Modulations of Insular Projections by Prior Belief Mediate the Precision of Prediction Error during Tactile Learning

Modulation of prefrontal couplings by prior belief-related responses in ventromedial prefrontal cortex

Humans and other animals can maintain constant payoffs in an uncertain environment by steadily re-evaluating and flexibly adjusting current strategy, which largely depends on the interactions between the prefrontal cortex (PFC) and mediodorsal thalamus (MD). While the ventromedial PFC (vmPFC) represents the level of uncertainty (i.e., prior belief about external states), it remains unclear how the brain recruits the PFC-MD network to re-evaluate decision strategy based on the uncertainty. Here, we leverage non-linear dynamic causal modeling on fMRI data to test how prior belief-dependent activity in vmPFC gates the information flow in the PFC-MD network when individuals switch their decision strategy. We show that the prior belief-related responses in vmPFC had a modulatory influence on the connections from dorsolateral PFC (dlPFC) to both, lateral orbitofrontal (lOFC) and MD. Bayesian parameter averaging revealed that only the connection from the dlPFC to lOFC surpassed the significant threshold, which indicates that the weaker the prior belief, the less was the inhibitory influence of the vmPFC on the strength of effective connections from dlPFC to lOFC. These findings suggest that the vmPFC acts as a gatekeeper for the recruitment of processing resources to re-evaluate the decision strategy in situations of high uncertainty.

Breathing in waves: Understanding respiratory-brain coupling as a gradient of predictive oscillations

Breathing plays a crucial role in shaping perceptual and cognitive processes by regulating the strength and synchronisation of neural oscillations. Numerous studies have demonstrated that respiratory rhythms govern a wide range of behavioural effects across cognitive, affective, and perceptual domains. Additionally, respiratory-modulated brain oscillations have been observed in various mammalian models and across diverse frequency spectra. However, a comprehensive framework to elucidate these disparate phenomena remains elusive. In this review, we synthesise existing findings to propose a neural gradient of respiratory-modulated brain oscillations and examine recent computational models of neural oscillations to map this gradient onto a hierarchical cascade of precision-weighted prediction errors. By deciphering the computational mechanisms underlying respiratory control of these processes, we can potentially uncover new pathways for understanding the link between respiratory-brain coupling and psychiatric disorders.

Analogous cognitive strategies for tactile learning in the rodent and human brain

Evolution has molded individual species' sensory capacities and abilities. In rodents, who mostly inhabit dark tunnels and burrows, the whisker-based somatosensory system has developed as the dominant sensory modality, essential for environmental exploration and spatial navigation. In contrast, humans rely more on visual and auditory inputs when collecting information from their surrounding sensory space in everyday life. As a result of such species-specific differences in sensory dominance, cognitive relevance and capacities, the evidence for analogous sensory-cognitive mechanisms across species remains sparse. However, recent research in rodents and humans yielded surprisingly comparable processing rules for detecting tactile stimuli, integrating touch information into percepts, and goal-directed rule learning. Here, we review how the brain, across species, harnesses such processing rules to establish decision-making during tactile learning, following canonical circuits from the thalamus and the primary somatosensory cortex up to the frontal cortex. We discuss concordances between empirical and computational evidence from micro- and mesoscopic circuit studies in rodents to findings from macroscopic imaging in humans. Furthermore, we discuss the relevance and challenges for future cross-species research in addressing mutual context-dependent evaluation processes underpinning perceptual learning.

Thalamic regulation of frontal interactions in human cognitive flexibility

Interactions across frontal cortex are critical for cognition. Animal studies suggest a role for mediodorsal thalamus (MD) in these interactions, but the computations performed and direct relevance to human decision making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which neuroimaging data were collected. Using a biologically-plausible learning rule, we found that the model MD thalamus compressed its cortical inputs (dorsolateral prefrontal cortex, dlPFC) underlying stimulus-response representations. Through direct feedback to dlPFC, this thalamic operation efficiently partitioned cortical activity patterns and enhanced task switching across different contingencies. To account for interactions with other frontal regions, we expanded the model to compute higher-order strategy signals outside dlPFC, and found that the MD offered a more efficient route for such signals to switch dlPFC activity patterns. Human fMRI data provided evidence that the MD engaged in feedback to dlPFC, and had a role in routing orbitofrontal cortex inputs when subjects switched behavioral strategy. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.

The expansion of frontal cortex during mammalian evolution suggested a prominent role in intelligent and adaptive behavior, overshadowing earlier parts of the brain. However, recent rodent studies have pointed to a role for the cognitive mediodorsal thalamus (MD) in sustaining and flexibly switching representations in the frontal cortex, but direct relevance to human decision-making are unclear. Here, inspired by animal work, we extended a neural model of an executive frontal-MD network and trained it on a human decision-making task for which human neuroimaging data were collected. We found that the model MD thalamus learned an abstract representation of its cortical inputs and provided direct feedback to frontal cortex leading to flexible computations and enhanced task switching. These abstract MD representations and ability to re-organize frontal computations created an efficient mechanism where MD can integrate input from other regions to select behavioral strategy dynamically. The model predicted an efficient route through the MD for frontal region interactions and we found consistent evidence in human neuroimaging data. Collectively, our findings contribute to the emerging evidence for thalamic regulation of frontal interactions in the human brain.

Confidence in Decision-Making during Probabilistic Tactile Learning Related to Distinct Thalamo–Prefrontal Pathways

The flexibility in adjusting the decision strategy from trial to trial is a prerequisite for learning in a probabilistic environment. Corresponding neural underpinnings remain largely unexplored. In the present study, 28 male humans were engaged in an associative learning task, in which they had to learn the changing probabilistic strengths of tactile sample stimuli. Combining functional magnetic resonance imaging with computational modeling, we show that an unchanged decision strategy over successively presented trials related to weakened functional connectivity between ventralmedial prefrontal cortex (vmPFC) and left secondary somatosensory cortex. The weaker the connection strength, the faster participants indicated their choice. If the decision strategy remained unchanged, participant’s decision confidence (i.e., prior belief) was related to functional connectivity between vmPFC and right pulvinar. While adjusting the decision strategy, we instead found confidence-related connections between left orbitofrontal cortex and left thalamic mediodorsal nucleus. The stronger the participant’s prior belief, the weaker the connection strengths. Together, these findings suggest that distinct thalamo–prefrontal pathways encode the confidence in keeping or changing the decision strategy during probabilistic learning. Low confidence in the decision strategy demands more thalamo–prefrontal processing resources, which is in-line with the theoretical accounts of the free-energy principle.