Brain signatures of chronic gut inflammation.

Gut inflammation is thought to modify brain activity and behaviour via modulation of the gut-brain axis. However, how relapsing and remitting exposure to peripheral inflammation over the natural history of inflammatory bowel disease (IBD) contributes to altered brain dynamics is poorly understood. Here, we used electroencephalography (EEG) to characterise changes in spontaneous spatiotemporal brain states in Crohn's Disease (CD) (n = 40) and Ulcerative Colitis (UC) (n = 30), compared to healthy individuals (n = 28). We first provide evidence of a significantly perturbed and heterogeneous microbial profile in CD, consistent with previous work showing enduring and long-standing dysbiosis in clinical remission. Results from our brain state assessment show that CD and UC exhibit alterations in the temporal properties of states implicating default-mode network, parietal, and visual regions, reflecting a shift in the predominance from externally to internally-oriented attentional modes. We investigated these dynamics at a finer sub-network resolution, showing a CD-specific and highly selective enhancement of connectivity between the insula and medial prefrontal cortex (mPFC), regions implicated in cognitive-interoceptive appraisal mechanisms. Alongside overall higher anxiety scores in CD, we also provide preliminary support to suggest that the strength of chronic interoceptive hyper-signalling in the brain co-occurs with disease duration. Together, our results demonstrate that a long-standing diagnosis of CD is, in itself, a key factor in determining the risk of developing altered brain network signatures.

Mental Imagery in Dreams of Congenitally Blind People.

It is unclear to what extent the absence of vision affects the sensory sensitivity for oneiric construction. Similarly, the presence of visual imagery in the mentation of dreams of congenitally blind people has been largely disputed. We investigate the presence and nature of oneiric visuo-spatial impressions by analysing 180 dreams of seven congenitally blind people identified from the online database DreamBank. A higher presence of auditory, haptic, olfactory, and gustatory sensation in dreams of congenitally blind people was demonstrated, when compared to normally sighted individuals. Nonetheless, oneiric visual imagery in reports of congenitally blind subjects was also noted, in opposition to some previous studies, and raising questions about the possible underlying neuro-mechanisms.

Establishing brain states in neuroimaging data.

The definition of a brain state remains elusive, with varying interpretations across different sub-fields of neuroscience-from the level of wakefulness in anaesthesia, to activity of individual neurons, voltage in EEG, and blood flow in fMRI. This lack of consensus presents a significant challenge to the development of accurate models of neural dynamics. However, at the foundation of dynamical systems theory lies a definition of what constitutes the 'state' of a system-i.e., a specification of the system's future. Here, we propose to adopt this definition to establish brain states in neuroimaging timeseries by applying Dynamic Causal Modelling (DCM) to low-dimensional embedding of resting and task condition fMRI data. We find that ~90% of subjects in resting conditions are better described by first-order models, whereas ~55% of subjects in task conditions are better described by second-order models. Our work calls into question the status quo of using first-order equations almost exclusively within computational neuroscience and provides a new way of establishing brain states, as well as their associated phase space representations, in neuroimaging datasets.

Sub-second Dopamine Signals during Risky Decision-Making in Patients with Impulse Control Disorder.

Background: Impulse Control Disorder (ICD) in Parkinson's disease is a behavioral addiction arising secondary to dopaminergic therapies, most often dopamine receptor agonists. Prior research implicates changes in striatal function and heightened dopaminergic activity in the dorsal striatum of patients with ICD. However, this prior work does not possess the temporal resolution required to investigate dopaminergic signaling during real-time progression through various stages of decision-making involving anticipation and feedback. Methods: We recorded high-frequency (10Hz) measurements of extracellular dopamine in the striatum of patients with (N=3) and without (N=3) a history of ICD secondary to dopamine receptor agonist therapy for Parkinson's disease symptoms. These measurements were made using carbon fiber microelectrodes during awake DBS neurosurgery and while participants performed a sequential decision-making task involving risky investment decisions and real monetary gains and losses. Per clinical standard-of-care, participants withheld all dopaminergic medications prior to the procedure. Results: Patients with ICD invested significantly more money than patients without ICD. On each trial, patients with ICD made smaller adjustments to their investment levels compared to patients without ICD. In patients with ICD, dopamine levels rose or fell on sub-second timescales in anticipation of investment outcomes consistent with increased or decreased confidence in a positive outcome, respectively; dopamine levels in patients without ICD were significantly more stable during this phase. After outcome revelation, dopamine levels in patients with ICD rose significantly more than in inpatients without ICD for better-than-expected gains. For worse-than-expected losses, dopamine levels in patients with ICD remained level whereas dopamine levels in patients without ICD fell. Conclusion: We report significantly increased risky behavior and exacerbated phasic dopamine signaling, on sub-second timescales, anticipating and following the revelation of the outcomes of risky decisions in patients with ICD. Notably, these results were obtained when patients who had demonstrated ICD in the past but were, at the time of surgery, in an off-medication state. Thus, it is unclear whether observed signals reflect an inherent predisposition for ICD that was revealed when dopamine receptor agonists were introduced or whether these observations were caused by the introduction of dopamine receptor agonists and the patients having experienced ICD symptoms in the past. Regardless, future work investigating dopamine's role in human cognition, behavior, and disease should consider the signals this system generates on sub-second timescales.

Sex differences in alpha-synucleinopathies: a systematic review.

Background: Past research indicates a higher prevalence, incidence, and severe clinical manifestations of alpha-synucleinopathies in men, leading to a suggestion of neuroprotective properties of female sex hormones (especially estrogen). The potential pathomechanisms of any such effect on alpha-synucleinopathies, however, are far from understood. With that aim, we undertook to systematically review, and to critically assess, contemporary evidence on sex and gender differences in alpha-synucleinopathies using a bench-to-bedside approach. Methods: In this systematic review, studies investigating sex and gender differences in alpha-synucleinopathies (Rapid Eye Movement (REM) Behavior Disorder (RBD), Parkinson's Disease (PD), Dementia with Lewy Bodies (DLB), Multiple System Atrophy (MSA)) from 2012 to 2022 were identified using electronic database searches of PubMed, Embase and Ovid. Results: One hundred sixty-two studies were included; 5 RBD, 6 MSA, 20 DLB and 131 PD studies. Overall, there is conclusive evidence to suggest sex-and gender-specific manifestation in demographics, biomarkers, genetics, clinical features, interventions, and quality of life in alpha-synucleinopathies. Only limited data exists on the effects of distinct sex hormones, with majority of studies concentrating on estrogen and its speculated neuroprotective effects. Conclusion: Future studies disentangling the underlying sex-specific mechanisms of alpha-synucleinopathies are urgently needed in order to enable novel sex-specific therapeutics.

Dynamic causal modeling reveals increased cerebellar- periaqueductal gray communication during fear extinction.

Introduction: The extinction of fear memories is an important component in regulating defensive behaviors, contributing toward adaptive processes essential for survival. The cerebellar medial nucleus (MCN) has bidirectional connections with the ventrolateral periaqueductal gray (vlPAG) and is implicated in the regulation of multiple aspects of fear, such as conditioned fear learning and the expression of defensive motor outputs. However, it is unclear how communication between the MCN and vlPAG changes during conditioned fear extinction. Methods: We use dynamic causal models (DCMs) to infer effective connectivity between the MCN and vlPAG during auditory cue-conditioned fear retrieval and extinction in the rat. DCMs determine causal relationships between neuronal sources by using neurobiologically motivated models to reproduce the dynamics of post-synaptic potentials generated by synaptic connections within and between brain regions. Auditory event related potentials (ERPs) during the conditioned tone offset were recorded simultaneously from MCN and vlPAG and then modeled to identify changes in the strength of the synaptic inputs between these brain areas and the relationship to freezing behavior across extinction trials. The DCMs were structured to model evoked responses to best represent conditioned tone offset ERPs and were adapted to represent PAG and cerebellar circuitry. Results: With the use of Parametric Empirical Bayesian (PEB) analysis we found that the strength of the information flow, mediated through enhanced synaptic efficacy from MCN to vlPAG was inversely related to freezing during extinction, i.e., communication from MCN to vlPAG increased with extinction. Discussion: The results are consistent with the cerebellum contributing to predictive processes that underpin fear extinction.

A primer on entropy in neuroscience.

Entropy is not just a property of a system - it is a property of a system and an observer. Specifically, entropy is a measure of the amount of hidden information in a system that arises due to an observer's limitations. Here we provide an account of entropy from first principles in statistical mechanics with the aid of toy models of neural systems. Specifically, we describe the distinction between micro and macrostates in the context of simplified binary-state neurons and the characteristics of entropy required to capture an associated measure of hidden information. We discuss the origin of the mathematical form of entropy via the indistinguishable re-arrangements of discrete-state neurons and show the way in which the arguments are extended into a phase space description for continuous large-scale neural systems. Finally, we show the ways in which limitations in neuroimaging resolution, as represented by coarse graining operations in phase space, lead to an increase in entropy in time as per the second law of thermodynamics. It is our hope that this primer will support the increasing number of studies that use entropy as a way of characterising neuroimaging timeseries and of making inferences about brain states.

Peripheral brain-derived neurotrophic factor (BDNF) in insomnia: A systematic review and meta-analysis.

The brain-derived neurotrophic factor (BDNF) is associated with emotional and cognitive functioning, and it is considered a transdiagnostic biomarker for mental disorders. Literature on insomnia related BDNF changes yielded contrasting results and it has never been synthetized using meta-analysis. To fill this gap, we conducted a systematic review and meta-analysis of case-control studies examining the levels of peripheric BDNF in individuals with insomnia and healthy controls using the PRISMA guidelines. PubMed, Scopus, Medline, PsycINFO and CINAHL were searched up to Nov 2022. Nine studies met the inclusion criteria and were assessed using the Newcastle-Ottawa Scale. Eight studies reported sufficient data for meta-analysis. Random-effects models showed lower BDNF in subjects with insomnia (n = 446) than in controls (n = 706) (Hedge's g = -0.86, 95% CI: -1.39 to -0.32, p = .002). Leave-one-out sensitivity analysis confirmed that the pooled effect size was robust and not driven by any single study. However, given the small sample size, the cross-sectional nature of the measurement, and the high heterogeneity of included data, the results should be cautiously interpreted. Progress in the study of BDNF in insomnia is clinically relevant to better understand the mechanisms that may explain the relationship between disturbed sleep and mental disorders.

Augmenting Human Selves Through Artificial Agents - Lessons From the Brain.

Much of current artificial intelligence (AI) and the drive toward artificial general intelligence (AGI) focuses on developing machines for functional tasks that humans accomplish. These may be narrowly specified tasks as in AI, or more general tasks as in AGI - but typically these tasks do not target higher-level human cognitive abilities, such as consciousness or morality; these are left to the realm of so-called "strong AI" or "artificial consciousness." In this paper, we focus on how a machine can augment humans rather than do what they do, and we extend this beyond AGI-style tasks to augmenting peculiarly personal human capacities, such as wellbeing and morality. We base this proposal on associating such capacities with the "self," which we define as the "environment-agent nexus"; namely, a fine-tuned interaction of brain with environment in all its relevant variables. We consider richly adaptive architectures that have the potential to implement this interaction by taking lessons from the brain. In particular, we suggest conjoining the free energy principle (FEP) with the dynamic temporo-spatial (TSD) view of neuro-mental processes. Our proposed integration of FEP and TSD - in the implementation of artificial agents - offers a novel, expressive, and explainable way for artificial agents to adapt to different environmental contexts. The targeted applications are broad: from adaptive intelligence augmenting agents (IA's) that assist psychiatric self-regulation to environmental disaster prediction and personal assistants. This reflects the central role of the mind and moral decision-making in most of what we do as humans.

A Control-Theoretical System for Modulating Hippocampal Gamma Oscillations Using Stimulation of the Posterior Cingulate Cortex.

Closed-loop stimulation for targeted modulation of brain signals has emerged as a promising strategy for episodic memory restoration. In parallel, closed-loop neuromodulation strategies have been applied to treat brain conditions including drug-resistant depression, Parkinson's Disease, and epilepsy. In this study, we seek to apply control theoretical principles to achieve closed loop modulation of hippocampal oscillatory activity. We focus on hippocampal gamma power, a signal with an established association for episodic memory processing, which may be a promising 'biomarker' for the modulation of memory performance. To develop a closed-loop stimulation paradigm that effectively modulates hippocampal gamma power, we use a novel data-set in which open-loop stimulation was applied to the posterior cingulate cortex and hippocampal gamma power was recorded during the encoding of episodic memories. The dataset was used to design and evaluate a linear quadratic integral (LQI) servo-controller in order to determine its viability for in-vivo use. In our simulation framework, we demonstrate that applying an LQI servo controller based on an autoregressive with exogenous input (ARX) plant model achieves effective control of hippocampal gamma power in 15 out of 17 experimental subjects. We demonstrate that we are able to modulate gamma power using stimulation thresholds that are physiologically safe and on time scales that are reasonable for application in a clinical system. We outline further experimentation to test our proposed system and compare our findings to emerging closed-loop neuromodulation strategies.

The role of cognitive control in the positive symptoms of psychosis.

BACKGROUND: Positive symptoms of psychosis (e.g., hallucinations) often limit everyday functioning and can persist despite adequate antipsychotic treatment. We investigated whether poor cognitive control is a mechanism underlying these symptoms. METHODS: 97 patients with early psychosis (30 with high positive symptoms (HS) and 67 with low positive symptoms (LS)) and 40 healthy controls (HC) underwent fMRI whilst performing a reward learning task with two conditions; low cognitive demand (choosing between neutral faces) and high cognitive demand (choosing between angry and happy faces - shown to induce an emotional bias). Decision and feedback phases were examined. RESULTS: Both patient groups showed suboptimal learning behaviour compared to HC and altered activity within a core reward network including occipital/lingual gyrus (decision), rostral Anterior Cingulate Cortex, left pre-central gyrus and Supplementary Motor Cortex (feedback). In the low cognitive demand condition, HS group showed significantly reduced activity in Supplementary Motor Area (SMA)/pre-SMA during the decision phase whilst activity was increased in LS group compared to HC. Recruitment of this region suggests a top-down compensatory mechanism important for control of positive symptoms. With additional cognitive demand (emotional vs. neutral contrast), HS patients showed further alterations within a subcortical network (increased left amygdala activity during decisions and reduced left pallidum and thalamus activity during feedback) compared to LS patients. CONCLUSIONS: The findings suggest a core reward system deficit may be present in both patient groups, but persistent positive symptoms are associated with a specific dysfunction within a network needed to integrate social-emotional information with reward feedback.

Oxytocin modulates neurocomputational mechanisms underlying prosocial reinforcement learning.

Humans often act in the best interests of others. However, how we learn which actions result in good outcomes for other people and the neurochemical systems that support this 'prosocial learning' remain poorly understood. Using computational models of reinforcement learning, functional magnetic resonance imaging and dynamic causal modelling, we examined how different doses of intranasal oxytocin, a neuropeptide linked to social cognition, impact how people learn to benefit others (prosocial learning) and whether this influence could be dissociated from how we learn to benefit ourselves (self-oriented learning). We show that a low dose of oxytocin prevented decreases in prosocial performance over time, despite no impact on self-oriented learning. Critically, oxytocin produced dose-dependent changes in the encoding of prediction errors (PE) in the midbrain-subgenual anterior cingulate cortex (sgACC) pathway specifically during prosocial learning. Our findings reveal a new role of oxytocin in prosocial learning by modulating computations of PEs in the midbrain-sgACC pathway.

Item-level analysis of mental health symptom trajectories during the COVID-19 pandemic in the UK: Associations with age, sex and pre-existing psychiatric conditions.

BACKGROUND: There is widespread concern regarding how the COVID-19 pandemic has affected mental health. Emerging meta-analyses suggest that the impact on anxiety/depression may have been transient, but much of the included literature has major methodological limitations. Addressing this topic rigorously requires longitudinal data of sufficient scope and scale, controlling for contextual variables, with baseline data immediately pre-pandemic. AIMS: To analyse self-report of symptom frequency from two largely UK-based longitudinal cohorts: Cohort 1 (N = 10,475, two time-points: winter pre-pandemic to UK first winter resurgence), and Cohort 2 (N = 10,391, two time-points, peak first wave to UK first winter resurgence). METHOD: Multinomial logistic regression applied at the item level identified sub-populations with greater probability of change in mental health symptoms. Permutation analyses characterised changes in symptom frequency distributions. Cross group differences in symptom stability were evaluated via entropy of response transitions. RESULTS: Anxiety was the most affected aspect of mental health. The profiles of change in mood symptoms was less favourable for females and older adults. Those with pre-existing psychiatric diagnoses showed substantially higher probability of very frequent symptoms pre-pandemic and elevated risk of transitioning to the highest levels of symptoms during the pandemic. Elevated mental health symptoms were evident across intra-COVID timepoints in Cohort 2. CONCLUSIONS: These findings suggest that mental health has been negatively affected by the pandemic, including in a sustained fashion beyond the first UK lockdown into the first winter resurgence. Women, and older adults, were more affected relative to their own baselines. Those with diagnoses of psychiatric conditions were more likely to experience transition to the highest levels of symptom frequency.

Estimating anisotropy directly via neural timeseries.

An isotropic dynamical system is one that looks the same in every direction, i.e., if we imagine standing somewhere within an isotropic system, we would not be able to differentiate between different lines of sight. Conversely, anisotropy is a measure of the extent to which a system deviates from perfect isotropy, with larger values indicating greater discrepancies between the structure of the system along its axes. Here, we derive the form of a generalised scalable (mechanically similar) discretized field theoretic Lagrangian that allows for levels of anisotropy to be directly estimated via timeseries of arbitrary dimensionality. We generate synthetic data for both isotropic and anisotropic systems and, by using Bayesian model inversion and reduction, show that we can discriminate between the two datasets - thereby demonstrating proof of principle. We then apply this methodology to murine calcium imaging data collected in rest and task states, showing that anisotropy can be estimated directly from different brain states and cortical regions in an empirical in vivo biological setting. We hope that this theoretical foundation, together with the methodology and publicly available MATLAB code, will provide an accessible way for researchers to obtain new insight into the structural organization of neural systems in terms of how scalable neural regions grow - both ontogenetically during the development of an individual organism, as well as phylogenetically across species.

Simultaneous brain, brainstem, and spinal cord pharmacological-fMRI reveals involvement of an endogenous opioid network in attentional analgesia.

Pain perception is decreased by shifting attentional focus away from a threatening event. This attentional analgesia engages parallel descending control pathways from anterior cingulate (ACC) to locus coeruleus, and ACC to periaqueductal grey (PAG) - rostral ventromedial medulla (RVM), indicating possible roles for noradrenergic or opioidergic neuromodulators. To determine which pathway modulates nociceptive activity in humans, we used simultaneous whole brain-spinal cord pharmacological-fMRI (N = 39) across three sessions. Noxious thermal forearm stimulation generated somatotopic-activation of dorsal horn (DH) whose activity correlated with pain report and mirrored attentional pain modulation. Activity in an adjacent cluster reported the interaction between task and noxious stimulus. Effective connectivity analysis revealed that ACC interacts with PAG and RVM to modulate spinal cord activity. Blocking endogenous opioids with Naltrexone impairs attentional analgesia and disrupts RVM-spinal and ACC-PAG connectivity. Noradrenergic augmentation with Reboxetine did not alter attentional analgesia. Cognitive pain modulation involves opioidergic ACC-PAG-RVM descending control which suppresses spinal nociceptive activity.

Microbiota links to neural dynamics supporting threat processing.

There is growing recognition that the composition of the gut microbiota influences behaviour, including responses to threat. The cognitive-interoceptive appraisal of threat-related stimuli relies on dynamic neural computations between the anterior insular (AIC) and the dorsal anterior cingulate (dACC) cortices. If, to what extent, and how microbial consortia influence the activity of this cortical threat processing circuitry is unclear. We addressed this question by combining a threat processing task, neuroimaging, 16S rRNA profiling and computational modelling in healthy participants. Results showed interactions between high-level ecological indices with threat-related AIC-dACC neural dynamics. At finer taxonomic resolutions, the abundance of Ruminococcus was differentially linked to connectivity between, and activity within the AIC and dACC during threat updating. Functional inference analysis provides a strong rationale to motivate future investigations of microbiota-derived metabolites in the observed relationship with threat-related brain processes.

World model learning and inference.

Understanding information processing in the brain-and creating general-purpose artificial intelligence-are long-standing aspirations of scientists and engineers worldwide. The distinctive features of human intelligence are high-level cognition and control in various interactions with the world including the self, which are not defined in advance and are vary over time. The challenge of building human-like intelligent machines, as well as progress in brain science and behavioural analyses, robotics, and their associated theoretical formalisations, speaks to the importance of the world-model learning and inference. In this article, after briefly surveying the history and challenges of internal model learning and probabilistic learning, we introduce the free energy principle, which provides a useful framework within which to consider neuronal computation and probabilistic world models. Next, we showcase examples of human behaviour and cognition explained under that principle. We then describe symbol emergence in the context of probabilistic modelling, as a topic at the frontiers of cognitive robotics. Lastly, we review recent progress in creating human-like intelligence by using novel probabilistic programming languages. The striking consensus that emerges from these studies is that probabilistic descriptions of learning and inference are powerful and effective ways to create human-like artificial intelligent machines and to understand intelligence in the context of how humans interact with their world.

Contextual perception under active inference.

Human social interactions depend on the ability to resolve uncertainty about the mental states of others. The context in which social interactions take place is crucial for mental state attribution as sensory inputs may be perceived differently depending on the context. In this paper, we introduce a mental state attribution task where a target-face with either an ambiguous or an unambiguous emotion is embedded in different social contexts. The social context is determined by the emotions conveyed by other faces in the scene. This task involves mental state attribution to a target-face (either happy or sad) depending on the social context. Using active inference models, we provide a proof of concept that an agent's perception of sensory stimuli may be altered by social context. We show with simulations that context congruency and facial expression coherency improve behavioural performance in terms of decision times. Furthermore, we show through simulations that the abnormal viewing strategies employed by patients with schizophrenia may be due to (i) an imbalance between the precisions of local and global features in the scene and (ii) a failure to modulate the sensory precision to contextualise emotions.