Psychiatric drugs, including antipsychotics and antidepressants, are widely prescribed, even in young and adolescent populations at early or subthreshold disease stages. However, their impact on brain structure remains elusive. Elucidating the relationship between psychotropic medication and structural brain changes could enhance the understanding of the potential benefits and risks associated with such treatment.

Investigation of the associations between psychiatric drug intake and longitudinal grey matter volume (GMV) changes in a transdiagnostic sample of young individuals at early stages of psychosis or depression using an unbiased data-driven approach.

The study sample comprised 247 participants (mean [SD] age = 25.06 [6.13] years, 50.61% male), consisting of young, minimally medicated individuals at clinical high-risk states for psychosis, individuals with recent-onset depression or psychosis, and healthy control individuals. Structural magnetic resonance imaging was used to obtain whole-brain voxel-wise GMV for all participants at two timepoints (mean [SD] time between scans = 11.15 [4.93] months). The multivariate sparse partial least squares (SPLS) algorithm (Monteiro et al. JNMEDT 2016; 271:182-194) was embedded in a nested cross-validation framework to identify parsimonious associations between the cumulative intake of psychiatric drugs, including commonly prescribed antipsychotics and antidepressants, and change in GMV between both timepoints, while additionally factoring in age, sex, and diagnosis. Furthermore, we correlated the retrieved SPLS results to personality domains (NEO-FFI) and childhood trauma (CTQ).

SPLS analysis revealed significant associations between the antipsychotic classes of benzamides, butyrophenones and thioxanthenes and longitudinal GMV decreases in cortical regions including the insula, posterior superior temporal sulcus as well as cingulate, postcentral, precentral, orbital and frontal gyri (Figure 1A-C). These brain regions corresponded most closely to the dorsal and ventral attention, somatomotor, salience and default network (Figure 1D). Furthermore, the medication signature was negatively associated with the personality domains extraversion, agreeableness and conscientiousness and positively associated with the CTQ domains emotional and physical neglect.

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Psychiatric drug intake over a period of one year was linked to distinct GMV reductions in key cortical hubs. These patterns were already visible in young individuals at early or subthreshold stages of mental illness and were further linked to childhood neglect and personality traits. Hence, a better and more in-depth understanding of the structural brain implications of medicating young and adolescent individuals might lead to more cautious, sustainable and targeted treatment strategies.

None Declared

The clinical high-risk state for psychosis (CHR) is associated with alterations in grey matter volume (GMV) in various regions such as the hippocampus (Vissink et al. BP:GOS 2022; 2(2) 147-152). Within the scope of the North American Prodrome Longitudinal Study (NAPLS-2; Cannon et al. AM J Psychiatry 2016; 173(10), 980-988), a publicly available risk calculator based on clinical variables was developed to assess the likelihood of individuals to transition to psychosis within a 2-year period.

In the current study, we aim to examine the association between GMV and NAPLS-2 risk scores calculated for individuals with CHR and recent-onset depression (ROD), taking a transdiagnostic approach on the transition to psychosis.

The sample consisted of 315 CHR (M = 23.85, SD = ± 5.64; female: 164) and 295 ROD (M = 25.11, SD = ± 6.21; female: 144) patients from the multi-site Personalised Prognostic Tools for Early Psychosis Management (PRONIA) Study (Koutsouleris et al. JAMA Psychiatry 2018; 57(11), 1156-1172). Risk scores were calculated using the six clinical and neurocognitive variables included in the NAPLS-2 risk calculator that were significant for predicting psychosis. Further, we derived smoothed GMV maps from T1-weighted structural magnetic resonance imaging using a full width at half maximum kernel size of 8 mm. We employed a multiple regression design in SPM12 to examine associations between risk scores and GMV. On the whole-brain level, we calculated permutation-based threshold-free cluster enhancement (TFCE) contrasts using the TFCE toolbox. Additionally, we calculated t-contrasts within a region-of-interest (ROI) analysis encompassing the hippocampus. All results were thresholded at p < 0.05 with family wise error correction to address multiple comparisons.

Our analysis revealed that linear GMV increases in the right middle and superior frontal gyrus (kE= 2726 voxels) were significantly associated with higher risk for psychosis transition within two years (see figure 1, highlighted in blue). In the ROI analysis, we found a significant negative linear association between GMV decreases in the left hippocampus (kE = 353 voxels) and higher risk for psychosis transition (see figure 1, highlighted in red).

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GMV reductions in the hippocampus have frequently been observed in CHR and psychosis patients (Vissink et al. BP:GOS 2022; 2(2) 147-152), therefore our results further highlight the crucial role of this region in the progression of the disease. There is limited evidence on GMV increases in CHR patients. However, the GMV increase we found in the frontal pole may reflect compensatory mechanisms of the brain in the development of psychosis. In addition, we were able to provide biological validation of the NAPLS-2 risk calculator and its assessment of risk for transition to psychosis.

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Understanding the factors contributing to optimal cognitive function throughout the aging process is essential to better understand successful cognitive aging. Processing speed is an age sensitive cognitive domain that usually declines early in the aging process; however, this cognitive skill is essential for other cognitive tasks and everyday functioning. Evaluating brain network interactions in cognitively healthy older adults can help us understand how brain characteristics variations affect cognitive functioning. Functional connections among groups of brain areas give insight into the brain’s organization, and the cognitive effects of aging may relate to this large-scale organization. To follow-up on our prior work, we sought to replicate our findings regarding network segregation’s relationship with processing speed. In order to address possible influences of node location or network membership we replicated the analysis across 4 different node sets.

Data were acquired as part of a multi-center study of 85+ cognitively normal individuals, the McKnight Brain Aging Registry (MBAR). For this analysis, we included 146 community-dwelling, cognitively unimpaired older adults, ages 85-99, who had undergone structural and BOLD resting state MRI scans and a battery of neuropsychological tests. Exploratory factor analysis identified the processing speed factor of interest. We preprocessed BOLD scans using fmriprep, Ciftify, and XCPEngine algorithms. We used 4 different sets of connectivity-based parcellation: 1)MBAR data used to define nodes and Power (2011) atlas used to determine node network membership, 2) Younger adults data used to define nodes (Chan 2014) and Power (2011) atlas used to determine node network membership, 3) Older adults data from a different study (Han 2018) used to define nodes and Power (2011) atlas used to determine node network membership, and 4) MBAR data used to define nodes and MBAR data based community detection used to determine node network membership.

Segregation (balance of within-network and between-network connections) was measured within the association system and three wellcharacterized networks: Default Mode Network (DMN), Cingulo-Opercular Network (CON), and Fronto-Parietal Network (FPN). Correlation between processing speed and association system and networks was performed for all 4 node sets.

We replicated prior work and found the segregation of both the cortical association system, the segregation of FPN and DMN had a consistent relationship with processing speed across all node sets (association system range of correlations: r=.294 to .342, FPN: r=.254 to .272, DMN: r=.263 to .273). Additionally, compared to parcellations created with older adults, the parcellation created based on younger individuals showed attenuated and less robust findings as those with older adults (association system r=.263, FPN r=.255, DMN r=.263).

This study shows that network segregation of the oldest-old brain is closely linked with processing speed and this relationship is replicable across different node sets created with varied datasets. This work adds to the growing body of knowledge about age-related dedifferentiation by demonstrating replicability and consistency of the finding that as essential cognitive skill, processing speed, is associated with differentiated functional networks even in very old individuals experiencing successful cognitive aging.

White matter hyperintensity (WMH) burden is greater, has a frontal-temporal distribution, and is associated with proxies of exposure to repetitive head impacts (RHI) in former American football players. These findings suggest that in the context of RHI, WMH might have unique etiologies that extend beyond those of vascular risk factors and normal aging processes. The objective of this study was to evaluate the correlates of WMH in former elite American football players. We examined markers of amyloid, tau, neurodegeneration, inflammation, axonal injury, and vascular health and their relationships to WMH. A group of age-matched asymptomatic men without a history of RHI was included to determine the specificity of the relationships observed in the former football players.

240 male participants aged 45-74 (60 unexposed asymptomatic men, 60 male former college football players, 120 male former professional football players) underwent semi-structured clinical interviews, magnetic resonance imaging (structural T1, T2 FLAIR, and diffusion tensor imaging), and lumbar puncture to collect cerebrospinal fluid (CSF) biomarkers as part of the DIAGNOSE CTE Research Project. Total WMH lesion volumes (TLV) were estimated using the Lesion Prediction Algorithm from the Lesion Segmentation Toolbox. Structural equation modeling, using Full-Information Maximum Likelihood (FIML) to account for missing values, examined the associations between log-TLV and the following variables: total cortical thickness, whole-brain average fractional anisotropy (FA), CSF amyloid ß42, CSF p-tau181, CSF sTREM2 (a marker of microglial activation), CSF neurofilament light (NfL), and the modified Framingham stroke risk profile (rFSRP). Covariates included age, race, education, APOE z4 carrier status, and evaluation site. Bootstrapped 95% confidence intervals assessed statistical significance. Models were performed separately for football players (college and professional players pooled; n=180) and the unexposed men (n=60). Due to differences in sample size, estimates were compared and were considered different if the percent change in the estimates exceeded 10%.

In the former football players (mean age=57.2, 34% Black, 29% APOE e4 carrier), reduced cortical thickness (B=-0.25, 95% CI [0.45, -0.08]), lower average FA (B=-0.27, 95% CI [-0.41, -.12]), higher p-tau181 (B=0.17, 95% CI [0.02, 0.43]), and higher rFSRP score (B=0.27, 95% CI [0.08, 0.42]) were associated with greater log-TLV. Compared to the unexposed men, substantial differences in estimates were observed for rFSRP (Bcontrol=0.02, Bfootball=0.27, 994% difference), average FA (Bcontrol=-0.03, Bfootball=-0.27, 802% difference), and p-tau181 (Bcontrol=-0.31, Bfootball=0.17, -155% difference). In the former football players, rFSRP showed a stronger positive association and average FA showed a stronger negative association with WMH compared to unexposed men. The effect of WMH on cortical thickness was similar between the two groups (Bcontrol=-0.27, Bfootball=-0.25, 7% difference).

These results suggest that the risk factor and biological correlates of WMH differ between former American football players and asymptomatic individuals unexposed to RHI. In addition to vascular risk factors, white matter integrity on DTI showed a stronger relationship with WMH burden in the former football players. FLAIR WMH serves as a promising measure to further investigate the late multifactorial pathologies of RHI.

The social defeat hypothesis (SDH) suggests that a chronic experience of social defeat increases the likelihood of the development of psychosis. The SDH indicates that a negative experience of exclusion leads to an increase in the baseline activity of the mesolimbic dopamine system (MDS), which in turn leads to the onset of psychosis. Social defeat models have previously been produced using animal models and preclinical literature; however, these theories have not fully been tested in human clinical samples. There have been studies implying changes in brain structure due to social defeat interactions; however, research evidence is varied.

This study aims to uncover whether exposure to SoDe has an impact on brain structure. Furthermore, we hope to understand if these changes are relevant to other mental health disorders.

698 (506 no SoDe, 191 SoDe) participants between the ages of 15-41 were recruited from the PRONIA-FP7 study. SoDe was measured from the self-reported questionnaires’ Bullying Scale’ and ‘The Everyday Discrimination Scale’. T1-weighted structural MRI data were processed; five 2 sample t-test analyses were carried out to compare the GMV differences in the entire sample and between the four groups.

The VBM analysis showed significant group interactions in the right thalamus proper when comparing participants who had experience SoDe to participants who had not experienced SoDe including all 4 groups along with left cerebral white matter differences. In the ROP subgroup, significant group interactions in the left cerebellum white matter were found along with right cerebral white matter, left cerebral white matter and right Thalamus proper.

The findings suggest that there are significant group interactions in thalamus and cerebral white matter. This is in keeping with some previous research suggesting volumetric changes in the thalamus due to stress and psychosis. Similarly for white matter there is some evidence suggesting differences due to SoDe and psychosis. However, there is a scarcity of research in this area with different research suggesting distinctive findings and therefore the evidence is inconclusive. In the ROP group analysis significant group interactions were present in the cerebellum due to SoDe experience. There is research suggesting the cerebellum’s role in multiple different aspects like social interaction, higher-order cognition, working memory, cognitive flexibility, and psychotic symptoms, with every research suggesting multiple different things the role of the cerebellum in SoDe in the ROP population is in question. Nonetheless this large-scale research presents some interesting novel finding and leads the way to a new area of research. Further analysis will explore the relationship between groups on markers of stress (CRP) and neuroinflammation as potential mediation of the environmental effects of SoDe.

None Declared

To evaluate the construct validity of the NIH Toolbox Cognitive Battery (NIH TB-CB) in the healthy oldest-old (85+ years old).

Our sample from the McKnight Brain Aging Registry consists of 179 individuals, 85 to 99 years of age, screened for memory, neurological, and psychiatric disorders. Using previous research methods on a sample of 85 + y/o adults, we conducted confirmatory factor analyses on models of NIH TB-CB and same domain standard neuropsychological measures. We hypothesized the five-factor model (Reading, Vocabulary, Memory, Working Memory, and Executive/Speed) would have the best fit, consistent with younger populations. We assessed confirmatory and discriminant validity. We also evaluated demographic and computer use predictors of NIH TB-CB composite scores.

Findings suggest the six-factor model (Vocabulary, Reading, Memory, Working Memory, Executive, and Speed) had a better fit than alternative models. NIH TB-CB tests had good convergent and discriminant validity, though tests in the executive functioning domain had high inter-correlations with other cognitive domains. Computer use was strongly associated with higher NIH TB-CB overall and fluid cognition composite scores.

The NIH TB-CB is a valid assessment for the oldest-old samples, with relatively weak validity in the domain of executive functioning. Computer use’s impact on composite scores could be due to the executive demands of learning to use a tablet. Strong relationships of executive function with other cognitive domains could be due to cognitive dedifferentiation. Overall, the NIH TB-CB could be useful for testing cognition in the oldest-old and the impact of aging on cognition in older populations.

People presenting with first-episode psychosis (FEP) have heterogenous outcomes. More than 40% fail to achieve symptomatic remission. Accurate prediction of individual outcome in FEP could facilitate early intervention to change the clinical trajectory and improve prognosis.

We aim to systematically review evidence for prediction models developed for predicting poor outcome in FEP.

A protocol for this study was published on the International Prospective Register of Systematic Reviews, registration number CRD42019156897. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidance, we systematically searched six databases from inception to 28 January 2021. We used the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies and the Prediction Model Risk of Bias Assessment Tool to extract and appraise the outcome prediction models. We considered study characteristics, methodology and model performance.

Thirteen studies reporting 31 prediction models across a range of clinical outcomes met criteria for inclusion. Eleven studies used logistic regression with clinical and sociodemographic predictor variables. Just two studies were found to be at low risk of bias. Methodological limitations identified included a lack of appropriate validation, small sample sizes, poor handling of missing data and inadequate reporting of calibration and discrimination measures. To date, no model has been applied to clinical practice.

Future prediction studies in psychosis should prioritise methodological rigour and external validation in larger samples. The potential for prediction modelling in FEP is yet to be realised.

We sought to determine who is involved in the care of a trauma patient.

We recorded hospital personnel involved in 24 adult Priority 1 trauma patient admissions for 12 h or until patient demise. Hospital personnel were delineated by professional background and role.

We cataloged 19 males and 5 females with a median age of 50-y-old (interquartile range [IQR], 35.5-67.5). The average number of hospital personnel involved was 79.71 (standard deviation, 17.62; standard error 3.6). A median of 51.2% (IQR, 43.4%-59.8%) of personnel were first involved within hour 1. More personnel were involved in direct versus indirect care (median 54.5 [IQR, 47.5-67.0] vs 25.0 [IQR, 22.0-30.5]; P < 0.0001). Median number of health-care professionals and auxiliary staff were 74.5 (IQR, 63.5-90.5) and 6.0 (IQR, 5.0-7.0), respectively. More personnel were first involved in hospital locations external to the emergency department (median, 53.0 [IQR, 41.5-63.0] vs 27.5 [IQR, 24.0-30.0]; P < 0.0001). No differences existed in total personnel by Injury Severity Score (P = 0.1266), day (P = 0.7270), or time of admission (P = 0.2098).

A large number of hospital personnel with varying job responsibilities respond to severe trauma. These data may guide hospital staffing and disaster preparedness policies.

Subanesthetic ketamine infusion therapy can produce fast-acting antidepressant effects in patients with major depression. How single and repeated ketamine treatment modulates the whole-brain functional connectome to affect clinical outcomes remains uncharacterized.

Data-driven whole brain functional connectivity (FC) analysis was used to identify the functional connections modified by ketamine treatment in patients with major depressive disorder (MDD). MDD patients (N = 61, mean age = 38, 19 women) completed baseline resting-state (RS) functional magnetic resonance imaging and depression symptom scales. Of these patients, n = 48 and n = 51, completed the same assessments 24 h after receiving one and four 0.5 mg/kg intravenous ketamine infusions. Healthy controls (HC) (n = 40, 24 women) completed baseline assessments with no intervention. Analysis of RS FC addressed effects of diagnosis, time, and remitter status.

Significant differences (p < 0.05, corrected) in RS FC were observed between HC and MDD at baseline in the somatomotor network and between association and default mode networks. These disruptions in FC in MDD patients trended toward control patterns with ketamine treatment. Furthermore, following serial ketamine infusions, significant decreases in FC were observed between the cerebellum and salience network (SN) (p < 0.05, corrected). Patient remitters showed increased FC between the cerebellum and the striatum prior to treatment that decreased following treatment, whereas non-remitters showed the opposite pattern.

Results support that ketamine treatment leads to neurofunctional plasticity between distinct neural networks that are shown as disrupted in MDD patients. Cortico-striatal-cerebellar loops that encompass the SN could be a potential biomarker for ketamine treatment.

As the pathophysiology of Covid-19 emerges, this paper describes dysphagia as a sequela of the disease, including its diagnosis and management, hypothesised causes, symptomatology in relation to viral progression, and concurrent variables such as intubation, tracheostomy and delirium, at a tertiary UK hospital.

During the first wave of the Covid-19 pandemic, 208 out of 736 patients (28.9 per cent) admitted to our institution with SARS-CoV-2 were referred for swallow assessment. Of the 208 patients, 102 were admitted to the intensive treatment unit for mechanical ventilation support, of which 82 were tracheostomised. The majority of patients regained near normal swallow function prior to discharge, regardless of intubation duration or tracheostomy status.

Dysphagia is prevalent in patients admitted either to the intensive treatment unit or the ward with Covid-19 related respiratory issues. This paper describes the crucial role of intensive swallow rehabilitation to manage dysphagia associated with this disease, including therapeutic respiratory weaning for those with a tracheostomy.

Neurobiological models of auditory verbal hallucination (AVH) have been advanced by symptom capture functional magnetic resonance imaging (fMRI), where participants self-report hallucinations during scanning. To date, regions implicated are those involved with language, memory and emotion. However, previous studies focus on chronic schizophrenia, thus are limited by factors, such as medication use and illness duration. Studies also lack detailed phenomenological descriptions of AVHs. This study investigated the neural correlates of AVHs in patients with first episode psychosis (FEP) using symptom capture fMRI with a rich description of AVHs. We hypothesised that intrusive AVHs would be associated with dysfunctional salience network activity.

Sixteen FEP patients with frequent AVH completed four psychometrically validated tools to provide an objective measure of the nature of their AVHs. They then underwent fMRI symptom capture, utilising general linear models analysis to compare activity during AVH to the resting brain.

Symptom capture of AVH was achieved in nine patients who reported intrusive, malevolent and uncontrollable AVHs. Significant activity in the right insula and superior temporal gyrus (cluster size 141 mm3), and the left parahippocampal and lingual gyri (cluster size 121 mm3), P < 0.05 FDR corrected, were recorded during the experience of AVHs.

These results suggest salience network dysfunction (in the right insula) together with memory and language processing area activation in intrusive, malevolent AVHs in FEP. This finding concurs with others from chronic schizophrenia, suggesting these processes are intrinsic to psychosis itself and not related to length of illness or prolonged exposure to antipsychotic medication.

The authors have not supplied their declaration of competing interest.

Major depressive disorder (MDD) is a common and disabling condition with well-established heritability and environmental risk factors. Gene–environment interaction studies in MDD have typically investigated candidate genes, though the disorder is known to be highly polygenic. This study aims to test for interaction between polygenic risk and stressful life events (SLEs) or childhood trauma (CT) in the aetiology of MDD.

The RADIANT UK sample consists of 1605 MDD cases and 1064 controls with SLE data, and a subset of 240 cases and 272 controls with CT data. Polygenic risk scores (PRS) were constructed using results from a mega-analysis on MDD by the Psychiatric Genomics Consortium. PRS and environmental factors were tested for association with case/control status and for interaction between them.

PRS significantly predicted depression, explaining 1.1% of variance in phenotype (p = 1.9 × 10−6). SLEs and CT were also associated with MDD status (p = 2.19 × 10−4 and p = 5.12 × 10−20, respectively). No interactions were found between PRS and SLEs. Significant PRSxCT interactions were found (p = 0.002), but showed an inverse association with MDD status, as cases who experienced more severe CT tended to have a lower PRS than other cases or controls. This relationship between PRS and CT was not observed in independent replication samples.

CT is a strong risk factor for MDD but may have greater effect in individuals with lower genetic liability for the disorder. Including environmental risk along with genetics is important in studying the aetiology of MDD and PRS provide a useful approach to investigating gene–environment interactions in complex traits.