AD) but no current assessment can identify these individuals in the absence of expensive biomarkers.ObjectiveWe examined differences in Cognivue Clarity performance between amyloid positive and amyloid negative individuals and whether Cognivue Clarity could differentiate True Controls (cognitively normal/amyloid negative), Preclinical AD (cognitively normal/amyloid positive), and MCI due to AD (MCI-AD, cognitively impaired/amyloid positive).MethodsCognivue Clarity was administered to all participants in the Bio-Hermes Study who also had amyloid PET and blood-based biomarkers. Performance was compared between biomarker-defined groups: True Controls (n = 297), Preclinical AD (n = 95), and MCI-AD (n = 113).ResultsCognivue Clarity global scores distinguished amyloid positive individuals from amyloid negative individuals (p < 0.001) and differentiated True Controls versus Preclinical AD (p = 0.014) and Preclinical AD versus MCI-AD (p < 0.001). Three subtests [Shape Discrimination (p = 0.004), Visual Salience (p = 0.008), Adaptive Motor Control (p = 0.004)] and the 3-test mean (p < 0.001) differentiated True Controls from Preclinical AD. The 3-test composite correlated with Amyloid PET (r = -0.433) and pTau217 (r = -0.400). The 3-test mean identified Preclinical AD in both White and Black participants.ConclusionsCognivue Clarity, a 10-min computerized battery, screens for individuals with cognitive impairment, characterizes amyloid positive individuals, and identifies Preclinical AD. This has great potential as a cost- and time-effective strategy to screen and enroll in AD prevention trials.

Control over a mixture of policies determines change of mind topology during continuous choice

Behavior is naturally organized into categorically distinct states with corresponding patterns of neural activity; how does the brain control those states? We propose that states are regulated by specific neural processes that implement meta-control that can blend simpler control processes. To test this hypothesis, we recorded from neurons in the dorsal anterior cingulate cortex (dACC) and dorsal premotor cortex (PMd) while macaques performed a continuous pursuit task with two moving prey that followed evasive strategies. We used a novel control theoretic approach to infer subjects' moment-to-moment latent control variables, which in turn dictated their blend of distinct identifiable control processes. We identified low-dimensional subspaces in neuronal responses that reflected the current strategy, the value of the pursued target, and the relative value of the two targets. The top two principal components of activity tracked changes of mind in abstract and change-type-specific formats, respectively. These results indicate that control of behavioral state reflects the interaction of brain processes found in dorsal prefrontal regions that implement a mixture over low-level control policies.

The Neural Correlates of Individual Differences in Reinforcement Learning during Pain Avoidance and Reward Seeking

Organisms learn to gain reward and avoid punishment through action-outcome associations. Reinforcement learning (RL) offers a critical framework to understand individual differences in this associative learning by assessing learning rate, action bias, pavlovian factor (i.e., the extent to which action values are influenced by stimulus values), and subjective impact of outcomes (i.e., motivation to seek reward and avoid punishment). Nevertheless, how these individual-level metrics are represented in the brain remains unclear. The current study leveraged fMRI in healthy humans and a probabilistic learning go/no-go task to characterize the neural correlates involved in learning to seek reward and avoid pain. Behaviorally, participants showed a higher learning rate during pain avoidance relative to reward seeking. Additionally, the subjective impact of outcomes was greater for reward trials and associated with lower response randomness. Our imaging findings showed that individual differences in learning rate and performance accuracy during avoidance learning were positively associated with activities of the dorsal anterior cingulate cortex, midcingulate cortex, and postcentral gyrus. In contrast, the pavlovian factor was represented in the precentral gyrus and superior frontal gyrus (SFG) during pain avoidance and reward seeking, respectively. Individual variation of the subjective impact of outcomes was positively predicted by activation of the left posterior cingulate cortex. Finally, action bias was represented by the supplementary motor area (SMA) and pre-SMA whereas the SFG played a role in restraining this action tendency. Together, these findings highlight for the first time the neural substrates of individual differences in the computational processes during RL.

Widespread coding of navigational variables in prefrontal cortex

To navigate effectively, we must represent information about our location in the environment. Traditional research highlights the role of the hippocampal complex in this process. Spurred by recent research highlighting the widespread cortical encoding of cognitive and motor variables previously thought to have localized function, we hypothesized that navigational variables would be likewise encoded widely, especially in the prefrontal cortex, which is associated with volitional behavior. We recorded neural activity from six prefrontal regions while macaques performed a foraging task in an open enclosure. In all regions, we found strong encoding of allocentric position, allocentric head direction, boundary distance, and linear and angular velocity. These encodings were not accounted for by distance, time to reward, or motor factors. The strength of coding of all variables increased along a ventral-to-dorsal gradient. Together, these results argue that encoding of navigational variables is not localized to the hippocampus and support the hypothesis that navigation is continuous with other forms of flexible cognition in the service of action.

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.

Automated pose estimation in primates

Understanding the behavior of primates is important for primatology, for psychology, and for biology more broadly. It is also important for biomedicine, where primates are an important model organism, and whose behavior is often an important variable of interest. Our ability to rigorously quantify behavior has, however, long been limited. On one hand, we can rigorously quantify low-information measures like preference, looking time, and reaction time; on the other, we can use more gestalt measures like behavioral categories tracked via ethogram, but at high cost and with high variability. Recent technological advances have led to a major revolution in behavioral measurement that offers affordable and scalable rigor. Specifically, digital video cameras and automated pose tracking software can provide measures of full-body position (i.e., pose) of primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to infer behavioral states, such as eating, sleeping, and mating. We call this technological approach behavioral imaging. In this review, we situate the behavioral imaging revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that primate centers and zoos will take on a more central role in relevant fields of research than they have in the past.

Selective modulation of cortical population dynamics during neuroprosthetic skill learning

Brain-machine interfaces (BMIs) provide a framework for studying how cortical population dynamics evolve over learning in a task in which the mapping between neural activity and behavior is precisely defined. Learning to control a BMI is associated with the emergence of coordinated neural dynamics in populations of neurons whose activity serves as direct input to the BMI decoder (direct subpopulation). While previous work shows differential modification of firing rate modulation in this population relative to a population whose activity was not directly input to the BMI decoder (indirect subpopulation), little is known about how learning-related changes in cortical population dynamics within these groups compare.To investigate this, we monitored both direct and indirect subpopulations as two macaque monkeys learned to control a BMI. We found that while the combined population increased coordinated neural dynamics, this increase in coordination was primarily driven by changes in the direct subpopulation. These findings suggest that motor cortex refines cortical dynamics by increasing neural variance throughout the entire population during learning, with a more pronounced coordination of firing activity in subpopulations that are causally linked to behavior.

The Promise of Behavioral Tracking Systems for Advancing Primate Animal Welfare

Recent years have witnessed major advances in the ability of computerized systems to track the positions of animals as they move through large and unconstrained environments. These systems have so far been a great boon in the fields of primatology, psychology, neuroscience, and biomedicine. Here, we discuss the promise of these technologies for animal welfare. Their potential benefits include identifying and reducing pain, suffering, and distress in captive populations, improving laboratory animal welfare within the context of the three Rs of animal research (reduction, refinement, and replacement), and applying our understanding of animal behavior to increase the "natural" behaviors in captive and wild populations facing human impact challenges. We note that these benefits are often incidental to the designed purpose of these tracking systems, a reflection of the fact that animal welfare is not inimical to research progress, but instead, that the aligned interests between basic research and welfare hold great promise for improvements to animal well-being.

Extending neural systems for navigation to hunting behavior

For decades, a central question in neuroscience has been: How does the brain support navigation? Recent research on navigation has explored how brain regions support the capacity to adapt to changes in the environment and track the distance and direction to goal locations. Here, we provide a brief review of this literature and speculate how these neural systems may be involved in another, parallel behavior-hunting. Hunting shares many of the same challenges as navigation. Like navigation, hunting requires the hunter to orient towards a goal while minimizing their distance from it while traveling. Likewise, hunting may require the accommodation of detours to locate prey or the exploitation of shortcuts for a quicker capture. Recent research suggests that neurons in the periaqueductal gray, hypothalamus, and dorsal anterior cingulate play key roles in such hunting behavior. In this review, we speculate on how these regions may operate functionally with other key brain regions involved in navigation, such as the hippocampus, to support hunting. Additionally, we posit that hunting in a group presents an additional set of challenges, where success relies on multicentric tracking and prediction of prey position as well as the position of co-hunters.

Think Fast!: Vervet Monkeys Assess the Risk of Being Displaced by a Dominant Competitor When Making Foraging Decisions

Foraging animals need to quickly assess the costs and benefits of different foraging decisions, including resource quantity, quality, preference, ease of access, dispersion, distance, and predation risk. Social animals also need to take social context into account and adapt foraging strategies that maximize net resource intake and minimize contest competition with conspecifics. We used an experimental approach to investigate how social context impacts wild vervet monkey (Chlorocebus pygerythrus) foraging decisions in a multi-destination pentagon array. We baited four platforms with less-preferred corn and one platform with a larger, preferred resource (half banana) that required handling time. We ran over 1,000 trials and found that when monkeys foraged alone, they usually took the path that minimized travel distance but prioritized the preferred-food platform when in competition. However, the foraging strategy chosen by low-ranking individuals depended on the handling skill of the decision maker (i.e., time it would take them to retrieve the banana), the relative rank of their audience members (i.e., who has priority-of-access to resources), and the distance audience members were from the experiment site (i.e., their travel time). When the risk of being displaced by a dominant competitor was low (because they were far away and/or because the decision-maker was skilled in retrieving the banana), low-ranking individuals chose a route that minimized travel costs. Conversely, when the risk of losing food to a dominant competitor was high, decision-makers rushed for the preferred-food platform at the onset of the trial. When the risk of displacement was moderate because a dominant audience member was at least 50 m away, low-ranking individuals partly prioritized the preferred-food platform but took the time to stop for one platform of corn on the way. This strategy increased the total amount of food obtained during the trial. These findings suggest that lower-ranking individuals, who experienced high contest competition at the foraging experiment, calculated the risk of being displaced by a dominant competitor when making foraging decisions. This experiment demonstrates that vervets go through a complex decision-making process that simultaneously considers the profitability of different foraging decisions and their social context.

The whole prefrontal cortex is premotor cortex

We propose that the entirety of the prefrontal cortex (PFC) can be seen as fundamentally premotor in nature. By this, we mean that the PFC consists of an action abstraction hierarchy whose core function is the potentiation and depotentiation of possible action plans at different levels of granularity. We argue that the apex of the hierarchy should revolve around the process of goal-selection, which we posit is inherently a form of optimization over action abstraction. Anatomical and functional evidence supports the idea that this hierarchy originates on the orbital surface of the brain and extends dorsally to motor cortex. Accordingly, our viewpoint positions the orbitofrontal cortex in a key role in the optimization of goal-selection policies, and suggests that its other proposed roles are aspects of this more general function. Our proposed perspective will reframe outstanding questions, open up new areas of inquiry and align theories of prefrontal function with evolutionary principles. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

Composition and decomposition of visuomotor maps during manual tracking

Adapting hand movements to changes in our body or the environment is essential for skilled motor behavior, as is the ability to flexibly combine experience gathered in separate contexts. However, it has been shown that when adapting hand movements to two different visuomotor perturbations in succession, interference effects can occur. Here, we investigate whether these interference effects compromise our ability to adapt to the superposition of the two perturbations. Participants tracked with a joystick, a visual target that followed a smooth but an unpredictable trajectory. Four separate groups of participants (total n = 83) completed one block of 50 trials under each of three mappings: one in which the cursor was rotated by 90° (ROTATION), one in which the cursor mimicked the behavior of a mass-spring system (SPRING), and one in which the SPRING and ROTATION mappings were superimposed (SPROT). The order of the blocks differed across groups. Although interference effects were found when switching between SPRING and ROTATION, participants who performed these blocks first performed better in SPROT than participants who had no prior experience with SPRING and ROTATION (i.e., composition). Moreover, participants who started with SPROT exhibited better performance under SPRING and ROTATION than participants who had no prior experience with each of these mappings (i.e., decomposition). Additional analyses confirmed that these effects resulted from components of learning that were specific to the rotational and spring perturbations. These results show that interference effects do not preclude the ability to compose/decompose various forms of visuomotor adaptation.NEW & NOTEWORTHY The ability to compose/decompose task representations is critical for both cognitive and behavioral flexibility. Here, we show that this ability extends to two forms of visuomotor adaptation in which humans have to perform visually guided hand movements. Despite the presence of interference effects when switching between visuomotor maps, we show that participants are able to flexibly compose or decompose knowledge acquired in previous sessions. These results further demonstrate the flexibility of sensorimotor adaptation in humans.

Aggression: How the anterior cingulate cortex helps to ensure a fair fight

Violent behavior is the product of a diverse network of neural structures. A new study shows that the anterior cingulate cortex is important for helping to restrain overly aggressive acts, even within a fight, to ensure animals match their behavioral intensity with the challenge posed by their opponents.