An active inference perspective for the amygdala complex

"Don't stop believing" - Decoding belief dynamics in the brain: An ALE meta-analysis of neural correlates in belief formation and updating

Understanding how individuals form and update their beliefs is a fundamental question in cognitive and social psychology. Belief formation (BF) refers to the initial development of an individual's belief, while belief updating (BU) pertains to the revision of existing beliefs in response to contradictory evidence. Although these two processes are often interwoven, they might operate through different neural mechanisms. This meta-analysis aims to synthesize the existing functional magnetic resonance imaging (fMRI) literature on BF and BU, with a particular focus on how BF is investigated. Approaches based on Theory of Mind paradigms, such as False Belief tasks, are often opposed to other approaches, emphasizing the role of individual or situational factors in belief formation. Notably, we propose that this differentiation might reflect the engagement of social and non-social dynamics within belief formation. Activation likelihood estimation (ALE) analysis revealed shared involvement of the Precuneus (PCu) in both BF and BU, while BF specifically engaged the bilateral activation of the Temporo-Parietal Junctions (TPJ). Additionally, social and non-social BF exhibited distinct neural correlates: social BF was associated with activity in the right TPJ, whereas non-social BF relied on the left dorsolateral prefrontal cortex (DLPFC). These findings support the hypothesis that BF and BU operate via partially dissociable neural networks and highlights the role of TPJ and PCu as essential hubs to build-up neural templates and enabling shifts in viewpoint necessary to adapt beliefs.

Decision-making balance in suicide: an active inference account

Introduction: Suicide is a behaviour whose motivation is challenging to explain as it can neither be rewarded nor punished since the agent no longer exists. The conventional explanation is that suicide is motivated as an escape from unresolvable psychological pain. However, despite suicide's high availability, its rates are very low (about 0.014% in the US). This speaks to high ambivalence as an essential feature of the suicidal mind.Method: To explicate the ambivalence of the suicidal mind, suicide has recently been framed within the active inference framework (AIF). AIF appears to be appropriate for conceptualising suicide because it is a theory of choice behaviour under uncertainty that, in suicide, cannot be resolved or, validated by experience. Moreover, AIF is based on the free-energy principle, which is proposed as a principle underwriting the very existence of sentient systems.Results: In this paper, we frame suicidal decision-making as the balance between the expected free energy of survival vs. suicide action policies. Based on this frame, we develop intuitions about the dynamics of suicidal decision-making. These intuitions are then proposed as guides for future research into suicidal decisions as well as suicide prevention.

Predictive coding in the human olfactory system

The human olfactory system is unusual. It deviates from the classical structure and function of other sensory cortices, and many of its basic computations remain mysterious. These idiosyncrasies have challenged the development of a clear and comprehensive theoretical framework in olfactory neuroscience. To address this challenge, we develop a theory of olfactory predictive coding that aims to unify diverse olfactory phenomena. Under this scheme, the olfactory system is not merely passively processing sensory information. Instead, it is actively issuing predictions about sensory inputs before they even arrive. We map this conceptual framework onto the micro- and macroscale neurobiology of the human olfactory system and review a variety of neurobiological, computational, and behavioral evidence in support of this scheme.

Galactooligosaccharides Attenuate Behavioural, Haematological and Immunological Abnormalities and Influence Gut Microbiota in Rats with Amygdala Hyperactivation Induced by Electrical Stimulation

The amygdala, especially its central nucleus (CeA), is one of the key brain structures regulating fear, anxiety and stress responses and is also involved in gut microbiota signal processing. Amygdala hyperactivity, as well as microbiota alterations, plays an important role in the pathophysiology of anxiety disorders, depression or post-traumatic stress disorder (PTSD). The present study determines whether 3 weeks of galactooligosaccharide (GOS) supplementation alleviates behavioural, haematological, immunological and gut microbiota disturbances induced by long-term electrical stimulation of the CeA in rats (Stim). The unsupplemented Stim group showed locomotor hyperactivity and higher anxiety (measured with an actometer and the elevated plus maze, respectively), as well as a decrease in white blood cells (WBCs), lymphocytes (LYMs), red blood cells (RBCs) and platelets (PLTs); an elevation of TNFα; a reduction in IL-10 concentration in plasma; and microbiota alterations as compared to the control (Sham) group. GOS supplementation alleviated all these Stim-induced adverse effects or even normalised them to the sham group level. The effect of GOS was comparable to citalopram and even more effective in WBC and PLT normalisation and IL-10 induction. The obtained results indicate the high therapeutic potential of GOS in anxiety and stress-related disorders. GOS supplementation may support conventional therapy or the prevention of PTSD, depression and anxiety disorders.

MRI textural plasticity in limbic gray matter associated with clinical response to electroconvulsive therapy for psychosis

Electroconvulsive therapy (ECT) is effective against treatment-resistant psychosis, but its mechanisms remain unclear. Conventional volumetry studies have revealed plasticity in limbic structures following ECT but with inconsistent clinical relevance, as they potentially overlook subtle histological alterations. Our study analyzed microstructural changes in limbic structures after ECT using MRI texture analysis and demonstrated a correlation with clinical response. 36 schizophrenia or schizoaffective patients treated with ECT and medication, 27 patients treated with medication only, and 70 healthy controls (HCs) were included in this study. Structural MRI data were acquired before and after ECT for the ECT group and at equivalent intervals for the medication-only group. The gray matter volume and MRI texture, calculated from the gray level size zone matrix (GLSZM), were extracted from limbic structures. After normalizing texture features to HC data, group-time interactions were estimated with repeated-measures mixed models. Repeated-measures correlations between clinical variables and texture were analyzed. Volumetric group-time interactions were observed in seven of fourteen limbic structures. Group-time interactions of the normalized GLSZM large area emphasis of the left hippocampus and the right amygdala reached statistical significance. Changes in these texture features were correlated with changes in psychotic symptoms in the ECT group but not in the medication-only group. These findings provide in vivo evidence that microstructural changes in key limbic structures, hypothetically reflected by MRI texture, are associated with clinical response to ECT for psychosis. These findings support the neuroplasticity hypothesis of ECT and highlight the hippocampus and amygdala as potential targets for neuromodulation in psychosis.

Knockdown of the type 1 cannabinoid receptor in the central amygdala increases both spontaneous and water deprivation-induced sodium intake in rats

Important inputs originating in the forebrain circumventricular organs and also in the central amygdala (CeA) trigger essential water deprivation (WD)-induced behaviors, such as thirst and sodium appetite. Together with the secretion of the neurohypophysial peptides arginine vasopressin (AVP) and oxytocin (OT), these behavioral responses seek to maintain the normalcy of extracellular fluid (ECF) osmolality and volume. Within this context, the main hypothesis tested by the present study was that CeA type 1 cannabinoid receptors (CB1Rs) were essential for the maintenance of body fluid homeostasis, particularly in response to WD challenge. We found that CeA CB1R knockdown increased spontaneous and WD-induced hypertonic saline intake, without significantly impacting water ingestion. In euhydrated rats, despite unaltered urinary volume, CB1R knockdown reduced urinary osmolality, and diminished urinary nitrate concentrations, suggesting reduced renal sodium excretion. No relevant changes were induced by CeA CB1R knockdown on urinary parameters following WD-induced rehydration, which is consistent with unaltered AVP and OT mRNA transcription and hormone release under the same experimental conditions. Taken together, the present data support the notion that CeA CB1Rs participate in both spontaneous and WD-induced NaCl intake, without significantly affecting neuroendocrine output. Given the well-described facilitatory CeA role on natriorexigenic responses, and the reported interplay between CB1Rs and γ-aminobutyric acid (GABA) within the CeA, the present findings suggest that CB1Rs may indirectly regulate sodium appetite through effects on CeA GABAergic neurotransmission.NEW & NOTEWORTHY CeA CB1R knockdown increased spontaneous and WD-induced hypertonic saline intake, without significantly impacting water ingestion. In euhydrated rats, despite unaltered urinary volume, CB1R knockdown reduced urinary osmolality, and diminished urinary nitrate concentrations, suggesting reduced renal sodium excretion. No relevant changes were induced by CeA CB1R knockdown on urinary parameters following WD-induced rehydration, which is consistent with unaltered AVP and OT mRNA transcription and hormone release under the same experimental conditions.

Understanding human amygdala function with artificial neural networks

The amygdala is a cluster of subcortical nuclei that receives diverse sensory inputs and projects to the cortex, midbrain, and other subcortical structures. Numerous accounts of amygdalar contributions to social and emotional behavior have been offered, yet an overarching description of amygdala function remains elusive. Here we adopt a computationally explicit framework that aims to develop a model of amygdala function based on the types of sensory inputs it receives, rather than individual constructs such as threat, arousal, or valence. Characterizing human fMRI signal acquired as male and female participants viewed a full-length film, we developed encoding models that predict both patterns of amygdala activity and self-reported valence evoked by naturalistic images. We use deep image synthesis to generate artificial stimuli that distinctly engage encoding models of amygdala subregions that systematically differ from one another in terms of their low-level visual properties. These findings characterize how the amygdala compresses high-dimensional sensory inputs into low-dimensional representations relevant for behavior.Significance Statement The amygdala is a cluster of subcortical nuclei critical for motivation, emotion, and social behavior. Characterizing the contribution of the amygdala to behavior has been challenging due to its structural complexity, broad connectivity, and functional heterogeneity. Here we use a combination of human neuroimaging and computational modeling to investigate how visual inputs relate to low-dimensional representations encoded in the amygdala. We find that the amygdala encodes an array of visual features, which systematically vary across specific nuclei and relate to the affective properties of the sensory environment.

The brain-heart axis: integrative cooperation of neural, mechanical and biochemical pathways

The neural and cardiovascular systems are pivotal in regulating human physiological, cognitive and emotional states, constantly interacting through anatomical and functional connections referred to as the brain-heart axis. When this axis is dysfunctional, neurological conditions can lead to cardiovascular disorders and, conversely, cardiovascular dysfunction can substantially affect brain health. However, the mechanisms and fundamental physiological components of the brain-heart axis remain largely unknown. In this Review, we elucidate these components and identify three primary pathways: neural, mechanical and biochemical. The neural pathway involves the interaction between the autonomic nervous system and the central autonomic network in the brain. The mechanical pathway involves mechanoreceptors, particularly those expressing mechanosensitive Piezo protein channels, which relay crucial information about blood pressure through peripheral and cerebrovascular connections. The biochemical pathway comprises many endogenous compounds that are important mediators of neural and cardiovascular function. This multisystem perspective calls for the development of integrative approaches, leading to new clinical specialties in neurocardiology.

Brain local structural connectomes and the subtypes of the medial temporal lobe parcellations

Objective

To investigate the quantitative characteristics and major subtypes of local structural connectomes for medial temporal lobe (MTL) parcellations.

Methods

The Q-Space Diffeomorphic Reconstruction (QSDR) method was used to track white matter fibers for the ROIs within MTL based on the integrating high-resolution T1 structural MR imaging and diffusion MR imaging of 100 adult Chinese individuals. Graph theoretical analysis was employed to construct the local structural connectome models for ROIs within MTL and acquire the network parameters. These connectivity matrices of these connectomes were classified into major subtypes undergoing hierarchical clustering.

Results

(1) In the local brain connectomes, the overall network features exhibited a low characteristic path length paired with moderate to high global efficiency, suggesting the effectiveness of the local brain connectome construction. The amygdala connectomes exhibited longer characteristic path length and weaker global efficiency than the ipsilateral hippocampus and parahippocampal connectomes. (2) The hubs of the amygdala connectomes were dispersed across the ventral frontal, olfactory area, limbic, parietal regions and subcortical nuclei, and the hubs the hippocampal connectomes were mainly situated within the limbic, parietal, and subcortical regions. The hubs distribution of the parahippocampal connectomes resembled the hippocampal structural connectomes, but lacking interhemispheric connections and connectivity with subcortical nuclei. (3) The subtypes of the brain local structural connectomes for each ROI were classified by hierarchical clustering, The subtypes of the bilateral amygdala connectomes were the amygdala-prefrontal connectome; the amygdala-ipsilateral or contralateral limbic connectome and the amygdala-posterior connectome. The subtypes of the bilateral hippocampal connectomes primarily included the hippocampus-ipsilateral or contralateral limbic connectome and the anterior temporal-hippocampus-ventral temporal-occipital connectome in the domain hemisphere. The subtypes of the parahippocampal connectomes exhibited resemblances to those of the hippocampus.

Conclusion

We have constructed the brain local connectomes of the MTL parcellations and acquired the network parameters to delineate the hubs distribution through graph theory analysis. The connectomes can be classified into different major subtypes, which were closely related to the functional connectivity.

An Honest Reckoning With the Amygdala and Mental Illness

Anxiety disorders are a leading source of human misery, morbidity, and premature mortality. Existing treatments are far from curative for many, underscoring the need to clarify the underlying neural mechanisms. Although many brain regions contribute, the amygdala has received the most intense scientific attention. Over the past several decades, this scrutiny has yielded a detailed understanding of amygdala function, but it has failed to produce new clinical assays, biomarkers, or cures. Rising to this urgent public health challenge demands an honest reckoning with the functional-neuroanatomical complexity of the amygdala and a shift from theories anchored on "the amygdala" to models centered on specific amygdala nuclei and cell types. This review begins by examining evidence from studies of rodents, monkeys, and humans for the "canonical model," the idea that the amygdala plays a central role in fear- and anxiety-related states, traits, and disorders. Next, the authors selectively highlight work indicating that the canonical model, while true, is overly simplistic and fails to adequately capture the actual state of the evidentiary record, the breadth of amygdala-associated functions and illnesses, or the complexity of the amygdala's functional architecture. The authors describe the implications of these facts for basic and clinical neuroimaging research. The review concludes with some general recommendations for grappling with the complexity of the amygdala and accelerating efforts to understand and more effectively treat amygdala-related psychopathology.

Understanding human amygdala function with artificial neural networks

The amygdala is a cluster of subcortical nuclei that receives diverse sensory inputs and projects to the cortex, midbrain and other subcortical structures. Numerous accounts of amygdalar contributions to social and emotional behavior have been offered, yet an overarching description of amygdala function remains elusive. Here we adopt a computationally explicit framework that aims to develop a model of amygdala function based on the types of sensory inputs it receives, rather than individual constructs such as threat, arousal, or valence. Characterizing human fMRI signal acquired as participants viewed a full-length film, we developed encoding models that predict both patterns of amygdala activity and self-reported valence evoked by naturalistic images. We use deep image synthesis to generate artificial stimuli that distinctly engage encoding models of amygdala subregions that systematically differ from one another in terms of their low-level visual properties. These findings characterize how the amygdala compresses high-dimensional sensory inputs into low-dimensional representations relevant for behavior.

Facing emotions: real-time fMRI-based neurofeedback using dynamic emotional faces to modulate amygdala activity

Introduction

Maladaptive functioning of the amygdala has been associated with impaired emotion regulation in affective disorders. Recent advances in real-time fMRI neurofeedback have successfully demonstrated the modulation of amygdala activity in healthy and psychiatric populations. In contrast to an abstract feedback representation applied in standard neurofeedback designs, we proposed a novel neurofeedback paradigm using naturalistic stimuli like human emotional faces as the feedback display where change in the facial expression intensity (from neutral to happy or from fearful to neutral) was coupled with the participant's ongoing bilateral amygdala activity.

Methods

The feasibility of this experimental approach was tested on 64 healthy participants who completed a single training session with four neurofeedback runs. Participants were assigned to one of the four experimental groups (n = 16 per group), i.e., happy-up, happy-down, fear-up, fear-down. Depending on the group assignment, they were either instructed to "try to make the face happier" by upregulating (happy-up) or downregulating (happy-down) the amygdala or to "try to make the face less fearful" by upregulating (fear-up) or downregulating (fear-down) the amygdala feedback signal.

Results

Linear mixed effect analyses revealed significant amygdala activity changes in the fear condition, specifically in the fear-down group with significant amygdala downregulation in the last two neurofeedback runs as compared to the first run. The happy-up and happy-down groups did not show significant amygdala activity changes over four runs. We did not observe significant improvement in the questionnaire scores and subsequent behavior. Furthermore, task-dependent effective connectivity changes between the amygdala, fusiform face area (FFA), and the medial orbitofrontal cortex (mOFC) were examined using dynamic causal modeling. The effective connectivity between FFA and the amygdala was significantly increased in the happy-up group (facilitatory effect) and decreased in the fear-down group. Notably, the amygdala was downregulated through an inhibitory mechanism mediated by mOFC during the first training run.

Discussion

In this feasibility study, we intended to address key neurofeedback processes like naturalistic facial stimuli, participant engagement in the task, bidirectional regulation, task congruence, and their influence on learning success. It demonstrated that such a versatile emotional face feedback paradigm can be tailored to target biased emotion processing in affective disorders.