Biology subjects

Moutoussis, M.

Publications and source records attributed to Moutoussis, M..

13 recordsLinked to original sources

bioRxiv2026

Temporal-difference valence-partitioned Bayesian brains work out whether others are caring or uncaring

Working out whether others care for us is crucial in personal relationships and when seeking professional help. It is often most difficult for those most in need, e.g. following interpersonal traumata. Here, we introduce a simple Caring Attributions task and elucidate key computational mechanisms involved and, using EEG, the cortical activity representing the degree of belief that another is beneficent or maleficent. We find evidence for a new type of neurocomputational processing: valence-partitioned temporal-difference inference (TD-Bayes). This employs primary processing about latent causes, but also separate channels to represent these different valenced attributions, inspired by value-partitioned associative learning (VPAL). TD-Bayes uses slow propagation of beliefs using temporal-difference updating. These models gave a very good account of behaviour, slightly better than VPAL, but crucially, their partitioned representations have stronger, distinct representations in ERP signals. They provide a promising inroad into the understanding of how people may jump to atttibutions about caring vs. uncaring others.

neuroscience↗

bioRxiv2025

Dopamine drives a positive reward bias on human reinforcement learning

Formal theories of reinforcement learning (RL) prescribe a clearly defined function for dopamine, namely modulating learning via reward prediction errors (RPEs). Yet, empirical evidence in humans remains scarce, and recent advances introducing noisy RL cast doubt on a simple one-to-one mapping between neurotransmitters and computational mechanisms. Here, we detail a double-blind, placebo-controlled, randomised pharmacological study using the dopamine precursor L-DOPA, while healthy volunteers performed a volatile two-armed bandit task. Behaviourally, L-DOPA decreased switching behaviour following below-average rewards. Algorithmic RL modelling of human behaviour supported a dual effect of L-DOPA on the rate and precision of learning. By leveraging recurrent neural networks (RNNs) as implementational models of RL, we explain this dual effect through a single inference-time modulation, whereby L-DOPA triggers a positive reward bias at the input of the recurrent layer that implements RL. Our findings highlight a unifying mechanism at the implementation level that explain seemingly disparate algorithmic effects of dopamine.

neuroscience↗

bioRxiv2025

Neurocomputational impairments in disambiguation of context as a key determinant of post-traumatic psychopathology

We combine theoretical biology and systems neuroscience to relate the mechanisms of post-traumatic stress disorder (PTSD) models to biopsychosocial complexity. Moving beyond fear conditioning, extinction and habits formed by associative learning, we consider conserved neuromodulatory mechanisms related to harm expectation, defensive mobilisation, and context (in)sensitivity. Our account of harm-mitigating, neurobehavioural responses furnishes a mechanistic understanding of maladaptive behaviour and impaired belief-updating, which are often underspecified in conceptual analyses. Grounded in neurobiological evidence, we specify PTSD-related impairment in terms of Bayesian belief-updating and establish an adaptive/maladaptive boundary using active inference simulation studies. The ensuing model plausibly illustrates PTSD pathogenesis, which remains understudied relative to aetiology. We locate pathogenesis in maladaptive precision following trauma, characterised by a distortion of ideal Bayesian updating via aberrant credit assignment. Future scientific and clinical studies can test the validity of these insights, which could underwrite our understanding of the phenomenology, diagnostics and ultimately treatment intervention in post-traumatic disorders.

neuroscience↗

bioRxiv2024

Indecision and recency-weighted evidence integration in non-clinical and clinical settings

Biases in information gathering are common in the general population and can reach pathological extremes in paralysing indecisiveness, as in obsessive-compulsive disorder (OCD). Here, we adopt a new perspective on information gathering and demonstrate an information integration bias where most recent information is over-weighted by means of evidence strength updates ({Delta}ES). In a large, crowd-sourced sample (N=5,237), we find reduced {Delta}ES-weighting drives indecisiveness along an OC spectrum. We replicate the attenuated {Delta}ES- weighting in a second lab-based study (N=105) that includes a transdiagnostic OC spectrum encompassing OCD and generalised anxiety patients. Using magnetoencephalography (MEG), we trace {Delta}ES signals to a late neural signal peaking around 920 ms. Critically, high-OC participants show an attenuated neural {Delta}ES signal in medio-frontal areas while other decision- relevant processes remain intact. Our findings establish biased information-weighting as a key driver of information gathering, where attenuated {Delta}ES can lead to indecisiveness across an OC spectrum.

neuroscience↗

bioRxiv2023

The role of serotonin and of perceived social differences in infering the motivation of others

Attributing motives to others is a crucial aspect of mentalizing, can be biased by prejudice, and also is affected by common psychiatric disorders. It is therefore important to understand in depth the mechanisms underpinning it. Toward improving models of mentalizing motives, we hypothesized that people quickly infer whether others motives are likely beneficial or detrimental, then refine their judgment ( Classify-refine). To test this, we used a modified Dictator game, a game theoretic task, where participants judged the likelihood of intent to harm vs. self-interest in economic decisions. Toward testing the role of serotonin in judgments of intent to harm, we delivered the task in a week-long, placebo vs. Citalopram study. Computational model comparison provided clear evidence for the superiority of Classify-refine models over traditional ones, strongly supporting the central hypothesis. Further, while Citalopram helped refine attributions about motives through learning, it did not induce more positive initial inferences about others motives. Finally, model comparison indicated a minimal role for racial bias within economic decisions for the large majority of our sample. Overall, these results support a proposal that classify-refine social cognition is adaptive, although relevant mechanisms of Serotonergic antidepressant action will need to be studied over longer time spans.

neuroscience↗

bioRxiv2020

A generic decision-making ability predicts psychopathology in adolescents and young adults and is reflected in distinct brain connectivity patterns

Decision-making underpins many important facets of our lives. Here, we assessed if a general ability factor underpins decision-making abilities. Using factor analysis of 32 decision-making measures in 830 adolescents and young adults, we identified a common factor we refer to as decision acuity that was distinct from IQ and reflected advantageous decision-making abilities. Decision acuity decreased with low general social functioning and aberrant thinking. Crucially, decision acuity and IQ had dissociable neural signatures in terms of resting-state functional connectivity involving specific neural networks. Finally, decision acuity was reliable and its relationship with functional connectivity was stable when measured in the same individuals 18 months later. We conclude that our behavioural and brain data demonstrate a new cognitive construct encapsulating ability to perform decision-making across distinct domains, and that the expression of this construct may be important for understanding psychopathology.

neuroscience↗

bioRxiv2019

Reinforcement learning as an intermediate phenotype in psychosis? Deficits sensitive to illness stage but not associated with polygenic risk of schizophrenia in the general population

BackgroundSchizophrenia is a complex disorder in which the causal relations between risk genes and observed clinical symptoms are not well understood and the explanatory gap is too wide to be clarified without considering an intermediary level. Thus, we aimed to test the hypothesis of a pathway from molecular polygenic influence to clinical presentation occurring via deficits in reinforcement learning. MethodsWe administered a reinforcement learning task (Go/NoGo) that measures reinforcement learning and the effect of Pavlovian bias on decision making. We modelled the behavioural data with a hierarchical Bayesian approach (hBayesDM) to decompose task performance into its underlying learning mechanisms. Study 1 included controls (n= 29, F|M=0.81), At Risk Mental State for psychosis (ARMS, n= 23, F|M=0.35) and FEP (First-episode psychosis, n= 26, F|M=0.18). Study 2 included healthy adolescents (n= 735, F|M= 1.06), 390 of whom had their polygenic risk scores for schizophrenia (PRSs) calculated. ResultsPatients with FEP showed significant impairments in overriding Pavlovian conflict, a lower learning rate and a lower sensitivity to both reward and punishment. Less widespread deficits were observed in ARMS. PRSs did not significantly predict performance on the task in the general population, which only partially correlated with measures of psychopathology. ConclusionsReinforcement learning deficits are observed in first episode psychosis and, to some extent, in those at clinical risk for psychosis, and were not predicted by molecular genetic risk for schizophrenia in healthy individuals. The study does not support the role of reinforcement learning as an intermediate phenotype in psychosis.

neuroscience↗

bioRxiv2019

Childhood socio-economic disadvantage predicts reduced myelin growth across adolescence and young adulthood

Socio-economic disadvantage (SED) increases exposure to life stressors. Animal research suggests early life stressors affect later neurodevelopment, including myelin developmental growth. To determine whether human childhood SED affects myelination in adolescence and early adulthood we measured the developmental increase of a sensitive myelin marker, magnetization transfer (MT), in a longitudinal study. Childhood SED was associated with globally reduced MT, as well as slower intra-cortical MT increase in widespread sensory-motor, cingulate, insular and prefrontal areas and subcortical areas. Parental education partially accounted for the SED effects on MT increase, while positive parenting provided a partial protection against the impact of SED. Thus, early socio-economic disadvantage, a vulnerability factor for a range of ill-health outcomes, is a risk factor for aberrant myelin growth during a critical developmental period that is associated with a high risk of psychiatric disorder.

neuroscience↗

bioRxiv2018

Brain-behaviour modes of covariation in healthy and clinically depressed young people

Understanding how variations in dimensions of psychometrics, IQ and demographics relate to changes in brain connectivity during the critical developmental period of adolescence and early adulthood is a major challenge. This has particular relevance for mental health disorders where a failure to understand these links might hinder the development of better diagnostic approaches and therapeutics. Here, we investigated this question in 306 adolescents and young adults (14-24y, 25 clinically depressed) using a multivariate statistical framework, based on canonical correlation analysis (CCA). By linking individual functional brain connectivity profiles to self-report questionnaires, IQ and demographic data we identified two distinct modes of covariation. The first mode mapped onto an externalization/internalization axis and showed a strong association with sex. The second mode mapped onto a well-being/distress axis independent of sex. Interestingly, both modes showed an association with age. Crucially, the changes in functional brain connectivity associated with changes in these phenotypes showed marked developmental effects. The findings point to a role for the default mode, frontoparietal and limbic networks in psychopathology and depression.

neuroscience↗

bioRxiv2018

Compulsivity and impulsivity are linked to distinct aberrant developmental trajectories of fronto-striatal myelination

The transition from adolescence into adulthood is a period where rapid brain development coincides with an enhanced incidence of psychiatric disorder. The precise developmental brain changes that account for this emergent psychiatric symptomatology remain obscure. Capitalising on a unique longitudinal dataset, that includes in-vivo myelin-sensitive magnetization transfer (MT) MRI, we show this transition period is characterised by brain-wide growth in MT, within both gray matter and adjacent juxta-cortical white matter. We show that an expression of common developmental psychiatric risk symptomatology in this otherwise healthy population, specifically compulsivity and impulsivity, is tied to regionally specific aberrant unfolding of these MT trajectories. This is most marked in frontal midline structures for compulsivity, and in lateral frontal areas for impulsivity. The findings highlight a brain developmental linkage for emergent psychiatric risk features, evident in regionally specific perturbations in the expansion of MT-related myelination.

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bioRxiv2018

Noradrenaline modulates decision urgency during sequential information gathering

Arbitrating between timely choice and extended information gathering is critical in effective decision making. Aberrant information gathering behaviour is said to be a feature of psychiatric disorders such as schizophrenia and obsessive-compulsive disorder. We know little about the neurocognitive control mechanisms that drive such information gathering. In a double-blind placebo-controlled drug study with 60 healthy humans (30 female), we examined the effects of noradrenaline and dopamine antagonism on information gathering. We show that modulating noradrenaline function with propranolol leads to decreased information gathering behaviour and this contrasts with no effect following a modulation of dopamine function. Using a Bayesian computational model, we show sampling behaviour is best explained when including an urgency signal that promotes commitment to an early decision. We demonstrate that noradrenaline blockade promotes the expression of this decision-related urgency signal during information gathering. We discuss the findings with respect to psychopathological conditions that are linked to aberrant information gathering.\n\nSignificance StatementKnowing when to stop gathering information and commit to an option is non-trivial. This is an important element in arbitrating between information gain and energy conservation. In this double-blind, placebo-controlled drug study, we investigated to role of catecholamines noradrenaline and dopamine on sequential information gathering. We found that blocking noradrenaline led to a decrease in information gathering, with no effect seen following dopamine blockade. Using a Bayesian computational model, we show that this noradrenaline effect is driven by an increased decision urgency, a signal that reflects an escalating subjective cost of sampling. The observation that noradrenaline modulates decision urgency suggests new avenues for treating patients that show information gathering deficits.

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bioRxiv2017

Cost evaluation during decision making in patients at early stages of psychosis

Jumping to conclusions during probabilistic reasoning is a cognitive bias reliably observed in psychosis, and linked to delusion formation. Although the reasons for this cognitive bias are unknown, one suggestion is that psychosis patients may view sampling information as more costly. However, previous computational modelling has provided evidence that patients with chronic schizophrenia jump to conclusion because of noisy decision making. We developed a novel version of the classical beads-task, systematically manipulating the cost of information gathering in four blocks. For 31 individuals with early symptoms of psychosis and 31 healthy volunteers, we examined the numbers of draws to decision when information sampling had no, a fixed, or an escalating cost. Computational modelling involved estimating a cost of information sampling parameter and a cognitive noise parameter. Overall patients sampled less information than controls. However, group differences in numbers of draws became less prominent at higher cost trials, where less information was sampled. The attenuation of group difference was not due to floor effects, as in the most costly block participants sampled more information than an ideal Bayesian agent. Computational modelling showed that, in the condition with no objective cost to information sampling, patients attributed higher costs to information sampling than controls (Mann-Whiney U=289, p=0.007), with marginal evidence of differences in noise parameter estimates (t=1.86 df=60, p=0.07). In patients, individual differences in severity of psychotic symptoms were statistically significantly associated with higher cost of information sampling (rho=0.6, p=0.001) but not with more cognitive noise (rho=0.27, p=0.14); in controls cognitive noise predicted aspects of schizotypy (preoccupation and distress associated with delusion-like ideation on the Peters Delusion Inventory). Using a psychological manipulation and computational modelling, we provide evidence that early psychosis patients jump to conclusions because of attributing higher costs to sampling information, not because of being primarily noisy decision makers.

neuroscience↗

bioRxiv2017

Developmental cognitive neuroscience using Latent Change Score models: A tutorial and applications

Assessing and analysing individual differences in change over time is of central scientific importance to developmental neuroscience. However, the literature is based largely on cross-sectional comparisons, which reflect a variety of influences and cannot directly represent change. We advocate using latent change score (LCS) models in longitudinal samples as a statistical framework to tease apart the complex processes underlying lifespan development in brain and behaviour using longitudinal data. LCS models provide a flexible framework that naturally accommodates key developmental questions as model parameters and can even be used, with some limitations, in cases with only two measurement occasions. We illustrate the use of LCS models with two empirical examples. In a lifespan cognitive training study (COGITO, N=204 (N=32 imaging) on two waves) we observe correlated change in brain and behaviour in the context of a high-intensity training intervention. In an adolescent development cohort (NSPN, N=176, two waves) we find greater variability in cortical thinning in males than in females. To facilitate the adoption of LCS by the developmental community, we provide analysis code that can be adapted by other researchers and basic primers in two freely available SEM software packages (lavaan and {Omega}nyx).\n\nHighlightsO_LIWe describe Latent change score modelling as a flexible statistical tool\nC_LIO_LIKey developmental questions can be readily formalized using LCS models\nC_LIO_LIWe provide accessible open source code and software examples to fit LCS models\nC_LIO_LIWhite matter structural change is negatively correlated with processing speed gains\nC_LIO_LIFrontal lobe thinning in adolescence is more variable in males than females\nC_LI

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