Predictive Psychiatry

Smartbands can be used to detect cigarette smoking and deliver real time smoking interventions. Brief mindfulness interventions have been found to reduce smoking.

Suicide risk is highly fluctuating. There is a need for predictors of short-term change in suicide risk to optimize risk assessment and treatment, especially among individuals who already attempted suicide.

Clinical high-risk psychosis (CHRp) samples can be heterogeneous, consisting essentially of people with not only psychotic-like experiences but also nonspecific symptoms that may reflect common mental disorders such as depression, anxiety, or substance abuse pathologies. Few studies have attempted to analyze and understand psychosis risk in relation to both environmental (ER) and psychopathological risk (PsR) factors. This study aimed to determine the clinical risk of psychosis in adolescents.

This study aims to explore the common genetic basis between respiratory diseases and to identify shared molecular and biological mechanisms.

This study investigates the prospective associations between short sleep duration, insomnia symptoms, and psychotic-like experiences (PLEs) in a large sample of Chinese adolescents. This study utilized a three-timepoint repeated cross-sectional survey with two nested longitudinal subsamples. A total of 17,722 adolescents were assessed at baseline (April 21 to May 12, 2021) and six months later (December 17 to 26, 2021). Out of these, 15,694 adolescents provided complete responses to the questions at baseline and one year later (May 17 - June 6, 2022). A self-administered questionnaire was used to measure sample characteristics (at baseline), sleep duration, insomnia symptoms, and PLEs (at each assessment), and negative life events (at two follow-ups). Baseline short sleep duration and insomnia symptoms predicted frequent PLEs at both follow-up assessments. Additionally, baseline frequent PLEs also predicted insomnia symptoms at six months and one year later. However, when controlling for confounders, PLEs at baseline only predicted short sleep duration at six months, and not at one year. This study reveals bidirectional prospective relationships between short sleep duration, insomnia symptoms, and PLEs, even after controlling for covariates. Therefore, it is crucial to assess both sleep patterns and PLEs in order to promote optimal sleep and mental health among adolescents.

Fostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students' academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear.

Moyamoya disease (MMD) is a rare noninflammatory disorder involving progressive intracranial vasculopathy and impaired cerebral blood flow in the anterior circulation, resulting in stroke and cognitive impairment. We aimed to characterize cognitive impairment and the possible predictive value of sociodemographic and clinical characteristics of adults with MMD.

The present study aimed to investigate whether the strength of mental health competencies and the severity of mental disorder symptoms, and their interaction, differ in the strength of their associations with several dimensions of well-being in Hungarian adult psychiatric and non-clinical samples. All respondent in the psychiatric sample (129 patients (44 male, 85 female)) and in the non-clinical community sample (253 adults (43 male, 210 female)) completed the Mental Health Test, six measures of well-being and mental health, and the Symptom Checklist-90-Revised. Including both mental health competencies and mental disorder symptoms in a regression model in both samples can predict patients' well-being even more accurately. Mental health competencies were positively related; mental disorder symptoms were negatively related to subjective well-being. In all models and in both samples, mental health competencies were found to be stronger determinants of well-being than mental disorder symptoms. The interaction of mental health competencies and mental disorder symptoms is no more predictive of well-being in either psychiatric or non-clinical samples than when the effects of each are considered separately. The assessment of mental health competencies has an important predictive value for well-being in the presence of psychopathological symptoms and/or mental disorders.

Alzheimer's disease (AD) is the most common cause of dementia, and the gradual deterioration of brain function eventually leads to death. Almost all AD patients suffer from neuropsychiatric symptoms (NPS), the emergence of which correlates with dysfunctional serotonergic systems. Our aim is to generate hindbrain organoids containing serotonergic neurons using human induced Pluripotent Stem Cells (iPSCs). Work presented here is laying the groundwork for the application of hindbrain organoids to evaluate individual differences in disease progression, NPS development, and pharmacological treatment response. Human peripheral blood mononuclear cells (PBMCs) from healthy volunteers (n = 3), an AD patient without NPS (n = 1), and AD patients with NPS (n = 2) were reprogrammed into iPSCs and subsequently differentiated into hindbrain organoids. The presence of serotonergic neurons was confirmed by quantitative reverse transcription PCR, flow cytometry, immunocytochemistry, and detection of released serotonin (5-HT). We successfully reprogrammed PBMCs into 6 iPSC lines, and subsequently generated hindbrain organoids from 6 individuals to study inter-patient variability using a precision medicine approach. To assess patient-specific treatment effects, organoids were treated with different concentrations of escitalopram oxalate, commonly prescribed for NPS. Changes in 5-HT levels before and after treatment with escitalopram were dose-dependent and variable across patients. Organoids from different people responded differently to the application of escitalopram in vitro. We propose that this 3D platform might be effectively used for drug screening purposes to predict patients with NPS most likely to respond to treatment in vivo and to understand the heterogeneity of treatment responses.

Epigenetic modifications, particularly DNA methylation (DNAm) in cord blood, are an important biological marker of how external exposures during gestation can influence the in-utero environment and subsequent offspring development. Despite the recognized importance of DNAm during gestation, comparative studies to determine the consistency of these epigenetic signals across different ethnic groups are largely absent. To address this gap, we first performed epigenome-wide association studies (EWAS) of gestational age (GA) using newborn cord blood DNAm comparatively in a white European (n = 342) and a South Asian (n = 490) birth cohort living in Canada. Then, we capitalized on established cord blood epigenetic GA clocks to examine the associations between maternal exposures, offspring characteristics and epigenetic GA, as well as GA acceleration, defined as the residual difference between epigenetic and chronological GA at birth.

The current genetic diagnostic workup of congenital cataract (CC) is mainly based on NGS panels, whereas exome sequencing (ES) has occasionally been employed. In this multicentre study, we investigated by ES the detection yield, mutational spectrum and genotype-phenotype correlations in a CC cohort recruited between 2020 and mid-2022. The cohort consisted of 67 affected individuals from 51 unrelated families and included both non-syndromic (75%) and syndromic (25%) phenotypes, with extra-CC ocular/visual features present in both groups (48% and 76%, respectively). The functional effect of variants was predicted by 3D modelling and hydropathy properties changes. Variant clustering was used for the in-depth assessment of genotype-phenotype correlations. A diagnostic (pathogenic or likely pathogenic) variant was identified in 19 out of 51 probands/families (~37%). In a further 14 probands/families a candidate variant was identified: in 12 families a VUS was detected, of which 9 were considered plausibly pathogenic (i.e., 4 or 5 points according to ACMG criteria), while in 2 probands ES identified a single variant in an autosomal recessive gene associated with CC. Eighteen probands/families, manifesting primarily non-syndromic CC (15/18, 83%), remained unsolved. The identified variants (8 P, 12 LP, 10 VUS-PP, and 5 VUS), half of which were unreported in the literature, affected five functional categories of genes involved in transcription/splicing, lens formation/homeostasis (i.e., crystallin genes), membrane signalling, cell-cell interaction, and immune response. A phenotype-specific variant clustering was observed in four genes (KIF1A, MAF, PAX6, SPTAN1), whereas variable expressivity and potential phenotypic expansion in two (BCOR, NHS) and five genes (CWC27, KIF1A, IFIH1, PAX6, SPTAN1), respectively. Finally, ES allowed to detect variants in six genes not commonly included in commercial CC panels. These findings broaden the genotype-phenotype correlations in one of the largest CC cohorts tested by ES, providing novel insights into the underlying pathogenetic mechanisms and emphasising the power of ES as first-tier test.

Cognitive rigidity, or difficulty adapting to changing demands, is commonly observed in anorexia nervosa. Less is known, however, about cognitive flexibility (CF) in bulimia nervosa (BN) and, particularly, adolescence. Clarifying this relation and best assessment practices may guide informed clinical decision-making. The current study compared how two measures of CF (i.e., Wisconsin Card Sort Task [WCST] and Trail Making Task [TMT]) relate to BN symptoms among adolescents.

This study examines the association between the coefficient of variation(%CV) of lithium levels and episode risk and frequency in bipolar patients maintaining serum lithium levels within the therapeutic range.

The typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes.

Emotion regulation (ER) is considered central in adolescent psychopathology, and ER strategies may change during challenging times, such as a global pandemic. Despite this, there remains a limited understanding of individual differences in ER mechanisms and their associations with psychopathology. This study examined whether and how cognitive reappraisal, expressive suppression, and self-compassion changed over COVID-19 and how these changes uniquely predicted adolescents' depressive symptoms.

Several psychotherapy protocols have been evaluated as adjuncts to pharmacotherapy for patients with bipolar disorder (BD). Little is known about their comparative effectiveness.

One third of people with psychosis become antipsychotic treatment-resistant and the underlying mechanisms remain unclear. We investigated whether altered cognitive control function is a factor underlying development of treatment resistance. We studied 50 people with early psychosis at a baseline visit (mean < 2 years illness duration) and follow-up visit (1 year later), when 35 were categorized at treatment-responsive and 15 as treatment-resistant. Participants completed an emotion-yoked reward learning task that requires cognitive control whilst undergoing fMRI and MR spectroscopy to measure glutamate levels from Anterior Cingulate Cortex (ACC). Changes in cognitive control related activity (in prefrontal cortex and ACC) over time were compared between treatment-resistant and treatment-responsive groups and related to glutamate. Compared to treatment-responsive, treatment-resistant participants showed blunted activity in right amygdala (decision phase) and left pallidum (feedback phase) at baseline which increased over time and was accompanied by a decrease in medial Prefrontal Cortex (mPFC) activity (feedback phase) over time. Treatment-responsive participants showed a negative relationship between mPFC activity and glutamate levels at follow-up, no such relationship existed in treatment-resistant participants. Reduced activity in right amygdala and left pallidum at baseline was predictive of treatment resistance at follow-up (67% sensitivity, 94% specificity). The findings suggest that deterioration in mPFC function over time, a key cognitive control region needed to compensate for an initial dysfunction within a social-emotional network, is a factor underlying development of treatment resistance in early psychosis. An uncoupling between glutamate and cognitive control related mPFC function requires further investigation that may present a future target for interventions.

Congenital heart disease affects 1% of infants and is associated with impaired neurodevelopment. Right- or left-sided sulcal features correlate with executive function among people with Tetralogy of Fallot or single ventricle congenital heart disease. Studies of multiple congenital heart disease types are needed to understand regional differences. Further, sulcal pattern has not been studied in people with d-transposition of the great arteries. Therefore, we assessed the relationship between sulcal pattern and executive function, general memory, and processing speed in a meta-regression of 247 participants with three congenital heart disease types (114 single ventricle, 92 d-transposition of the great arteries, and 41 Tetralogy of Fallot) and 94 participants without congenital heart disease. Higher right hemisphere sulcal pattern similarity was associated with improved executive function (Pearson r = 0.19, false discovery rate-adjusted P = 0.005), general memory (r = 0.15, false discovery rate P = 0.02), and processing speed (r = 0.17, false discovery rate P = 0.01) scores. These positive associations remained significant in for the d-transposition of the great arteries and Tetralogy of Fallot cohorts only in multivariable linear regression (estimated change β = 0.7, false discovery rate P = 0.004; β = 4.1, false discovery rate P = 0.03; and β = 5.4, false discovery rate P = 0.003, respectively). Duration of deep hypothermic circulatory arrest was also associated with outcomes in the multivariate model and regression tree analysis. This suggests that sulcal pattern may provide an early biomarker for prediction of later neurocognitive challenges among people with congenital heart disease.

Only a third of individuals with mild cognitive impairment (MCI) progress to dementia of the Alzheimer's type (DAT). Identifying biomarkers that distinguish individuals with MCI who will progress to DAT (MCI-Converters) from those who will not (MCI-Non-Converters) remains a key challenge in the field. In our study, we evaluate whether the individual rates of loss of volumes of the Hippocampus and entorhinal cortex (EC) with age in the MCI stage can predict progression to DAT. Using data from 758 MCI patients in the Alzheimer's Disease Neuroimaging Database, we employ Linear Mixed Effects (LME) models to estimate individual trajectories of regional brain volume loss over 12 years on average. Our approach involves three key analyses: (1) mapping age-related volume loss trajectories in MCI-Converters and Non-Converters, (2) using logistic regression to predict progression to DAT based on individual rates of hippocampal and EC volume loss, and (3) examining the relationship between individual estimates of these volumetric changes and cognitive decline across different cognitive functions-episodic memory, visuospatial processing, and executive function. We find that the loss of Hippocampal volume is significantly more rapid in MCI-Converters than Non-Converters, but find no such difference in EC volumes. We also find that the rate of hippocampal volume loss in the MCI stage is a significant predictor of conversion to DAT, while the rate of volume loss in the EC and other additional regions is not. Finally, individual estimates of rates of regional volume loss in both the Hippocampus and EC, and other additional regions, correlate strongly with individual rates of cognitive decline. Across all analyses, we find significant individual variation in the initial volumes and the rates of changes in volume with age in individuals with MCI. This study highlights the importance of personalized approaches in predicting AD progression, offering insights for future research and intervention strategies.

Low app engagement is a central barrier to digital mental health efficacy. With mindfulness-based mental health apps growing in popularity, there is a need for new understanding of factors influencing engagement. This study utilized digital phenotyping to understand real-time patterns of engagement around app-based mindfulness. Different engagement metrics are presented that measure both the total number of app-based activities participants completed each week, as well as the proportion of days that participants engaged with the app each week. Data were derived from two iterations of a four-week study exploring app engagement in college students ( = 169). This secondary analysis investigated the relationships between general and mindfulness-based app engagement with passive data metrics (sleep duration, home time, and screen duration) at a weekly level, as well as the relationship between demographics and engagement. Additional clinically focused analysis was performed on three case studies of participants with high mindfulness activity completion. Demographic variables such as gender, race/ethnicity, and age lacked a significant association with mindfulness app-based engagement. Passive data variables such as sleep and screen duration were significant predictors for different metrics of general and mindfulness-based app engagement at a weekly level. There was a significant interaction effect for screen duration between the number of mindfulness activities completed and whether or not the participant received a mindfulness notification. K-means clusters analyses using passive data features to predict mindfulness activity completion had low performance. While there are no simple solutions to predicting engagement with mindfulness apps, utilizing digital phenotyping approaches at a population and personal level offers new potential. The signal from digital phenotyping warrants more investigation; even small increases in engagement with mindfulness apps may have a tremendous impact given their already high prevalence of engagement, availability, and potential to engage patients across demographics.

Infertility patients, often in high distress, are entitled to being informed about their mental status compared to normative data. The objective of this study was to revalidate and test the accuracy of the SCREENIVF, a self-reported tool for screening psychological maladjustment in the assisted reproduction context. A cross-sectional, questionnaire-based online survey was carried out between December 2019 and February 2023 in a consecutive sample of female patients (N = 645, response rate 22.9%) in a university-based assisted reproduction center in Hungary. Confirmatory factor analysis and cluster and ROC analyses were applied to test validity, sensitivity and specificity in relation to Beck Depression Inventory (BDI) scores. Model fit was optimal (chi-square = 630.866, p < 0.001; comparative fit index = 0.99; root-mean-square error of approximation = 0.018 (90% CI 0.013-0.023); standardized-root-mean-square-residual = 0.044), and all dimensions were reliable (α > 0.80). A specific combination of cutoffs correctly predicted 87.4% of BDI-scores possibly indicative of moderate-to-severe depression (χ(1) = 220.608, p < 0.001, Nagelkerke R = 0.462, J = 66.4). The Hungarian version of the SCREENIVF is a valid and reliable tool, with high accuracy in predicting BDI-scores. Low response rate may affect generalizability. The same instrument with different cutoffs can serve various clinical goals.

The World Health Organization defines mental health as a combination of two dimensions: the negative dimension, or negative mental health, which indicates the presence of mental disorders, symptoms, and problems, and the positive dimension, or positive mental health, which includes emotions and positive personal characteristics such as self-esteem, resilience against environmental challenges, a sense of integrity, and self-efficacy. The aim of the present study was to investigate the role of internet addiction and academic resilience in predicting the mental health of high school students in Tehran, Iran.

Major depressive disorder (MDD) and bipolar disorder (BD) are prevalent psychiatric conditions linked to inflammatory processes. However, it is unclear whether associations of immune cells with these disorders are likely to be causal.

To advance the emergence of circadian-based therapies, this study characterized how psychiatric symptoms fluctuate across the day and vary between individuals. Using a dimensional approach, we determined how chronotype relates to 13 psychiatric traits, and modeled the temporal development of symptoms throughout the day using generalized additive mixed effects models. In this preregistered study, a subclinical sample completed 13 psychiatric trait scales and a chronotype scale at baseline (N = 515, n = 404 women, 109 men, n = 2 non-binary, M age = 32.4 years, range 18-77), followed by 22 psychiatric symptoms and behaviors rated repeatedly between ~08:00-00:00 (n = 410). Key findings are that 11 out of 13 psychiatric traits were associated with being an evening-type, ranging from depression to obsessive comulsive disorder, social anxiety, and delusional ideation, while only mania was associated with being a morning-type. Four distinct psychiatric trait factors were identified, each predicting worse symptom levels throughout the day. Fatigue-related symptoms exhibited strong time-of-day changes with evening-types experiencing worse fatigue in the morning and morning-types in the evening. Evening-types had considerably lower drive and motivation than morning-types from morning to early evening. Evening-types also had more pronounced negative emotional symptoms and ADHD-type symptoms in the evening, particularly among those high in psychiatric trait factors. These findings identified important research targets that hold promise for improving mental health outcomes, such as strategies to boost morning motivation. Furthermore, the results emphasize the relevance of incorporating circadian factors, including chronotype, into translational psychiatric research and interventions.

Therapeutic drug monitoring (TDM) is strongly recommended for olanzapine due to its high pharmacokinetic variability. This study aimed to investigate the impact of various clinical factors on olanzapine plasma concentrations in patients with psychiatric disorders.

A key focus of sports science research is the identification of quantitative assessments that can predict players' on-field performance and developmental potential. Despite efforts to establish predictive models, there are few validated measures that show reliable associations and large gaps in understanding. Here, we test a multidimensional battery of assessments developed through the USA Baseball, Prospect Development Pipeline that capture strength and functional movement abilities, and anthropometric characteristics, in a two-year cohort of collegiate baseball players from the Appalachian League. Swing propensity metrics for Zone Contact Percentage (ZCP: proportion pitches in strike zone swung at and hit) and Hard-Hit Percentage (HHP: proportion in-play balls with exit velocity ≥ 95 mph) were calculated on 189 players. Models testing hierarchical combinations of anthropometric and anthropometric plus assessment data were implemented using nested cross-validation with random forest and elastic net regression. Results indicate that anthropometric features account for 29% of variance in ZCP and 50-55% of HHP, while the addition of assessment contributed an additional 1-3% to ZCP and 5-12% to HHP, with top predictors coming from PDP strength and power assessments. These findings delineate contributions of andromorphic and physical abilities to in-game baseball performance using a validated assessment battery and advanced game statistics.

Atomoxetine is a drug widely used for the treatment of the attention deficit hyperactivity disorder (ADHD) with reduced risk of adverse motor reactions and chemical dependence. However, the pharmacokinetics characteristics as well as the toxicological risk of atomoxetine deserves further investigation to comprehensively analyze the therapeutic and safety aspects of this drug. This study aimed to predict the physicochemical profile and medicinal chemistry characteristics of atomoxetine, alongside its pharmacokinetic properties-namely absorption, distribution, metabolism, and excretion-as well as its toxicology (ADMET) potential through the utilization of web-based in silico tools. This research emphasizes predicted physicochemical, medicinal chemistry, and absorption parameters of atomoxetine that could influence the efficacy and safety of this drug for ADHD treatment. Additionally, atomoxetine also presents noteworthy predicted risks of hepatotoxicity, cardiotoxicity, neurotoxicity, nephrotoxicity, respiratory system toxicity, skin toxicity, and carcinogenicity. These findings underscore the necessity for further assessments of atomoxetine's safety profile, particularly considering different patient populations and durations of drug treatment. The data reported here from in silico predictions suggest that closer monitoring is warranted when atomoxetine is administered to patients with ADHD. Moreover, controlled studies detailing reliable protocols for personalized dosing, considering the multifactorial variability in metabolism efficiency and toxicological potential, would enable a more comprehensive assessment of atomoxetine's safety profile.

Alcohol and substance use are increasing in older adults, many of whom have depression, and treatment in this context may be more hazardous. We assessed alcohol and other substance use patterns in older adults with treatment-resistant depression (TRD). We examined patient characteristics associated with higher alcohol consumption and examined the moderating effect of alcohol on the association between clinical variables and falls during antidepressant treatment.

NMDA receptors (NMDAR) may be crucial to working memory (WM). Computational models predict that they sustain neural firing and produce associative memory, which may underpin maintaining and binding information respectively. We test this in patients with antibodies to NMDAR (n=10, female) and compare them with healthy control participants (n=55, 20 male, 35 female). Patients were tested after recovery with a task that separates two aspects of WM: sustaining attention and feature binding. Participants had to remember two colored arrows. Then attention was directed to one of them. After a variable delay, they reported the direction of either the same arrow (congruent cue), or of the other arrow (incongruent cue). We asked how congruency affected recall precision and measured types of error. Patients had difficulty in both sustaining attention to an item over time and feature binding. Controls were less precise after longer delays and incongruent cues. In contrast, patients did not benefit from congruent cues at longer delays (Group x Congruency [long condition], p=0.041), indicating they could not sustain attention. Additionally, patients reported the wrong item (misbinding errors) more than controls after congruent cues (Group x Delay [congruent condition], main effect of group, p=<0.001). Our results suggest NMDARs are critical for both maintaining attention and feature binding. Computational theories suggest NMDA receptors (NMDARs) are critical for actively maintaining information, while other theories propose they allow us to associate or "bind" objects features together. This is the first causal test in humans of the role of NMDARs in actively maintaining attention in working memory and feature binding. We find patients have difficulty with both these processes in support of computational models. Notably, we demonstrate that patients with NMDA receptor-antibody encephalitis are an ideal model condition to study roles of receptors in human cognition. Secondly, few studies follow these patients long after treatment. Our findings demonstrate a specific long-term neuropsychological deficit, previously unreported to our knowledge, that highlights the need for greater focus on neurocognitive rehabilitation with these patients.

Plasma β-amyloid-1-42/1-40 (Aβ42/40), phosphorylated-tau (P-tau), glial fibrillary acidic protein (GFAP), and neurofilament light (NfL) have been widely examined in Alzheimer disease (AD), but little is known about their reflection of copathologies, clinical importance, and predictive value in dementia with Lewy bodies (DLB). We aimed to evaluate associations of these biomarkers with CSF amyloid, cognition, and core features in DLB.

Cognitive functions, such as learning and memory processes, depend on effective communication between brain regions which is facilitated by white matter tracts (WMT). We investigated the microstructural properties and the contribution of WMT to extinction learning and memory in a predictive learning task. Forty-two healthy participants completed an extinction learning paradigm without a fear component. We examined differences in microstructural properties using diffusion tensor imaging to identify underlying neural connectivity and structural correlates of extinction learning and their potential implications for the renewal effect. Participants with good acquisition performance exhibited higher fractional anisotropy (FA) in WMT including the bilateral inferior longitudinal fasciculus (ILF) and the right temporal part of the cingulum (CNG). This indicates enhanced connectivity and communication between brain regions relevant to learning and memory resulting in better learning performance. Our results suggest that successful acquisition and extinction performance were linked to enhanced structural connectivity. Lower radial diffusivity (RD) in the right ILF and right temporal part of the CNG was observed for participants with good acquisition learning performance. This observation suggests that learning difficulties associated with increased RD may potentially be due to less myelinated axons in relevant WMT. Also, participants with good acquisition performance were more likely to show a renewal effect. The results point towards a potential role of structural integrity in extinction-relevant WMT for acquisition and extinction.

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

WHAT IS KNOWN ON THE SUBJECT?: Dual diagnosis is one of the leading causes of disability globally. There is limited evidence on mental health nurses' attitudes towards consumers with dual diagnosis. WHAT DOES THE PAPER ADD TO EXISTING KNOWLEDGE?: Mental health nurses have positive attitudes towards consumers with dual diagnosis. A positive attitude at work is influenced by various factors, including feeling that one's role is appropriate and legitimate. This also includes receiving support in that role, being motivated to work, having confidence in completing tasks and feeling satisfied with one's job.

Insomnia and repetitive negative thinking (RNT) are both prevalent among cancer survivors, yet little work has investigated their interrelationship. To explore the hypothesis that RNT and insomnia are related, we conducted secondary analyses on data from a pilot clinical trial of cognitive behavioral therapy for insomnia (CBT-I) for cancer survivors.

Tele-assessment of autism in early childhood has increased. However, it is unclear how autism screening tools (M-CHAT-R, DCI) function as part of tele-assessment and relate to a commonly used tele-assessment instrument, the TAP. 361 families from a clinically referred sample of children (mean age: 27.63 months, sd = 4.86 months) completed the M-CHAT-R and DCI prior to a tele-assessment visit utilizing the TAP. Data was collected on demographic background, measure scores, and diagnostic outcome. No significant differences in measure scores or diagnostic findings emerged in age at referral, age group, age at diagnosis, or child sex, ethnicity, or racial background. The M-CHAT-R and DCI correlated strongly and positively. Older age was associated with lower risk scores on screening instruments. Children with autism had significantly higher scores on all screener and subdomain scores, with the exception of DCI Behavior. Subdomains of the DCI emerged as the strongest predictor of diagnostic outcome. Both the DCI total score and the M-CHAT-R significantly related to diagnostic outcome and TAP score in this tele-assessment model, regardless of child age or sex. Findings also support use of the DCI for children under 24 months of age.

Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.

Drug-resistant epilepsy (DRE) affects about one-third of people with epilepsy (PWE). Our study aims to estimate the DRE prevalence and its predictive factors in Morocco. A cross-sectional study was conducted over 18 months. PWE with clinical diagnosis of epilepsy, and with an antiseizure treatment duration >12 months were examined in the neurology, neurosurgery, psychiatry, and pediatrics departments, of different sampled clinical sectors for the Casablanca-Settat region. Sociodemographic and clinical data were collected using a questionnaire during consultations. Antiseizure multi-therapy, a seizure freedom duration <12 months, compliance, and adequate posology were the determining factors for classifying DRE. Data were analyzed using Statistical Package for Social Sciences (SPSS) software, version 21.0. Statistical significance was set at p < 0.05 and logistic regression was performed to determine the predictive factors. In our sample of 446 PWE, the median age is 25 years (IQR: 11.75-44.00). The DRE estimated prevalence was 29.4 %. Pseudo-resistant epilepsy (PRE) was 18.0 %. Multivariate logistic regression analysis reports that single marital status (ORa = 1.94; CI95%: 1.02-3.71), comorbidities and concomitant affections (ORa = 2.14; CI95%: 1.27-3.59), structural etiology (ORa = 1.96; CI95%: 1.16-3.30), pre-ictal aura (ORa = 1.90; CI95%: 1.09-3.29), inter-ictal EEG abnormalities (ORa = 2.45; CI95%: 1.24-4.84) and allopathic treatment use (ORa = 2.10; CI95%: 1.30-3.39) are the predictive factors for DRE. We report an alarming DRE prevalence. Associated factors found may contribute to the prognosis and early management. PWE awareness, facilitating healthcare access and the development of epilepsy surgery are the key points to limit DRE in Morocco and prevent its various complications, especially for the pediatric population.

Work on the "double empathy problem" (DEP) is rapidly growing in academic and applied settings (e.g., clinical practice). It is most popular in research on conditions, like autism, which are characterized by social cognitive difficulties. Drawing from this literature, we propose that, while research on the DEP has the potential to improve understanding of both typical and atypical social processes, it represents a striking example of a weak derivation chain in psychological science. The DEP is poorly conceptualized, and we find that it is being conflated with many other constructs (i.e., reflecting the "jingle-jangle" fallacy). We provide examples to show how this underlies serious problems with translating theoretical claims into empirical predictions and evidence. To start tackling these problems, we propose that DEP research needs reconsideration, particularly through a better synthesis with the cognitive neuroscience literature on social interaction. Overall, we argue for a strengthening of the derivation chain pertaining to the DEP, toward more robust research on (a)typical social cognition. Until then, we caution against the translation of DEP research into applied settings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Alcohol consumption affects sleep both in healthy populations and in patients with alcohol use disorder (AUD). However, sleep has typically not been considered within AUD pharmacotherapy trials. We used data from a completed gabapentin clinical treatment trial to explore the medication's effect on patient-rated insomnia measured by a standard insomnia rating (Insomnia Severity Index [ISI]) and whether this influenced gabapentin's effects on alcohol consumption.

Biomarkers of neuronal, glial cells and inflammation in traumatic brain injury (TBI) are available but they do not specifically reflect the damage to synapses, which represent the bulk volume of the brain. Experimental models have demonstrated extensive involvement of synapses in acute TBI, but biomarkers of synaptic damage in human patients have not been explored.

Shame is experienced as a threat to social self, and so activates threat-protective responses. There is evidence that shame has trauma-like characteristics, suggesting it can be understood within the same conceptual framework as trauma and dissociation. Evidence for causal links among trauma, dissociation, and psychosis thus warrant the investigation of how shame may influence causal mechanisms for psychosis symptoms.

Lifetime and temporal co-occurrence of substance use disorders (SUDs) is common and compared with individual SUDs is characterized by greater severity, additional psychiatric comorbidities, and worse outcomes. Here, we review evidence for the role of generalized genetic liability to various SUDs. Coaggregation of SUDs has familial contributions, with twin studies suggesting a strong contribution of additive genetic influences undergirding use disorders for a variety of substances (including alcohol, nicotine, cannabis, and others). GWAS have documented similarly large genetic correlations between alcohol, cannabis, and opioid use disorders. Extending these findings, recent studies have identified multiple genomic loci that contribute to common risk for these SUDs and problematic tobacco use, implicating dopaminergic regulatory and neuronal development mechanisms in the pathophysiology of generalized SUD genetic liability, with certain signals demonstrating cross-species and translational validity. Overlap with genetic signals for other externalizing behaviors, while substantial, does not explain the entirety of the generalized genetic signal for SUD. Polygenic scores (PGS) derived from the generalized genetic liability to SUDs outperform PGS for individual SUDs in prediction of serious mental health and medical comorbidities. Going forward, it will be important to further elucidate the etiology of generalized SUD genetic liability by incorporating additional SUDs, evaluating clinical presentation across the lifespan, and increasing the granularity of investigation (e.g., specific transdiagnostic criteria) to ultimately improve the nosology, prevention, and treatment of SUDs.

Gilles de la Tourette syndrome (GTS) is a disorder characterised by motor and vocal tics, which may represent habitual actions as a result of enhanced learning of associations between stimuli and responses (S-R). In this study, we investigated how adults with GTS and healthy controls (HC) learn two types of regularities in a sequence: statistics (non-adjacent probabilities) and rules (predefined order). Participants completed a visuomotor sequence learning task while EEG was recorded. To understand the neurophysiological underpinnings of these regularities in GTS, multivariate pattern analyses on the temporally decomposed EEG signal as well as sLORETA source localisation method were conducted. We found that people with GTS showed superior statistical learning but comparable rule-based learning compared to HC participants. Adults with GTS had different neural representations for both statistics and rules than HC adults; specifically, adults with GTS maintained the regularity representations longer and had more overlap between them than HCs. Moreover, over different time scales, distinct fronto-parietal structures contribute to statistical learning in the GTS and HC groups. We propose that hyper-learning in GTS is a consequence of the altered sensitivity to encode complex statistics, which might lead to habitual actions.

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. PRACTITIONER POINTS: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.

Non-communicable chronic diseases (NCDs) have become a major global health concern. They constitute the leading cause of disabilities, increased morbidity, mortality, and socio-economic disasters worldwide. Medical condition-specific digital biomarker (DB) panels have emerged as valuable tools to manage NCDs. DBs refer to the measurable and quantifiable physiological, behavioral, and environmental parameters collected for an individual through innovative digital health technologies, including wearables, smart devices, and medical sensors. By leveraging digital technologies, healthcare providers can gather real-time data and insights, enabling them to deliver more proactive and tailored interventions to individuals at risk and patients diagnosed with NCDs. Continuous monitoring of relevant health parameters through wearable devices or smartphone applications allows patients and clinicians to track the progression of NCDs in real time. With the introduction of digital biomarker monitoring (DBM), a new quality of primary and secondary healthcare is being offered with promising opportunities for health risk assessment and protection against health-to-disease transitions in vulnerable sub-populations. DBM enables healthcare providers to take the most cost-effective targeted preventive measures, to detect disease developments early, and to introduce personalized interventions. Consequently, they benefit the quality of life (QoL) of affected individuals, healthcare economy, and society at large. DBM is instrumental for the paradigm shift from reactive medical services to 3PM approach promoted by the European Association for Predictive, Preventive, and Personalized Medicine (EPMA) involving 3PM experts from 55 countries worldwide. This position manuscript consolidates multi-professional expertise in the area, demonstrating clinically relevant examples and providing the roadmap for implementing 3PM concepts facilitated through DBs.

Attachment theory has served as an influential framework for understanding psychopathology, partly due to reliable assessment methodology. The influence of insecure attachment on attitudes toward the body and the impact this might have for the development of psychopathology is however less well elucidated.

Academic underachievement refers to school performance which falls below expectations. Focusing on the pivotal first stage of education, we explored a quantitative measure of underachievement using genomically predicted achievement delta (GPAΔ), which reflects the difference between observed and expected achievement predicted by genome-wide polygenic scores. We analyzed the relationship between GPAΔ at age 7 and achievement trajectories from ages 7 to 16, using longitudinal data from 4175 participants in the Twins Early Development Study to assess empirically the extent to which students regress to their genomically predicted levels by age 16. We found that the achievement of underachievers and overachievers who deviated from their genomic predictions at age 7 regressed on average by one-third towards their genomically predicted levels. We also found that GPAΔ at age 7 was as predictive of achievement trajectories as a traditional ability-based index of underachievement. Targeting GPAΔ underachievers might prove cost-effective because such interventions seem more likely to succeed by going with the genetic flow rather than swimming upstream, helping GPAΔ underachievers reach their genetic potential as predicted by their GPS. However, this is a hypothesis that needs to be tested in intervention research investigating whether GPAΔ underachievers respond better to the intervention than other underachievers. We discuss the practicality of genomic indices in assessing underachievement.

Hyperactivity and inattention, the symptoms of ADHD, are marked by high levels of heritability and intergenerational transmission. Two distinct pathways of genetic intergenerational transmission are distinguished: direct genetic transmission when parental genetic variants are passed to the child's genome and genetic nurture when the parental genetic background contributes to the child's outcomes through rearing environment. This study assessed genetic contributions to hyperactivity and inattention in childhood through these transmission pathways.

A stronger preference for immediate rewards has been reported in individuals with ADHD and other disorders. However, the consistency of the associations between this preference and psychiatric conditions as well as functional outcomes have been questioned. Research on its association with longitudinal outcomes is scarce.

Schizophrenia is routinely referred to as a neurodevelopmental disorder, but the role of brain development in a disorder typically diagnosed during early adult life is enigmatic. The authors revisit the neurodevelopmental model of schizophrenia with genomic insights from the most recent schizophrenia clinical genetic association studies, transcriptomic and epigenomic analyses from human postmortem brain studies, and analyses from cellular models that recapitulate neurodevelopment. Emerging insights into schizophrenia genetic risk continue to converge on brain development, particularly stages of early brain development, that may be perturbed to deviate from a typical, normative course, resulting in schizophrenia clinical symptomatology. As the authors explicate, schizophrenia genetic risk is likely dynamic and context dependent, with effects of genetic risk varying spatiotemporally, across the neurodevelopmental continuum. Optimizing therapeutic strategies for the heterogeneous collective of individuals with schizophrenia may likely be guided by leveraging markers of genetic risk and derivative functional insights, well before the emergence of psychosis. Ultimately, rather than a focus on therapeutic intervention during adolescence or adulthood, principles of prediction and prophylaxis in the pre- and perinatal and neonatal stages may best comport with the biology of schizophrenia to address the early-stage perturbations that alter the normative neurodevelopmental trajectory.

Frailty is a complex trait. Twin studies and high-powered Genome Wide Association Studies conducted in the UK Biobank have demonstrated a strong genetic basis of frailty. The present study utilized summary statistics from a Genome Wide Association Study on the Frailty Index to create and test the predictive power of frailty polygenic risk scores (PRS) in two independent samples - the Lothian Birth Cohort 1936 (LBC1936) and the English Longitudinal Study of Ageing (ELSA) aged 67-84 years. Multiple regression models were built to test the predictive power of frailty PRS at five time points. Frailty PRS significantly predicted frailty, measured via the FI, at all-time points in LBC1936 and ELSA, explaining 2.1% (β = 0.15, 95%CI, 0.085-0.21) and 1.8% (β = 0.14, 95%CI, 0.10-0.17) of the variance, respectively, at age ~ 68/ ~ 70 years (p < 0.001). This work demonstrates that frailty PRS can predict frailty in two independent cohorts, particularly at early ages (~ 68/ ~ 70). PRS have the potential to be valuable instruments for identifying those at risk for frailty and could be important for controlling for genetic confounders in epidemiological studies.

Identifying predictors of treatment response to repetitive transcranial magnetic stimulation (rTMS) remain elusive in treatment-resistant depression (TRD). Leveraging electronic medical records (EMR), this retrospective cohort study applied supervised machine learning (ML) to sociodemographic, clinical, and treatment-related data to predict depressive symptom response (>50% reduction on PHQ-9) and remission (PHQ-9 < 5) following rTMS in 232 patients with TRD (mean age: 54.5, 63.4% women) treated at the University of California, San Diego Interventional Psychiatry Program between 2017 and 2023. ML models were internally validated using nested cross-validation and Shapley values were calculated to quantify contributions of each feature to response prediction. The best-fit models proved reasonably accurate at discriminating treatment responders (Area under the curve (AUC): 0.689 [0.638, 0.740], p < 0.01) and remitters (AUC 0.745 [0.692, 0.797], p < 0.01), though only the response model was well-calibrated. Both models were associated with significant net benefits, indicating their potential utility for clinical decision-making. Shapley values revealed that patients with comorbid anxiety, obesity, concurrent psychiatric medication use, and more chronic TRD were less likely to respond or remit following rTMS. Patients with trauma and former tobacco users were more likely to respond. Furthermore, delivery of intermittent theta burst stimulation and more rTMS sessions were associated with superior outcomes. These findings highlight the potential of ML-guided techniques to guide clinical decision-making for rTMS treatment in patients with TRD to optimize therapeutic outcomes.

Evidence suggests a possible relationship between exposure to childhood adversity (CA) and functional impairment in psychosis. However, the impact of CA on long-term outcomes of psychotic disorders remains poorly understood.

Most theories of suicide propose within-person changes in psychological states cause suicidal thoughts/behaviors; however, most studies use between-person analyses. Thus, there are little empirical data exploring current theories in the way they are hypothesized to occur. We used a form of statistical modeling called group iterative multiple model estimation (GIMME) to explore one theory of suicide: The Interpersonal Theory of Suicide (IPTS). GIMME estimates personalized statistical models for each individual and associations shared across individuals. Data were from a real-time monitoring study of individuals with a history of suicidal thoughts/behavior (adult sample: participants = 111, observations = 25,242; adolescent sample: participants = 145, observations = 26,182). Across both samples, none of theorized IPTS effects (i.e., contemporaneous effect from hopeless to suicidal thinking) were shared at the group level. There was significant heterogeneity in the personalized models, suggesting there are different pathways through which different people come to experience suicidal thoughts/behaviors. These findings highlight the complexity of suicide risk and the need for more personalized approaches to assessment and prediction.

Angiogenic imbalances, characterized by an excess of antiangiogenic factors (soluble fms-like tyrosine kinase 1 [sFlt-1]) and reduced angiogenic factors (VEGF and placental growth factor [PlGF]), contribute to the mechanisms of disease in preeclampsia. The ratio of sFlt-1 to PlGF has been used as a biomarker for preeclampsia, but cut-off values may vary with gestational age and assay platform.

Obstructive sleep apnea (OSA) is associated with hypertension. However, the differential mechanisms underlying OSA-related hypertension between normal-weight vs. obese patients is limited.

Functional neurological disorder (FND) is a common neuropsychiatric condition with established diagnostic criteria and effective treatments but for which the underlying neuropathophysiological mechanisms remain incompletely understood. Recent neuroimaging studies have revealed FND as a multi-network brain disorder, unveiling alterations across limbic, self-agency, attentional/salience, and sensorimotor networks. However, the relationship between identified brain alterations and disease progression or improvement is less explored.

Major depressive disorder (MDD) is a psychiatric illness that can jeopardize the normal growth and development of adolescents. Approximately 40% of adolescent patients with MDD exhibit resistance to conventional antidepressants, leading to the development of Treatment-Resistant Depression (TRD). TRD is associated with severe impairments in social functioning and learning ability and an elevated risk of suicide, thereby imposing an additional societal burden. In this study, we conducted plasma metabolomic analysis on 53 adolescents diagnosed with first-episode drug-naïve MDD (FEDN-MDD), 53 adolescents with TRD, and 56 healthy controls (HCs) using hydrophilic interaction liquid chromatography-mass spectrometry (HILIC-MS) and reversed-phase liquid chromatography-mass spectrometry (RPLC-MS). We established a diagnostic model by identifying differentially expressed metabolites and applying cluster analysis, metabolic pathway analysis, and multivariate linear support vector machine (SVM) algorithms. Our findings suggest that adolescent TRD shares similarities with FEDN-MDD in five amino acid metabolic pathways and exhibits distinct metabolic characteristics, particularly tyrosine and glycerophospholipid metabolism. Furthermore, through multivariate receiver operating characteristic (ROC) analysis, we optimized the area under the curve (AUC) and achieved the highest predictive accuracy, obtaining an AUC of 0.903 when comparing FEDN-MDD patients with HCs and an AUC of 0.968 when comparing TRD patients with HCs. This study provides new evidence for the identification of adolescent TRD and sheds light on different pathophysiologies by delineating the distinct plasma metabolic profiles of adolescent TRD and FEDN-MDD.

Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60-0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70-0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70-0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.

Τhe aim of this longitudinal study was to investigate the effect of the COVID-19 pandemic on the mental health and quality of life (QoL) of the general population in the region of Attica, Greece, during the third year of the pandemic (2022), and tο compare the findings with those of a survey conducted in the first year (2020). Our sample consisted of 130 participants and the study was conducted through phone interviews. The instruments used were: the World Health Organisation QoL instrument, the Depression-Anxiety-Stress Scale, the Body Vigilance Scale, the Dimensional Obsessive-Compulsive Scale, as well as socio-demographic data and questions on stressors related to COVID-19. The findings of the study were the following: (1) Regarding the comparison of the variables between the first and the third year of the pandemic in the total sample: a) In comparison to the first year, in the third year we observed a significant decrease in negative feelings caused by the pandemic; b) obsessive compulsive (OC) and hypochondriacal symptomatology were significantly reduced, and the fact that participants felt safe following vaccination had a statistically significant effect on this decrease; c) job insecurity was aggravated; d) QoL remained low and even deteriorated in the Environment domain; f) no changes were found in Depression-Stress. (2) Regarding participants who were contaminated, there was a significant increase in negative feelings during the third year of the pandemic. Moreover, QoL decreased in the Physical, Psychological health, Environment domains, as well as in OC symptomatology. (3) Depression-Stress, hypochondriacal symptomatology, and the case of contamination were the predominant factors negatively associated with the dependent variables of QoL. (4) Vaccination was found to contribute to high levels of the QoL Environment domain score. (5) Anxiety, hypochondriacal symptomatology, fear of contamination, and negative feelings seemed to predict OC symptomatology. (6) The most vulnerable groups, in terms of QoL and mental health, were men, older and lower-educated people. Overall, it was found that the negative psychosocial impact of the pandemic persisted, especially on people who had fallen ill during the third year of the pandemic. Therefore, targeted psychotherapeutic interventions should be implemented, especially for those who got infected.

Chemotherapy is notorious for causing behavioral side effects (e.g., cognitive decline). Notably, the gut microbiome has recently been reported to communicate with the brain to affect behavior, including cognition. Thus, the aim of this clinical longitudinal, observational study was to determine whether chemotherapy-induced disruption of the gut microbial community structure relates to cognitive decline and circulating inflammatory signals. Fecal samples, blood, and cognitive measures were collected from 77 patients with breast cancer before, during, and after chemotherapy. Chemotherapy altered the gut microbiome community structure and increased circulating TNF-α. Both the chemotherapy-induced changes in microbial relative abundance and decreased microbial diversity were related to elevated circulating pro-inflammatory cytokines, TNF-α and IL-6. Participants reported subjective cognitive decline during chemotherapy, which was not related to changes in the gut microbiome or inflammatory markers. In contrast, a decrease in overall objective cognition was related to a decrease in microbial diversity, independent of circulating cytokines. Stratification of subjects, via a reliable change index based on all 4 objective cognitive tests, identified objective cognitive decline in 35% of the subjects. Based on a differential microbial abundance analysis, those characterized by cognitive decline had unique taxonomic shifts (Faecalibacterium, Bacteroides, Fusicatenibacter, Erysipelotrichaceae UCG-003, and Subdoligranulum) over chemotherapy treatment compared to those without cognitive decline. Taken together, gut microbiome change was associated with cognitive decline during chemotherapy, independent of chemotherapy-induced inflammation. These results suggest that microbiome-related strategies may be useful for predicting and preventing behavioral side effects of chemotherapy.

Chronic low-grade inflammation is observed across mental disorders and is associated with difficult-to-treat-symptoms of anhedonia and functional brain changes - reflecting a potential transdiagnostic dimension. Previous investigations have focused on distinct illness categories in those with enduring illness, with few exploring inflammatory changes. We sought to identify an inflammatory signal and associated brain function underlying anhedonia among young people with recent onset psychosis (ROP) and recent onset depression (ROD).

This paper analyzed the role of depression as a mediator in the association between bullying, cyberbullying, and suicide risk in adolescent females. A total of 751 Colombian adolescent females (M= 13.71, SD=1.897), who were administered the Plutchik Suicide Risk Scale, the Beck Depression Inventory, the European Bullying Intervention Project Questionnaire and Cyberbullying. Bullying victimization and aggression and cyberbullying victimization were found to contribute statistically significant effects that explaining 22 % of the variance in depression. The variables of victimization in bullying and cyberbullying and depression explained 64 % of the variance in suicidal risk, and depression mediated the association between victim and aggressor roles in bullying and cyberbullying in predicting suicidal risk, whose total direct and indirect effects are statistically significant. The findings support the role of depression as a mediating variable between bullying and cyberbullying and suicidal risk in female adolescents and highlight the importance of focusing prevention and intervention efforts on risk factors for depression and suicidal behavior in cases of bullying and cyberbullying.

Postpartum blues (PPB) is a frequent syndrome of sad mood, crying spells, anxiety, restlessness, reduced appetite, and irritability, typically peaking day 5 postpartum. When severe, it greatly increases risk for later postpartum depression. This trial compared a dietary supplement to placebo on PPB severity. The supplement was designed to counter downstream effects of elevated monoamine oxidase A level, implicated in causing PPB.

Detecting patients with a high-risk profile for treatment-resistant schizophrenia (TRS) can be beneficial for implementing individually adapted therapeutic strategies and better understanding the TRS etiology. The aim of this study was to explore, with machine learning methods, the impact of demographic and clinical patient characteristics on TRS prediction, for already established risk factors and unexplored ones. This was a retrospective study of 500 patients admitted during 2020 to the University Hospital Group for Paris Psychiatry. We hypothesized potential TRS risk factors. The selected features were coded into structured variables in a new dataset, by processing patients discharge summaries and medical narratives with natural-language processing methods. We compared three machine learning models (XGBoost, logistic elastic net regression, logistic regression without regularization) for predicting TRS outcome. We analysed feature impact on the models, suggesting the following factors as markers of a high-risk TRS profile: early age at first contact with psychiatry, antipsychotic treatment interruptions due to non-adherence, absence of positive symptoms at baseline, educational problems and adolescence mental disorders in the personal psychiatric history. Specifically, we found a significant association with TRS outcome for age at first contact with psychiatry and medication non-adherence. Our findings on TRS risk factors are consistent with the review of the literature and suggest potential in using early pathophysiologic features for TRS prediction. Results were encouraging with the use of natural-langage processing techniques to leverage raw data provided by discharge summaries, combined with machine leaning models. These findings are a promising step for helping clinicians adapt their guidelines to early detection of TRS.

Electronic (e-)cigarettes may help adult cigarette smokers achieve cigarette cessation, depending on patterns of e-cigarette use. Among cigarette smokers who do not use e-cigarettes, it is unclear if and how a-priori intentions for use are related to uptake patterns. Longitudinal studies have focused on established e-cigarette users or adolescent and young adult populations exclusively.

As an established treatment for major depressive disorder (MDD), cognitive behavioral therapy (CBT) is now implemented and assessed in internet-based formats that, when combined with smartphone apps, enable secure text messaging. As an adjunct to such internet-based CBT (ICBT) approaches, text messaging has been associated with increased adherence and therapeutic alliance.

Social environment is a key determinant of substance use, but cannabis-related social network analysis is not common, in part due to the assessment burden of comprehensive egocentric social network analysis.

Applying effort-based decision-making tasks provides insights into specific variables influencing choice behaviors. The current review summarizes the structural and functional neuroanatomy of effort-based decision-making. Across 39 examined studies, the review highlights the ventromedial prefrontal cortex in forming reward-based predictions, the ventral striatum encoding expected subjective values driven by reward size, the dorsal anterior cingulate cortex for monitoring choices to maximize rewards, and specific motor areas preparing for effort expenditure. Neuromodulation techniques, along with shifting environmental and internal states, are promising novel treatment interventions for altering neural alterations underlying decision-making. Our review further articulates the translational promise of this construct into the development, maintenance and treatment of psychiatric conditions, particularly those characterized by reward-, effort- and valuation-related deficits.

Foundational mathematical abilities, acquired in early childhood, are essential for success in our technology-driven society. Yet, the neurobiological mechanisms underlying individual differences in children's mathematical abilities and learning outcomes remain largely unexplored. Leveraging one of the largest multicohort datasets from children at a pivotal stage of knowledge acquisition, we first establish a replicable mathematical ability-related imaging phenotype (MAIP). We then show that brain gene expression profiles enriched for candidate math ability-related genes, neuronal signaling, synaptic transmission, and voltage-gated potassium channel activity contributed to the MAIP. Furthermore, the similarity between MAIP gene expression signatures and brain structure, acquired before intervention, predicted learning outcomes in two independent math tutoring cohorts. These findings advance our knowledge of the interplay between neuroanatomical, transcriptomic, and molecular mechanisms underlying mathematical ability and reveal predictive biomarkers of learning. Our findings have implications for the development of personalized education and interventions.

Coronary computed tomography angiography (CCTA) is the first line investigation for chest pain, and it is used to guide revascularisation. However, the widespread adoption of CCTA has revealed a large group of individuals without obstructive coronary artery disease (CAD), with unclear prognosis and management. Measurement of coronary inflammation from CCTA using the perivascular fat attenuation index (FAI) Score could enable cardiovascular risk prediction and guide the management of individuals without obstructive CAD. The Oxford Risk Factors And Non-invasive imaging (ORFAN) study aimed to evaluate the risk profile and event rates among patients undergoing CCTA as part of routine clinical care in the UK National Health Service (NHS); to test the hypothesis that coronary arterial inflammation drives cardiac mortality or major adverse cardiac events (MACE) in patients with or without CAD; and to externally validate the performance of the previously trained artificial intelligence (AI)-Risk prognostic algorithm and the related AI-Risk classification system in a UK population.

Deep brain stimulation (DBS) targeting the ventral intermediate (Vim) nucleus of the thalamus is an effective treatment for essential tremor (ET). We studied 15 ​ET patients undergoing DBS to a major input/output tract of the Vim, the dentato-rubro-thalamic tract (DRTt), using resting state functional MRI (rsfMRI) to evaluate connectivity differences between DBS ON and OFF and elucidate significant regions most influential in impacting tremor control and/or concomitant gait ataxia. Anatomical/functional 1.5T MRIs were acquired and replicated for each DBS state. Tremor severity and gait ataxia severity were scored with DBS ON at optimal stimulation parameters and immediately upon DBS OFF. Whole brain analysis was performed using dual regression analysis followed by randomized permutation testing for multiple correction comparison. Regions of interest (ROI) analysis was also performed. All 15 patients had tremor improvement between DBS ON/OFF (p ​< ​0.001). Whole brain analysis revealed significant connectivity changes between states in the left pre-central gyrus and left supplemental motor area. Group analysis of ROIs revealed that, with threshold p ​< ​0.05, in DBS ON vs. OFF both tremor duration and tremor improvement were significantly correlated to changes in connectivity. A sub-group analysis of patients with greater ataxia had significantly decreased functional connectivity between multiple ROIs in the cortex and cerebellum when DBS was ON compared to OFF. Stimulation of the DRTt and concordant improvement of tremor resulted in connectivity changes seen in multiple regions outside the motor network; when combined with both structural and electrophysiologic connectivity, this may help to serve as a biomarker to improve DBS targeting and possibly predict outcome.

Post-traumatic stress disorder (PTSD) lacks clear biomarkers in clinical practice. Language as a potential diagnostic biomarker for PTSD is investigated in this study. We analyze an original cohort of 148 individuals exposed to the November 13, 2015, terrorist attacks in Paris. The interviews, conducted 5-11 months after the event, include individuals from similar socioeconomic backgrounds exposed to the same incident, responding to identical questions and using uniform PTSD measures. Using this dataset to collect nuanced insights that might be clinically relevant, we propose a three-step interdisciplinary methodology that integrates expertise from psychiatry, linguistics, and the Natural Language Processing (NLP) community to examine the relationship between language and PTSD. The first step assesses a clinical psychiatrist's ability to diagnose PTSD using interview transcription alone. The second step uses statistical analysis and machine learning models to create language features based on psycholinguistic hypotheses and evaluate their predictive strength. The third step is the application of a hypothesis-free deep learning approach to the classification of PTSD in our cohort. Results show that the clinical psychiatrist achieved a diagnosis of PTSD with an AUC of 0.72. This is comparable to a gold standard questionnaire (Area Under Curve (AUC) ≈ 0.80). The machine learning model achieved a diagnostic AUC of 0.69. The deep learning approach achieved an AUC of 0.64. An examination of model error informs our discussion. Importantly, the study controls for confounding factors, establishes associations between language and DSM-5 subsymptoms, and integrates automated methods with qualitative analysis. This study provides a direct and methodologically robust description of the relationship between PTSD and language. Our work lays the groundwork for advancing early and accurate diagnosis and using linguistic markers to assess the effectiveness of pharmacological treatments and psychotherapies.

Cocaine Use Disorder (CUD) is an important health issue, associated with structural brain abnormalities. However, the impact of the route of administration and their predictive value for relapse remain unknown.

There are individual effects of alexithymia, childhood maltreatment, impulsivity, and some biological markers on aggression and psychological distress in schizophrenia. However, the combined effects of these psychological and biological markers have not yet been fully studied. This study therefore aimed to investigate the influence of these psychological and biological markers on aggression and psychological distress (e.g., depression and anxiety) in inpatients with schizophrenia (n = 355). Participants completed self-report and clinician-rated scales, and blood samples were collected. There were no significant differences between patients with and without alexithymia regarding biological markers. Patients with childhood maltreatment exhibited higher levels of free triiodothyronine (FT3) and C-reactive protein (CRP), as well as lower total cholesterol (TC) levels, compared to non-traumatized individuals. Aggression was positively predicted by psychological distress, alexithymia, childhood maltreatment, impulsivity, CRP, and FT3, and negatively by TC and low-density lipoprotein cholesterol. Negative symptoms, childhood maltreatment, alexithymia, aggression, and CRP positively, and high-density lipoprotein cholesterol negatively emerged as predictors of psychological distress. The study highlights the connections between childhood maltreatment, alexithymia, impulsivity, and potentially related biological dysregulation in explaining aggression and negative mood states as a bio-psychological model of aggression and mood in schizophrenia.

Cannabis and nicotine/tobacco products (NTP) are commonly co-used in adolescence and young adulthood; however, limited research has been done on predictive health behaviors to co-use. The current study is a preliminary investigation into the relationships of modifiable health behaviors on cannabis and NTP co-use in adolescents and young adults.

Habits are familiar behaviors triggered by cues, not outcome predictability, and are insensitive to changes in the environment. They are adaptive under many circumstances but can be considered antecedent to compulsions and intrusive thoughts that drive persistent, potentially maladaptive behavior. Whether compulsive-like and habitual behaviors share neural substrates is still being determined. Here, we investigated mice bred to display inflexible reward-seeking behaviors that are insensitive to action consequences. We found that these mice demonstrate habitual response biases and compulsive-like grooming behavior that was reversible by fluoxetine and ketamine. They also suffer dendritic spine attrition on excitatory neurons in the orbitofrontal cortex (OFC). Nevertheless, synaptic melanocortin 4 receptor (MC4R), a factor implicated in compulsive behavior, is preserved, leading to the hypothesis that Mc4r+ OFC neurons may drive aberrant behaviors. Repeated chemogenetic stimulation of Mc4r+ OFC neurons triggered compulsive and not inflexible or habitual response biases in otherwise typical mice. Thus, Mc4r+ neurons within the OFC appear to drive compulsive-like behavior that is dissociable from habitual behavior. Understanding which neuron populations trigger distinct behaviors may advance efforts to mitigate harmful compulsions.

Reducing harm from combustible cigarette use among women of reproductive age (WRA) is critical given their potential vulnerability to multigenerational adverse impacts of cigarette smoking. Although electronic nicotine delivery systems (ENDS) are not approved smoking cessation aids in the US, many WRA who smoke report using ENDS to help quit smoking. Associations between ENDS use patterns and smoking-cessation efforts among US WRA remain unclear.

Research has demonstrated an association between elevated systemic inflammation and changes in brain function. Affective areas of the brain involved in processing threat (e.g., amygdala) and reward (e.g., nucleus accumbens [NAcc]) appear to be sensitive to inflammation. Early life stress (ELS), such as experiencing low socioeconomic status (SES), may also potentiate this association, but relevant evidence has come primarily from cross-sectional studies of brain function. It is unclear whether similar associations are present between ELS, inflammation, and brain structure, particularly in typically developing populations.

Recent studies on autism spectrum disorder (ASD) have identified recurring states dominated by similar coactivation pattern (CAP) and revealed associations between dysfunction in seed-based large-scale brain networks and clinical symptoms. However, the presence of abnormalities in moment-to-moment whole-brain dynamics in ASD remains uncertain. In this study, we employed seed-free CAP analysis to identify transient brain activity configurations and investigate dynamic abnormalities in ASD. We utilized a substantial multisite resting-state fMRI dataset consisting of 354 individuals with ASD and 446 healthy controls (HCs, from HC groups and 2). CAP were generated from a subgroup of all HC subjects (HC group 1) through temporal K-means clustering, identifying four CAPs. These four CAPs exhibited either the activation or inhibition of the default mode network (DMN) and were grouped into two pairs with opposing spatial CAPs. CAPs for HC group 2 and ASD were identified by their spatial similarity to those for HC group 1. Compared with individuals in HC group 2, those with ASD spent more time in CAPs involving the ventral attention network but less time in CAPs related to executive control and the dorsal attention network. Support vector machine analysis demonstrated that the aberrant dynamic characteristics of CAPs achieved an accuracy of 74.87% in multisite classification. In addition, we used whole-brain dynamics to predict symptom severity in ASD. Our findings revealed whole-brain dynamic functional abnormalities in ASD from a single transient perspective, emphasizing the importance of the DMN in abnormal dynamic functional activity in ASD and suggesting that temporally dynamic techniques offer novel insights into time-varying neural processes.

Children can be reliably diagnosed with autism as early as 3 years of age, and early interventions are initiated. There is often a significant gap between the age of onset of symptoms (2-3 years) and diagnosis (8-10 years) in Africa. We conducted a study to validate the Social Communication Questionnaire (SCQ) as a screening instrument in a rural setting in Kenya. The study was conducted along the Kenyan Coast. Study participants included 172 children with a neurodevelopmental disorder (NDD) diagnosis (84 of which were autism) and 112 controls. Internal consistency was evaluated through the use of Cronbach's alpha, confirmatory factor analysis (CFA) with maximum likelihood procedure to assess the conceptual model for the SCQ. Additionally, the sensitivity and specificity of cut-off scores using ROC analysis and item difficulties and discrimination quality using an IRT framework were also assessed. Factor analysis revealed an adequate fitting model for the three-factor DSM-IV-TR (root mean squared error of approximation (RMSEA) = 0.050; Comparative Fit Index (CFI) = 0.974; Tucker-Lewis Index (TLI) = 0.973) and two-factor DSM-5 factor structure (RMSEA = 0.050; CFI = 0.972; TLI = 0.974). The reliability coefficient alphas for the whole group for all items (Cronbach's α = 0.90) and all three domains (Cronbach's α = 0.68-0.84) were acceptable to excellent. The recommended cut-off score of 15 yielded 72% sensitivity and 100% specificity in the ASD group compared to the typically developing group. We provide early evidence of the adequate factor structure and good internal consistency of the SCQ. We also note that the recommended cut-off yielded sufficient predictive validity.

To survive in ever-changing environments, living organisms need to continuously combine the ongoing external inputs they receive, representing present conditions, with their dynamical internal state, which includes influences of past experiences. It is still unclear in general, however 1) how this happens at the molecular and cellular levels and 2) how the corresponding molecular and cellular processes are integrated with the behavioral responses of the organism. Here, we address these issues by modeling mathematically a particular behavioral paradigm in a minimal model organism, namely chemotaxis in the nematode C. elegans. Specifically, we use a long-standing collection of elegant experiments on salt chemotaxis in this animal, in which the migration direction varies depending on its previous experience. Our model integrates the molecular, cellular, and organismal levels to reproduce the experimentally observed experience-dependent behavior. The model proposes specific molecular mechanisms for the encoding of current conditions and past experiences in key neurons associated with this response, predicting the behavior of various mutants associated with those molecular circuits.

Accurately predicting functional outcomes for unresponsive patients with acute brain injury is a medical, scientific and ethical challenge. This prospective study assesses how a multimodal approach combining various numbers of behavioral, neuroimaging and electrophysiological markers affects the performance of outcome predictions. We analyzed data from 349 patients admitted to a tertiary neurointensive care unit between 2009 and 2021, categorizing prognoses as good, uncertain or poor, and compared these predictions with observed outcomes using the Glasgow Outcome Scale-Extended (GOS-E, levels ranging from 1 to 8, with higher levels indicating better outcomes). After excluding cases with life-sustaining therapy withdrawal to mitigate the self-fulfilling prophecy bias, our findings reveal that a good prognosis, compared with a poor or uncertain one, is associated with better one-year functional outcomes (common odds ratio (95% CI) for higher GOS-E: OR = 14.57 (5.70-40.32), P < 0.001; and 2.9 (1.56-5.45), P < 0.001, respectively). Moreover, increasing the number of assessment modalities decreased uncertainty (OR = 0.35 (0.21-0.59), P < 0.001) and improved prognostic accuracy (OR = 2.72 (1.18-6.47), P = 0.011). Our results underscore the value of multimodal assessment in refining neuroprognostic precision, thereby offering a robust foundation for clinical decision-making processes for acutely brain-injured patients. ClinicalTrials.gov registration: NCT04534777 .

Securing adequate data privacy is critical for the productive utilization of data. De-identification, involving masking or replacing specific values in a dataset, could damage the dataset's utility. However, finding a reasonable balance between data privacy and utility is not straightforward. Nonetheless, few studies investigated how data de-identification efforts affect data analysis results. This study aimed to demonstrate the effect of different de-identification methods on a dataset's utility with a clinical analytic use case and assess the feasibility of finding a workable tradeoff between data privacy and utility.

Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse.

The hypoperfusion intensity ratio is a surrogate marker for collateral status and a predictor of infarct growth, malignant cerebral edema, and hemorrhagic transformation. Its utility to predict a poor NIHSS score and early neurologic deterioration after mechanical thrombectomy for large vessel (LVO) versus distal and medium vessel occlusions (DMVO) has not been investigated. The objective of this study was to determine whether the higher hypoperfusion intensity ratio is associated with a worse NIHSS score at 24 hours post-mechanical thrombectomy and early neurologic deterioration in LVO versus DMVO acute ischemic stroke.

Buprenorphine is an effective evidence-based medication for opioid use disorder (OUD). Yet premature discontinuation undermines treatment effectiveness, increasing the risk of mortality and overdose. We developed and evaluated a machine learning (ML) framework for predicting buprenorphine care discontinuity within 12 months following treatment initiation.

Understanding and dealing with chronic nonspecific pain (CNP) is the important entity at primary care hospital. Chronic nonspecific multiple-site pain [CNMSP] of unknown etiology creates diagnostic and therapeutic challenges for primary care physicians due to lack of guidance regarding evaluation and treatment.

This study aims to examine the potential influence of RS4680 (), RS16965628 (), and RS1019385 () polymorphisms on the therapeutic response to repetitive transcranial magnetic stimulation (rTMS) and selective serotonin reuptake inhibitors (SSRIs) in individuals with obsessive-compulsive disorder (OCD).

Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction.

This study aimed to investigate the similarities and differences in risk factors for suicide among adult and adolescent women in South Korea and identify subtypes of suicidal ideation or suicide attempt in each group.

Autism Spectrum Disorder (ASD) is a common neurodevelopmental disorder emerging in early childhood, with heterogeneous clinical outcomes across individuals. This study aims to recognize neuroimaging genetic factors associated with outcomes of ASD after a 4-year follow-up.

This article illustrates novel quantitative methods to estimate classification consistency in machine learning models used for screening measures. Screening measures are used in psychology and medicine to classify individuals into diagnostic classifications. In addition to achieving high accuracy, it is ideal for the screening process to have high classification consistency, which means that respondents would be classified into the same group every time if the assessment was repeated. Although machine learning models are increasingly being used to predict a screening classification based on individual item responses, methods to describe the classification consistency of machine learning models have not yet been developed. This article addresses this gap by describing methods to estimate classification inconsistency in machine learning models arising from two different sources: sampling error during model fitting and measurement error in the item responses. These methods use data resampling techniques such as the bootstrap and Monte Carlo sampling. These methods are illustrated using three empirical examples predicting a health condition/diagnosis from item responses. R code is provided to facilitate the implementation of the methods. This article highlights the importance of considering classification consistency alongside accuracy when studying screening measures and provides the tools and guidance necessary for applied researchers to obtain classification consistency indices in their machine learning research on diagnostic assessments. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The 18-24 age group has a much higher rate of depression risk than other age groups, and this age group has the highest proportion among users of mobile social media. The relationship between the use of mobile social media and depressive mood is inconsistent and the mechanism of action is controversial.

The aim of this study was to examine the correlation between the level of mobile phone dependence among college students and their experience of academic burnout. Additionally, the study sought to explore the potential mediating effect of study engagement and the moderating role of love.

There is evidence that adverse childhood experiences (ACEs) and negative life events (NLEs) are associated with major depression (MDD).

The cause of Developmental Coordination Disorder (DCD) is unknown, but neuroimaging evidence suggests that DCD may be related to altered brain development. Children with DCD show less structural and functional connectivity compared to typically developing (TD) children, but few studies have examined cortical volume in children with DCD. The purpose of this study was to investigate cortical grey matter volume using voxel-based morphometry (VBM) in children with DCD compared to TD children.

In recent years, several theoretical models have been suggested as complementary to the adaptative information processing model of eye movement desensitization and reprocessing therapy. A narrative review of such models was conducted to assess the contributions of each, as well as their convergences, contradictions, and potential complementarity. Seven theoretical models were identified. All focus on the effects of EMDR therapy as a comprehensive psychotherapy approach with its principles, procedures, and protocols. Several refer to concepts related to propositional or predictive processing theories. Overall, the contribution of these proposals does appear to bolster Shapiro's original AIP model, potentially offering additional depth and breadth to case conceptualization and treatment planning in clinical practice, as well as a more precise theoretical understanding. The current exploratory comparative analysis may serve as a preliminary baseline to guide research into the relative merit of suggested theoretical proposals to enhance current standards for the clinical practice and teaching of EMDR therapy.

As the most commonly used illicit substance, cannabis is gaining global acceptance through increasing legalization efforts. This shift intensifies the need for research to guide policymakers and healthcare providers in harm reduction and treatment strategies. Nonetheless, the relationship between psychopathological symptoms and cannabis use remains inadequately understood.