Biology subjects

Fonagy, P.

Publications and source records attributed to Fonagy, P..

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↗

bioRxiv2023

Resilient functioning after childhood adversity is associated with altered structural brain network topology in adolescence.

Childhood adversity is one of the strongest predictors of adolescent mental illness. Therefore, it is critical that the mechanisms that aid resilient functioning in individuals exposed to childhood adversity are better understood. Here, we examined whether resilient functioning was related to structural brain network topology. We quantified resilient functioning at the individual level as psychosocial functioning adjusted for the severity of childhood adversity in a large sample of adolescents (N=2406, aged 14-24). Next, we examined nodal degree (the number of connections that brain regions have in a network) using brain-wide cortical thickness measures in a representative subset (N=275) using a sliding window approach. We found that higher resilient functioning was associated with lower nodal degree of multiple regions including the dorsolateral prefrontal cortex, the medial prefrontal cortex, and the posterior superior temporal sulcus (z > 1.645). During adolescence, decreases in nodal degree are thought to reflect a normative developmental process that is part of the extensive remodelling of structural brain network topology. Prior findings in this sample showed that decreased nodal degree was associated with age, as such our findings of negative associations between nodal degree and resilient functioning may therefore potentially resemble a more mature structural network configuration in individuals with higher resilient functioning.

neuroscience↗

bioRxiv2020

Sexually dimorphic development of depression-related brain networks during healthy human adolescence

We hypothesized that there are sexual differences in human brain network development underlying the female > male divergence in adolescent depression. We tested for sex differences in parameters of brain network development (accelerated longitudinal fMRI, N=298 healthy adolescents, each scanned 1 to 3 times). Sexually divergent development of functional connectivity was located in default mode network (DMN), limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of divergent adolescent development was co-located with (i) a map of functional dysconnectivity associated with adult major depressive disorder (MDD); and (ii) an adult brain gene expression pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. Sexual divergence in disruptive development of DMN, limbic and subcortical functional networks is potentially relevant to the increased risk of depression in adolescent females.

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

Conservative and disruptive modes of adolescent change in brain functional connectivity

Adolescent changes in human brain function are not entirely understood. Here we used multi-echo functional magnetic resonance imaging (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in N=298 healthy adolescents. Participants were aged 14-26 years and were scanned on two or more occasions at least 6 months apart. We found two distinct modes of age-related change in FC: \"conservative\" and \"disruptive\". Conservative development was characteristic of primary cortex, which was strongly connected at 14 years and became even more connected in the period 14-26 years. Disruptive development was characteristic of association cortex, hippocampus and amygdala, which were not strongly connected at 14 years but became more strongly connected during adolescence. We defined the maturational index (MI) as the signed coefficient of the linear relationship between baseline FC (at 14 years, FC14) and adolescent change in FC ({triangleup}FC14-26). Disruptive systems (with negative MI) were functionally specialised for social cognition and autobiographical memory and were significantly co-located with prior maps of aerobic glycolysis (AG), AG-related gene expression, post-natal expansion of cortical surface area, and adolescent shrinkage of cortical depth. We conclude that human brain organization is disrupted during adolescence by the emergence of strong functional connectivity of subcortical nuclei and association cortical areas, representing metabolically expensive re-modelling of synaptic connectivity between brain regions that were not strongly connected in childhood. We suggest that this re-modelling process may support emergence of social skills and self-awareness during healthy human adolescence.

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

Schizotypy-related magnetization of cortex in healthy adolescence is co-located with expression of schizophrenia risk genes

BackgroundGenetic risk is thought to drive clinical variation on a spectrum of schizophrenia-like traits but the underlying changes in brain structure that mechanistically link genomic variation to schizotypal experience and behaviour are unclear. MethodsWe assessed schizotypy using a self-reported questionnaire, and measured magnetization transfer (MT), as a putative micro-structural MRI marker of intra-cortical myelination, in 68 brain regions, in 248 healthy young people (aged 14-25 years). We used normative adult brain gene expression data, and partial least squares (PLS) analysis, to find the weighted gene expression pattern that was most co-located with the cortical map of schizotypy-related magnetization (SRM). ResultsMagnetization was significantly correlated with schizotypy in bilateral posterior cingulate cortex and precuneus (and for disorganized schizotypy also in medial prefrontal cortex; all FDR-corrected P < 0.05), which are regions of the default mode network specialized for social and memory functions. The genes most positively weighted on the whole genome expression map co-located with SRM were enriched for genes that were significantly down-regulated in two prior case-control histological studies of brain gene expression in schizophrenia. Conversely, the most negatively weighted genes were enriched for genes that were transcriptionally up-regulated in schizophrenia. Positively weighted (down-regulated) genes were enriched for neuronal, specifically inter-neuronal, affiliations and coded a network of proteins comprising a few highly interactive "hubs" such as parvalbumin and calmodulin. ConclusionsMicrostructural MRI maps of intracortical magnetization can be linked to both the behavioural traits of schizotypy and to prior histological data on dysregulated gene expression in schizophrenia.

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.

neuroscience↗

bioRxiv2017

Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex

Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network (SCN) from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we use this to define, transcriptomic brain networks (TBN) by estimating gene co-expression between pairs of cortical regions. Finally, we explore the hypothesis that TBN and the SCN are coupled.\n\nTBN and SCN were correlated across connection weights and showed qualitatively similar complex topological properties. There were differences between networks in degree and distance distributions. However, cortical areas connected to each other within modules of the SCN network had significantly higher levels of whole genome co-expression than expected by chance.\n\nNodes connected in the SCN had significantly higher levels of expression and co-expression of a Human Supragranular Enriched (HSE) gene set that are known to be important for large-scale cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not completely related to the common constraint of physical distance on both networks.

neuroscience↗

bioRxiv2017

Morphometric Similarity Networks Detect Microscale Cortical Organisation And Predict Inter-Individual Cognitive Variation

Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping, based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organisation comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs to tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.

neuroscience↗

bioRxiv2017

Adolescent Tuning Of Association Cortex In Human Structural Brain Networks

Motivated by prior data on local cortical shrinkage and intracortical myelination, we predicted age-related changes in topological organisation of cortical structural networks during adolescence. We estimated structural correlation from magnetic resonance imaging measures of cortical thickness at 308 regions in a sample of N=297 healthy participants, aged 14-24 years. We used a novel sliding-window analysis to measure age-related changes in network attributes globally, locally and in the context of several community partitions of the network. We found that the strength of structural correlation generally decreased as a function of age. Association cortical regions demonstrated a sharp decrease in nodal degree (hubness) from 14 years, reaching a minimum at approximately 19 years, and then levelling off or even slightly increasing until 24 years. Greater and more prolonged age-related changes in degree of cortical regions within the brain network were associated with faster rates of adolescent cortical myelination and shrinkage. The brain regions that demonstrated the greatest age-related changes were concentrated within prefrontal modules. We conclude that human adolescence is associated with biologically plausible changes in structural imaging markers of brain network organization, consistent with the concept of tuning or consolidating anatomical connectivity between frontal cortex and the rest of the connectome.

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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