Neural dynamics of emotion and cognition: From trajectories to underlying neural geometry

Comprehensive investigation of predictive processing: A cross- and within-cognitive domains fMRI meta-analytic approach

Predictive processing (PP) stands as a predominant theoretical framework in neuroscience. While some efforts have been made to frame PP within a cognitive domain-general network perspective, suggesting the existence of a "prediction network," these studies have primarily focused on specific cognitive domains or functions. The question of whether a domain-general predictive network that encompasses all well-established cognitive domains exists remains unanswered. The present meta-analysis aims to address this gap by testing the hypothesis that PP relies on a large-scale network spanning across cognitive domains, supporting PP as a unified account toward a more integrated approach to neuroscience. The Activation Likelihood Estimation meta-analytic approach was employed, along with Meta-Analytic Connectivity Mapping, conjunction analysis, and behavioral decoding techniques. The analyses focused on prediction incongruency and prediction congruency, two conditions likely reflective of core phenomena of PP. Additionally, the analysis focused on a prediction phenomena-independent dimension, regardless of prediction incongruency and congruency. These analyses were first applied to each cognitive domain considered (cognitive control, attention, motor, language, social cognition). Then, all cognitive domains were collapsed into a single, cross-domain dimension, encompassing a total of 252 experiments. Results pertaining to prediction incongruency rely on a defined network across cognitive domains, while prediction congruency results exhibited less overall activation and slightly more variability across cognitive domains. The converging patterns of activation across prediction phenomena and cognitive domains highlight the role of several brain hubs unfolding within an organized large-scale network (Dynamic Prediction Network), mainly encompassing bilateral insula, frontal gyri, claustrum, parietal lobules, and temporal gyri. Additionally, the crucial role played at a cross-domain, multimodal level by the anterior insula, as evidenced by the conjunction and Meta-Analytic Connectivity Mapping analyses, places it as the major hub of the Dynamic Prediction Network. Results support the hypothesis that PP relies on a domain-general, large-scale network within whose regions PP units are likely to operate, depending on the context and environmental demands. The wide array of regions within the Dynamic Prediction Network seamlessly integrate context- and stimulus-dependent predictive computations, thereby contributing to the adaptive updating of the brain's models of the inner and external world.

Targeting Human Glucocorticoid Receptors in Fear Learning: A Multiscale Integrated Approach to Study Functional Connectivity

Fear extinction is a phenomenon that involves a gradual reduction in conditioned fear responses through repeated exposure to fear-inducing cues. Functional brain connectivity assessments, such as functional magnetic resonance imaging (fMRI), provide valuable insights into how brain regions communicate during these processes. Stress, a ubiquitous aspect of life, influences fear learning and extinction by changing the activity of the amygdala, prefrontal cortex, and hippocampus, leading to enhanced fear responses and/or impaired extinction. Glucocorticoid receptors (GRs) are key to the stress response and show a dual function in fear regulation: while they enhance the consolidation of fear memories, they also facilitate extinction. Accordingly, GR dysregulation is associated with anxiety and mood disorders. Recent advancements in cognitive neuroscience underscore the need for a comprehensive understanding that integrates perspectives from the molecular, cellular, and systems levels. In particular, neuropharmacology provides valuable insights into neurotransmitter and receptor systems, aiding the investigation of mechanisms underlying fear regulation and potential therapeutic targets. A notable player in this context is cortisol, a key stress hormone, which significantly influences both fear memory reconsolidation and extinction processes. Gaining a thorough understanding of these intricate interactions has implications in terms of addressing psychiatric disorders related to stress. This review sheds light on the complex interactions between cognitive processes, emotions, and their neural bases. In this endeavor, our aim is to reshape the comprehension of fear, stress, and their implications for emotional well-being, ultimately aiding in the development of therapeutic interventions.

Decoding defensive systems

Our understanding of the neuronal circuits and mechanisms of defensive systems has been primarily dominated by studies focusing on the contribution of individual cells in the processing of threat-predictive cues, defensive responses, the extinction of such responses and the contextual modulation of threat-related behavior. These studies have been key in establishing threat-related circuits and mechanisms. Yet, they fall short in answering long-standing questions related to the integrative processing of distinct threatening cues, behavioral states induced by threat-related events, or the bridging from sensory processing of threat-related cues to specific defensive responses. Recent conceptual and technical developments has allowed the monitoring of large populations of neurons, which in addition to advanced analytic tools, have improved our understanding of how collective neuronal activity supports threat-related behaviors. In this review, we discuss the current knowledge of neuronal population codes within threat-related networks, in the context of aversive motivated behavior and the study of defensive systems.

Predictive processing models and affective neuroscience

The neural bases of affective experience remain elusive. Early neuroscience models of affect searched for specific brain regions that uniquely carried out the computations that underlie dimensions of valence and arousal. However, a growing body of work has failed to identify these circuits. Research turned to multivariate analyses, but these strategies, too, have made limited progress. Predictive processing models offer exciting new directions to address this problem. Here, we use predictive processing models as a lens to critique prevailing functional neuroimaging research practices in affective neuroscience. Our review highlights how much work relies on rigid assumptions that are inconsistent with a predictive processing approach. We outline the central aspects of a predictive processing model and draw out their implications for research in affective and cognitive neuroscience. Predictive models motivate a reformulation of "reverse inference" in cognitive neuroscience, and placing a greater emphasis on external validity in experimental design.

Adult trkB Signaling in Parvalbumin Interneurons is Essential to Prefrontal Network Dynamics

Inhibitory interneurons expressing parvalbumin (PV) are central to cortical network dynamics, generation of γ oscillations, and cognition. Dysfunction of PV interneurons disrupts cortical information processing and cognitive behavior. Brain-derived neurotrophic factor (BDNF)/tyrosine receptor kinase B (trkB) signaling regulates the maturation of cortical PV interneurons but is also implicated in their adult multidimensional functions. Using a novel viral strategy for cell-type-specific and spatially restricted expression of a dominant-negative trkB (trkB.DN), we show that BDNF/trkB signaling is essential to the integrity and maintenance of prefrontal PV interneurons in adult male and female mice. Reduced BDNF/trkB signaling in PV interneurons in the medial prefrontal cortex (mPFC) resulted in deficient PV inhibition and increased baseline local field potential (LFP) activity in a broad frequency band. The altered network activity was particularly pronounced during increased activation of the prefrontal network and was associated with changed dynamics of local excitatory neurons, as well as decreased modulation of the LFP, abnormalities that appeared to generalize across stimuli and brain states. In addition, our findings link reduced BDNF/trkB signaling in prefrontal PV interneurons to increased aggression. Together our investigations demonstrate that BDNF/trkB signaling in PV interneurons in the adult mPFC is essential to local network dynamics and cognitive behavior. Our data provide direct support for the suggested association between decreased trkB signaling, deficient PV inhibition, and altered prefrontal circuitry.SIGNIFICANCE STATEMENT Brain-derived neurotrophic factor (BDNF)/tyrosine receptor kinase B (trkB) signaling promotes the maturation of inhibitory parvalbumin (PV) interneurons, neurons central to local cortical dynamics, γ rhythms, and cognition. Here, we used a novel viral approach for reduced BDNF/trkB signaling in PV interneurons in the medial prefrontal cortex (mPFC) to establish the role of BDNF/trkB signaling in adult prefrontal network activities. Reduced BDNF/trkB signaling caused pronounced morphologic alterations, reduced PV inhibition, and deficient prefrontal network dynamics. The altered network activity appeared to manifest across stimuli and brain states and was associated with aberrant local field potential (LFP) activities and increased aggression. The results demonstrate that adult BDNF/trkB signaling is essential to PV inhibition and prefrontal circuit function and directly links BDNF/trkB signaling to network integrity in the adult brain.

The Locus Coeruleus- Norepinephrine System in Stress and Arousal: Unraveling Historical, Current, and Future Perspectives

Arousal may be understood on a spectrum, with excessive sleepiness, cognitive dysfunction, and inattention on one side, a wakeful state in the middle, and hypervigilance, panic, and psychosis on the other side. However, historically, the concepts of arousal and stress have been challenging to define as measurable experimental variables. Divergent efforts to study these subjects have given rise to several disciplines, including neurobiology, neuroendocrinology, and cognitive neuroscience. We discuss technological advancements that chronologically led to our current understanding of the arousal system, focusing on the multifaceted nucleus locus coeruleus. We share our contemporary perspective and the hypotheses of others in the context of our current technological capabilities and future developments that will be required to move forward in this area of research.

Psychological mechanisms and functions of 5-HT and SSRIs in potential therapeutic change: Lessons from the serotonergic modulation of action selection, learning, affect, and social cognition

Uncertainty regarding which psychological mechanisms are fundamental in mediating SSRI treatment outcomes and wide-ranging variability in their efficacy has raised more questions than it has solved. Since subjective mood states are an abstract scientific construct, only available through self-report in humans, and likely involving input from multiple top-down and bottom-up signals, it has been difficult to model at what level SSRIs interact with this process. Converging translational evidence indicates a role for serotonin in modulating context-dependent parameters of action selection, affect, and social cognition; and concurrently supporting learning mechanisms, which promote adaptability and behavioural flexibility. We examine the theoretical basis, ecological validity, and interaction of these constructs and how they may or may not exert a clinical benefit. Specifically, we bridge crucial gaps between disparate lines of research, particularly findings from animal models and human clinical trials, which often seem to present irreconcilable differences. In determining how SSRIs exert their effects, our approach examines the endogenous functions of 5-HT neurons, how 5-HT manipulations affect behaviour in different contexts, and how their therapeutic effects may be exerted in humans - which may illuminate issues of translational models, hierarchical mechanisms, idiographic variables, and social cognition.

Admiring the Great Mountain: A Celebration Special Issue in Honor of Stephen Grossberg's 80th Birthday

This editorial summarizes selected key contributions of Prof. Stephen Grossberg and describes the papers in this 80th birthday special issue in his honor. His productivity, creativity, and vision would each be enough to mark a scientist of the first caliber. In combination, they have resulted in contributions that have changed the entire discipline of neural networks. Grossberg has been tremendously influential in engineering, dynamical systems, and artificial intelligence as well. Indeed, he has been one of the most important mentors and role models in my career, and has done so with extraordinary generosity and encouragement. All authors in this special issue have taken great pleasure in hereby commemorating his extraordinary career and contributions.