Combining multi-site MRI datasets increases statistical power and model generalisability but may be hindered by variability between sites. Harmonisation methods aim to remove potentially confounding variance while preserving biologically meaningful signals. However, this can be challenging, as each T1-weighted image reflects both scanner properties (e.g., field strength, sequence parameters) and individual biological characteristics (e.g., age, sex, ethno-cultural background, and pathology). Two image-based (HACA3, IGUANe) and two feature-based (neuroHarmonize, neuroCombat) harmonisation methods were assessed using T1-weighted brain imaging data from the Psy-ShareD database; 564 participants (295 schizophrenia, 269 controls) from seven studies acquired across 5 sites from the Psy-ShareD database. We trained several models to classify sites, schizophrenia diagnosis, age, and symptom levels. Site-classification accuracy was high for unharmonised data (90.1%) and for HACA3 (92.2%), slightly reduced with IGUANe (86.6%), and near chance for feature-based methods (4.2% neuroHarmonize; 1.8% neuroCombat), indicating effective bias removal. We fitted several models predicting biological signals including diagnosis, age, and symptom levels across different harmonisation methods. In most cases, classification with harmonised data performed at least as well as with unharmonised data. Generally, feature-based methods best remove site-related variance, but image-based approaches remain a promising avenue for preserving individual biological differences. This work provides practical guidance for selecting harmonisation strategies in multi-site psychiatric neuroimaging, depending on whether the priority is bias reduction or preservation of subject-level variability. ### Competing Interest Statement PA has been funded by FrieslandCampina and Nedra, GKM consults for Ieso Digital Health. RU reports consultancy from Vitaris and Springer Healthcare unrelated to the current work. ### Funding Statement This work was funded by the United Kingdom Medical Research Council grant number MR/X010651/1 and delivered through the National Institute for Health and Care Research (NIHR) Maudsley Biomedical Research Centre (BRC). This work was supported in part by the Japan Agency for Medical Research and Development (AMED) Grant Number JP24wm0625302. All research at the Department of Psychiatry in the University of Cambridge is supported by the NIHR Cambridge Biomedical Research Centre (NIHR203312) and the NIHR Applied Research Collaboration East of England. RU is supported by the NIHR Oxford Health Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. A list of funders and acknowledgements for Psy-ShareD datasets can be found at https://psyshared.com/Team.html ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: London - Dulwich Research Ethics Committee Title of the Research Database: Psychosis MRI Shared Data Resource (Psy- ShareD), V1 REC reference: 25/LO/0184 IRAS project ID: 352347 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The data used in the study is available via the Psychsis MRI ShareD Data Resource (psyshared.com)

Positive and negative schizotypy reflect distinct patterns of subclinical traits in the general population associated with neurodevelopmental and schizophrenia-spectrum pathologies. Yet, a comprehensive characterization of the unique and shared neuroanatomical signatures of these schizotypy dimensions is lacking. Leveraging 3D brain MRI data from 2730 unmedicated healthy individuals, we identified neuroanatomical profiles of positive and negative schizotypy and systematically compared them with disorder-specific, microarchitectural, neurotransmitter-level, and connectome measures. Positive and negative schizotypy were associated with distinct cortical signatures, of predominantly thinner frontal and thicker paralimbic cortical areas, respectively. These cortical signatures of positive and negative schizotypy were differentially linked to brain-wide cortical patterns of schizophrenia-spectrum (clinical high-risk for psychosis, schizophrenia) and neurodevelopmental conditions (ADHD, autism spectrum disorder and 22q11.2 deletion syndrome). Additionally, the positive and negative schizotypy-related cortical profiles mapped onto different local attributes of gene expression, cortical myelination, D1, and histamine receptor distributions. Network models further showed that positive and negative schizotypy cortical signatures were spatially associated with cortical hubs, suggesting that highly interconnected regions are more vulnerable to the morphological differences associated with both schizotypy dimensions. Finally, predominantly sensorimotor-to-association and paralimbic areas emerged as epicenters with connectivity profiles significantly linked to the schizotypy-related cortical patterns. Collectively, this study identified cortical signatures of positive and negative schizotypy traits that are embedded along multiple scales of cortical organization and neuropsychiatric pathologies. Our work yields novel insights into how neurobiology and brain architecture may guide neuroanatomical vulnerability and resilience to psychopathology in the general population.

In the era of data-intensive science, managing and verifying research processes that include raw data, analysis scripts, workflows, and documentation, remains a major challenge, particularly in complex fields like neuroimaging were multiple processing and analyses stages can take place. Fragmented tools, inconsistent documentation, and poor platform integration continue to undermine reproducibility. While current Electronic Lab Notebooks (ELNs) aid organization, they often lack cross-platform interoperability, authorship certification, and immutable auditability. To address these gaps, this study proposes a novel ELN framework IntegriLAB, that consolidates traditional data, document and code repositories into a centralized, web-based system for end-to-end project tracking. As a proof of concept, our case study integrates DataLad, Overleaf and GitHub. It allows real-time monitoring across the research cycle while preserving familiar workflows. A key innovation is the use of LabTrace built on green blockchain technology to certify research activities through cryptographically signed, immutable records, ensuring data integrity and verifiable authorship with minimal environmental impact. By unifying project management, secure data handling, and blockchain-based verification, the proposed ELN advances reproducibility, fosters trust, and strengthens collaborative research practices.

Multi-voxel pattern analysis (MVPA)-based fMRI neurofeedback is a promising tool for modulating brain states and influencing cognition and behaviour, yet its effectiveness remains unclear. This systematic review investigates its efficacy across emotion regulation, fear conditioning, associative and perceptual learning, attention, craving, semantic neurofeedback and motor rehabilitation, incorporating a meta-analytic synthesis of neural effects. A systematic search of PubMed, Web of Science, IEEE, and DPBL databases identified 29 studies. Published studies exhibit considerable variability in protocol designs and methodological aspects related to the implementation of MVPA-based neurofeedback. Overall, the global meta-analysis revealed a moderate, statistically significant effect of MVPA-based neurofeedback on neural outcomes, with subgroup analysis indicating a similar moderate effect in emotion regulation. Most studies support neural modulation following neurofeedback training across multiple domains, though behavioural outcomes are less consistent. Fear reduction, attention and perceptual learning studies demonstrated both neural and behavioural changes, while associative learning and craving reduction studies showed evidence of neural regulation but unclear behavioural outcomes. Studies on motor rehabilitation and semantic neurofeedback demonstrated neural modulation but lacked behavioural assessments. Emotion regulation studies consistently supported neural modulation, however, only a few studies reported behavioural improvements. While these findings highlight the potential of neurofeedback, they also emphasise the need for standardized methodologies and clearer theoretical frameworks. Clarifying terminology used to define different approaches and addressing MVPA-specific methodological considerations – such as preprocessing, motion correction, and classifier selection - will be critical for refining neurofeedback protocols and unlocking the full potential of MVPA-based neurofeedback for both clinical and research applications.

Normative models of brain morphometry quantify individual deviations from typical anatomical patterns and hold promise for enhancing clinical decision-making. However, their clinical utility depends critically on demonstrating generalizability across diverse ethnoracial populations. We previously developed sex-specific, race-neutral normative models for cortical thickness, surface area, and subcortical volumes using brain scans from a large international sample of healthy individuals, as part of the CentileBrain Project, a global initiative to provide open-access, neuroimaging reference models. The primary aim of the present study was to empirically evaluate the generalizability and accuracy of these pretrained models across multiple ethnoracial groups. To this end, we tested model performance in independent samples of healthy individuals from Africa, Asia, Europe, and the Americas, with ethnoracial classification defined either by self-identification or genetic ancestry (N = 4,862). We further compared performance against normative models developed exclusively from a single-population Chinese cohort. Across all groups, as well as in the pooled sample, the pretrained CentileBrain models demonstrated consistently high accuracy, with relative mean absolute error values below 10% for subcortical volume and surface area and below 5% for cortical thickness. Model performance was highly concordant across self-identified and ancestry-defined groups. In a separate analysis, the CentileBrain models performed comparably to a population-specific model when applied to an independent ancestry-matched sample. These findings provide empirical support for the generalizability of race-neutral normative models developed on large and diverse samples and underscore their potential utility for individualized neuroimaging assessment across ethnoracially diverse populations.

Background:The hippocampus plays a critical role in psychosis, with reduced volume observed across the psychosis continuum. These structural changes are associated with cognitive deficits, symptom severity, and increased risk of psychosis progression. Elevated hippocampal perfusion and glutamate/GABA (gamma-aminobutyric acid) imbalance further suggest metabolic dysregulation as a key mechanism. Gut microbiota composition can influence hippocampal metabolism, but their interplay remains to be explored. Methods:In this cross-sectional study, we recruited 142 healthy participants from the general population, yielding 69 individuals with high schizotypy (HS) and 72 individuals with low schizotypy. All underwent clinical and cognitive testing, multimodal neuroimaging, and gut microbiota analysis via 16S ribosomal RNA gene sequencing. Hippocampal subfield volumes (structural magnetic resonance imaging), perfusion (arterial spin labeling) and glutamate/GABA levels (proton magnetic resonance spectroscopy), and microbial taxa (abundance, diversity, enterotypes) were assessed. Results:Group comparisons of cognition, multimodal neuroimaging, and gut microbiome composition did not reveal significant differences after correction for multiple comparisons. Within the HS group, glutamate (r = 0.38, p = .003) and GABA (r = -0.36, p = .003) ratios were linked to social withdrawal. Across the entire sample, left hippocampal subfield volumes and glutamate/GABA levels differed significantly between predominant gut microbial enterotypes. Conclusions:Our results suggest a potential relationship between aberrant gut microbial composition and hippocampal alterations in people with positive schizotypy from the general population. Our findings inform future large-scale research that further explores specific mechanisms of gut microbiome-hippocampus interactions in psychosis and the potential of tailored microbial interventions targeting hippocampal-mediated symptoms.

ABSTRACTThe brain integrates multiple scales of description, from the level of cells and molecules to large-scale networks and behaviour, and understanding the relationships between these layers may be fundamental to advancing our understanding of how the brain works in health and disease. Recent neuroimaging research has shown that alterations in brain function that are associated with schizophrenia spectrum disorders (SSD) are already present in young adults at clinical high-risk for psychosis (CHR-P), yet the cellular and molecular determinants of these alterations are not well understood. Here, combining regional cerebral blood flow (rCBF) data with existing transcriptomic and neurotransmitter data, we show that cell-types involved in stress response and inflammation, as well as the dopamine, acetylcholine, GABAA and NMDA receptor systems, align as shared and distinct cellular and neurochemical signatures of rCBF phenotypes in people with SSD and those at CHR-P. Decoding the biological pathways involved in neuroimaging-based psychosis phenotypes may provide a basis for the development of novel interventions.

Brain network architecture is anticipated to influence future grey matter loss in individuals at Clinical High Risk (CHR) for psychosis. However, existing studies on grey matter structural network properties in CHR are scarce and constrained by small sample sizes. Here, we examined network topology differences comparing a) CHR versus healthy controls (HC); b) CHR who transitioned to psychosis (CHR-T) versus those who did not (CHR-NT); and c) different subsyndromes. We included structural scans from 1842 CHR individuals and 1417 HC individuals from 31 sites within the Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) consortium. At the global level, CHR individuals exhibited lower structural covariance (q < 0.001; Cohen’s d = 0.164) and less optimal structural network configuration than HC (lower global efficiency and clustering coefficient, d = 0.100,0.087, qs <= 0.027). Though no global difference between CHR-T and CHR-NT, network distinctiveness of the frontal and temporal surface area networks was higher in CHR-T than CHR-NT (d = 0.223,0.237) and HC (d = 0.208,0.219) (qs < 0.001). Network distinctiveness of the frontal cortical thickness network was lower in CHR-T (d = 0.218, q < 0.001) than CHR-NT and HC (d = 0.165, q < 0.001). Importantly, higher network distinctiveness was associated with worse positive symptoms in CHR-NT (frontal surface area, q = 0.008, R2 = 0.013) and at trend with worse negative symptoms in CHR-T (frontal thickness, q = 0.063, R2 = 0.049). Further, the brief intermittent psychotic syndrome subgroup showed more severe network alterations. Together, brain structural networks inform symptoms and the risk of transition to psychosis in CHR individuals.

Neuroimaging research in the field of schizophrenia and other psychotic disorders has sought to investigate neuroanatomical markers, relative to healthy control groups. In recent decades, a large number of structural magnetic resonance imaging (MRI) studies have been funded and undertaken, but their small sample sizes and heterogeneous methods have led to inconsistencies across findings. To tackle this, efforts have been made to combine datasets across studies and sites. While notable recent multicentre initiatives and the resulting meta- and mega-analytical outputs have progressed the field, efforts have generally been restricted to MRI scans in one or two illness stages, often overlook patient heterogeneity, and study populations have rarely been globally representative of the diversity of patients who experience psychosis. Furthermore, access to these datasets is often restricted to consortia members who can contribute data, likely from research institutions located in high-income countries. The Psychosis MRI Shared Data Resource (Psy-ShareD) is a new open access structural MRI data sharing partnership that will host pre-existing structural T1-weighted MRI data collected across multiple sites worldwide, including the Global South. MRI T1 data included in Psy-ShareD will be available in image and feature-level formats, having been harmonised using state-of-the-art approaches. All T1 data will be linked to demographic and illness-related (diagnosis, symptoms, medication status) measures, and in a number of datasets, IQ and cognitive data, and medication history will also be available, allowing subgroup and dimensional analyses. Psy-ShareD will be free-to-access for all researchers. Importantly, comprehensive data catalogues, scientific support and training resources will be available to facilitate use by early career researchers and build capacity in the field. We are actively seeking new collaborators to contribute further T1 data. Collaborators will benefit in terms of authorships, as all publications arising from Psy-ShareD will include data contributors as authors.

Healthy diets are believed to be associated with a reduced risk of experiencing common mental disorders (CMDs) and related symptomatology (such as ruminative thinking), and with healthier brain chemistry and structure, especially in the frontal regions implicated in CMDs, cognitive control, and food choice. Nevertheless, there is very limited research on the relationship between diet health/quality and brain function. In this study we assessed the associations between adherence to the Mediterranean diet and resting state functional connectivity (rs-FC) of the prefrontal cortex (PFC) with the whole brain and whether this connectivity would be associated with ruminative thinking as a transdiagnostic factor for CMDs. Thirty-seven adults (Mean Age = 25.57, SD = 7.18) completed the Mediterranean Diet Adherence Screener (MEDAS) and were classified into high- and low-quality diet groups and completed the Ruminative Response Scale. All participants underwent resting-state functional MRI (fMRI) to determine whole-brain rs-FC of the medial prefrontal cortex (mPFC). Participants in the high MEDAS group (vs. low MEDAS group) exhibited significantly greater rs-FC of the mPFC seed with the thalamus, caudate and putamen. Additionally, the strength of rs-FC of the mPFC seed with these regions was positively associated with the MEDAS scores across groups in both crude and adjusted models. There were no significant associations between the strength of rs-FC of the mPFC seed with the cluster of voxels with the thalamus, caudate, and putamen and ruminative thinking. This work shows that healthy dietary patterns are associated with rs-FC in the frontal-subcortical circuitry in healthy volunteers. Considering the implications of the dysregulation of this circuity, adhering to healthy dietary patterns may offer a promising alternative/complementary method to improve CMDs, cognitive control, and food choices.

Abnormalities in functional brain networks (functional connectome) are increasingly implicated in people at Clinical High Risk for Psychosis (CHR-P). Intranasal oxytocin, a potential novel treatment for the CHR-P state, modulates network topology in healthy individuals. However, its connectomic effects in people at CHR-P remain unknown. Forty-seven men (30 CHR-P and 17 healthy controls) received acute challenges of both intranasal oxytocin 40 IU and placebo in two parallel randomised, double-blind, placebo-controlled cross-over studies which had similar but not identical designs. Multi-echo resting-state fMRI data was acquired at approximately 1 h post-dosing. Using a graph theoretical approach, the effects of group (CHR-P vs healthy control), treatment (oxytocin vs placebo) and respective interactions were tested on graph metrics describing the topology of the functional connectome. Group effects were observed in 12 regions (all pFDR < 0.05) most localised to the frontoparietal network. Treatment effects were found in 7 regions (all pFDR < 0.05) predominantly within the ventral attention network. Our major finding was that many effects of oxytocin on network topology differ across CHR-P and healthy individuals, with significant interaction effects observed in numerous subcortical regions strongly implicated in psychosis onset, such as the thalamus, pallidum and nucleus accumbens, and cortical regions which localised primarily to the default mode network (12 regions, all pFDR < 0.05). Collectively, our findings provide new insights on aberrant functional brain network organisation associated with psychosis risk and demonstrate, for the first time, that oxytocin modulates network topology in brain regions implicated in the pathophysiology of psychosis in a clinical status (CHR-P vs healthy control) specific manner.

ObjectivesCommon mental disorders (CMD) are associated with impaired frontal excitatory/inhibitory (E/I) balance and reduced grey matter volume (GMV). Larger GMV (in the areas that are implicated in CMD-pathology) and improved CMD-symptomatology have been observed in individuals who adhere to high quality diets. Moreover, preclinical studies have shown altered neurometabolites (primarily gamma-aminobutyric acid: GABA and glutamate: GLU) in relation to diet quality. However, neurochemical correlates of diet quality and how these neurobiological changes are associated with CMD and with its transdiagnostic factor, rumination, is unknown in humans. Therefore, in this study, we examined the associations between diet quality and frontal cortex neuro-chemistry and structure, as well as CMD and rumination in humans.MethodsThirty adults were classified into high and low diet quality groups and underwent 1H-MRS to measure medial prefrontal cortex (mPFC) metabolite concentrations and volumetric imaging to measure GMV.ResultsLow (vs High) diet quality group had reduced mPFC-GABA and elevated mPFC-GLU concentrations, as well as reduced right precentral gyrus (rPCG) GMV. However, CMD and rumination were not associated with diet quality. Notably, we observed a significant negative correlation between rumination and rPCG-GMV and a marginally significant association between rumination and mPFC-GLU concentrations. There was also a marginally significant association between mPFC-GLU concentrations and rPCG-GMV.DiscussionAdhering to unhealthy dietary patterns may be associated with compromised E/I balance, and this could affect GMV, and subsequently, rumination.

OBJECTIVE:Auditory hallucinations (hearing voices) have been associated with a range of altered cognitive functions, pertaining to signal detection, source-monitoring, memory, inhibition and language processes. Yet, empirical results are inconsistent. Despite this, several theoretical models of auditory hallucinations persist, alongside increasing emphasis on the utility of a multidimensional framework. Thus, clarification of current evidence across the broad scope of proposed mechanisms is warranted. METHOD:A systematic search of the Web of Science, PubMed and Scopus databases was conducted. Records were screened to confirm the use of an objective behavioural cognitive task, and valid measurement of hallucinations specific to the auditory modality. RESULTS:Auditory hallucinations were primarily associated with difficulties in perceptual decision-making (i.e. reduced sensitivity/accuracy for signal-noise discrimination; liberal responding to ambiguity), source-monitoring (i.e. self-other and temporal context confusion), working memory and language function (i.e. reduced verbal fluency). Mixed or limited support was observed for perceptual feature discrimination, imagery vividness/illusion susceptibility, source-monitoring for stimulus form and spatial context, recognition and recall memory, executive functions (e.g. attention, inhibition), emotion processing and language comprehension/hemispheric organisation. CONCLUSIONS:Findings were considered within predictive coding and self-monitoring frameworks. Of concern was the portion of studies which - despite offering auditory-hallucination-specific aims and inferences - employed modality-general measures, and/or diagnostic-based contrasts with psychologically healthy individuals. This review highlights disparities within the literature between theoretical conceptualisations of auditory hallucinations and the body of rigorous empirical evidence supporting such inferences. Future cognitive investigations, beyond the schizophrenia-spectrum, which explicitly define and measure the timeframe and sensory modality of hallucinations, are recommended.

Introduction The impact of the clinical high-risk for psychosis (CHR-P) construct is dependent on accurately predicting outcomes. Individuals with brief limited intermittent psychotic symptoms (BLIPS) have higher risk of developing a first episode of psychosis (FEP) compared to individuals with attenuated psychotic symptoms (APS). Supplementing subgroup stratification with information from candidate biomarkers based on neurobiological parameters, such as resting-state, regional cerebral blood flow (rCBF), may help refine risk estimates. Based on previous evidence, we hypothesized that individuals with BLIPS would exhibit increased rCBF compared to APS in key regions linked to dopaminergic pathways. Methods Data from four studies were combined using ComBat (to account for between-study differences) to analyse rCBF in 150 age- and sex-matched subjects ( n = 30 healthy controls [HCs], n = 80 APS, n = 20 BLIPS and n = 20 FEP). Global gray matter (GM) rCBF was examined in addition to region-of-interest (ROI) analyses in bilateral/left/right frontal cortex, hippocampus and striatum. Group differences were assessed using general linear models: (i) alone; (ii) with global GM rCBF as a covariate; (iii) with global GM rCBF and smoking status as covariates. Significance was set at p < 0.05. Results Whole-brain voxel-wise analyses and Bayesian ROI analyses were also conducted. No significant group differences were found in global [ F (3,143) = 1,41, p = 0.24], bilateral frontal cortex [ F (3,143) = 1.01, p = 0.39], hippocampus [ F (3,143) = 0.63, p = 0.60] or striatum [ F (3,143) = 0.52, p = 0.57] rCBF. Similar null findings were observed in lateralized ROIs ( p > 0.05). All results were robust to addition of covariates ( p > 0.05). No significant clusters were identified in whole-brain voxel-wise analyses ( p > 0.05 FWE ). Weak-to-moderate evidence was found for an absence of rCBF differences between APS and BLIPS in Bayesian ROI analyses. Conclusion On this evidence, APS and BLIPS are unlikely to be neurobiologically distinct. Due to this and the weak-to-moderate evidence for the null hypothesis, future research should investigate larger samples of APS and BLIPS through collaboration across large-scale international consortia.

Recent studies suggest that transcranial direct current stimulation (tDCS) applied over the prefrontal cortex (PFaC) may enhance episodic memory ability. As such, there is ongoing interest in the therapeutic potential of this technique in age-related memory decline. At the same time, the findings are not yet conclusive regarding the magnitude of this effect, and assumptions regarding underlying brain mechanisms of stimulation-induced changes in behaviour are yet to be tested in detail. Here, we evaluated the effect of tDCS over left PFC on verbal episodic memory in young adults. Two separate randomized, double-blind, sham-controlled experiments were carried out using (1) incidental learning followed by a recognition test and (2) intentional learning followed by a free recall. In both studies, participants performed a learning task with active or sham tDCS during the encoding period, followed by retrieval tasks on the same day and the next day. The results suggest that, contrary to expectations, active tDCS did not enhance memory performance relative to sham tDCS. Possible reasons behind the lack of enhancement effects are discussed, including the possibility that memory enhancement effects of tDCS may be smaller than first thought. Scientific practices that could improve estimation accuracy in the field are also discussed.

The circular inference (CI) computational model assumes a corruption of sensory data by prior information and vice versa, leading at the extremes to 'see what we expect' (through prior amplification) and/or to 'expect what we see' (through sensory amplification). Although a CI mechanism has been reported in a schizophrenia population, it has not been investigated in individuals experiencing psychosis-like experiences, such as people with high schizotypy traits. Furthermore, the neurobiological basis of CI, such as the link between hierarchical amplifications, excitatory neurotransmission, and resting state functional connectivity (RSFC), remains untested. The participants included in the present study consisted of a subsample of those recruited in a study previously published by our group, Kozhuharova et al. (2021b). We included 36 participants with High (n=18) and Low (n=18) levels of schizotypy who completed a probabilistic reasoning task (the Fisher task) for which individual confidence levels were obtained and fitted to the CI model. Participants also underwent a 1H-Magnetic Resonance Spectroscopy (MRS) scan to measure medial prefrontal cortex (mPFC) glutamate metabolite levels, and a functional Magnetic Resonance Imaging (fMRI) scan to measure RSFC of the medial prefrontal cortex (mPFC). People with high levels of schizotypy exhibited changes in CI parameters, altered cortical excitatory neurotransmission and RSFC that were all associated with sensory amplification. Our findings capture a multimodal signature of CI that is observable in people early in the psychosis spectrum.