Socio-demographic variation in diagnosis of and prescribing for common mental illnesses among children and young people during the COVID-19 pandemic: time series analysis of primary care electronic health records.

BACKGROUND: The impact of the COVID-19 pandemic on the mental health of children and young people (CYP) has been widely reported. Primary care electronic health records were utilised to examine trends in the diagnosing, recording and treating of these common mental disorders by ethnicity and social deprivation in Greater Manchester, England. METHODS: Time-series analyses conducted using Greater Manchester Care Record (GMCR) data examined all diagnosed episodes of anxiety disorders and depression and prescribing of anxiolytics and antidepressants among patients aged 6-24 years. The 41-month observation period was split into three epochs: Pre-pandemic (1/2019-2/2020); Pandemic Phase 1 (3/2020-6/2021); Pandemic Phase 2 (7/2021-5/2022). Rate ratios for all CYP specific to sex, age, ethnicity, and neighbourhood-level Indices of Multiple Deprivation (IMD) quintile were modelled using negative binomial regression. RESULTS: Depression and anxiety disorder rates were highest in females, CYP aged 19-24, and White and 'Other' ethnic groups. During Pandemic Phase 1, rates for these diagnoses fell in all demographic subgroups and then rose to similar levels as those recorded pre-pandemic. In Pandemic Phase 2, rates in Black and Mixed-ethnicity females rose to a significantly greater degree (by 54% and 62%, respectively) than those in White females. Prescribing rates increased throughout the study period, with significantly greater rises observed in non-White females and males. The temporal trends were mostly homogeneous across deprivation quintiles. CONCLUSION: The observed fluctuations in frequency of recorded common mental illness diagnoses likely reflect service accessibility and patients' differential propensities to consult as well as changing levels of distress and psychopathology in the population. However, psychotropic medication prescribing increased throughout the observation period, possibly indicating a sustained decline in mental health among CYP, and also clinicians' responses to problems presented. The comparatively greater increases in frequencies of diagnosis recording and medication prescribing among ethnic minority groups warrants further investigation.

5-year mental health outcomes for children and adolescents presenting with psychiatric symptoms to general practitioners in England: a retrospective cohort study.

BACKGROUND: Little information is available on the clinical trajectories of children and adolescents who attend general practice (GP) with psychiatric symptoms. We aimed to examine 5-year service use in English primary care for children and adolescents with neurodevelopmental or mental health symptoms or diagnoses. METHODS: In this retrospective cohort study, we used anonymised primary care health records from the Clinical Practice Research Datalink Aurum database (CPRD-Aurum). We identified children and adolescents (aged 3-18 years) presenting to primary care in England between Jan 1, 2000, and May 9, 2016, with a symptom or diagnosis of a mental health, behavioural, or neurodevelopmental condition. Participants were excluded if they had less than 1 year of follow-up. We followed up participants from their index date until either death, transfer out of the practice, or the end of data collection on May 5, 2021, and for trajectory analysis we limited follow-up to 5 years. We used group-based multi-trajectory models to identify clusters with similar trajectories over 5 years of follow-up for three primary outcomes: mental health-related GP contacts, psychotropic medication prescriptions, and specialist mental health-care contact. We did survival analysis to examine the associations between trajectory-group membership and hospital admission for self-harm or death by suicide, as indicators of severe psychiatric distress. FINDINGS: We included 369 340 children and adolescents, of whom 180 863 (49·0%) were girls, 188 438 (51·0%) were boys, 39 (<0·1%) were of indeterminate gender, 290 125 (78·6%) were White, 9161 (2·5%) were South Asian, 10 418 (2·8%) were Black, 8115 (2·2%) were of mixed ethnicity, and 8587 (2·3%) were other ethnicities, and the median age at index presentation was 13·6 years (IQR 8·4-16·7). In the best-fitting, seven-group, group-based multi-trajectory model, over a 5-year period, the largest group (low contact; 207 985 [51·2%]) had low rates of additional service contact or psychotropic prescriptions. The other trajectory groups were moderate, non-pharmacological contact (43 836 [13·0%]); declining contact (25 469 [8·7%]); year-4 escalating contact (18 277 [6·9%]); year-5 escalating contact (18 139; 5·2%); prolonged GP contact (32 147 [8·6%]); and prolonged specialist contact (23 487 [6·5%]). Non-White ethnicity and presentation in earlier study years (eg, 2000-2004) were associated with low-contact group membership. The prolonged specialist-contact group had the highest risk of hospital admission for self-harm (hazard ratio vs low-contact group 2·19 [95% CI 2·03-2·36]) and suicide (2·67 [1·72-4·14]). INTERPRETATION: Most children and adolescents presenting to primary care with psychiatric symptoms or diagnoses have low or declining rates of ongoing contact. If these trajectories reflect symptomatic improvement, these findings provide reassurance for children and adolescents and their caregivers. However, these trajectories might reflect an unmet need for some children and adolescents. FUNDING: National Institute for Health and Care Research and the Wellcome Trust.

S-LSTM-ATT: a hybrid deep learning approach with optimized features for emotion recognition in electroencephalogram.

Purpose: Human emotion recognition using electroencephalograms (EEG) is a critical area of research in human-machine interfaces. Furthermore, EEG data are convoluted and diverse; thus, acquiring consistent results from these signals remains challenging. As such, the authors felt compelled to investigate EEG signals to identify different emotions. Methods: A novel deep learning (DL) model stacked long short-term memory with attention (S-LSTM-ATT) model is proposed for emotion recognition (ER) in EEG signals. Long Short-Term Memory (LSTM) and attention networks effectively handle time-series EEG data and recognise intrinsic connections and patterns. Therefore, the model combined the strengths of the LSTM model and incorporated an attention network to enhance its effectiveness. Optimal features were extracted from the metaheuristic-based firefly optimisation algorithm (FFOA) to identify different emotions efficiently. Results: The proposed approach recognised emotions in two publicly available standard datasets: SEED and EEG Brainwave. An outstanding accuracy of 97.83% in the SEED and 98.36% in the EEG Brainwave datasets were obtained for three emotion indices: positive, neutral and negative. Aside from accuracy, a comprehensive comparison of the proposed model's precision, recall, F1 score and kappa score was performed to determine the model's applicability. When applied to the SEED and EEG Brainwave datasets, the proposed S-LSTM-ATT achieved superior results to baseline models such as Convolutional Neural Networks (CNN), Gated Recurrent Unit (GRU) and LSTM. Conclusion: Combining an FFOA-based feature selection (FS) and an S-LSTM-ATT-based classification model demonstrated promising results with high accuracy. Other metrics like precision, recall, F1 score and kappa score proved the suitability of the proposed model for ER in EEG signals.

Aberrant oscillatory activity in neurofibromatosis type 1: an EEG study of resting state and working memory.

BACKGROUND: Neurofibromatosis type 1 (NF1) is a genetic neurodevelopmental disorder commonly associated with impaired cognitive function. Despite the well-explored functional roles of neural oscillations in neurotypical populations, only a limited number of studies have investigated oscillatory activity in the NF1 population. METHODS: We compared oscillatory spectral power and theta phase coherence in a paediatric sample with NF1 (N = 16; mean age: 13.03 years; female: n = 7) to an age/sex-matched typically developing control group (N = 16; mean age: 13.34 years; female: n = 7) using electroencephalography measured during rest and during working memory task performance. RESULTS: Relative to typically developing children, the NF1 group displayed higher resting state slow wave power and a lower peak alpha frequency. Moreover, higher theta power and frontoparietal theta phase coherence were observed in the NF1 group during working memory task performance, but these differences disappeared when controlling for baseline (resting state) activity. CONCLUSIONS: Overall, results suggest that NF1 is characterised by aberrant resting state oscillatory activity that may contribute towards the cognitive impairments experienced in this population. TRIAL REGISTRATION: ClinicalTrials.gov, NCT03310996 (first posted: October 16, 2017).

Temporal trends in eating disorder and self-harm incidence rates among adolescents and young adults in the UK in the 2 years since onset of the COVID-19 pandemic: a population-based study.

BACKGROUND: Self-harm and eating disorders share multiple risk factors, with onset typically during adolescence or early adulthood. We aimed to examine the incidence rates of these psychopathologies among young people in the UK in the 2 years following onset of the COVID-19 pandemic. METHODS: We conducted a population-based study using the primary care electronic health records of patients aged 10-24 years in the UK Clinical Practice Research Datalink (CPRD). The observation period was from Jan 1, 2010, to March 31, 2022. We calculated the monthly incidence rates of eating disorders and self-harm according to the first record of each outcome. On the basis of antecedent trends between January, 2010, and February, 2020, negative binomial regression models were fitted to predict monthly incidence rates after the pandemic began in March, 2020. Percentage differences between observed and expected incidence were calculated to indicate changes since the onset of the pandemic, with stratification by sex, age, and deprivation quintile. FINDINGS: The primary care health records of 9 184 712 patients aged 10-24 years (4 836 226 [52·7%] female patients and 4 348 486 [47·3%] male patients; n=1881 general practices) were included for analysis. The incidence rates of eating disorders and self-harm among girls were higher than expected between March 1, 2020, and March 31, 2022. The observed incidence of eating disorders was 42·4% (95% CI 25·7-61·3) higher than expected for girls aged 13-16 years, and 32·0% (13·3-53·8) higher than expected for girls aged 17-19 years, whereas other age groups showed little difference between observed and expected incidence. Similarly, the increase in self-harm incidence was driven by girls aged 13-16 years, for whom the observed incidence was 38·4% (20·7-58·5) higher than expected. By contrast, among boys in all age groups, the incidence rates of eating disorders and self-harm were lower than, or close to, the expected rates. Among boys, the observed incidence of eating disorders was 22·8% (9·2-34·4) lower than expected, and the observed incidence of self-harm was 11·5% (3·6-18·7) lower than expected. The estimated increases in eating disorder and self-harm incidence among girls aged 13-16 years were largely attributable to increases within less deprived communities. INTERPRETATION: Although causes are uncertain, increased incidence of eating disorder diagnoses and self-harm among teenage girls in the UK during the first 2 years of the COVID-19 pandemic highlight an urgent need for intervention. Early identification of mental health difficulties by primary care clinicians is necessary. Timely access to treatments and sufficient support from general practitioners and mental health services needs to be available to manage presenting problems and to prevent exacerbations of conditions. FUNDING: National Institute for Health and Care Research.

A Targeted, Low-Throughput Compound Screen in a Drosophila Model of Neurofibromatosis Type 1 Identifies Simvastatin and BMS-204352 as Potential Therapies for Autism Spectrum Disorder (ASD).

Autism spectrum disorder (ASD) is a common neurodevelopmental condition for which there are no pharmacological therapies that effectively target its core symptomatology. Animal models of syndromic forms of ASD, such as neurofibromatosis type 1, may be of use in screening for such treatments. Drosophila larvae lacking Nf1 expression exhibit tactile hypersensitivity following mechanical stimulation, proposed to mirror the sensory sensitivity issues comprising part of the ASD diagnostic criteria. Such behavior is associated with synaptic dysfunction at the neuromuscular junction (NMJ). Both phenotypes may thus provide tractable outputs with which to screen for potential ASD therapies. In this study, we demonstrate that, while loss of Nf1 expression within the embryo is sufficient to impair NMJ synaptic transmission in the larva, constitutive Nf1 knock-down is required to induce tactile hypersensitivity, suggesting that a compound must be administered throughout development to rescue this behavior. With such a feeding regime, we identify two compounds from a targeted, low-throughput screen that significantly and consistently reduce, but do not fully rescue, tactile hypersensitivity in Nf1P1 larvae. These are the HMG CoA-reductase inhibitor simvastatin, and the BKCa channel activator BMS-204352. At the NMJ, both compounds induce a significant reduction in the enhanced spontaneous transmission frequency of Nf1P1 larvae, though again not to the level of vehicle-treated controls. However, both compounds fully rescue the increased quantal size of Nf1P1 mutants, with simvastatin also fully rescuing their reduced quantal content. Thus, the further study of both compounds as potential ASD interventions is warranted.

A novel convolution bi-directional gated recurrent unit neural network for emotion recognition in multichannel electroencephalogram signals.

BACKGROUND: Recognising emotions in humans is a great challenge in the present era and has several applications under affective computing. Deep learning (DL) is found as a successful tool for prediction of human emotions in different modalities. OBJECTIVE: To predict 3D emotions with high accuracy in multichannel physiological signals, i.e. electroencephalogram (EEG). METHODS: A hybrid DL model consisting of convolutional neural network (CNN) and gated recurrent units (GRU) is proposed in this work for emotion recognition in EEG data. CNN has the capability of learning abstract representation, whereas GRU can explore temporal correlation. A bi-directional variation of GRU is used here to learn features in both directions. Discrete and dimensional emotion indices are recognised in two publicly available datasets SEED and DREAMER, respectively. A fused feature of energy and Shannon entropy (𝐸𝑛𝑆𝐸→) and energy and differential entropy (𝐸𝑛𝐷𝐸→) are fed in the proposed classifier to improve the efficiency of the model. RESULTS: The performance of the presented model is measured in terms of average accuracy, which is obtained as 86.9% and 93.9% for SEED and DREAMER datasets, respectively. CONCLUSION: The proposed convolution bi-directional gated recurrent unit neural network (CNN-BiGRU) model outperforms most of the state-of-the-art and competitive hybrid DL models, which indicates the effectiveness of emotion recognition using EEG signals and provides a scientific base for the implementation in human-computer interaction (HCI).

Efficacy of novel attention-based gated recurrent units transformer for depression detection using electroencephalogram signals.

Purpose: Depression is a global challenge causing psychological and intellectual problems that require efficient diagnosis. Electroencephalogram (EEG) signals represent the functional state of the human brain and can help build an accurate and viable technique for the early prediction and treatment of depression. Methods: An attention-based gated recurrent units transformer (AttGRUT) time-series model is proposed to efficiently identify EEG perturbations in depressive patients. Statistical, spectral and wavelet features were first extracted from the 60-channel EEG signal data. Then, two feature selection techniques, recursive feature elimination and the Boruta algorithm, both with Shapley additive explanations, were utilised for selecting essential features. Results: The proposed model outperformed the two baseline and two hybrid time-series models-long short-term memory (LSTM), gated recurrent units (GRU), convolutional neural network-LSTM (CNN-LSTM), and CNN-GRU-achieving an accuracy of up to 98.67%. Feature selection considerably increased the performance across all time-series models. Conclusion: Based on the obtained results, novel feature selection greatly affected the results of the baseline and hybrid time-series models. The proposed AttGRUT can be implemented and tested in other domains by using different modalities for prediction. Supplementary Information: The online version contains supplementary material available at 10.1007/s13755-022-00205-8.

A Review on Opportunities and Limitations of Membrane Bioreactor Configuration in Biofuel Production.

Biofuels are a clean and renewable source of energy that has gained more attention in recent years; however, high energy input and processing cost during the production and recovery process restricted its progress. Membrane technology offers a range of energy-saving separation for product recovery and purification in biorefining along with biofuel production processes. Membrane separation techniques in combination with different biological processes increase cell concentration in the bioreactor, reduce product inhibition, decrease chemical consumption, reduce energy requirements, and further increase product concentration and productivity. Certain membrane bioreactors have evolved with the ability to deal with different biological production and separation processes to make them cost-effective, but there are certain limitations. The present review describes the advantages and limitations of membrane bioreactors to produce different biofuels with the ability to simplify upstream and downstream processes in terms of sustainability and economics.

Infant excitation/inhibition balance interacts with executive attention to predict autistic traits in childhood.

BACKGROUND: Autism is proposed to be characterised by an atypical balance of cortical excitation and inhibition (E/I). However, most studies have examined E/I alterations in older autistic individuals, meaning that findings could in part reflect homeostatic compensation. To assess the directionality of effects, it is necessary to examine alterations in E/I balance early in the lifespan before symptom emergence. Recent explanatory frameworks have argued that it is also necessary to consider how early risk features interact with later developing modifier factors to predict autism outcomes. METHOD: We indexed E/I balance in early infancy by extracting the aperiodic exponent of the slope of the electroencephalogram (EEG) power spectrum ('1/f'). To validate our index of E/I balance, we tested for differences in the aperiodic exponent in 10-month-old infants with (n = 22) and without (n = 27) neurofibromatosis type 1 (NF1), a condition thought to be characterised by alterations to cortical inhibition. We then tested for E/I alterations in a larger heterogeneous longitudinal cohort of infants with and without a family history of neurodevelopmental conditions (n = 150) who had been followed to early childhood. We tested the relevance of alterations in E/I balance and our proposed modifier, executive attention, by assessing whether associations between 10-month aperiodic slope and 36-month neurodevelopmental traits were moderated by 24-month executive attention. Analyses adjusted for age at EEG assessment, sex and number of EEG trials. RESULTS: Infants with NF1 were characterised by a higher aperiodic exponent, indicative of greater inhibition, supporting our infant measure of E/I. Longitudinal analyses showed a significant interaction between aperiodic slope and executive attention, such that higher aperiodic exponents predicted greater autistic traits in childhood, but only in infants who also had weaker executive functioning abilities. LIMITATIONS: The current study relied on parent report of infant executive functioning-type abilities; future work is required to replicate effects with objective measures of cognition. CONCLUSIONS: Results suggest alterations in E/I balance are on the developmental pathway to autism outcomes, and that higher executive functioning abilities may buffer the impact of early cortical atypicalities, consistent with proposals that stronger executive functioning abilities may modify the impact of a wide range of risk factors.

Loss of NF1 in Drosophila Larvae Causes Tactile Hypersensitivity and Impaired Synaptic Transmission at the Neuromuscular Junction.

Autism spectrum disorder (ASD) is a neurodevelopmental condition in which the mechanisms underlying its core symptomatology are largely unknown. Studying animal models of monogenic syndromes associated with ASD, such as neurofibromatosis type 1 (NF1), can offer insights into its etiology. Here, we show that loss of function of the Drosophila NF1 ortholog results in tactile hypersensitivity following brief mechanical stimulation in the larva (mixed sexes), paralleling the sensory abnormalities observed in individuals with ASD. Mutant larvae also exhibit synaptic transmission deficits at the glutamatergic neuromuscular junction (NMJ), with increased spontaneous but reduced evoked release. While the latter is homeostatically compensated for by a postsynaptic increase in input resistance, the former is consistent with neuronal hyperexcitability. Indeed, diminished expression of NF1 specifically within central cholinergic neurons induces both excessive neuronal firing and tactile hypersensitivity, suggesting the two may be linked. Furthermore, both impaired synaptic transmission and behavioral deficits are fully rescued via knock-down of Ras proteins. These findings validate NF1 -/- Drosophila as a tractable model of ASD with the potential to elucidate important pathophysiological mechanisms.SIGNIFICANCE STATEMENT Autism spectrum disorder (ASD) affects 1-2% of the overall population and can considerably impact an individual's quality of life. However, there are currently no treatments available for its core symptoms, largely because of a poor understanding of the underlying mechanisms involved. Examining how loss of function of the ASD-associated NF1 gene affects behavior and physiology in Drosophila may shed light on this. In this study, we identify a novel, ASD-relevant behavioral phenotype in NF1 -/- larvae, namely an enhanced response to mechanical stimulation, which is associated with Ras-dependent synaptic transmission deficits indicative of neuronal hyperexcitability. Such insights support the use of Drosophila neurofibromatosis type 1 (NF1) models in ASD research and may provide outputs for genetic or pharmacological screens in future studies.

Prediction of Global Psychological Stress and Coping Induced by the COVID-19 Outbreak: A Machine Learning Study.

Background: Artificial intelligence and machine learning have enormous potential to deal efficiently with a wide range of issues that traditional sciences may be unable to address. Neuroscience, particularly psychiatry, is one of the domains that could potentially benefit from artificial intelligence and machine learning. This study aims to predict Stress and assess Coping with stress mechanisms during the COVID-19 pandemic and, therefore, help establish a successful intervention to manage distress. Methods: COVIDiSTRESS global survey data was used in this study and comprised 70 652 respondents after pre-processing. Binary classification is performed for predicting Stress and Coping with stress, while 2 ensemble machine learning algorithms, deep super learner and cascade deep forest, and state-of-the-art methods are explored for classification. Correlation attribute evaluation is used for feature significance. Statistical analysis, such as Cronbach's alpha, demographic statistics, Pearson's correlation coefficient, independent sample t-test, and 95% CI, is also performed. Results: Globally, females, the younger population, and those in COVID-19 risk groups are observed to possess higher levels of stress. Trust, Loneliness, and Distress are found to be the primary predictors of Stress, whereas the significant predictors for coping with stress are identified as Social Provision, Extroversion, and Agreeableness. Deep super learner and cascade deep forest outperformed the state-of-the-art methods with an accuracy of up to 88.42%. Conclusions: By comparing different classifiers, we can conclude that multi-layer ensemble outperforms all. Another aim of this study, is the ability to regulate demographic and negative psychological states with a goal of medical interventions and to work towards building multiple coping strategies to reduce stress and promote resilience and recovery from COVID-19.

Non-invasive brain stimulation modulates GABAergic activity in neurofibromatosis 1.

Neurofibromatosis 1 (NF1) is a single-gene disorder associated with cognitive phenotypes common to neurodevelopmental conditions such as autism. GABAergic dysregulation underlies working memory impairments seen in NF1. This mechanistic experimental study investigates whether application of anodal transcranial direct current stimulation (atDCS) can modulate GABA and working memory in NF1. Thirty-one NF1 adolescents 11-18 years, were recruited to this single-blind sham-controlled cross-over randomized trial. AtDCS or sham stimulation was applied to the left Dorsolateral Prefrontal Cortex (DLPFC) and MR Spectroscopy was collected before and after intervention in the left DLPFC and occipital cortex. Task-related functional MRI was collected before, during, and after stimulation. Higher baseline GABA+ in the left DLPFC was associated with faster response times on baseline working memory measures. AtDCS was seen to significantly reduced GABA+ and increase brain activation in the left DLPFC as compared to sham stimulation. Task performance was worse in the aTDCS group during stimulation but no group differences in behavioural outcomes were observed at the end of stimulation. Although our study suggests aTDCS modulates inhibitory activity in the DLPFC, further work is needed to determine whether repeated sessions of atDCS and strategies such as alternating current stimulation offer a better therapeutic approach.

Early Developmental Trajectories in Infants With Neurofibromatosis 1.

Objective: To examine the trajectories of cognitive, motor and behavioural development in infants with NF1 compared to infants without a family history of neurodevelopmental difficulties. Study design: Infants with NF1 and low-risk controls were recruited from 5 months of age and followed longitudinally. Data from standardised tests was gathered at 5, 10 and 14 months and developmental trajectories of motor, language, behaviour, sleep, social development and parent-infant interaction were examined. Linear mixed modelling was used to estimate group differences in cognitive and behavioural measures over time. Results: No group differences were observed on Mullen Scale of Early Learning, overall adaptive functioning, temperament or behavioural measures. There were no group differences observed on measures of social communication or parent-infant interaction. Over the course of development, the NF1 group slept less and took more time to settle to sleep as compared to the control group. Maternal education was significantly associated with cognitive and behavioural developmental outcomes in both groups. Conclusion: Cognitive, social and behavioural impairments are a cause of significant functional morbidity in children with NF1. This report is the first study to investigate the trajectories of cognitive, motor and behavioural development in infancy in NF1. Our results demonstrate that overall cognitive and behavioural developmental trajectories of the NF1 group in the infancy period are similar to controls. Given previous reports of delayed development in the NF1 cohort by 40 months, early clinical interventions strategies to promote sleep hygiene may be beneficial to optimise developmental outcomes.

Management of anxiety disorders among children and adolescents in UK primary care: A cohort study.

BACKGROUND: Anxiety disorders are common in childhood and adolescence but evidence-based guidance on their management is limited in the UK. In the absence of guidelines, we examined what treatment young people with anxiety disorders receive in primary care in the year following diagnosis. METHOD: We delineated a cohort of individuals diagnosed with anxiety disorders aged 10-18 using the Clinical Practice Research Datalink (CPRD). We estimated the annual prevalence of antidepressant and anxiolytic prescribing and referrals to mental health services in the year following diagnosis between 2003 and 2019 via Poisson models, adjusted for age, gender, and practice-level deprivation. RESULTS: 34,490 out of 52,358 (66 %) individuals were not prescribed or referred in the year following diagnosis. Those registered to practices in the most deprived compared to the least deprived areas were less likely to be referred (PR 0.80, 95%CI 0.76-0.84) and prescribed antidepressants (PR 0.77, 95%CI 0.72-0.82). Referrals increased 2003-2008 (22-28 %) and then declined until 2019 (28-21 %). Antidepressant prescribing decreased substantially between 2003 and 2005 (18-11 %) and then increased slightly between 2006 and 2019 (11-13 %). Anxiolytic prescribing declined between 2003 and 2019 (10-2 %). LIMITATIONS: Prescriptions in the CPRD are not coupled with information about indication. Some prescriptions may therefore have been incorrectly attributed to the treatment of anxiety disorders. CONCLUSION: The continued use of antidepressants necessitates the development of evidence-based guidance. The lower likelihood of being prescribed medication and/or referred among young people in more deprived practice populations, where incidence of anxiety disorder and other mental illnesses is higher, must also be investigated and rectified.

Neuroanatomical correlates of working memory performance in Neurofibromatosis 1.

Introduction: Neurofibromatosis 1 (NF1) is a single-gene disorder associated with cognitive impairments, particularly with deficits in working memory. Prior research indicates that brain structure is affected in NF1, but it is unclear how these changes relate to aspects of cognition. Methods: 29 adolescents aged 11-17 years were compared to age and sex-matched controls. NF1 subjects were assessed using detailed multimodal measurements of working memory at baseline followed by a 3T MR scan. A voxel-based morphometry approach was used to estimate the total and regional gray matter(GM) volumetric differences between the NF1 and control groups. The working memory metrics were subjected to a principal component analysis (PCA) approach. Results: The NF1 groups showed increased gray matter volumes in the thalamus, corpus striatum, dorsal midbrain and cerebellum bilaterally in the NF1 group as compared to controls. Principal component analysis on the working memory metrics in the NF1 group yielded three independent factors reflecting high memory load, low memory load and auditory working memory. Correlation analyses revealed that increased volume of posterior cingulate cortex, a key component of the default mode network (DMN) was significantly associated with poorer performance on low working memory load tasks. Conclusion: These results are consistent with prior work showing larger subcortical brain volumes in the NF1 cohort. The strong association between posterior cingulate cortex volume and performance on low memory load conditions supports hypotheses of deficient DMN structural development, which in turn may contribute to the cognitive impairments in NF1.

The seventh international RASopathies symposium: Pathways to a cure-expanding knowledge, enhancing research, and therapeutic discovery.

RASopathies are a group of genetic disorders that are caused by genes that affect the canonical Ras/mitogen-activated protein kinase (MAPK) signaling pathway. Despite tremendous progress in understanding the molecular consequences of these genetic anomalies, little movement has been made in translating these findings to the clinic. This year, the seventh International RASopathies Symposium focused on expanding the research knowledge that we have gained over the years to enhance new discoveries in the field, ones that we hope can lead to effective therapeutic treatments. Indeed, for the first time, research efforts are finally being translated to the clinic, with compassionate use of Ras/MAPK pathway inhibitors for the treatment of RASopathies. This biannual meeting, organized by the RASopathies Network, brought together basic scientists, clinicians, clinician scientists, patients, advocates, and their families, as well as representatives from pharmaceutical companies and the National Institutes of Health. A history of RASopathy gene discovery, identification of new disease genes, and the latest research, both at the bench and in the clinic, were discussed.

Cognitive and Electrophysiological Correlates of Working Memory Impairments in Neurofibromatosis Type 1.

Neurofibromatosis 1 (NF1) is a single gene disorder associated with working Memory (WM) impairments. The aim of this study was to investigate P300 event-related potential (ERP) associated with WM in NF1. Sixteen adolescents with NF1 were compared with controls on measures of WM and EEG was recorded during a WM nback task. The NF1 group showed poorer performance on measures of WM as compared to the control group. No group differences were observed in P300 amplitude at Pz, but P300 latency was shorter in the NF1 group. Topographic analyses of P300 amplitude showed group differences indicating neural processing differences in the NF1 group relative to controls, which possibly contribute to the cognitive deficits seen in this population.

Risk factors for nonfatal self-harm and suicide among adolescents: two nested case-control studies conducted in the UK Clinical Practice Research Datalink.

BACKGROUND: The characteristics of adolescents who die by suicide have hitherto been examined in uncontrolled study designs, thereby precluding examination of risk factors. The degree to which antecedents of nonfatal self-harm and suicide at young age differ remains unknown. METHOD: We delineated two nested case-control studies of patients aged 10-19 years using the Clinical Practice Research Datalink with interlinked hospital and national mortality records. Cases were adolescents who between 1st January 2003 and 31st December 2018 had died from suicide (N = 324) - study 1; experienced their first self-harm episode (N = 56,008) - study 2. In both studies, cases were matched on sex, age and practice-level deprivation quintile to 25 controls. By fitting conditional logistic regression, we examined how risks varied according to psychiatric diagnoses, prescribed psychotropic medication, patterns of clinical contact and area-level deprivation. RESULTS: Suicides occurred more often among boys (66%), but self-harm was more common in girls (68%). Most individuals who self-harmed or died from suicide presented to their GP at least once in the preceding year (85% and 75% respectively). Only a third of cases had one of the examined diagnostic categories recorded. Depression was most strongly associated with elevated risks for both outcomes (self-harm: OR 7.9; 95% CI 7.8-8.2; suicide: OR 7.4; 95% CI 5.5-9.9). Except for autism spectrum disorder, all other diagnostic categories were linked with similar risk elevations for self-harm as for suicide. Whilst self-harm risk rose incrementally with increasing levels of area-level deprivation, suicide risks did not. CONCLUSIONS: We observed few marked differences in risk factor profiles for nonfatal self-harm versus suicide. As most adolescents who had harmed themselves or died by suicide were known to services in the preceding year, their underlying pathology may not be adequately identified and treated. Our findings highlight the need for a multiagency approach to treatment and prevention.