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Student- and School-Level Factors Associated With Mental Health and Well-Being in Early Adolescence.
OBJECTIVE: Adolescence is a key developmental window that may determine long-term mental health. As schools may influence mental health of students, this study aimed to examine the association of school-level characteristics with students' mental health over time. METHOD: Longitudinal data from a cluster randomized controlled trial comprising 8,376 students (55% female; aged 11-14 years at baseline) across 84 schools in the United Kingdom were analyzed. Data collection started in academic years 2016/2017 (cohort 1) and 2017/2018 (cohort 2), with follow-up at 1, 1.5, and 2 years. Students' mental health (risk for depression [Center for Epidemiologic Studies Depression Scale], social-emotional-behavioral difficulties [Strength and Difficulties Questionnaire]) and well-being (Warwick-Edinburgh Mental Well-being Scale) and their relationship with student- and school-level characteristics were explored using multilevel regression models. RESULTS: Mental health difficulties and poorer well-being increased over time, particularly in girls. Differences among schools represented a small but statistically significant proportion of variation (95% CI) in students' mental health at each time point: depression, 1.7% (0.9%-2.5%) to 2.5% (1.6%-3.4%); social-emotional-behavioral difficulties, 1.9% (1.1%-2.7%) to 2.8% (2.1%-3.5%); and well-being, 1.8% (0.9%-2.7%) to 2.2% (1.4%-3.0%). Better student-rated school climate analyzed as a time-varying factor at the student and school level was associated with lower risk of depression (regression coefficient [95%CI] student level: -4.25 [-4.48, -4.01]; school level: -4.28 [-5.81, -2.75]), fewer social-emotional-behavioral difficulties (student level: -2.46 [-2.57, -2.35]; school level: -2.36 [-3.08, -1.63]), and higher well-being (student level: 3.88 [3.70, 4.05]; school-level: 4.28 [3.17, 5.38]), which was a stable relationship. CONCLUSION: Student-rated school climate predicted mental health in early adolescence. Policy and system interventions that focus on school climate may promote students' mental health.
A Common Genetic Factor Underlies Genetic Risk for Gynaecological and Reproductive Disorders and Is Correlated with Risk to Depression.
INTRODUCTION: Sex steroid hormone fluctuations may underlie both reproductive disorders and sex differences in lifetime depression prevalence. Previous studies report high comorbidity among reproductive disorders and between reproductive disorders and depression. This study sought to assess the multivariate genetic architecture of reproductive disorders and their loading onto a common genetic factor and investigated whether this latent factor shares a common genetic architecture with female depression, including perinatal depression (PND). METHOD: Using UK Biobank and FinnGen data, genome-wide association meta-analyses were conducted for nine reproductive disorders, and genetic correlation between disorders was estimated. Genomic Structural Equation Modelling identified a latent genetic factor underlying disorders, accounting for their significant genetic correlations. SNPs significantly associated with both latent factor and depression were identified. RESULTS: Excellent model fit existed between a latent factor underlying five reproductive disorders (χ2 (5) = 6.4; AIC = 26.4; CFI = 1.00; SRMR = 0.03) with high standardised loadings for menorrhagia (0.96, SE = 0.05); ovarian cysts (0.94, SE = 0.05); endometriosis (0.83, SE = 0.05); menopausal symptoms (0.77, SE = 0.10); and uterine fibroids (0.65, SE = 0.05). This latent factor was genetically correlated with PND (rG = 0.37, SE = 0.15, p = 1.4e-03), depression in females only (rG = 0.48, SE = 0.06, p = 7.2e-11), and depression in both males and females (MD) (rG = 0.35, SE = 0.03, p = 1.8e-30), with its top locus associated with FSHB/ARL14EP (rs11031006; p = 9.1e-33). SNPs intronic to ESR1, significantly associated with the latent factor, were also associated with PND, female depression, and MD. CONCLUSION: A common genetic factor, correlated with depression, underlies risk of reproductive disorders, with implications for aetiology and treatment. Genetic variation in ESR1 is associated with reproductive disorders and depression, highlighting the importance of oestrogen signalling for both reproductive and mental health.
Schizophrenia-associated somatic copy-number variants from 12,834 cases reveal recurrent NRXN1 and ABCB11 disruptions.
While germline copy-number variants (CNVs) contribute to schizophrenia (SCZ) risk, the contribution of somatic CNVs (sCNVs)-present in some but not all cells-remains unknown. We identified sCNVs using blood-derived genotype arrays from 12,834 SCZ cases and 11,648 controls, filtering sCNVs at loci recurrently mutated in clonal blood disorders. Likely early-developmental sCNVs were more common in cases (0.91%) than controls (0.51%, p = 2.68e-4), with recurrent somatic deletions of exons 1-5 of the NRXN1 gene in five SCZ cases. Hi-C maps revealed ectopic, allele-specific loops forming between a potential cryptic promoter and non-coding cis-regulatory elements upon 5' deletions in NRXN1. We also observed recurrent intragenic deletions of ABCB11, encoding a transporter implicated in anti-psychotic response, in five treatment-resistant SCZ cases and showed that ABCB11 is specifically enriched in neurons forming mesocortical and mesolimbic dopaminergic projections. Our results indicate potential roles of sCNVs in SCZ risk.
Reduced coupling between offline neural replay events and default mode network activation in schizophrenia.
Schizophrenia is characterized by an abnormal resting state and default mode network brain activity. However, despite intense study, the mechanisms linking default mode network dynamics to neural computation remain elusive. During rest, sequential hippocampal reactivations, known as 'replay', are played out within default mode network activation windows, highlighting a potential role of replay-default mode network coupling in memory consolidation and model-based mental simulation. Here, we test a hypothesis of reduced replay-default mode network coupling in schizophrenia, using magnetoencephalography and a non-spatial sequence learning task designed to elicit off-task (i.e. resting state) neural replay. Participants with a diagnosis of schizophrenia (n = 28, mean age 28.2 years, range 20-40, 6 females, 13 not taking antipsychotic medication) and non-clinical control participants (n = 29, mean age 28.1 years, range 18-45, 6 females, matched at group level for age, intelligence quotient, gender, years in education and working memory) underwent a magnetoencephalography scan both during task completion and during a post-task resting state session. We used neural decoding to infer the time course of default mode network activation (time-delay embedding hidden Markov model) and spontaneous neural replay (temporally delayed linear modelling) in resting state magnetoencephalography data. Using multiple regression, we then quantified the extent to which default mode network activation was uniquely predicted by replay events that recapitulated the learned task sequences (i.e. 'task-relevant' replay-default mode network coupling). In control participants, replay-default mode network coupling was augmented following sequence learning, an augmentation that was specific for replay of task-relevant (i.e. learned) state transitions. This task-relevant replay-default mode network coupling effect was significantly reduced in schizophrenia (t(52) = 3.93, P = 0.018). Task-relevant replay-default mode network coupling predicted memory maintenance of learned sequences (ρ(52) = 0.31, P = 0.02). Importantly, reduced task-relevant replay-default mode network coupling in schizophrenia was not explained by differential replay or altered default mode network dynamics between groups nor by reference to antipsychotic exposure. Finally, task-relevant replay-default mode network coupling during rest correlated with stimulus-evoked default mode network modulation as measured in a separate task session. In the context of a proposed functional role of replay-default mode network coupling, our findings shed light on the functional significance of default mode network abnormalities in schizophrenia and provide for a consilience between task-based and resting state default mode network findings in this disorder.
Reward processing in schizophrenia and its relation to Mu opioid receptor availability and negative symptoms: A [11C]-carfentanil PET and fMRI study.
BACKGROUND: Reward processing deficits are a core feature of schizophrenia and are thought to underlie negative symptoms. Pre-clinical evidence suggests that opioid neurotransmission is linked to reward processing. However, the contribution of Mu Opioid Receptor (MOR) signalling to the reward processing abnormalities in schizophrenia is unknown. Here, we examined the association between MOR availability and the neural processes underlying reward anticipation in patients with schizophrenia using multimodal neuroimaging. METHOD: 37 subjects (18 with Schizophrenia with moderate severity negative symptoms and 19 age and sex-matched healthy controls) underwent a functional MRI scan while performing the Monetary Incentive Delay (MID) task to measure the neural response to reward anticipation. Participants also had a [11C]-carfentanil PET scan to measure MOR availability. RESULTS: Reward anticipation was associated with increased neural activation in a widespread network of brain regions including the striatum. Patients with schizophrenia had both significantly lower MOR availability in the striatum as well as striatal hypoactivation during reward anticipation. However, there was no association between MOR availability and striatal neural activity during reward anticipation in either patient or controls (Pearson's Correlation, controls df = 17, r = 0.321, p = 0.18, patients df = 16, r = 0.295, p = 0.24). There was no association between anticipation-related neural activation and negative symptoms (r = -0.120, p = 0.14) or anhedonia severity (social r = -0.365, p = 0.14 physical r = -0.120, p = 0.63). CONCLUSIONS: Our data suggest reduced MOR availability in schizophrenia might not underlie striatal hypoactivation during reward anticipation in patients with established illness. Therefore, other mechanisms, such as dopamine dysfunction, warrant further investigation as treatment targets for this aspect of the disorder.
The effect of antipsychotics on glutamate levels in the anterior cingulate cortex and clinical response: A 1H-MRS study in first-episode psychosis patients.
INTRODUCTION: Glutamatergic dysfunction is implicated in the pathophysiology of schizophrenia. It is unclear whether glutamatergic dysfunction predicts response to treatment or if antipsychotic treatment influences glutamate levels. We investigated the effect of antipsychotic treatment on glutamatergic levels in the anterior cingulate cortex (ACC), and whether there is a relationship between baseline glutamatergic levels and clinical response after antipsychotic treatment in people with first episode psychosis (FEP). MATERIALS AND METHODS: The sample comprised 25 FEP patients; 22 completed magnetic resonance spectroscopy scans at both timepoints. Symptoms were assessed using the Positive and Negative Syndrome Scale (PANSS). RESULTS: There was no significant change in glutamate [baseline 13.23 ± 2.33; follow-up 13.89 ± 1.74; t(21) = -1.158, p = 0.260], or Glx levels [baseline 19.64 ± 3.26; follow-up 19.66 ± 2.65; t(21) = -0.034, p = 0.973]. There was no significant association between glutamate or Glx levels at baseline and the change in PANSS positive (Glu r = 0.061, p = 0.777, Glx r = -0.152, p = 0.477), negative (Glu r = 0.144, p = 0.502, Glx r = 0.052, p = 0.811), general (Glu r = 0.110, p = 0.607, Glx r = -0.212, p = 0.320), or total scores (Glu r = 0.078, p = 0.719 Glx r = -0.155, p = 0.470). CONCLUSION: These findings indicate that treatment response is unlikely to be associated with baseline glutamatergic metabolites prior to antipsychotic treatment, and there is no major effect of antipsychotic treatment on glutamatergic metabolites in the ACC.
The Watts Connectedness Scale: a new scale for measuring a sense of connectedness to self, others, and world.
RATIONALE: A general feeling of disconnection has been associated with mental and emotional suffering. Improvements to a sense of connectedness to self, others and the wider world have been reported by participants in clinical trials of psychedelic therapy. Such accounts have led us to a definition of the psychological construct of 'connectedness' as 'a state of feeling connected to self, others and the wider world'. Existing tools for measuring connectedness have focused on particular aspects of connectedness, such as 'social connectedness' or 'nature connectedness', which we hypothesise to be different expressions of a common factor of connectedness. Here, we sought to develop a new scale to measure connectedness as a construct with these multiple domains. We hypothesised that (1) our scale would measure three separable subscale factors pertaining to a felt connection to 'self', 'others' and 'world' and (2) improvements in total and subscale WCS scores would correlate with improved mental health outcomes post psychedelic use. OBJECTIVES: To validate and test the 'Watts Connectedness Scale' (WCS). METHODS: Psychometric validation of the WCS was carried out using data from three independent studies. Firstly, we pooled data from two prospective observational online survey studies. The WCS was completed before and after a planned psychedelic experience. The total sample of completers from the online surveys was N = 1226. Exploratory and confirmatory factor analysis were performed, and construct and criterion validity were tested. A third dataset was derived from a double-blind randomised controlled trial (RCT) comparing psilocybin-assisted therapy (n = 27) with 6 weeks of daily escitalopram (n = 25) for major depressive disorder (MDD), where the WCS was completed at baseline and at a 6-week primary endpoint. RESULTS: As hypothesised, factor analysis of all WCS items revealed three main factors with good internal consistency. WCS showed good construct validity. Significant post-psychedelic increases were observed for total connectedness scores (η2 = 0.339, p
The histamine system and cognitive function: An in vivo H3 receptor PET imaging study in healthy volunteers and patients with schizophrenia.
BACKGROUND: The histamine-3 receptor (H3R) is an auto- and heteroreceptor that inhibits the release of histamine and other neurotransmitters. Post-mortem evidence has found altered H3R expression in patients with psychotic disorders, which may underlie cognitive impairment associated with schizophrenia (CIAS). AIMS: We used positron emission tomography (PET) imaging to compare brain uptake of an H3R selective tracer between patients with schizophrenia and matched controls (healthy individuals). Regions of interest included the dorsolateral prefrontal cortex (DLPFC) and striatum. We explored correlations between tracer uptake and symptoms, including cognitive domains. METHODS: A total of 12 patients and 12 matched controls were recruited to the study and were assessed with psychiatric and cognitive rating scales. They received a PET scan using the H3R-specific radioligand [11C]MK-8278 to determine H3R availability. RESULTS: There was no statistically significant difference in tracer uptake between patients and controls in the DLPFC (t19 = 0.79, p = 0.44) or striatum (t21 = 1.18, p = 0.25). An exploratory analysis found evidence for lower volume of distribution in the left cuneus (pFWE-corrected = 0.01). DLPFC tracer uptake was strongly correlated with cognition in controls (trail making test (TMT) A: r = 0.77, p = 0.006; TMT B: rho = 0.74, p = 0.01), but not in patients (TMT A: r = -0.18, p = 0.62; TMT B: rho = -0.06, p = 0.81). CONCLUSIONS: These findings indicate H3R in the DLPFC might play a role in executive function and this is disrupted in schizophrenia in the absence of major alterations in H3R availability as assessed using a selective radiotracer for H3R. This provides further evidence for the role of H3R in CIAS.
Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling.
BACKGROUND: Natural language processing (NLP) holds promise to transform psychiatric research and practice. A pertinent example is the success of NLP in the automatic detection of speech disorganization in formal thought disorder (FTD). However, we lack an understanding of precisely what common NLP metrics measure and how they relate to theoretical accounts of FTD. We propose tackling these questions by using deep generative language models to simulate FTD-like narratives by perturbing computational parameters instantiating theory-based mechanisms of FTD. METHODS: We simulated FTD-like narratives using Generative-Pretrained-Transformer-2 by either increasing word selection stochasticity or limiting the model's memory span. We then examined the sensitivity of common NLP measures of derailment (semantic distance between consecutive words or sentences) and tangentiality (how quickly meaning drifts away from the topic) in detecting and dissociating the 2 underlying impairments. RESULTS: Both parameters led to narratives characterized by greater semantic distance between consecutive sentences. Conversely, semantic distance between words was increased by increasing stochasticity, but decreased by limiting memory span. An NLP measure of tangentiality was uniquely predicted by limited memory span. The effects of limited memory span were nonmonotonic in that forgetting the global context resulted in sentences that were semantically closer to their local, intermediate context. Finally, different methods for encoding the meaning of sentences varied dramatically in performance. CONCLUSIONS: This work validates a simulation-based approach as a valuable tool for hypothesis generation and mechanistic analysis of NLP markers in psychiatry. To facilitate dissemination of this approach, we accompany the paper with a hands-on Python tutorial.
The effects of AUT00206, a novel Kv3.1/3.2 potassium channel modulator, on task-based reward system activation: a test of mechanism in schizophrenia.
The pathophysiology of schizophrenia involves abnormal reward processing, thought to be due to disrupted striatal and dopaminergic function. Consistent with this hypothesis, functional magnetic resonance imaging (fMRI) studies using the monetary incentive delay (MID) task report hypoactivation in the striatum during reward anticipation in schizophrenia. Dopamine neuron activity is modulated by striatal GABAergic interneurons. GABAergic interneuron firing rates, in turn, are related to conductances in voltage-gated potassium 3.1 (Kv3.1) and 3.2 (Kv3.2) channels, suggesting that targeting Kv3.1/3.2 could augment striatal function during reward processing. Here, we studied the effect of a novel potassium Kv3.1/3.2 channel modulator, AUT00206, on striatal activation in patients with schizophrenia, using the MID task. Each participant completed the MID during fMRI scanning on two occasions: once at baseline, and again following either 4 weeks of AUT00206 or placebo treatment. We found a significant inverse relationship at baseline between symptom severity and reward anticipation-related neural activation in the right associative striatum (r = -0.461, p = 0.035). Following treatment with AUT00206, there was a significant increase in reward anticipation-related activation in the left associative striatum (t(13) = 4.23, peak-level p(FWE)
Relationship Between Replay-Associated Ripples and Hippocampal N-Methyl-D-Aspartate Receptors: Preliminary Evidence From a PET-MEG Study in Schizophrenia.
BACKGROUND AND HYPOTHESES: Hippocampal replay and associated high-frequency ripple oscillations are among the best-characterized phenomena in resting brain activity. Replay/ripples support memory consolidation and relational inference, and are regulated by N-methyl-D-aspartate receptors (NMDARs). Schizophrenia has been associated with both replay/ripple abnormalities and NMDAR hypofunction in both clinical samples and genetic mouse models, although the relationship between these 2 facets of hippocampal function has not been tested in humans. STUDY DESIGN: Here, we avail of a unique multimodal human neuroimaging data set to investigate the relationship between the availability of (intrachannel) NMDAR binding sites in hippocampus, and replay-associated ripple power, in 16 participants (7 nonclinical participants and 9 people with a diagnosis of schizophrenia, PScz). Each participant had both a [18F]GE-179 positron emission tomography (PET) scan (to measure NMDAR availability, V T ) and a magnetoencephalography (MEG) scan (to measure offline neural replay and associated high-frequency ripple oscillations, using Temporally Delayed Linear Modeling). STUDY RESULTS: We show a positive relationship between hippocampal NMDAR availability and replay-associated ripple power. This linkage was evident across control participants (r(5) = .94, P = .002) and PScz (r(7) = .70, P = .04), with no group difference. CONCLUSIONS: Our findings provide preliminary evidence for a relationship between hippocampal NMDAR availability and replay-associated ripple power in humans, and haverelevance for NMDAR hypofunction theories of schizophrenia.
Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.
Human cognition is underpinned by structured internal representations that encode relationships between entities in the world (cognitive maps). Clinical features of schizophrenia-from thought disorder to delusions-are proposed to reflect disorganization in such conceptual representations. Schizophrenia is also linked to abnormalities in neural processes that support cognitive map representations, including hippocampal replay and high-frequency ripple oscillations. Here, we report a computational assay of semantically guided conceptual sampling and exploit this to test a hypothesis that people with schizophrenia (PScz) exhibit abnormalities in semantically guided cognition that relate to hippocampal replay and ripples. Fifty-two participants [26 PScz (13 unmedicated) and 26 age-, gender-, and intelligence quotient (IQ)-matched nonclinical controls] completed a category- and letter-verbal fluency task, followed by a magnetoencephalography (MEG) scan involving a separate sequence-learning task. We used a pretrained word embedding model of semantic similarity, coupled to a computational model of word selection, to quantify the degree to which each participant's verbal behavior was guided by semantic similarity. Using MEG, we indexed neural replay and ripple power in a post-task rest session. Across all participants, word selection was strongly influenced by semantic similarity. The strength of this influence showed sensitivity to task demands (category > letter fluency) and predicted performance. In line with our hypothesis, the influence of semantic similarity on behavior was reduced in schizophrenia relative to controls, predicted negative psychotic symptoms, and correlated with an MEG signature of hippocampal ripple power (but not replay). The findings bridge a gap between phenomenological and neurocomputational accounts of schizophrenia.
The Structural Organization and Construct Validity Evidence of the Brazilian Versions of the Mysticism Scale and the Ego-Dissolution Inventory in a Major Religion of the Ayahuasca
Mystical experiences and ego dissolution are essential to understanding the lasting psychological effects of psychedelics or even natural religious experience. The main objective of the article is to present evidence of construct validity in the adaptation to Brazilian Portuguese of the Hood Mysticism Scale (HMS) and the Ego-Dissolution Inventory (EDI)-8 through two psychometric techniques: the smallest space analysis technique and the factorial analysis. Design: a cross-sectional survey. The sample consisted of 1414 members of the União do Vegetal religion. The smallest space analysis (SSA) identified three distinct regions in HMS: introversive mysticism, extroversive mysticism, and interpretation. The SSA for EDI-8 suggests four distinct regions, which were conceptualized as (1) loss of ego, (2) total dissolution, (3) ego quiet, and (4) internal–external unity. In addition, a confirmatory factor analysis (CFA) was used to test the factorial replicability of the HMS and EDI-8. The CFA presented acceptable support for the dimensional structures tested. Conclusion: Results show good reliability and validity indicators, which endorse the Portuguese HMS and EDI-8 application in future research.
Effect of polygenic risk for schizophrenia on cardiac structure and function: a UK Biobank observational study.
BACKGROUND: Cardiovascular disease is a major cause of excess mortality in people with schizophrenia. Several factors are responsible, including lifestyle and metabolic effects of antipsychotics. However, variations in cardiac structure and function are seen in people with schizophrenia in the absence of cardiovascular disease risk factors and after accounting for lifestyle and medication. Therefore, we aimed to explore whether shared genetic causes contribute to these cardiac variations. METHODS: For this observational study, we used data from the UK Biobank and included White British or Irish individuals without diagnosed schizophrenia with variable polygenic risk scores for the condition. To test the association between polygenic risk score for schizophrenia and cardiac phenotype, we used principal component analysis and regression. Robust regression was then used to explore the association between the polygenic risk score for schizophrenia and individual cardiac phenotypes. We repeated analyses with fibro-inflammatory pathway-specific polygenic risk scores for schizophrenia. Last, we investigated genome-wide sharing of common variants between schizophrenia and cardiac phenotypes using linkage disequilibrium score regression. The primary outcome was principal component regression. FINDINGS: Of 33 353 individuals recruited, 32 279 participants had complete cardiac MRI data and were included in the analysis, of whom 16 625 (51·5%) were female and 15 654 (48·5%) were male. 1074 participants were excluded on the basis of incomplete cardiac MRI data (for all phenotypes). A model regressing polygenic risk scores for schizophrenia onto the first five cardiac principal components of the principal components analysis was significant (F=5·09; p=0·00012). Principal component 1 captured a pattern of increased cardiac volumes, increased absolute peak diastolic strain rates, and reduced ejection fractions; polygenic risk scores for schizophrenia and principal component 1 were negatively associated (β=-0·01 [SE 0·003]; p=0·017). Similar to the principal component analysis results, for individual cardiac phenotypes, we observed negative associations between polygenic risk scores for schizophrenia and indexed right ventricular end-systolic volume (β=-0·14 [0·04]; p=0·0013, pFDR=0·015), indexed right ventricular end-diastolic volume (β=-0·17 [0·08]); p=0·025; pFDR=0·082), and absolute longitudinal peak diastolic strain rates (β=-0·01 [0·003]; p=0·0024, pFDR=0·015), and a positive association between polygenic risk scores for schizophrenia and right ventricular ejection fraction (β=0·09 [0·03]; p=0·0041, pFDR=0·015). Models examining the transforming growth factor-β (TGF-β)-specific and acute inflammation-specific polygenic risk scores for schizophrenia found significant associations with the first five principal components (F=2·62, p=0·022; F=2·54, p=0·026). Using linkage disequilibrium score regression, we observed genetic overlap with schizophrenia for right ventricular end-systolic volume and right ventricular ejection fraction (p=0·0090, p=0·0077). INTERPRETATION: High polygenic risk scores for schizophrenia are associated with decreased cardiac volumes, increased ejection fractions, and decreased absolute peak diastolic strain rates. TGF-β and inflammatory pathways might be implicated, and there is evidence of genetic overlap for some cardiac phenotypes. Reduced absolute peak diastolic strain rates indicate increased myocardial stiffness and diastolic dysfunction, which increases risk of cardiac disease. Thus, genetic risk for schizophrenia is associated with cardiac structural changes that can worsen cardiac outcomes. Further work is required to determine whether these associations are specific to schizophrenia or are also seen in other psychiatric conditions. FUNDING: National Institute for Health Research, Maudsley Charity, Wellcome Trust, Medical Research Council, Academy of Medical Sciences, Edmond J Safra Foundation, British Heart Foundation.
Value and values in inclusive design
As an ethical design approach embedding with the human value of inclusiveness, inclusive design could contribute to economic value creation. However, research on the relationship between economic value and human values in inclusive design has seldom been explored. This preliminary literature review focuses on how value and values have been discussed in inclusive design research. The findings first present the evolving conceptions of inclusive design that formulate and transform the understanding of value and values. Then, existing literature on the economic value of inclusive design, and inclusive design for human values at both individual and social levels are reviewed respectively. We categorize these disparate discussions into ‘value creation’ and ‘value distribution’ and propose opportunities for an integrated approach that would bridge discussions on the economic value and human values in future research.
Adaptive learning from outcome contingencies in eating-disorder risk groups.
Eating disorders are characterised by altered eating patterns alongside overvaluation of body weight or shape, and have relatively low rates of successful treatment and recovery. Notably, cognitive inflexibility has been implicated in both the development and maintenance of eating disorders, and understanding the reasons for this inflexibility might indicate avenues for treatment development. We therefore investigate one potential cause of this inflexibility: an inability to adjust learning when outcome contingencies change. We recruited (n = 82) three groups of participants: those who had recovered from anorexia nervosa (RA), those who had high levels of eating disorder symptoms but no formal diagnosis (EA), and control participants (HC). They performed a reinforcement learning task (alongside eye-tracking) in which the volatility of wins and losses was independently manipulated. We predicted that both the RA and EA groups would adjust their learning rates less than the control participants. Unexpectedly, the RA group showed elevated adjustment of learning rates for both win and loss outcomes compared to control participants. The RA group also showed increased pupil dilation to stable wins and reduced pupil dilation to stable losses. Their learning rate adjustment was associated with the difference between their pupil dilation to volatile vs. stable wins. In conclusion, we find evidence that learning rate adjustment is unexpectedly higher in those who have recovered from anorexia nervosa, indicating that the relationship between eating disorders and cognitive inflexibility may be complex. Given our findings, investigation of noradrenergic agents may be valuable in the field of eating disorders.
Young People's Mental Health Changes, Risk, and Resilience During the COVID-19 Pandemic.
IMPORTANCE: As young people's mental health difficulties increase, understanding risk and resilience factors under challenging circumstances becomes critical. OBJECTIVE: To explore the outcomes of the COVID-19 pandemic on secondary school students' mental health difficulties, as well as the associations with individual, family, friendship, and school characteristics. DESIGN, SETTING, AND PARTICIPANTS: For this cohort study, follow-up data from the My Resilience in Adolescence (MYRIAD) cluster randomized clinical trial were collected across 2 representative UK cohorts. Mainstream UK secondary schools with a strategy and structure to deliver social-emotional learning, with an appointed head teacher, and that were not rated "inadequate" in their latest official inspection were recruited. A total of 5663 schools were approached, 532 showed interest, and 84 consented. Cohort 1 included 12 schools and 864 students, and cohort 2 included 72 schools and 6386 students. COVID-19 was declared a pandemic after cohort 1 had completed all assessments (September 2018 to January 2020), but cohort 2 had not (September 2019 to June 2021). EXPOSURES: Cohort 2 was exposed to the COVID-19 pandemic, including 3 national lockdowns. Associations of individual, family, friendship, and school characteristics with students' mental health were explored. MAIN OUTCOMES AND MEASURES: Changes in students' risk for depression (Center for Epidemiological Studies-Depression scale); social, emotional, and behavioral difficulties (Strengths and Difficulties Questionnaire); and mental well-being (Warwick-Edinburgh Mental Well-Being Scale). RESULTS: Of the 7250 participants included, the mean (SD) age was 13.7 (0.6) years, 3947 (55.4%) identified as female, and 5378 (73.1%) self-reported their race as White. Twelve schools and 769 of the 864 students (89.0%) in cohort 1 and 54 schools and 2958 of the 6386 students (46.3%) in cohort 2 provided data and were analyzed. Mental health difficulties increased in both cohorts but to a greater extent among students exposed to the pandemic, including for risk of depression (adjusted mean difference [AMD], 1.91; 95% CI, 1.07-2.76); social, emotional, and behavioral difficulties (AMD, 0.76; 95% CI, 0.33-1.18); and mental well-being (AMD, -2.08; 95% CI, -2.80 to -1.36). Positive school climate, high home connectedness, and having a friend during lockdown were protective factors during the pandemic. Female gender and initial low risk for mental health difficulties were associated with greater mental health deteriorations. Partial school attendance during lockdown was associated with better adjustment than no attendance when returning to school. CONCLUSIONS AND RELEVANCE: This cohort study of secondary school students demonstrated that to promote mental health and adjustment, policy interventions should foster home connectedness, peer friendship, and school climate; avoid full school closures; and consider individual differences.
The arrow of time of brain signals in cognition: Potential intriguing role of parts of the default mode network.
A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamics-inspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, 'arrow of time', in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.