Brains use predictive models to facilitate the processing of expected stimuli or planned actions. Under a predictive model, surprising (low probability) stimuli or actions necessitate the immediate ...reallocation of processing resources, but they can also signal the need to update the underlying predictive model to reflect changes in the environment. Surprise and updating are often correlated in experimental paradigms but are, in fact, distinct constructs that can be formally defined as the Shannon information (I S) and Kullback–Leibler divergence (D KL) associated with an observation. In a saccadic planning task, we observed that distinct behaviors and brain regions are associated with surprise/I S and updating/D KL. Although surprise/I S was associated with behavioral reprogramming as indexed by slower reaction times, as well as with activity in the posterior parietal cortex human lateral intraparietal area (LIP), the anterior cingulate cortex (ACC) was specifically activated during updating of the predictive model (D KL). A second saccade-sensitive region in the inferior posterior parietal cortex (human 7a), which has connections to both LIP and ACC, was activated by surprise and modulated by updating. Pupillometry revealed a further dissociation between surprise and updating with an early positive effect of surprise and late negative effect of updating on pupil area. These results give a computational account of the roles of the ACC and two parietal saccade regions, LIP and 7a, by which their involvement in diverse tasks can be understood mechanistically. The dissociation of functional roles between regions within the reorienting/reprogramming network may also inform models of neurological phenomena, such as extinction and Balint syndrome, and neglect.
Ventromedial prefrontal cortex has been linked to choice evaluation and decision-making in humans but understanding the role it plays is complicated by the fact that little is known about the ...corresponding area of the macaque brain. We recorded activity in macaques using functional magnetic resonance imaging during two very different value-guided decision-making tasks. In both cases ventromedial prefrontal cortex activity reflected subjective choice values during decision-making just as in humans but the relationship between the blood oxygen level-dependent signal and both decision-making and choice value was inverted and opposite to the relationship seen in humans. In order to test whether the ventromedial prefrontal cortex activity related to choice values is important for decision-making we conducted an additional lesion experiment; lesions that included the same ventromedial prefrontal cortex region disrupted normal subjective evaluation of choices during decision-making.
Purpose
To estimate dynamic off‐resonance due to vigorous body motion in accelerated fMRI of awake behaving nonhuman primates (NHPs) using the echo‐planar imaging reference navigator, in order to ...attenuate the effects of time‐varying off‐resonance on the reconstruction.
Methods
In NHP fMRI, the animal’s head is usually head‐posted, and the dynamic off‐resonance is mainly caused by motion in body parts that are distant from the brain and have low spatial frequency. Hence, off‐resonance at each frame can be approximated as a spatially linear perturbation of the off‐resonance at a reference frame, and is manifested as a relative linear shift in k‐space. Using GRAPPA operators, we estimated these shifts by comparing the navigator at each time frame with that at the reference frame. Estimated shifts were then used to correct the data at each frame. The proposed method was evaluated in phantom scans, simulations, and in vivo data.
Results
The proposed method is shown to successfully estimate spatially low‐order dynamic off‐resonance perturbations, including induced linear off‐resonance perturbations in phantoms, and is able to correct retrospectively corrupted data in simulations. Finally, it is shown to reduce ghosting artifacts and geometric distortions by up to 20% in simultaneous multislice in vivo acquisitions in awake‐behaving NHPs.
Conclusion
A method is proposed that does not need sequence modification or extra acquisitions and makes accelerated awake behaving NHP imaging more robust and reliable, reducing the gap between what is possible with NHP protocols and state‐of‐the‐art human imaging.
Animals learn from the past to make predictions. These predictions are adjusted after prediction errors, i.e., after surprising events. Generally, most reward prediction errors models learn the ...average expected amount of reward. However, here we demonstrate the existence of distinct mechanisms for detecting other types of surprising events. Six macaques learned to respond to visual stimuli to receive varying amounts of juice rewards. Most trials ended with the delivery of either 1 or 3 juice drops so that animals learned to expect 2 juice drops on average even though instances of precisely 2 drops were rare. To encourage learning, we also included sessions during which the ratio between 1 and 3 drops changed. Additionally, in all sessions, the stimulus sometimes appeared in an unexpected location. Thus, 3 types of surprising events could occur: reward amount surprise (i.e., a scalar reward prediction error), rare reward surprise, and visuospatial surprise. Importantly, we can dissociate scalar reward prediction errors-rewards that deviated from the average reward amount expected-and rare reward events-rewards that accorded with the average reward expectation but that rarely occurred. We linked each type of surprise to a distinct pattern of neural activity using functional magnetic resonance imaging. Activity in the vicinity of the dopaminergic midbrain only reflected surprise about the amount of reward. Lateral prefrontal cortex had a more general role in detecting surprising events. Posterior lateral orbitofrontal cortex specifically detected rare reward events regardless of whether they followed average reward amount expectations, but only in learnable reward environments.
Abstract Staying engaged is necessary to maintain goal-directed behaviors. Despite this, engagement exhibits continuous, intrinsic fluctuations. Even in experimental settings, animals, unlike most ...humans, repeatedly and spontaneously move between periods of complete task engagement and disengagement. We, therefore, looked at behavior in male macaques ( macaca mulatta) in four tasks while recording fMRI signals. We identified consistent autocorrelation in task disengagement. This made it possible to build models capturing task-independent engagement. We identified task general patterns of neural activity linked to impending sudden task disengagement in mid-cingulate gyrus. By contrast, activity centered in perigenual anterior cingulate cortex (pgACC) was associated with maintenance of performance across tasks. Importantly, we carefully controlled for task-specific factors such as the reward history and other motivational effects, such as response vigor, in our analyses. Moreover, we showed pgACC activity had a causal link to task engagement: transcranial ultrasound stimulation of pgACC changed task engagement patterns.
Lesions of primary visual cortex (V1) lead to loss of conscious visual perception with significant impact on human patients. Understanding the neural consequences of such damage may aid the ...development of rehabilitation methods. In this rare case of a Rhesus macaque (monkey S), likely born without V1, the animal's in-group behaviour was unremarkable, but visual task training was impaired. With multi-modal magnetic resonance imaging, visual structures outside of the lesion appeared normal. Visual stimulation under anaesthesia with checkerboards activated lateral geniculate nucleus of monkey S, while full-field moving dots activated pulvinar. Visual cortical activation was sparse but included face patches. Consistently across lesion and control monkeys, functional connectivity analysis revealed an intact network of bilateral dorsal visual areas temporally correlated with V5/MT activation, even without V1. Despite robust subcortical responses to visual stimulation, we found little evidence for strengthened subcortical input to V5/MT supporting residual visual function or blindsight-like phenomena.
Social behavior is coordinated by a network of brain regions, including those involved in the perception of social stimuli and those involved in complex functions, such as inferring perceptual and ...mental states and controlling social interactions. The properties and function of many of these regions in isolation are relatively well understood, but less is known about how these regions interact while processing dynamic social interactions. To investigate whether the functional connectivity between brain regions is modulated by social context, we collected fMRI data from male monkeys (
) viewing videos of social interactions labeled as "affiliative," "aggressive," or "ambiguous." We show activation related to the perception of social interactions along both banks of the superior temporal sulcus, parietal cortex, medial and lateral frontal cortex, and the caudate nucleus. Within this network, we show that fronto-temporal functional connectivity is significantly modulated by social context. Crucially, we link the observation of specific behaviors to changes in functional connectivity within our network. Viewing aggressive behavior was associated with a limited increase in temporo-temporal and a weak increase in cingulate-temporal connectivity. By contrast, viewing interactions where the outcome was uncertain was associated with a pronounced increase in temporo-temporal, and cingulate-temporal functional connectivity. We hypothesize that this widespread network synchronization occurs when cingulate and temporal areas coordinate their activity when more difficult social inferences are being made.
Processing social information from our environment requires the activation of several brain regions, which are concentrated within the frontal and temporal lobes. However, little is known about how these areas interact to facilitate the processing of different social interactions. Here we show that functional connectivity within and between the frontal and temporal lobes is modulated by social context. Specifically, we demonstrate that viewing social interactions where the outcome was unclear is associated with increased synchrony within and between the cingulate cortex and temporal cortices. These findings suggest that the coordination between the cingulate and temporal cortices is enhanced when more difficult social inferences are being made.
The ability to attribute thoughts to others, also called theory of mind (TOM), has been extensively studied in humans; however, its evolutionary origins have been challenged. Computationally, the ...basis of TOM has been interpreted within the predictive coding framework and associated with activity in the temporoparietal junction (TPJ). Here, we revealed, using a nonlinguistic task and functional magnetic resonance imaging, that activity in a region of the macaque middle superior temporal cortex was specifically modulated by the predictability of social situations. As in human TPJ, this region could be distinguished from other temporal regions involved in face processing. Our result suggests the existence of a precursor for the TOM ability in the last common ancestor of human and Old World monkeys.
Controversy surrounds the role of the temporoparietal junction (TPJ) area of the human brain. Although TPJ has been implicated both in reorienting of attention and social cognition, it is still ...unclear whether these functions have the same neural basis. Indeed, whether TPJ is a precisely identifiable cortical region or a cluster of subregions with separate functions is still a matter of debate. Here, we examined the structural and functional connectivity of TPJ, testing whether TPJ is a unitary area with a heterogeneous functional connectivity profile or a conglomerate of regions with distinctive connectivity. Diffusion-weighted imaging tractrography-based parcellation identified 3 separate regions in TPJ. Resting-state functional connectivity was then used to establish which cortical networks each of these subregions participates in. A dorsal cluster in the middle part of the inferior parietal lobule showed resting-state functional connectivity with, among other areas, lateral anterior prefrontal cortex. Ventrally, an anterior TPJ cluster interacted with ventral prefrontal cortex and anterior insula, while a posterior TPJ cluster interacted with posterior cingulate, temporal pole, and anterior medial prefrontal cortex. These results indicate that TPJ can be subdivided into subregions on the basis of its structural and functional connectivity.
In an ever-changing environment, organisms learn about non-random events and use that information to form predictive models to facilitate the processing of expected stimuli or planned actions and ...their outcomes. Predictions about stimuli enable for an efficient and appropriate response and predictions about action outcomes determine the value and therefore likelihood of repetition of that action. In primate brains, these models have been described in the framework of spatial attention and reward expectation. Unexpected observations in space trigger an attentional reorienting process and surprising action outcomes are defined as reward prediction errors. These error signals can be used by the brain to update and refine its internal model that contains probabilistic information about events in the environment (Chapter 1). In what follows, I will report effects of spatial surprise and unexpected rewards on behaviour and brain activity in humans and macaques. I will show how humans react to visually surprising events and how they use that information to form future predictions (Chapter 2). Brains reacting to surprise have been primarily described in the attention literature but there are dissociable networks for an attentional shift and for an expectation updating process following unexpected but informative observations (Chapter 3). Macaques also form spatial predictions and violations of these predictions result in a behavioural cost. Furthermore, both expectations about the size of a reward and prediction errors influence behaviour (Chapter 4). FMRI in macaques performing a task reveals a network of reward sensitive areas. Nodes of this network however carry different information about the reward history and therefore play different roles in forming a reward expectation. Spatially surprising events activate networks similar to those described in human fMRI studies (Chapter 5). In summary, unexpected observations and action outcomes influence behaviour and activate brain networks that process the surprise as well as form new predictions.
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