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Preclinical models of psychosis propose that hippocampal glutamatergic neuron hyperactivity drives increased striatal dopaminergic activity, which underlies the development of psychotic symptoms. The aim of this study was to examine the relationship between hippocampal glutamate and subcortical dopaminergic function in people at clinical high risk for psychosis, and to assess the association with the development of psychotic symptoms. 1H-MRS was used to measure hippocampal glutamate concentrations, and 18F-DOPA PET was used to measure dopamine synthesis capacity in 70 subjects (51 people at clinical high risk for psychosis and 19 healthy controls). Clinical assessments were undertaken at baseline and follow-up (median 15 months). Striatal dopamine synthesis capacity predicted the worsening of psychotic symptoms at follow-up (r = 0.35; p < 0.05), but not transition to a psychotic disorder (p = 0.22), and was not significantly related to hippocampal glutamate concentration (p = 0.13). There were no differences in either glutamate (p = 0.5) or dopamine (p = 0.5) measures in the total patient group relative to controls. Striatal dopamine synthesis capacity at presentation predicts the subsequent worsening of sub-clinical total and psychotic symptoms, consistent with a role for dopamine in the development of psychotic symptoms, but is not strongly linked to hippocampal glutamate concentrations.

Preclinical models propose that increased hippocampal activity drives subcortical dopaminergic dysfunction and leads to psychosis-like symptoms and behaviors. Here, we used multimodal neuroimaging to examine the relationship between hippocampal regional cerebral blood flow (rCBF) and striatal dopamine synthesis capacity in people at clinical high risk (CHR) for psychosis and investigated its association with subsequent clinical and functional outcomes. Ninety-five participants (67 CHR and 28 healthy controls) underwent arterial spin labeling MRI and 18F-DOPA PET imaging at baseline. CHR participants were followed up for a median of 15 months to determine functional outcomes with the global assessment of function (GAF) scale and clinical outcomes using the comprehensive assessment of at-risk mental states (CAARMS). CHR participants with poor functional outcomes (follow-up GAF < 65, n = 25) showed higher rCBF in the right hippocampus compared to CHRs with good functional outcomes (GAF ≥ 65, n = 25) (pfwe = 0.026). The relationship between rCBF in this right hippocampal region and striatal dopamine synthesis capacity was also significantly different between groups (pfwe = 0.035); the association was negative in CHR with poor outcomes (pfwe = 0.012), but non-significant in CHR with good outcomes. Furthermore, the correlation between right hippocampal rCBF and striatal dopamine function predicted a longitudinal increase in the severity of positive psychotic symptoms within the total CHR group (p = 0.041). There were no differences in rCBF, dopamine, or their associations in the total CHR group relative to controls. These findings indicate that altered interactions between the hippocampus and the subcortical dopamine system are implicated in the pathophysiology of adverse outcomes in the CHR state.

Preclinical and human studies suggest that hippocampal dysfunction is a key factor in the onset of psychosis. People at Clinical High Risk for psychosis (CHR-P) present with a clinical syndrome that can include social withdrawal and have a 20–35% risk of developing psychosis in the next 2 years. Recent research shows that resting hippocampal blood flow is altered in CHR-P individuals and predicts adverse clinical outcomes, such as non-remission/transition to frank psychosis. Previous work in healthy males indicates that a single dose of intranasal oxytocin has positive effects on social function and marked effects on resting hippocampal blood flow. The present study examined the effects of intranasal oxytocin on hippocampal blood flow in CHR-P individuals. In a double-blind, placebo-controlled, crossover design, 30 CHR-P males were studied using pseudo-continuous Arterial Spin Labelling on 2 occasions, once after 40IU intranasal oxytocin and once after placebo. The effects of oxytocin on left hippocampal blood flow were examined in a region-of-interest analysis of data acquired at 22–28 and at 30–36 minutes post-intranasal administration. Relative to placebo, administration of oxytocin was associated with increased hippocampal blood flow at both time points (p = .0056; p = .034), although the effect at the second did not survive adjustment for the effect of global blood flow. These data indicate that oxytocin can modulate hippocampal function in CHR-P individuals and therefore merits further investigation as a candidate novel treatment for this group.

Preclinical rodent models suggest that psychosis involves alterations in the activity and glutamatergic function in the hippocampus, driving dopamine activity through projections to the striatum. The extent to which this model applies to the onset of psychosis in clinical subjects is unclear. We assessed whether interactions between hippocampal glutamatergic function and activity/striatal connectivity are associated with adverse clinical outcomes in people at clinical high-risk (CHR) for psychosis. We measured functional Magnetic Resonance Imaging of hippocampal activation/connectivity, and 1H-Magnetic Resonance Spectroscopy of hippocampal glutamatergic metabolites in 75 CHR participants and 31 healthy volunteers. At follow-up, 12 CHR participants had transitioned to psychosis and 63 had not. Within the clinical high-risk cohort, at follow-up, 35 and 17 participants had a poor or a good functional outcome, respectively. The onset of psychosis (ppeakFWE = 0.003, t = 4.4, z = 4.19) and a poor functional outcome (ppeakFWE < 0.001, t = 5.52, z = 4.81 and ppeakFWE < 0.001, t = 5.25, z = 4.62) were associated with a negative correlation between the hippocampal activation and hippocampal Glx concentration at baseline. In addition, there was a negative association between hippocampal Glx concentration and hippocampo-striatal connectivity (ppeakFWE = 0.016, t = 3.73, z = 3.39, ppeakFWE = 0.014, t = 3.78, z = 3.42, ppeakFWE = 0.011, t = 4.45, z = 3.91, ppeakFWE = 0.003, t = 4.92, z = 4.23) in the total CHR sample, not seen in healthy volunteers. As predicted by preclinical models, adverse clinical outcomes in people at risk for psychosis are associated with altered interactions between hippocampal activity and glutamatergic function.

Abstract Background Early psychosis in first-episode psychosis (FEP) and clinical high-risk (CHR) individuals has been associated with alterations in mean regional measures of brain morphology. Examination of variability in brain morphology could assist in quantifying the degree of brain structural heterogeneity in clinical relative to healthy control (HC) samples. Methods Structural magnetic resonance imaging data were obtained from CHR (n = 71), FEP (n = 72), and HC individuals (n = 55). Regional brain variability in cortical thickness (CT), surface area (SA), and subcortical volume (SV) was assessed with the coefficient of variation (CV). Furthermore, the person-based similarity index (PBSI) was employed to quantify the similarity of CT, SA, and SV profile of each individual to others within the same diagnostic group. Normative modeling of the PBSI-CT, PBSI-SA, and PBSI-SV was used to identify CHR and FEP individuals whose scores deviated markedly from those of the healthy individuals. Results There was no effect of diagnosis on the CV for any regional measure (P > .38). CHR and FEP individuals differed significantly from the HC group in terms of PBSI-CT (P < .0001), PBSI-SA (P < .0001), and PBSI-SV (P = .01). In the clinical groups, normative modeling identified 32 (22%) individuals with deviant PBSI-CT, 12 (8.4%) with deviant PBSI-SA, and 21 (15%) with deviant PBSI-SV; differences of small effect size indicated that individuals with deviant PBSI scores had lower IQ and higher psychopathology. Conclusions Examination of brain structural variability in early psychosis indicated heterogeneity at the level of individual profiles and encourages further large-scale examination to identify individuals that deviate markedly from normative reference data.

The brain‐age‐gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine‐learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G‐brainAGE and L‐brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22–37 years) participating in the Human Connectome Project. Sex differences were determined in G‐brainAGE and L‐brainAGE. Random forest regression was used to determine sex‐specific associations between G‐brainAGE and non‐imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L‐brainAGE showed sex‐specific differences; in females, compared to males, L‐brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G‐brainAGE were minimal, associations between G‐brainAGE and non‐imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G‐brainAGE was self‐identification as non‐white in males and systolic blood pressure in females. The results demonstrate the value of applying sex‐specific analyses and machine learning methods to advance our understanding of sex‐related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions. In this study machine learning was used to examine sex differences in global and localized brainAGE and their non‐imaging correlates in healty young adults from the Human Connectome Project. Males and females showed different regional patterns of brain ageing which were influenced by different non‐imaging characteristics. These results demonstrate the value of applying sex‐specific analyses and machine learning methods to advance our understanding of factors that influence the rate of brain ageing.

Using machine learning, we recently decomposed the neuroanatomical heterogeneity of established schizophrenia to discover two volumetric subgroups—a ‘lower brain volume’ subgroup (SG1) and an ‘higher striatal volume’ subgroup (SG2) with otherwise normal brain structure. In this study, we investigated whether the MRI signatures of these subgroups were also already present at the time of the first-episode of psychosis (FEP) and whether they were related to clinical presentation and clinical remission over 1-, 3-, and 5-years. We included 572 FEP and 424 healthy controls (HC) from 4 sites (Sao Paulo, Santander, London, Melbourne) of the PHENOM consortium. Our prior MRI subgrouping models (671 participants; USA, Germany, and China) were applied to both FEP and HC. Participants were assigned into 1 of 4 categories: subgroup 1 (SG1), subgroup 2 (SG2), no subgroup membership (‘None’), and mixed SG1 + SG2 subgroups (‘Mixed’). Voxel-wise analyses characterized SG1 and SG2 subgroups. Supervised machine learning analyses characterized baseline and remission signatures related to SG1 and SG2 membership. The two dominant patterns of ‘lower brain volume’ in SG1 and ‘higher striatal volume’ (with otherwise normal neuromorphology) in SG2 were identified already at the first episode of psychosis. SG1 had a significantly higher proportion of FEP (32%) vs. HC (19%) than SG2 (FEP, 21%; HC, 23%). Clinical multivariate signatures separated the SG1 and SG2 subgroups (balanced accuracy = 64%; p  < 0.0001), with SG2 showing higher education but also greater positive psychosis symptoms at first presentation, and an association with symptom remission at 1-year, 5-year, and when timepoints were combined. Neuromorphological subtypes of schizophrenia are already evident at illness onset, separated by distinct clinical presentations, and differentially associated with subsequent remission. These results suggest that the subgroups may be underlying risk phenotypes that could be targeted in future treatment trials and are critical to consider when interpreting neuroimaging literature.

BackgroundHigh-resolution structural MRI has been widely used in clinical research to detect and quantify subtle brain changes in patient populations. Findings from prospective, longitudinal studies show structural brain abnormalities as well as progressive gray matter changes over time in individuals at clinical high risk for psychosis compared to healthy subjects. In recent years, research in this field has seen an increase in multicentre neuroimaging projects, such as EU-GEI, PSYSCAN, PRONIA and NAPLS. Additional sources of variance, alongside known technological and biological factors, may be introduced when MRI images are acquired and combined from different sites. It is imperative for longitudinal multicentre studies to determine the accuracy of quantitative MRI measurements and account for systematic differences both between scanners and across scanning sessions. This is particularly true within psychosis research where morphometric changes as small as 3% or less are expected.MethodsSix healthy participants were scanned on four separate occasions over a two-month period at King’s College London; twice on a GE SIGNA HDx 3T scanner used locally in the EU-GEI High Risk Study and twice on a GE MR750 3T scanner used locally in the PSYSCAN study. Both scanners implemented the ADNI-2 T1 protocol which is used globally across the EU-GEI and PSYSCAN consortia. Structural imaging data was segmented using the FreeSurfer 6.0 longitudinal pipeline. Intraclass correlation coefficients (ICCs) with a two-way mixed effects model of absolute agreement were calculated to assess intra- and inter-scanner reliability of brain morphometry. For volumetric studies, ICC values greater than 0.9 indicate ‘excellent’ reliability. Reliability analyses of key regions implicated in psychosis included gray matter volume estimates of the hippocampus, insula, lateral ventricle, orbitofrontal cortex and anterior cingulate cortex, and average cortical thickness measurements of the whole brain, parahippocampus and superior frontal cortex.ResultsGray matter volume estimates of all structures yielded ‘excellent’ reliability for both intra-scanner (ICCs of 0.979 – 0.998) and inter-scanner analyses (ICCs of 0.976 – 0.999). Intra-scanner reliability for mean cortical thickness measurements was ‘excellent’ for right total cortex, resulting in an ICC of 0.901, but otherwise ‘good’ for left and total cortex, parahippocampus, superior frontal cortex (ICCs of 0.754 – 0.875). Inter-scanner reliability for mean cortical thickness estimates were most variable across the brain structures. Here, results demonstrated ‘excellent’ reliability for the parahippocampus and left total cortex (ICCs of 0.907 – 0.965), ‘good’ for total cortex (ICC of 0.835), ‘moderate’ for right total cortex, right and total superior frontal cortex (ICCs of 0.520 – 0.676), and ‘poor’ for the left superior frontal cortex which produced an ICC of 0.470. Overall, mean cortical thickness estimates of the superior frontal cortex from two different MR scanners showed the least reliability.DiscussionResults confirmed highly reliable estimates for gray matter volumes in all brain structures, both from images acquired within the same scanner and across two different scanners. However, the findings indicated increased variability of mean cortical thickness estimates, particularly between scanners, which should be considered when interpreting study findings. Multicentre structural neuroimaging within the field of psychosis is becoming more common and it must be acknowledged that combining MRI data in multicentre studies will contribute additional sources of variance and potential bias with certain brain regions affected more than others.

Psychosis has been proposed to develop from dysfunction in a hippocampal-striatal-midbrain circuit, leading to aberrant salience processing. Here, we used functional magnetic resonance imaging (fMRI) during novelty salience processing to investigate this model in people at clinical high risk (CHR) for psychosis according to their subsequent clinical outcomes. Seventy-six CHR participants as defined using the Comprehensive Assessment of At-Risk Mental States (CAARMS) and 31 healthy controls (HC) were studied while performing a novelty salience fMRI task that engaged an a priori hippocampal-striatal-midbrain circuit of interest. The CHR sample was then followed clinically for a mean of 59.7 months (~5 y), when clinical outcomes were assessed in terms of transition (CHR-T) or non-transition (CHR-NT) to psychosis (CAARMS criteria): during this period, 13 individuals (17%) developed a psychotic disorder (CHR-T) and 63 did not. Functional activation and effective connectivity within a hippocampal-striatal-midbrain circuit were compared between groups. In CHR individuals compared to HC, hippocampal response to novel stimuli was significantly attenuated (P = .041 family-wise error corrected). Dynamic Causal Modelling revealed that stimulus novelty modulated effective connectivity from the hippocampus to the striatum, and from the midbrain to the hippocampus, significantly more in CHR participants than in HC. Conversely, stimulus novelty modulated connectivity from the midbrain to the striatum significantly less in CHR participants than in HC, and less in CHR participants who subsequently developed psychosis than in CHR individuals who did not become psychotic. Our findings are consistent with preclinical evidence implicating hippocampal-striatal-midbrain circuit dysfunction in altered salience processing and the onset of psychosis.