Algorithms and Psychiatry

Digital phenotyping can be an innovative and unobtrusive way to improve the detection of insomnia. This study explores the correlations between smartphone usage features (SUF) and insomnia symptoms and their predictive value for detecting insomnia symptoms.

Exposure to adverse childhood experiences (ACEs) confers a higher risk of developing depression in adulthood, yet the mediation of inflammation remains under debate. To test this model, we conducted a systematic review and two-stage structural equation modelling meta-analysis of studies reporting correlations between ACEs before age 18, inflammatory markers and depression severity in adulthood. Scopus, Pubmed, Medline, PsycInfo, and CINAHL were searched up to 2 October 2023. Twenty-two studies reporting data on C-reactive protein (CRP, n = 12,935), interleukin-6 (IL-6, n = 4108), tumour necrosis factor-α (TNF-α, n = 2256) and composite measures of inflammation (n = 1674) were included. Unadjusted models revealed that CRP (β = 0.003, 95 % LBCI 0.0002 to 0.0068), IL-6 (β = 0.003, 95 % LBCI 0.001 to 0.006), and composite inflammation (β = 0.009, 95 % LBCI 0.004 to 0.018) significantly mediated the association between ACEs and adult depression. The mediation effects no longer survived after adjusting for BMI; however, a serial mediation model revealed that BMI and IL-6 sequentially mediated the association between ACEs and depression (β = 0.002, 95 % LBCI 0.0005 to 0.0046), accounting for 14.59 % and 9.94 % of the variance of IL-6 and depressive symptoms, respectively. Due to the cross-sectional nature of assessment of inflammation and depression findings should be approached with caution; however, results suggest that complex interactions of psychoneuroimmunological and metabolic factors underlie the association between ACEs and adulthood depression.

Through the application of machine learning algorithms to neuroimaging data the brain age methodology was shown to provide a useful individual-level biological age prediction and identify key brain regions responsible for the prediction. In this study, we present the methodology of constructing a rhesus macaque brain age model using a machine learning algorithm and discuss the key predictive brain regions in comparison to the human brain, to shed light on cross-species primate similarities and differences. Structural information of the brain (e.g., parcellated volumes) from brain magnetic resonance imaging of 43 rhesus macaques were used to develop brain atlas-based features to build a brain age model that predicts biological age. The best-performing model used 22 selected features and achieved an R of 0.72. We also identified interpretable predictive brain features including Right Fronto-orbital Cortex, Right Frontal Pole, Right Inferior Lateral Parietal Cortex, and Bilateral Posterior Central Operculum. Our findings provide converging evidence of the parallel and comparable brain regions responsible for both non-human primates and human biological age prediction.

Anxiety disorders are among the most common mental health disorders in the middle aged and older population. Because older individuals are more likely to have multiple comorbidities or increased frailty, the impact of anxiety disorders on their overall well-being is exacerbated. Early identification of anxiety disorders using machine learning (ML) can potentially mitigate the adverse consequences associated with these disorders.

Perinatal arterial ischemic stroke (PAIS) is a focal vascular brain injury presumed to occur between the fetal period and the first 28 days of life. It is the leading cause of hemiparetic cerebral palsy. Multiple maternal, intrapartum, delivery, and fetal factors have been associated with PAIS, but studies are limited by modest sample sizes and complex interactions between factors. Machine learning approaches use large and complex data sets to enable unbiased identification of clinical predictors but have not yet been applied to PAIS. We combined large PAIS data sets and used machine learning methods to identify clinical PAIS factors and compare this data-driven approach with previously described literature-driven clinical prediction models.

Metabolic syndrome (MetS) is common following first-episode psychosis (FEP), contributing to substantial morbidity and mortality. The Psychosis Metabolic Risk Calculator (PsyMetRiC), a risk prediction algorithm for MetS following a FEP diagnosis, was developed in the United Kingdom and has been validated in other European populations. However, the predictive accuracy of PsyMetRiC in Chinese populations is unknown.

Effect sizes of psychotherapies currently stagnate at a low-to-moderate level. Personalizing psychotherapy by algorithm-based modular procedures promises improved outcomes, greater flexibility, and a better fit between research and practice. However, evidence for the feasibility and efficacy of modular-based psychotherapy, using a personalized treatment algorithm, is lacking. This proof-of-concept randomized controlled trial was conducted in 70 adult outpatients with a primary DSM-5 diagnosis of major depressive disorder, a score higher than 18 on the 24-item Hamilton Rating Scale for Depression (HRSD-24), at least one comorbid psychiatric diagnosis according to the Structured Clinical Interview for DSM-5 (SCID-5), a history of at least "moderate to severe" childhood maltreatment on at least one domain of the Childhood Trauma Questionnaire (CTQ), and exceeding the cut-off value on at least one of three measures of early trauma-related transdiagnostic mechanisms: the Rejection Sensitivity Questionnaire (RSQ), the Interpersonal Reactivity Index (IRI), and the Difficulties in Emotion Regulation Scale-16 (DERS-16). Patients were randomized to 20 sessions of either standard cognitive-behavioral therapy alone (CBT) or CBT plus transdiagnostic modules according to a mechanism-based treatment algorithm (MoBa), over 16 weeks. We aimed to assess the feasibility of MoBa, and to compare MoBa vs. CBT with respect to participants' and therapists' overall satisfaction and ratings of therapeutic alliance (using the Working Alliance Inventory - Short Revised, WAI-SR), efficacy, impact on early trauma-related transdiagnostic mechanisms, and safety. The primary outcome for efficacy was the HRSD-24 score at post-treatment. Secondary outcomes included, among others, the rate of response (defined as a reduction of the HRSD-24 score by at least 50% from baseline and a score <16 at post-treatment), the rate of remission (defined as a HRSD-24 score ≤8 at post-treatment), and improvements in early trauma-related mechanisms of social threat response, hyperarousal, and social processes/empathy. We found no difficulties in the selection of the transdiagnostic modules in the individual patients, applying the above-mentioned cut-offs, and in the implementation of MoBa. Both participants and therapists reported higher overall satisfaction and had higher WAI-SR ratings with MoBa than CBT. Both approaches led to major reductions of depressive symptoms at post-treatment, with a non-significant superiority of MoBa over CBT. Patients randomized to MoBa were nearly three times as likely to experience remission at the end of therapy (29.4% vs. 11.4%; odds ratio, OR = 3.2, 95% CI: 0.9-11.6). Among mechanism-based outcomes, MoBa patients showed a significantly higher post-treatment effect on social processes/empathy (p<0.05) compared to CBT patients, who presented an exacerbation on this domain at post-treatment. Substantially less adverse events were reported for MoBa compared to CBT. These results suggest the feasibility and acceptability of an algorithm-based modular psychotherapy complementing CBT in depressed patients with psychiatric comorbidities and early trauma. While initial evidence of efficacy was observed, potential clinical advantages and interindividual heterogeneity in treatment outcomes will have to be investigated in fully powered confirmation trials.

Abnormal emotion processing is a core feature of schizophrenia spectrum disorders (SSDs) that encompasses multiple operations. While deficits in some areas have been well-characterized, we understand less about abnormalities in the emotion processing that happens through language, which is highly relevant for social life. Here, we introduce a novel method using deep learning to estimate emotion processing rapidly from spoken language, testing this approach in male-identified patients with SSDs (n = 37) and healthy controls (n = 51). Using free responses to evocative stimuli, we derived a measure of appropriateness, or "emotional alignment" (EA). We examined psychometric characteristics of EA and its sensitivity to a single-dose challenge of oxytocin, a neuropeptide shown to enhance the salience of socioemotional information in SSDs. Patients showed impaired EA relative to controls, and impairment correlated with poorer social cognitive skill and more severe motivation and pleasure deficits. Adding EA to a logistic regression model with language-based measures of formal thought disorder (FTD) improved classification of patients versus controls. Lastly, oxytocin administration improved EA but not FTD among patients. While additional validation work is needed, these initial results suggest that an automated assay using spoken language may be a promising approach to assess emotion processing in SSDs.

Patent foramen ovale (PFO) is frequently identified in young patients with cryptogenic ischaemic stroke. Potential stroke mechanisms include paradoxical embolism from a venous clot which traverses the PFO, in situ clot formation within the PFO, and atrial arrhythmias due to electrical signalling disruption. The purpose of this guideline is to provide recommendations for diagnosing, treating, and long-term managing patients with ischaemic stroke and PFO. Conversely, Transient Ischaemic Attack (TIA) was not considered an index event in this context because only one RCT involved TIA patients. However, this subgroup analysis showed no significant differences between TIA and stroke outcomes. The working group identified questions and outcomes, graded evidence, and developed recommendations following the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach and the European Stroke Organisation (ESO) standard operating procedure for guideline development. This document underwent peer-review by independent experts and members of the ESO Guideline Board and Executive Committee. The working group acknowledges the current evidentiary gap in delineating an unequivocal diagnostic algorithm for the detection of PFO. Although transoesophageal echocardiography is conventionally held as the most accurate diagnostic tool for PFO identification, its status as the 'gold standard' remains unsubstantiated by rigorously validated evidence. We found high-quality evidence to recommend PFO closure plus antiplatelet therapy in selected patients aged 18-60 years in whom no other evident cause of stroke is found but a PFO (i.e. PFO-associated stroke). The PASCAL classification system can be used to select such candidates for PFO closure. Patients with both a large right-to-left shunt and an atrial septal aneurysm benefit most from PFO closure. There is insufficient evidence to make an evidence-based recommendation on PFO closure in patients older than 60 and younger than 18 years. We found low quality evidence to suggest against PFO closure in patients with unlikely PFO-related stroke according to the PASCAL classification, except in specific scenarios (Expert Consensus). We suggest against long-term anticoagulation in patients with PFO-associated stroke unless anticoagulation is indicated for other medical reasons. Regarding the long-term AF monitoring after PFO closure, the working group concluded that there remains significant uncertainty regarding the risks and benefits associated with the use of long-term cardiac monitoring, such as implantable loop recorders. This document provides additional guidance, in the form of evidence-based recommendations or expert consensus statements, on diagnostic methods for PFO detection, and medical management after PFO closure.

Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed.

Little is known about the incidence of osteoporosis testing and treatment in individuals with schizophrenia, who may be more likely to fracture. Using competing risk models, we found that schizophrenia was associated with lower incidence of testing or treatment. Implications are for understanding barriers and solutions for this disadvantaged group.

Patients with chronic limb-threatening ischemia (CLTI) face a high long-term mortality risk. Identifying novel mortality predictors and risk profiles would enable individual health care plan design and improved survival. We aimed to leverage a random survival forest machine-learning algorithm to identify long-term all-cause mortality predictors in patients with CLTI undergoing peripheral vascular intervention.

Clinical data analysis relies on effective methods and appropriate data. Recognizing distinctive clinical services and service functions may lead to improved decision-making. Our first objective is to categorize analytical methods, data sources, and algorithms used in current research on information analysis and decision support in child and adolescent mental health services (CAMHS). Our secondary objective is to identify the potential for data analysis in different clinical services and functions in which data-driven decision aids can be useful.

Cognitive-control theories assume that the experience of response conflict can trigger control adjustments. However, while some approaches focus on adjustments that impact the selection of the present response (in trial N), other approaches focus on adjustments in the next upcoming trial  (N + 1). We aimed to trace control adjustments over time by quantifying cortical noise by means of the fitting oscillations and one over f algorithm, a measure of aperiodic activity. As predicted, conflict trials increased the aperiodic exponent in a large sample of 171 healthy adults, thus indicating noise reduction. While this adjustment was visible in trial N already, it did not affect response selection before the next trial. This suggests that control adjustments do not affect ongoing response-selection processes but prepare the system for tighter control in the next trial. We interpret the findings in terms of a conflict-induced switch from metacontrol flexibility to metacontrol persistence, accompanied or even implemented by a reduction of cortical noise.

Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges.

Both music and language are found in all known human societies, yet no studies have compared similarities and differences between song, speech, and instrumental music on a global scale. In this Registered Report, we analyzed two global datasets: (i) 300 annotated audio recordings representing matched sets of traditional songs, recited lyrics, conversational speech, and instrumental melodies from our 75 coauthors speaking 55 languages; and (ii) 418 previously published adult-directed song and speech recordings from 209 individuals speaking 16 languages. Of our six preregistered predictions, five were strongly supported: Relative to speech, songs use (i) higher pitch, (ii) slower temporal rate, and (iii) more stable pitches, while both songs and speech used similar (iv) pitch interval size and (v) timbral brightness. Exploratory analyses suggest that features vary along a "musi-linguistic" continuum when including instrumental melodies and recited lyrics. Our study provides strong empirical evidence of cross-cultural regularities in music and speech.

To develop a claims-based algorithm to determine the setting of a disease diagnosis.

The prospective study of youths at clinical high risk (CHR) for psychosis, including neuroimaging, can identify neural signatures predictive of psychosis outcomes using algorithms that integrate complex information. Here, to identify risk and psychosis conversion, we implemented multiple kernel learning (MKL), a multimodal machine learning approach allowing patterns from each modality to inform each other. Baseline multimodal scans (n = 74, 11 converters) included structural, resting-state functional imaging, and diffusion-weighted data. Multimodal MKL outperformed unimodal models (AUC = 0.73 vs. 0.66 in predicting conversion). Moreover, patterns learned by MKL were robust to training set variations, suggesting it can identify cross-modality redundancies and synergies to stabilize the predictive pattern. We identified many predictors consistent with the literature, including frontal cortices, cingulate, thalamus, and striatum. This highlights the advantage of methods that leverage the complex pathophysiology of psychosis.

Determining the causality of Adverse Drug Reactions (ADRs) is essential for management and prevention of future occurrences. The WHO-Uppsala Monitoring Centre (UMC) system is recommended under the Pharmacovigilance Program of India whereas Naranjo's algorithm is commonly utilized by clinicians, but their agreement remains a subject of investigation. This study aims to compare the inter-rater agreement between these two scales for causality assessment of ADRs. In this cross-sectional study, two groups of pharmacovigilance experts were given a set of total 399 anonymized individual case safety reports, collected over six months. The raters were blinded to each other's assessments and applied the WHO-UMC system and Naranjo algorithm to each case independently. Inter-rater agreement was then evaluated utilizing Cohen's kappa. The suspected ADRs were also comprehensively analysed on parameters like age, sex, route of administration, speciality, organ system affected, most common drug categories and individual drugs, outcome of ADRs. Analysis of 399 suspected ADRs revealed that mean age of patients was 36.8 ± 18.0 years, females were more frequently affected, highest proportion of reports were from psychiatry inpatients, seen with antipsychotic drugs, involved the central nervous system, with oral administration, and 91% resolved. On causality assessment by the WHO-UMC system, 53.3% were "Certain" whereas Naranjo's algorithm categorized 96.74% of ADRs as "Probable". Cohen's kappa showed a "Minimal" agreement (0.22) between WHO-UMC and Naranjo system of causality assessment. The considerable lack of agreement between the two commonly employed systems of causality assessment of ADRs warrants further investigation into specific factors influencing the disagreement to improve the accuracy of causality assessments.

Neuropsychiatric symptoms affect the majority of dementia patients. Past studies report high rates of potentially inappropriate prescribing of psychotropic medications in this population. We investigate differences in neuropsychiatric diagnoses and psychotropic medication prescribing in a local US cohort by sex and race.

Sex differences exist in the prevalence and clinical manifestation of several mental disorders, suggesting that sex-specific brain phenotypes may play key roles. Previous research used machine learning models to classify sex from imaging data of the whole brain and studied the association of class probabilities with mental health, potentially overlooking regional specific characteristics.

There is an urgent need worldwide for qualified health professionals. High attrition rates among health professionals, combined with a predicted rise in life expectancy, further emphasize the need for additional health professionals. Work-related stress is a major concern among health professionals, affecting both the well-being of health professionals and the quality of patient care.

Technological burden and medical complexity are significant drivers of clinician burnout. Electronic health record(EHR)-based population health management tools can be used to identify high-risk patient populations and implement prophylactic health practices. Their impact on clinician burnout, however, is not well understood. Our objective was to assess the relationship between ratings of EHR-based population health management tools and clinician burnout.

In Europe, concentrations of ∆-tetrahydrocannabinol (THC) in cannabis resin (also known as hash) have risen markedly in the past decade, potentially increasing risks of mental health disorders. Current approaches to international drug monitoring cannot distinguish between different types of cannabis resin which may have contrasting health effects due to THC and cannabidiol (CBD) content. Here, we compared concentrations of THC and CBD in different types of cannabis resin collected in Europe (either Moroccan-type, or Dutch-type). We then tested the ability of machine learning algorithms to classify the type of cannabis resin (either Moroccan-type, or Dutch-type) using routinely collected monitoring data on THC and CBD. Finally, we applied the optimal algorithm to new samples collected in countries where the type of cannabis resin was unknown, the UK and Denmark. Results showed that overall, Dutch-type samples had higher THC (Hedges' g = 2.39) and lower CBD (Hedges' g = 0.81) than Moroccan-type samples. A Support Vector Machine algorithm achieved classification accuracy exceeding 95%, with little variation in this estimate, good interpretability, and plausibility. It made contrasting predictions about the type of cannabis resin collected in the UK (94% Moroccan-type; 6% Dutch-type) and Denmark (36% Moroccan-type; 64% Dutch-type). In conclusion, we provide proof-of-concept evidence for the potential of machine learning to inform international drug monitoring. Our findings should not be interpreted as objective confirmatory evidence but suggest that Dutch-type cannabis resin has higher THC concentrations than Moroccan-type cannabis resin, which may contribute to variation in drug markets and health outcomes for people who use cannabis in Europe.

Digital reconstruction has been instrumental in deciphering how in vitro neuron architecture shapes information flow. Emerging approaches reconstruct neural systems as networks with the aim of understanding their organization through graph theory. Computational tools dedicated to this objective build models of nodes and edges based on key cellular features such as somata, axons, and dendrites. Fully automatic implementations of these tools are readily available, but they may also be purpose-built from specialized algorithms in the form of multi-step pipelines. Here we review software tools informing the construction of network models, spanning from noise reduction and segmentation to full network reconstruction. The scope and core specifications of each tool are explicitly defined to assist bench scientists in selecting the most suitable option for their microscopy dataset. Existing tools provide a foundation for complete network reconstruction, however more progress is needed in establishing morphological bases for directed/weighted connectivity and in software validation.

Rates of suicide have increased by over 35% since 1999. Despite concerted efforts, our ability to predict, explain, or treat suicide risk has not significantly improved over the past 50 years.

Alzheimer's disease (AD) is a neurodegenerative condition for which there is currently no available medication that can stop its progression. Previous studies suggest that mild cognitive impairment (MCI) is a phase that precedes the disease. Therefore, a better understanding of the molecular mechanisms behind MCI conversion to AD is needed.

This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.

Psychotherapies, such as cognitive behavioral therapy (CBT), currently have the strongest evidence of durable symptom changes for most psychological disorders, such as anxiety disorders. Nevertheless, only about half of individuals treated with CBT benefit from it. Predictive algorithms, including digital assessments and passive sensing features, could better identify patients who would benefit from CBT, and thus, improve treatment choices.

Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop a machine learning (ML) model for POD prediction in older patients, in close cooperation with the PAWEL (patient safety, cost-effectiveness and quality of life in elective surgery) project.

Depression is a prevalent mental health condition worldwide but there is limited data on its presentation and associated symptoms in primary care settings in low- and middle-income countries like Nepal. This study aims to assess the prevalence of depression, its hallmark and other associated symptoms that meet the Diagnostic and Statistical Manual (DSM-5) criteria in primary healthcare facilities in Nepal. The collected information will be used to determine the content of a mobile app-based clinical guidelines for better detection and management of depression in primary care.

Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.

The development of robust tools for segmenting cellular and sub-cellular neuronal structures lags behind the massive production of high-resolution 3D images of neurons in brain tissue. The challenges are principally related to high neuronal density and low signal-to-noise characteristics in thick samples, as well as the heterogeneity of data acquired with different imaging methods. To address this issue, we design a framework which includes sample preparation for high resolution imaging and image analysis. Specifically, we set up a method for labeling thick samples and develop SENPAI, a scalable algorithm for segmenting neurons at cellular and sub-cellular scales in conventional and super-resolution STimulated Emission Depletion (STED) microscopy images of brain tissues. Further, we propose a validation paradigm for testing segmentation performance when a manual ground-truth may not exhaustively describe neuronal arborization. We show that SENPAI provides accurate multi-scale segmentation, from entire neurons down to spines, outperforming state-of-the-art tools. The framework will empower image processing of complex neuronal circuitries.

Psychosis-associated diagnostic codes are increasingly being utilized as case definitions for electronic health record (EHR)-based algorithms to predict and detect psychosis. However, data on the validity of psychosis-related diagnostic codes is limited. We evaluated the positive predictive value (PPV) of International Classification of Diseases (ICD) codes for psychosis.

In the modern day, multimedia and digital resources play a crucial role in demystifying complex topics and improving communication. Additionally, images, videos, and documents speed data administration, fostering both individual and organizational efficiency. Healthcare providers use tools like X-rays, MRIs, and CT scans to improve diagnostic and therapeutic capacities, highlighting the importance of these tools in contemporary communication, data processing, and healthcare. Protecting medical data becomes essential for maintaining patient confidentiality and service dependability in a time when digital assets are crucial to the healthcare industry. In order to overcome this issue, this study analyses the DWT-HD-SVD algorithm-based invisible watermarking in medical data. The main goal is to verify medical data by looking at a DWT-based hybrid technique used on X-ray images with various watermark sizes (256*256, 128*128, 64*64). The algorithm's imperceptibility and robustness are examined using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) and are analyzed using Normalized Connection (NC), Bit Error Rate (BER), and Bit Error Rate (BCR) in order to evaluate its resistance to various attacks. The results show that the method works better with smaller watermark sizes than it does with larger ones.

It is of vital importance to establish an objective and reliable model to facilitate the early diagnosis and intervention of internet gaming disorder (IGD). A total of 133 patients with IGD and 110 healthy controls (HCs) were included. We extracted radiomic features of subcortical structures in high-resolution T1-weighted MRI. Different combinations of four feature selection methods (analysis of variance, Kruskal-Wallis, recursive feature elimination and relief) and ten classification algorithms were used to identify the most robust combined models for distinguishing IGD patients from HCs. Furthermore, a nomogram incorporating radiomic signatures and independent clinical factors was developed. Calibration curve and decision curve analyses were used to evaluate the nomogram. The combination of analysis of variance selector and logistic regression classifier identified that the radiomic model constructed with 20 features from the right caudate nucleus and amygdala showed better IGD screening performance. The radiomic model produced good areas under the curves (AUCs) in the training, validation and test cohorts (AUCs of 0.961, 0.903 and 0.895, respectively). In addition, sex, internet addiction test scores and radiomic scores were included in the nomogram as independent risk factors for IGD. Analysis of the correction curve and decision curve showed that the clinical-radiomic model has good reliability (C-index: 0.987). The nomogram incorporating radiomic features of subcortical structures and clinical characteristics achieved satisfactory classification performance and could serve as an effective tool for distinguishing IGD patients from HCs.

To assess utility and accuracy of a gestational age-based screening targeting premature infants to detect congenital hypothyroidism.

Chronic inflammation is a hallmark of age-related disease states. The effectiveness of inflammatory proteins including C-reactive protein (CRP) in assessing long-term inflammation is hindered by their phasic nature. DNA methylation (DNAm) signatures of CRP may act as more reliable markers of chronic inflammation. We show that inter-individual differences in DNAm capture 50% of the variance in circulating CRP (N = 17,936, Generation Scotland). We develop a series of DNAm predictors of CRP using state-of-the-art algorithms. An elastic-net-regression-based predictor outperformed competing methods and explained 18% of phenotypic variance in the Lothian Birth Cohort of 1936 (LBC1936) cohort, doubling that of existing DNAm predictors. DNAm predictors performed comparably in four additional test cohorts (Avon Longitudinal Study of Parents and Children, Health for Life in Singapore, Southall and Brent Revisited, and LBC1921), including for individuals of diverse genetic ancestry and different age groups. The best-performing predictor surpassed assay-measured CRP and a genetic score in its associations with 26 health outcomes. Our findings forge new avenues for assessing chronic low-grade inflammation in diverse populations.

Artificial intelligence (AI) such as ChatGPT by OpenAI holds great promise to improve the quality of life of patients with dementia and their caregivers by providing high-quality responses to their questions about typical dementia behaviors. So far, however, evidence on the quality of such ChatGPT responses is limited. A few recent publications have investigated the quality of ChatGPT responses in other health conditions. Our study is the first to assess ChatGPT using real-world questions asked by dementia caregivers themselves.

To differentiate early-onset schizophrenia (EOS) from early-onset bipolar disorder (EBD) using surface-based morphometry measurements and brain volumes using machine learning (ML) algorithms.

The relationship between schizophrenia spectrum disorders (SSD) and violent offending has long been the subject of research. The present study attempts to identify the content of delusions, an understudied factor in this regard, that differentiates between violent and non-violent offenses. Limitations, clinical relevance, and future directions are discussed. Employing a retrospective study design, machine learning algorithms and a comprehensive set of variables were applied to a sample of 366 offenders with a schizophrenia spectrum disorder in a Swiss forensic psychiatry department. Taking into account the different contents and affects associated with delusions, eight variables were identified as having an impact on discriminating between violent and non-violent offenses with an AUC of 0.68, a sensitivity of 30.8%, and a specificity of 91.9%, suggesting that the variables found are useful for discriminating between violent and non-violent offenses. Delusions of grandiosity, delusional police and/or army pursuit, delusional perceived physical and/or mental injury, and delusions of control or passivity were more predictive of non-violent offenses, while delusions with aggressive content or delusions associated with the emotions of anger, distress, or agitation were more frequently associated with violent offenses. Our findings extend and confirm current research on the content of delusions in patients with SSD. In particular, we found that the symptoms of threat/control override (TCO) do not directly lead to violent behavior but are mediated by other variables such as anger. Notably, delusions traditionally seen as symptoms of TCO, appear to have a protective value against violent behavior. These findings will hopefully help to reduce the stigma commonly and erroneously associated with mental illness, while supporting the development of effective therapeutic approaches.

Previous investigations have revealed substantial differences in neuroimaging characteristics between healthy controls (HCs) and individuals diagnosed with schizophrenia (SCZ). However, we are not entirely sure how brain activity links to symptoms in schizophrenia, and there is a need for reliable brain imaging markers for treatment prediction.

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

A challenging goal of neural coding is to characterize the neural representations underlying visual perception. To this end, multi-unit activity (MUA) of macaque visual cortex was recorded in a passive fixation task upon presentation of faces and natural images. We analyzed the relationship between MUA and latent representations of state-of-the-art deep generative models, including the conventional and feature-disentangled representations of generative adversarial networks (GANs) (i.e., z- and w-latents of StyleGAN, respectively) and language-contrastive representations of latent diffusion networks (i.e., CLIP-latents of Stable Diffusion). A mass univariate neural encoding analysis of the latent representations showed that feature-disentangled w representations outperform both z and CLIP representations in explaining neural responses. Further, w-latent features were found to be positioned at the higher end of the complexity gradient which indicates that they capture visual information relevant to high-level neural activity. Subsequently, a multivariate neural decoding analysis of the feature-disentangled representations resulted in state-of-the-art spatiotemporal reconstructions of visual perception. Taken together, our results not only highlight the important role of feature-disentanglement in shaping high-level neural representations underlying visual perception but also serve as an important benchmark for the future of neural coding.

Magnetic resonance guided transcranial focused ultrasound holds great promises for treating neurological disorders. This technique relies on skull aberration correction which requires computed tomography (CT) scans of the skull of the patients. Recently, ultra-short time-echo (UTE) magnetic resonance (MR) sequences have unleashed the MRI potential to reveal internal bone structures. In this study, we measure the efficacy of transcranial aberration correction using UTE images. Approach. We compare the efficacy of transcranial aberration correction using UTE scans to CT based correction on four skulls and two targets using a clinical device (Exablate Neuro, Insightec, Israel). We also evaluate the performance of a custom ray tracing algorithm using both UTE and CT estimates of acoustic properties and compare these against the performance of the manufacturer's proprietary aberration correction software. Main results. UTE estimated skull maps in Hounsfield units (HU) had a mean absolute error of 242 ± 20 HU (n=4). The UTE skull maps were sufficiently accurate to improve pressure at the target (no correction: 0.44 ± 0.10, UTE correction: 0.79 ± 0.05, manufacturer CT: 0.80 ± 0.05), pressure confinement ratios (no correction: 0.45 ± 0.10, UTE correction: 0.80 ± 0.05, manufacturer CT: 0.81 ± 0.05), and targeting error (no correction: 1.06 ± 0.42 mm, UTE correction 0.30 ± 0.23 mm, manufacturer CT: 0.32 ± 0.22) (n=8 for all values). When using CT, our ray tracing algorithm performed slightly better than UTE based correction with pressure at the target (UTE: 0.79 ± 0.05, CT: 0.84 ± 0.04), pressure confinement ratios (UTE: 0.80 ± 0.05, CT: 0.84 ± 0.04), and targeting error (UTE: 0.30 ± 0.23 mm, CT: 0.17 ± 0.15). Significance. These 3D transcranial measurements suggest that UTE sequences could replace CT scans in the case of MR guided focused ultrasound with minimal reduction in performance which will avoid ionizing radiation exposure to the patients and reduce procedure time and cost. .

Effective prevention of severe mental disorders (SMD), including non-psychotic unipolar mood disorders (UMD), non-psychotic bipolar mood disorders (BMD), and psychotic disorders (PSY), rely on accurate knowledge of the duration, first presentation, time course and transdiagnosticity of their prodromal stages. Here we present a retrospective, real-world, cohort study using electronic health records, adhering to RECORD guidelines. Natural language processing algorithms were used to extract monthly occurrences of 65 prodromal features (symptoms and substance use), grouped into eight prodromal clusters. The duration, first presentation, and transdiagnosticity of the prodrome were compared between SMD groups with one-way ANOVA, Cohen's f and d. The time course (mean occurrences) of prodromal clusters was compared between SMD groups with linear mixed-effects models. 26,975 individuals diagnosed with ICD-10 SMD were followed up for up to 12 years (UMD = 13,422; BMD = 2506; PSY = 11,047; median[IQR] age 39.8[23.7] years; 55% female; 52% white). The duration of the UMD prodrome (18[36] months) was shorter than BMD (26[35], d = 0.21) and PSY (24[38], d = 0.18). Most individuals presented with multiple first prodromal clusters, with the most common being non-specific ('other'; 88% UMD, 85% BMD, 78% PSY). The only first prodromal cluster that showed a medium-sized difference between the three SMD groups was positive symptoms (f = 0.30). Time course analysis showed an increase in prodromal cluster occurrences approaching SMD onset. Feature occurrence across the prodromal period showed small/negligible differences between SMD groups, suggesting that most features are transdiagnostic, except for positive symptoms (e.g. paranoia, f = 0.40). Taken together, our findings show minimal differences in the duration and first presentation of the SMD prodromes as recorded in secondary mental health care. All the prodromal clusters intensified as individuals approached SMD onset, and all the prodromal features other than positive symptoms are transdiagnostic. These results support proposals to develop transdiagnostic preventive services for affective and psychotic disorders detected in secondary mental healthcare.

To establish significant risk factors for the development of adverse drug effects (ADEs) in children and adolescents with an acute psychotic episode taking antipsychotics. The research team randomly selected 15 patient records each month for 3 years (2016-2018). Overall, 450 patient records were included (223 boys and 227 girls, mean age was 14.52 ± 2.21 years). Adverse effects were identified using the standard algorithm of the Global Trigger Tool method. A "trigger" is an indication that an adverse reaction is likely to occur, e.g., an antihistamine prescription on a prescribing list. When a trigger was detected, the case history was studied in further detail to confirm the occurrence of ADEs. We divided patients into two groups: the "children" group (under 12 years old) and the "adolescents" group (13 years and older). Data were analyzed using the statistical package IBM SPSS Statistics 23.0. Of the 450 patient records, 402 (89.3%) had at least one trigger detected. In total, 126 case histories contained evidence of ADE (28%). The total number of ADEs per 1000 patient days was 5.39 and the number of ADEs per 100 admissions was 32.0. Among adolescents, two or more triggers per patient were significantly more frequently identified (61.3% vs. 44.6%; = 0.001). ADEs were rare in "Children" compared with "Adolescents" (13.8% vs. 30.4%; = 0.006). The logistic regression analysis confirmed high predictive role of "Adolescence" (odds ratio [OR] = 2.58; 95% confidence interval [CI] 1.22-5.4; = 0.013), "Polypharmacy" (OR = 1.96; 95% CI 1.23-3.1; = 0.004), and "First-life hospitalization" (OR = 2.17; 95% CI 1.34-3.48; = 0.001) for ADE fact in patient records. We found that significant risk factors for ADEs to antipsychotics in patients with acute psychotic episode were adolescence (13 years and older), polypharmacy, and first-life hospitalization. The fact that children (i.e., younger than 13 years of age) are less likely to experience ADEs was not associated with high-risk drugs or higher doses in our study.

Depression is a global burden with profound personal and economic consequences. Previous studies have reported that the amount of physical activity is associated with depression. However, the relationship between the temporal patterns of physical activity and depressive symptoms is poorly understood. In this exploratory study, we hypothesize that a particular temporal pattern of daily physical activity could be associated with depressive symptoms and might be a better marker than the total amount of physical activity.

Electroencephalogram (EEG) provides noninvasive measures of brain activity and is found to be valuable for the diagnosis of some chronic disorders. Specifically, pre-treatment EEG signals in the alpha and theta frequency bands have demonstrated some association with antidepressant response, which is well-known to have a low response rate. We aim to design an integrated pipeline that improves the response rate of patients with major depressive disorder by developing a treatment policy guided by the resting state pre-treatment EEG recordings and other treatment effects modifiers. First, we design an innovative automatic site-specific EEG preprocessing pipeline to extract features with stronger signals than raw data. We then estimate the conditional average treatment effect (CATE) using causal forests and use a doubly robust technique to improve efficiency in the estimation of the average treatment effect. We present evidence of heterogeneity in the treatment effect and the modifying power of the EEG features, as well as a significant average treatment effect, a result that cannot be obtained with conventional methods. Finally, we employ an efficient policy learning algorithm to learn an optimal depth-2 treatment assignment decision tree and compare its performance with Q-Learning and outcome-weighted learning via simulation studies and an application to a large multi-site, double-blind, randomized controlled clinical trial, EMBARC.

There are significant challenges to identifying which individuals require intervention following exposure to trauma, and a need for strategies to identify and provide individuals at risk for developing PTSD with timely interventions. The present study seeks to identify a minimal set of trauma-related symptoms, assessed during the weeks following traumatic exposure, that can accurately predict PTSD. Participants were 2185 adults (Mean age=36.4 years; 64% women; 50% Black) presenting for emergency care following traumatic exposure. Participants received a 'flash survey' with 6-8 varying symptoms (from a pool of 26 trauma symptoms) several times per week for eight weeks following the trauma exposure (each symptom assessed ∼6 times). Features (mean, sd, last, worst, peak-end scores) from the repeatedly assessed symptoms were included as candidate variables in a CART machine learning analysis to develop a pragmatic predictive algorithm. PTSD (PCL-5 ≥38) was present for 669 (31%) participants at the 8-week follow-up. A classification tree with three splits, based on mean scores of nervousness, rehashing, and fatigue, predicted PTSD with an Area Under the Curve of 0.836. Findings suggest feasibility for a 3-item assessment protocol, delivered once per week, following traumatic exposure to assess and potentially facilitate follow-up care for those at risk.

Machine learning algorithms hold promise for developing precision medicine approaches to addiction treatment yet have been used sparingly to identify predictors of alcohol-related problems. Recursive partitioning, a machine learning algorithm, can identify salient predictors and clinical cut points that can guide treatment. This study aimed to identify predictors and cut points of alcohol-related problems and to examine result stability in two separate, large data sets of college student drinkers ( = 5,090 and 2,808). Four regression trees were grown using the "rpart" package in R. Seventy-one predictors were classified as demographics (e.g., age), alcohol use indicators (e.g., typical quantity/frequency), or psychosocial indicators (e.g., anxiety). Predictors and cut points were extracted and used to manually recreate the tree in the other data set to test result stability. Outcome variables were alcohol-related problems as measured by the Alcohol Use Disorder Identification Test and Brief Young Adult Alcohol Consequences Questionnaire. Coping with depression, conformity motives, binge drinking frequency, typical/heaviest quantity, drunk frequency, serious harm reduction protective behavioral strategies, substance use, and psychosis symptoms best predicted alcohol-related problems across the four trees; coping with depression (cut point range: 1.83-2.17) and binge drinking frequency (cut point range: 1.5-2.5) were the most common splitting variables. Model fit indices suggest relatively stable results accounting for 17%-30% of the variance. Results suggest the nine salient predictors, particularly coping with depression motives scores around 2 and binge drinking frequency around two to three times per month, are important targets to consider when treating alcohol-related problems for college students. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dictated by consumer ratings and concealed algorithms, high levels of online visibility are granted to certain sleep apps on mainstream modes of app selection. Yet, it remains unclear to what extent these highly visible apps are evidence-based. The objectives of this review were to identify and describe the apps with the greatest online visibility when searching for a sleep app and to assess the claimed and actual research associated with them.

The use of chatbots in mental health support has increased exponentially in recent years, with studies showing that they may be effective in treating mental health problems. More recently, the use of visual avatars called digital humans has been introduced. Digital humans have the capability to use facial expressions as another dimension in human-computer interactions. It is important to study the difference in emotional response and usability preferences between text-based chatbots and digital humans for interacting with mental health services.

Stroke is the second leading cause of death across the globe. Early screening and risk detection could provide early intervention and possibly prevent its incidence. Imaging modalities, including 1D-Transcranial Doppler Ultrasound (1D-TCD) or Transcranial Color-code sonography (TCCS), could only provide low spatial resolution or 2D image information, respectively. Notably, 3D imaging modalities including CT have high radiation exposure, whereas MRI is expensive and cannot be adopted in patients with implanted devices. This study proposes an alternative imaging solution for reconstructing 3D Doppler ultrasound geared towards providing a screening tool for the 3D vessel structure of the brain.

Health care providers, particularly nursing staff, are at risk of physical or emotional abuse from patients. This abuse has been associated with increased use of physical and pharmacological restraints on patients, poor patient outcomes, high staff turnover, and reduced job satisfaction.

Bipolar Disorder (BD) is a complex disease. It is heterogeneous, both at the phenotypic and genetic level, although the extent and impact of this heterogeneity is not fully understood. One way to assess this heterogeneity is to look for patterns in the subphenotype data, identify a more phenotypically homogeneous set of subjects, and perform a genome-wide association-study (GWAS) and subsequent secondary analyses restricted to this homogeneous subset. Because of the variability in how phenotypic data was collected by the various BD studies over the years, homogenizing the phenotypic data is a challenging task, and so is replication. As members of the Psychiatric Genomics Consortium (PGC), we have access to the raw genotypes of 18,711 BD cases and 29,738 controls. This amount of data makes it possible for us to set aside the intricacies of phenotype and allow the genetic data itself to determine which subjects define a homogeneous genetic subgroup. In this paper, we leverage recent advances in heterogeneity analysis to look for distinct homogeneous genetic BD subgroups (or biclusters) that manifest the broad phenotype we think of as Bipolar Disorder. As our data was generated by 27 studies and genotyped on a variety of platforms (OMEX, Affymetrix, Illumina), we use a biclustering algorithm capable of covariate-correction. Covariate-correction is critical if we wish to distinguish disease-related signals from those which are a byproduct of ancestry, study or genotyping platform. We rely on the raw genotyped data and do not include any data generated through imputation. We first apply this covariate-corrected biclustering algorithm to a cohort of 2524 BD cases and 4106 controls from the Bipolar Disease Research Network (BDRN: OMEX). We find evidence of genetic heterogeneity delineating a statistically significant bicluster comprising a subset of BD cases which exhibits a disease-specific pattern of differential-expression across a subset of SNPs. This pattern replicates across the remaining data-sets collected by the PGC containing 5781/8289 (OMEX), 3581/7591 (Illumina), and 6825/9752(Affymetrix) cases/controls, respectively. This bicluster includes subjects diagnosed with bipolar type-I, as well as subjects diagnosed with bipolar type-II. However, the bicluster is enriched for bipolar type-I over type-II and may represent a collection of correlated genetic risk-factors. By investigating the bicluster-informed polygenic-risk-scoring (PRS), we find that the disease-specific pattern highlighted by the bicluster can be leveraged to eliminate noise from our GWAS analyses and improve not only risk prediction, particularly when using only a relatively small subset (e.g., ~ 1%) of the available SNPs, but also SNP replication. Though our primary focus is only the analysis of disease-related signal, we also identify replicable control-related heterogeneity. Covariate-corrected biclustering of raw genetic data appears to be a promising route for untangling heterogeneity and identifying replicable homogeneous genetic subtypes of complex disease. It may also prove useful in identifying protective effects within the control group. This approach circumvents some of the difficulties presented by subphenotype data collected by meta-analyses or 23 andMe, e.g., missingness, assessment variation, and reliance on self-report.

Post-hoc analysis to evaluate the effect of daridorexant on sleep architecture in people with insomnia, focusing on features associated with hyperarousal.

Primary care physicians often lack resources and training to correctly diagnose and manage chronic insomnia disorder. Tools supporting chronic insomnia diagnosis and management could fill this critical gap. A survey was conducted to understand insomnia disorder diagnosis and treatment practices among primary care physicians, and to evaluate a diagnosis and treatment algorithm on its use, to identify ways to optimize it specifically for these providers.

Asking questions is common in conversations, and while asking questions, we need to listen carefully to what others say and consider the perspective our questions adopt. However, difficulties persist in verifying the effect of asking questions on older adults' cognitive function due to the lack of a standardized system for conducting experiments at participants' homes.

Recently, we developed Research and Diagnostic Algorithm Rules (RADAR) to assess the clinical and pathway features of mood disorders. The aims of this paper are to review a) the methodology for developing continuous RADAR scores that describe the clinical and pathway features of schizophrenia, and b) a new method to visualize the clinical status of patients and the pathways implicated in RADAR graphs. We review how to interpret clinical RADAR scores, which serve as valuable tools for monitoring the staging of illness, lifetime suicidal behaviors, overall severity of illness, a general cognitive decline index, and a behavior-cognitive-psychosocial (BCPS) index that represents the "defect"; and b) pathway RADAR scores which reflect various protective (including the compensatory immune- inflammatory system) and adverse (including neuro-immune, neuro-oxidative, and neurotoxic biomarkers) outcome pathways. Using RADAR scores and machine learning, we created new, qualitatively different types of schizophrenia, such as major neurocognitive psychosis and simple psychosis. We also made RADAR graphs, which give us a quick way to compare the patient's clinical condition and pathways to those of healthy controls. We generated a personalized fingerprint for each patient, encompassing various clinical and pathway features of the disorder represented through RADAR graphs. The latter is utilized in clinical practice to assess the clinical condition of patients and identify treatment-required pathways to mitigate the risk of recurrent episodes, worsening BCPS, and increasing staging. The quantitative clinical RADAR scores should be used in schizophrenia research as dependent variables and regressed on the pathway RADAR scores.

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated depression symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals: sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from sensed-behaviors should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.

Digital interventions, including apps and websites, can be effective for reducing alcohol consumption. However, many are not evidence- or theory-informed and have not been evaluated. We tested the effectiveness of the Drink Less app for reducing alcohol consumption compared with usual digital care in the UK.

The neurodevelopmental outcomes of preterm infants can be stratified based on the level of prematurity. We explored brain structural networks in extremely preterm (EP; < 28 weeks of gestation) and very-to-late (V-LP; ≥ 28 and < 37 weeks of gestation) preterm infants at term-equivalent age to predict 2-year neurodevelopmental outcomes. Using MRI and diffusion MRI on 62 EP and 131 V-LP infants, we built a multimodal feature set for volumetric and structural network analysis. We employed linear and nonlinear machine learning models to predict the Bayley Scales of Infant and Toddler Development, Third Edition (BSID-III) scores, assessing predictive accuracy and feature importance. Our findings revealed that models incorporating local connectivity features demonstrated high predictive performance for BSID-III subsets in preterm infants. Specifically, for cognitive scores in preterm (variance explained, 17%) and V-LP infants (variance explained, 17%), and for motor scores in EP infants (variance explained, 15%), models with local connectivity features outperformed others. Additionally, a model using only local connectivity features effectively predicted language scores in preterm infants (variance explained, 15%). This study underscores the value of multimodal feature sets, particularly local connectivity, in predicting neurodevelopmental outcomes, highlighting the utility of machine learning in understanding microstructural changes and their implications for early intervention.

Optimal anesthetic strategy for the endovascular treatment of stroke is still under debate. Despite scarce data concerning anesthetic management for medium and distal vessel occlusions (MeVOs) some centers empirically support a general anesthesia (GA) strategy in these patients.

Attention-deficit/hyperactivity disorder (ADHD) is associated with various cognitive, behavioral, and mood symptoms that complicate diagnosis and treatment. The heterogeneity of these symptoms may also vary depending on certain sociodemographic factors. It is therefore important to establish more homogenous symptom profiles in patients with ADHD and determine their association with the patient's sociodemographic makeup. The current study used unsupervised machine learning to identify symptom profiles across various cognitive, behavioral, and mood symptoms in adults with ADHD. It was then examined whether symptom profiles differed based on relevant sociodemographic factors.

The objective of this study is to determine demographic and diagnostic distributions of physical pain recorded in clinical notes of a mental health electronic health records database by using natural language processing and examine the overlap in recorded physical pain between primary and secondary care.

The response variability to repetitive transcranial magnetic stimulation (rTMS) challenges the effective use of this treatment option in patients with schizophrenia. This variability may be deciphered by leveraging predictive information in structural MRI, clinical, sociodemographic, and genetic data using artificial intelligence. We developed and cross-validated rTMS response prediction models in patients with schizophrenia drawn from the multisite RESIS trial. The models incorporated pre-treatment sMRI, clinical, sociodemographic, and polygenic risk score (PRS) data. Patients were randomly assigned to receive active (N = 45) or sham (N = 47) rTMS treatment. The prediction target was individual response, defined as ≥20% reduction in pre-treatment negative symptom sum scores of the Positive and Negative Syndrome Scale. Our multimodal sequential prediction workflow achieved a balanced accuracy (BAC) of 94% (non-responders: 92%, responders: 95%) in the active-treated group and 50% in the sham-treated group. The clinical, clinical + PRS, and sMRI-based classifiers yielded BACs of 65%, 76%, and 80%, respectively. Apparent sadness, inability to feel, educational attainment PRS, and unemployment were most predictive of non-response in the clinical + PRS model, while grey matter density reductions in the default mode, limbic networks, and the cerebellum were most predictive in the sMRI model. Our sequential modelling approach provided superior predictive performance while minimising the diagnostic burden in the clinical setting. Predictive patterns suggest that rTMS responders may have higher levels of brain grey matter in the default mode and salience networks which increases their likelihood of profiting from plasticity-inducing brain stimulation methods, such as rTMS. The future clinical implementation of our models requires findings to be replicated at the international scale using stratified clinical trial designs.

Depression is prevalent among Operation Enduring Freedom and Operation Iraqi Freedom (OEF/OIF) Veterans, yet rates of Veteran mental health care utilization remain modest. The current study examined: factors in electronic health records (EHR) associated with lack of treatment initiation and treatment delay; the accuracy of regression and machine learning models to predict initiation of treatment.

With advances in our understanding regarding the neurochemical underpinnings of neurological and psychiatric diseases, there is an increased demand for advanced computational methods for neurochemical analysis. Despite having a variety of techniques for measuring tonic extracellular concentrations of neurotransmitters, including voltammetry, enzyme-based sensors, amperometry, and in vivo microdialysis, there is currently no means to resolve concentrations of structurally similar neurotransmitters from mixtures in the in vivo environment with high spatiotemporal resolution and limited tissue damage. Since a variety of research and clinical investigations involve brain regions containing electrochemically similar monoamines, such as dopamine and norepinephrine, developing a model to resolve the respective contributions of these neurotransmitters is of vital importance. Here we have developed a deep learning network, DiscrimNet, a convolutional autoencoder capable of accurately predicting individual tonic concentrations of dopamine, norepinephrine, and serotonin from both in vitro mixtures and the in vivo environment in anesthetized rats, measured using voltammetry. The architecture of DiscrimNet is described, and its ability to accurately predict in vitro and unseen in vivo concentrations is shown to vastly outperform a variety of shallow learning algorithms previously used for neurotransmitter discrimination. DiscrimNet is shown to generalize well to data captured from electrodes unseen during model training, eliminating the need to retrain the model for each new electrode. DiscrimNet is also shown to accurately predict the expected changes in dopamine and serotonin after cocaine and oxycodone administration in anesthetized rats in vivo. DiscrimNet therefore offers an exciting new method for real-time resolution of in vivo voltammetric signals into component neurotransmitters.

Optical coherence tomography (OCT) and electroretinography (ERG) studies have revealed structural and functional retinal alterations in individuals with schizophrenia spectrum disorders (SSD). However, it remains unclear which specific retinal layers are affected, how the retina, brain, and clinical symptomatology are connected, and how alterations of the visual system are related to genetic disease risk.

To better assess the pathology of neurodegenerative disorders and the efficacy of neuroprotective interventions, it is necessary to develop biomarkers that can accurately capture age-related biological changes in the human brain. Brain serotonin 2A receptors (5-HT2AR) show a particularly profound age-related decline and are also reduced in neurodegenerative disorders, such as Alzheimer's disease. This study investigates whether the decline in 5-HT2AR binding, measured in vivo using positron emission tomography (PET), can be used as a biomarker for brain aging. Specifically, we aim to (1) predict brain age using 5-HT2AR binding outcomes, (2) compare 5-HT2AR-based predictions of brain age to predictions based on gray matter (GM) volume, as determined with structural magnetic resonance imaging (MRI), and (3) investigate whether combining 5-HT2AR and GM volume data improves prediction. We used PET and MR images from 209 healthy individuals aged between 18 and 85 years (mean = 38, std = 18) and estimated 5-HT2AR binding and GM volume for 14 cortical and subcortical regions. Different machine learning algorithms were applied to predict chronological age based on 5-HT2AR binding, GM volume, and the combined measures. The mean absolute error (MAE) and a cross-validation approach were used for evaluation and model comparison. We find that both the cerebral 5-HT2AR binding (mean MAE = 6.63 years, std = 0.74 years) and GM volume (mean MAE = 6.95 years, std = 0.83 years) predict chronological age accurately. Combining the two measures improves the prediction further (mean MAE = 5.54 years, std = 0.68). In conclusion, 5-HT2AR binding measured using PET might be useful for improving the quantification of a biomarker for brain aging.

This study aimed to compare the breastfeeding (BF) duration of the younger siblings of children with ASD in an enriched-likelihood cohort for autism spectrum disorder (ASD), and to determine whether longer BF duration was associated with differences in neurodevelopmental outcomes in this cohort. Information on BF practices was collected via surveys in the MARBLES (Markers of Autism Risk in Babies-Learning Early Signs) study. Developmental evaluations, including the Mullen Scales of Early Learning and the Autism Diagnostic Observation Schedule, were conducted by expert clinicians. Participants' neurodevelopmental outcome was classified by an algorithm into three groups: typical development, ASD, and non-typical development. The median duration of BF was 10.70 months (interquartile range of 12.07 months). There were no significant differences in the distribution of duration of BF among the three neurodevelopmental outcome categories. Children in this enriched-likelihood cohort who were breastfed for > 12 months had significantly higher scores on cognitive testing compared to those who were breastfed for 0-3 months. There was no significant difference in ASD symptomatology or ASD risk based on BF duration.

Sharing data from clinical studies can accelerate scientific progress, improve transparency, and increase the potential for innovation and collaboration. However, privacy concerns remain a barrier to data sharing. Certain concerns, such as reidentification risk, can be addressed through the application of anonymization algorithms, whereby data are altered so that it is no longer reasonably related to a person. Yet, such alterations have the potential to influence the data set's statistical properties, such that the privacy-utility trade-off must be considered. This has been studied in theory, but evidence based on real-world individual-level clinical data is rare, and anonymization has not broadly been adopted in clinical practice.

Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

Memory clinic patients are a heterogeneous population representing various aetiologies of pathological aging. It is unknown if divergent spatiotemporal progression patterns of brain atrophy, as previously described in Alzheimer's disease (AD) patients, are prevalent and clinically meaningful in this group of older adults. To uncover distinct atrophy subtypes, we applied the Subtype and Stage Inference (SuStaIn) algorithm to baseline structural MRI data from 813 participants enrolled in the DELCODE cohort (mean ± SD age = 70.67 ± 6.07 years, 52% females). Participants were cognitively unimpaired (CU; n = 285) or fulfilled diagnostic criteria for subjective cognitive decline (SCD; n = 342), mild cognitive impairment (MCI; n = 118), or dementia of the Alzheimer's type (n = 68). Atrophy subtypes were compared in baseline demographics, fluid AD biomarker levels, the Preclinical Alzheimer Cognitive Composite (PACC-5), as well as episodic memory and executive functioning. PACC-5 trajectories over up to 240 weeks were examined. To test if baseline atrophy subtype and stage predicted clinical trajectories before manifest cognitive impairment, we analysed PACC-5 trajectories and MCI conversion rates of CU and SCD participants. Limbic-predominant and hippocampal-sparing atrophy subtypes were identified. Limbic-predominant atrophy first affected the medial temporal lobes, followed by further temporal and, finally, the remaining cortical regions. At baseline, this subtype was related to older age, more pathological AD biomarker levels, APOE ε4 carriership, and an amnestic cognitive impairment. Hippocampal-sparing atrophy initially occurred outside the temporal lobe with the medial temporal lobe spared up to advanced atrophy stages. This atrophy pattern also affected individuals with positive AD biomarkers and was associated with more generalised cognitive impairment. Limbic-predominant atrophy, in all and in only unimpaired participants, was linked to more negative longitudinal PACC-5 slopes than observed in participants without or with hippocampal-sparing atrophy and increased the risk of MCI conversion. SuStaIn modelling was repeated in a sample from the Swedish BioFINDER-2 cohort. Highly similar atrophy progression patterns and associated cognitive profiles were identified. Cross-cohort model generalizability, both on the subject and group level, were excellent, indicating reliable performance in previously unseen data. The proposed model is a promising tool for capturing heterogeneity among older adults at early at-risk states for AD in applied settings. The implementation of atrophy subtype- and stage-specific end-points may increase the statistical power of pharmacological trials targeting early AD.

While there is data assessing the test performance of artificial intelligence (AI) chatbots, including the Generative Pre-trained Transformer 4.0 (GPT 4) chatbot (ChatGPT 4.0), there is scarce data on its diagnostic accuracy of clinical cases. We assessed the large language model (LLM), ChatGPT 4.0, on its ability to answer questions from the United States Medical Licensing Exam (USMLE) Step 2, as well as its ability to generate a differential diagnosis based on corresponding clinical vignettes from published case reports. A total of 109 Step 2 Clinical Knowledge (CK) practice questions were inputted into both ChatGPT 3.5 and ChatGPT 4.0, asking ChatGPT to pick the correct answer. Compared to its previous version, ChatGPT 3.5, we found improved accuracy of ChatGPT 4.0 when answering these questions, from 47.7 to 87.2% (p = 0.035) respectively. Utilizing the topics tested on Step 2 CK questions, we additionally found 63 corresponding published case report vignettes and asked ChatGPT 4.0 to come up with its top three differential diagnosis. ChatGPT 4.0 accurately created a shortlist of differential diagnoses in 74.6% of the 63 case reports (74.6%). We analyzed ChatGPT 4.0's confidence in its diagnosis by asking it to rank its top three differentials from most to least likely. Out of the 47 correct diagnoses, 33 were the first (70.2%) on the differential diagnosis list, 11 were second (23.4%), and three were third (6.4%). Our study shows the continued iterative improvement in ChatGPT's ability to answer standardized USMLE questions accurately and provides insights into ChatGPT's clinical diagnostic accuracy.

The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

Dementia has a long prodromal stage with various pathophysiological manifestations; however, the progression of pre-diagnostic changes remains unclear. We aimed to determine the evolutional trajectories of multiple-domain clinical assessments and health conditions up to 15 years before the diagnosis of dementia.

Intelligent predictive models for autistic symptoms based on neuroimaging datasets were beneficial for the precise intervention of patients with ASD. The goals of this study were twofold: investigating predictive models for autistic symptoms and discovering the brain connectivity patterns for ASD-related behaviors. To achieve these goals, we obtained a cohort of patients with ASD from the ABIDE project. The autistic symptoms were measured using the Autism Diagnostic Observation Schedule (ADOS). The anatomical MRI datasets were preprocessed using the Freesurfer package, resulting in regional morphological features. For each individual, the interregional morphological network was constructed using a novel feature distance-based method. The predictive models for autistic symptoms were built using the support vector regression (SVR) algorithm with feature selection method. The predicted autistic symptoms (i.e., ADOS social score, ADOS behavior) were significantly correlated to the original measures. The most predictive features for ADOS social scores were located in the bilateral fusiform. The most predictive features for ADOS behavior scores were located in the temporal pole and the lingual gyrus. In summary, the autistic symptoms could be predicted using the interregional morphological connectivity and machine learning. The interregional morphological connectivity could be a potential biomarker for autistic symptoms.

Data from the UK suggests 13-55 % of depression patients experience some level of treatment resistance. However, little is known about how physicians manage inadequate response to antidepressants in primary care. This study aimed to explore the incidence of inadequate response to antidepressants in UK primary care. One-hundred-eighty-four medication-free patients with low mood initiated antidepressant treatment and monitored severity of depression symptoms, using the QIDS-SR16, for 48 weeks. Medication changes, visits to healthcare providers, and health-related quality of life were also recorded. Patients were classified into one of four response types based on their QIDS scores at three study timepoints: persistent inadequate responders (<50 % reduction in baseline QIDS at all timepoints), successful responders (≥50 % reduction in baseline QIDS at all timepoints), slow responders (≥50 % reduction in QIDS at week 48, despite earlier inadequate responses), and relapse (initial ≥50 % reduction in baseline QIDS, but inadequate response by week 48). Forty-eight weeks after initiating treatment 47 % of patients continued to experience symptoms of depression (QIDS >5), and 20 % of patients had a persistent inadequate response. Regardless of treatment response, 96 % (n = 176) of patients did not visit their primary care physician over the 40-week follow-up period. These results suggest that despite receiving treatment, a considerable proportion of patients with low mood remain unwell and fail to recover. Monitoring depression symptoms remotely can enable physicians to identify inadequate responders, allowing patients to be reassessed or referred to secondary services, likely improving patients' quality of life and reducing the socioeconomic impacts of chronic mental illness.

Numerous physiological processes are cyclical, but sampling these processes densely enough to perform frequency decomposition and subsequent analyses can be challenging. Mathematical approaches for decomposition and reconstruction of sparsely and irregularly sampled signals are well established but have been under-utilized in physiological applications. We developed a basis pursuit denoising with polynomial detrending (BPWP) model that recovers oscillations and trends from sparse and irregularly sampled timeseries. We validated this model on a unique dataset of long-term inter-ictal epileptiform discharge (IED) rates from human hippocampus recorded with a novel investigational device with continuous local field potential sensing. IED rates have well established circadian and multiday cycles related to sleep, wakefulness, and seizure clusters. Given sparse and irregular samples of IED rates from multi-month intracranial EEG recordings from ambulatory humans, we used BPWP to compute narrowband spectral power and polynomial trend coefficients and identify IED rate cycles in three subjects. In select cases, we propose that random and irregular sampling may be leveraged for frequency decomposition of physiological signals. Trial Registration: NCT03946618.

: Inherited metabolic disorders (IEMs) can be represented in children and adolescents by psychiatric disorders. The early diagnosis of IEMs is crucial for clinical outcome and treatment. The aim of this review is to analyze the most recurrent and specific psychiatric features related to IEMs in pediatrics, based on the onset type and psychiatric phenotypes. Following the PRISMA Statement, a systematic literature review was performed using a predefined algorithm to find suitable publications in scientific databases of interest. After removing duplicates and screening titles and abstracts, suitable papers were analyzed and screened for inclusion and exclusion criteria. Finally, the data of interest were retrieved from the remaining articles. The results of this study are reported by type of symptoms onset (acute and chronic) and by possible psychiatric features related to IEMs. Psychiatric phenomenology has been grouped into five main clinical manifestations: mood and anxiety disorders; schizophrenia-spectrum disorders; catatonia; eating disorders; and self-injurious behaviors. The inclusion of a variety of psychiatric manifestations in children and adolescents with different IEMs is a key strength of this study, which allowed us to explore the facets of seemingly different disorders in depth, avoiding possible misdiagnoses, with the related delay of early and appropriate treatments.

Deconvolution is an efficient approach for detecting cell-type-specific (cs) transcriptomic signals without cellular segmentation. However, this type of methods may require a reference profile from the same molecular source and tissue type. Here, we present a method to dissect bulk proteome by leveraging tissue-matched transcriptome and proteome without using a proteomics reference panel. Our method also selects the proteins contributing to the cellular heterogeneity shared between bulk transcriptome and proteome. The deconvoluted result enables downstream analyses such as cs-protein Quantitative Trait Loci (cspQTL) mapping. We benchmarked the performance of this multimodal deconvolution approach through CITE-seq pseudo bulk data, a simulation study, and the bulk multi-omics data from human brain normal tissues and breast cancer tumors, individually, showing robust and accurate cell abundance quantification across different datasets. This algorithm is implemented in a tool MICSQTL that also provides cspQTL and multi-omics integrative visualization, available at https://bioconductor.org/packages/MICSQTL .

Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.

We developed a video-based tool to quantitatively assess the Glabellar Tap Reflex (GTR) in patients with idiopathic Parkinson's disease (iPD) as well as healthy age-matched participants. We also video-graphically assessed the effect of dopaminergic medication on the GTR in iPD patients, as well as the frequency and blinking duration of reflex and non-reflex blinks. The Glabellar Tap Reflex is a clinical sign seen in patients e.g. suffering from iPD. Reliable tools to quantify this sign are lacking.

Unbiased data-driven omic approaches are revealing the molecular heterogeneity of Alzheimer disease. Here, we used machine learning approaches to integrate high-throughput transcriptomic, proteomic, metabolomic, and lipidomic profiles with clinical and neuropathological data from multiple human AD cohorts. We discovered 4 unique multimodal molecular profiles, one of them showing signs of poor cognitive function, a faster pace of disease progression, shorter survival with the disease, severe neurodegeneration and astrogliosis, and reduced levels of metabolomic profiles. We found this molecular profile to be present in multiple affected cortical regions associated with higher Braak tau scores and significant dysregulation of synapse-related genes, endocytosis, phagosome, and mTOR signaling pathways altered in AD early and late stages. AD cross-omics data integration with transcriptomic data from an SNCA mouse model revealed an overlapping signature. Furthermore, we leveraged single-nuclei RNA-seq data to identify distinct cell-types that most likely mediate molecular profiles. Lastly, we identified that the multimodal clusters uncovered cerebrospinal fluid biomarkers poised to monitor AD progression and possibly cognition. Our cross-omics analyses provide novel critical molecular insights into AD.

Artificial intelligence (AI) is rapidly transforming the field of medicine, announcing a new era of innovation and efficiency. Among AI programs designed for general use, ChatGPT holds a prominent position, using an innovative language model developed by OpenAI. Thanks to the use of deep learning techniques, ChatGPT stands out as an exceptionally viable tool, renowned for generating human-like responses to queries. Various medical specialties, including rheumatology, oncology, psychiatry, internal medicine, and ophthalmology, have been explored for ChatGPT integration, with pilot studies and trials revealing each field's potential benefits and challenges. However, the field of genetics and genetic counseling, as well as that of rare disorders, represents an area suitable for exploration, with its complex datasets and the need for personalized patient care. In this review, we synthesize the wide range of potential applications for ChatGPT in the medical field, highlighting its benefits and limitations. We pay special attention to rare and genetic disorders, aiming to shed light on the future roles of AI-driven chatbots in healthcare. Our goal is to pave the way for a healthcare system that is more knowledgeable, efficient, and centered around patient needs.

In translational animal study aimed at evaluation of the effectiveness of innovative methods for treating cerebral stroke, including regenerative cell technologies, of particular importance is evaluation of the dynamics of changes in the volume of the cerebral infarction in response to therapy. Among the methods for assessing the focus of infarction, MRI is the most effective and convenient tool for use in preclinical studies. This review provides a description of MR pulse sequences used to visualize cerebral ischemia at various stages of its development, and a detailed description of the MR semiotics of cerebral infarction. A comparison of various methods for morphometric analysis of the focus of a cerebral infarction, including systems based on artificial intelligence for a more objective measurement of the volume of the lesion, is also presented.

Postpartum psychosis (PPP) is a psychiatric emergency that generally warrants acute inpatient care. PPP is marked by the sudden onset of affective and psychotic symptoms with a rapid deterioration in mental state. Evidence suggests that PPP is a discrete disorder on the bipolar disorder spectrum with a distinct treatment profile and prognosis.

Major depressive disorder is the leading cause of mental health-related burden globally and up to one-third of major depressive disorder patients never achieve remission. Transcranial Direct Current Stimulation is a non-invasive intervention used to treat individuals diagnosed with major depressive disorder and bipolar disorder. Since the last transcranial direct current stimulation review specifically focusing on cognitive symptoms in major depressive disorder, twice as many papers have been published.

The Suicide Crisis Syndrome (SCS) describes a suicidal mental state marked by entrapment, affective disturbance, loss of cognitive control, hyperarousal, and social withdrawal that has predictive capacity for near-term suicidal behavior. The Suicide Crisis Inventory-2 (SCI-2), a reliable clinical tool that assesses SCS, lacks a short form for use in clinical settings which we sought to address with statistical analysis. To address this need, a community sample of 10,357 participants responded to an anonymous survey after which predictive performance for suicidal ideation (SI) and SI with preparatory behavior (SI-P) was measured using logistic regression, random forest, and gradient boosting algorithms. Four-fold cross-validation was used to split the dataset in 1,000 iterations. We compared rankings to the SCI-Short Form to inform the short form of the SCI-2. Logistic regression performed best in every analysis. The SI results were used to build the SCI-2-Short Form (SCI-2-SF) utilizing the two top ranking items from each SCS criterion. SHAP analysis of the SCI-2 resulted in meaningful rankings of its items. The SCI-2-SF, derived from these rankings, will be tested for predictive validity and utility in future studies.

Alcohol cravings are considered a major factor in relapse among individuals with alcohol use disorder (AUD). This study aims to investigate the frequency and triggers of cravings in the daily lives of people with alcohol-related issues. Large amounts of data are analyzed with Artificial Intelligence (AI) methods to identify possible groupings and patterns.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ). While most rs-fMRI studies being conducted in AD and SZ compare patients to healthy controls, it is also of interest to directly compare AD and SZ patients with each other to identify potential biomarkers shared between the disorders. However, comparing patient groups collected in different studies can be challenging due to potential confounds, such as differences in the patient's age, scan protocols, etc. In this study, we compared and contrasted resting-state functional network connectivity (rs-FNC) of 162 patients with AD and late mild cognitive impairment (LMCI), 181 schizophrenia patients, and 315 cognitively normal (CN) subjects. We used confounder-controlled rs-FNC and applied machine learning algorithms (including support vector machine, logistic regression, random forest, and k-nearest neighbor) and deep learning models (i.e., fully-connected neural networks) to classify subjects in binary and three-class categories according to their diagnosis labels (e.g., AD, SZ, and CN). Our statistical analysis revealed that FNC between the following network pairs is stronger in AD compared to SZ: subcortical-cerebellum, subcortical-cognitive control, cognitive control-cerebellum, and visual-sensory motor networks. On the other hand, FNC is stronger in SZ than AD for the following network pairs: subcortical-visual, subcortical-auditory, subcortical-sensory motor, cerebellum-visual, sensory motor-cognitive control, and within the cerebellum networks. Furthermore, we observed that while AD and SZ disorders each have unique FNC abnormalities, they also share some common functional abnormalities that can be due to similar neurobiological mechanisms or genetic factors contributing to these disorders' development. Moreover, we achieved an accuracy of 85% in classifying subjects into AD and SZ where default mode, visual, and subcortical networks contributed the most to the classification and accuracy of 68% in classifying subjects into AD, SZ, and CN with the subcortical domain appearing as the most contributing features to the three-way classification. Finally, our findings indicated that for all classification tasks, except AD vs. SZ, males are more predictable than females.

The COVID-19 pandemic severely affected populations of all age groups. The elderly are a high-risk group and are highly vulnerable to COVID-19. Assistive software chatbots can enhance the mental health status of the elderly by providing support and companionship. The objective of this study was to validate a Thai artificial chatmate for the elderly during the COVID-19 pandemic and floods.

The aging brain represents the primary risk factor for many neurodegenerative disorders. Whole-brain oscillations may contribute novel early biomarkers of aging. Here, we investigated the dynamic oscillatory neural activities across lifespan (from 18 to 88 years) using resting Magnetoencephalography (MEG) in a large cohort of 624 individuals. Our aim was to examine the patterns of oscillation microstates during the aging process. By using a machine-learning algorithm, we identify four typical clusters of microstate patterns across different age groups and different frequency bands: left-to-right topographic MS1, right-to-left topographic MS2, anterior-posterior MS3 and fronto-central MS4. We observed a decreased alpha duration and an increased alpha occurrence for sensory-related microstate patterns (MS1 & MS2). Accordingly, theta and beta changes from MS1 & MS2 may be related to motor decline that increased with age. Furthermore, voluntary 'top-down' saliency/attention networks may be reflected by the increased MS3 & MS4 alpha occurrence and complementary beta activities. The findings of this study advance our knowledge of how the aging brain shows dysfunctions in neural state transitions. By leveraging the identified microstate patterns, this study provides new insights into predicting healthy aging and the potential neuropsychiatric cognitive decline.

Artificial intelligence (AI) is the ability of a computer machine to display human capabilities such as reasoning, learning, planning, and creativity. Such processing technology receives the data (already prepared or collected), processes them, using models and algorithms, and answers questions about forecasting and decision-making. AI systems are also able to adapt their behavior by analyzing the effects of previous actions and working then autonomously. Artificial intelligence is already present in our lives, even if it often goes unnoticed (shopping networked, home automation, vehicles). Even in the medical field, artificial intelligence can be used to analyze large amounts of medical data and discover matches and patterns to improve diagnosis and prevention. In forensic medicine, the applications of AI are numerous and are becoming more and more valuable.

While meaningless gross motor imitation (GMI) is a common challenge for children diagnosed with autism spectrum disorder (ASD), this topic has not attracted much attention and few appropriate test paradigms have been developed.

The emergence of a new coronavirus strain caused the COVID-19 pandemic. While vaccines effectively control the infection, it's important to acknowledge the potential for side effects, including rare cases like psychosis, which may increase with the rising number of vaccinations.

Neuronal ceroid lipofuscinoses (NCLs) are a growing group of neurodegenerative storage diseases, in which specific features are sought to facilitate the creation of a universal diagnostic algorithm in the future. In our ultrastructural studies, the group of NCLs was represented by the CLN2 disease caused by a defect in the TPP1 gene encoding the enzyme tripeptidyl-peptidase 1. A 3.5-year-old girl was affected by this disease. Due to diagnostic difficulties, the spectrum of clinical, enzymatic, and genetic tests was extended to include analysis of the ultrastructure of cells from a rectal biopsy. The aim of our research was to search for pathognomonic features of CLN2 and to analyse the mitochondrial damage accompanying the disease. In the examined cells of the rectal mucosa, as expected, filamentous deposits of the curvilinear profile (CVP) type were found, which dominated quantitatively. Mixed deposits of the CVP/fingerprint profile (FPP) type were observed less frequently in the examined cells. A form of inclusions of unknown origin, not described so far in CLN2 disease, were wads of osmophilic material (WOMs). They occurred alone or co-formed mixed deposits. In addition, atypically damaged mitochondria were observed in muscularis mucosae. Their deformed cristae had contact with inclusions that looked like CVPs. Considering the confirmed role of the c subunit of the mitochondrial ATP synthase in the formation of filamentous lipopigment deposits in the group of NCLs, we suggest the possible significance of other mitochondrial proteins, such as mitochondrial contact site and cristae organizing system (MICOS), in the formation of these deposits. The presence of WOMs in the context of searching for ultrastructural pathognomonic features in CLN2 disease also requires further research.