Accurate assessment of residual cognition in patients with disorders of consciousness (DoC) remains challenging due to their limited behavioral responsiveness. Statistical learning (SL), defined as the ability to extract regularities from sensory input, reflects neural processing associated with residual cognition. This study examined how SL is preserved across different levels of consciousness and whether neural markers of SL can inform diagnosis and prognosis in patients with DoC.

We employed an auditory paradigm with structured trisyllabic pseudowords and recorded electroencephalography (EEG) from 46 patients and 25 healthy controls. Neural entrainment to statistical regularities was quantified using frequency-tagging analysis.

The within-group effect pattern was observed in the healthy control group and in the minimally conscious state (MCS) group, whereas the unresponsive wakefulness syndrome (UWS) group did not show a significant effect. Moreover, stronger neural learning predicted better behavioral scores in patients with MCS, but not in patients with UWS. Further analyses showed that SL-related effect correlated significantly with behavioral scores and 6-month outcomes in the traumatic group, whereas no such association was observed in the non-traumatic group. Importantly, SL-based EEG models effectively distinguished healthy controls from patients and further differentiated MCS from UWS patients. Combining the SL model with clinical behavioral performance could enable a more precise assessment of patients' level of consciousness.

These findings suggest that SL may require a minimal level of consciousness in the integration of structured regularities and that it may serve as an index for clinical diagnosis and prognosis in patients with DoC.

This exploratory small sample study aimed to investigate dynamic functional connectivity (dFC) abnormalities in adolescent depression associated with childhood trauma through resting-state electroencephalography (EEG), focusing on identifying atypical patterns in key brain networks and their correlation with clinical symptoms.

Resting-state EEG data were recorded from adolescents diagnosed with depression, encompassing two subtypes: individuals with a history of childhood trauma (CTD) and those without such a history (NCTD). Additionally, data were collected from a matched group of healthy controls (HCs). Childhood trauma was assessed using the Childhood Trauma Questionnaire-Short Form (CTQ-SF). Depression severity was evaluated using the 17-item Hamilton Depression Rating Scale (HAMD-17). dFC was analyzed using the sliding window method and weighted phase-lag index, followed by K-means clustering to identify key connectivity states across frequency bands. Spearman correlation analysis was performed to explore the relationship between dFC matrix characteristics and clinical features in the CTD group.

The final sample comprised 25 individuals in the CTD group, 27 in the NCTD group, and 32 HCs. The CTD group exhibited an earlier onset and more severe depression compared to the NCTD group, with significant differences (p < 0.001). In the CTQ-SF assessment, the CTD group scored significantly higher on emotional abuse, emotional neglect, and somatic neglect compared to both the HC and NCTD groups (p < 0.001). dFC analysis and K-means clustering revealed three distinct brain states (S1, S2, S3). These brain states correlated with CTQ-SF scores in the CTD group.

Resting EEG with dFC analysis and K-means clustering could be biomarkers for identifying adolescent depression linked to childhood trauma, aiding personalized trauma-focused interventions and monitoring treatment efficacy.

People with dementia and their caregivers regularly face unmet needs, not only in dementia care, but also in self-care, education, training, and the use of digital tools. National dementia policies, clinical guidelines, and best practices can improve dementia care and quality of life across Europe. This study examined how far current national guidelines and good practices address these domains, and where important gaps remain. The objective was to identify unmet needs in national guidelines and good practices on self-care, education and training, and digital tools for people with dementia and their caregivers.

We conducted a cross-country comparative analysis in countries participating in the JADE Health collaboration: Bulgaria, Croatia, Denmark, Germany, Latvia, Lithuania, Poland, Spain, and Ukraine. Country partners provided national and regional dementia guidelines and descriptions of good practices, which were systematically reviewed and compared against selected best practices from the EU Best Practice Portal, focusing on alignment and gaps in the three domains.

We found substantial variation between countries in the maturity, scope, and implementation of dementia guidelines and good practices. Some countries, such as Denmark and Germany, have relatively advanced policy frameworks and clinical guidance, while others, such as Croatia, rely on promising but regionally limited programs, and several, including Bulgaria and Latvia, still depend on fragmented services. Across all countries, integrated approaches that combine structured self-care support, systematic education and training, and scalable digital tools were rare.

Best practices explicitly addressing self-care, education and training, and digital tools remain limited and unevenly implemented. Strengthening and disseminating such practices is essential to guide the further development of dementia care across Europe and to reduce persistent unmet needs among people with dementia and their caregivers.

Individuals with neurodevelopmental disorders (NDDs) are at increased risk of having sleep difficulties. A variety of sleep problems have been reported in Angelman syndrome (AS), Prader-Willi syndrome (PWS), and Rett syndrome (RTT). The present study intended to expand an earlier characterization of sleep difficulties in a large AS, PWS, and RTT sample by analyzing subscales of standardized sleep questionnaires.

Scores from children (2-18 years) with AS (n = 74), PWS (n = 90) or RTT (n = 241) on components of the Children's Sleep Habits Questionnaire (CSHQ), the Sleep-Related Breathing Disorder (SRBD) scale, and the Pediatric Daytime Sleepiness Scale (PDSS) were compared between NDDs and with those from a group of neurotypical siblings (n = 282). Additional comparisons of scores after a 12-month follow-up evaluation, in a subset of individuals, were also performed. Data were analyzed using nonparametric tests and, for changes over time, both cross-sectionally and longitudinally.

Comparisons with neurotypical children showed that night waking and snoring were increased in the three NDDs while parasomnias and daytime sleepiness only in AS and RTT. Children with RTT also had the highest scores on measures of disordered breathing. At the 12-month follow-up, scores decreased in neurotypical children but had variable courses in the NDDs, with increased disordered breathing scores characterizing AS and RTT. There was high agreement among disordered breathing measures, but not among daytime sleepiness scales. Overall, CSHQ scores were relatively stable within NDDs.

Sleep questionnaires revealed disorder-specific profiles of sleep problems that could assist in their identification and management. The CSHQ and the SRBD, including their subscales, appear to be consistent measures particularly for sleep-disordered breathing and, therefore, suitable for clinical and research use in severe NDDs. Follow-up studies should expand the range of instruments to include objective measures in the characterization of sleep abnormalities in AS, PWS, and RTT.

Prior evidence for the ketogenic diet (KD) interventions in treatment-resistant depression (TRD) was very limited when recruitment for the present pilot trial began on 1 November 2023. This pilot trial aimed to examine feasibility, adherence, safety, and preliminary clinical signals in patients with TRD treated with a 6-week KD intervention.

This prospective, single-arm, open-label pilot trial was conducted in adult patients with TRD who had a baseline Montgomery-Åsberg Depression Rating Scale (MADRS) score of at least 20. The intervention consisted of a 6-week KD; preexisting psychopharmacological treatment had to be stable before study entry and was not changed during the intervention. Adherence was monitored by daily fasting capillary β-hydroxybutyrate measurements. The MADRS was assessed at baseline and after 2, 4, and 6 weeks in all participants. A response was defined as a ≥ 50% reduction in the MADRS score from baseline; remission was defined as a MADRS score of ≤10.

A total of 13 eligible patients were offered study participation; 5 declined, and 8 were enrolled. Four patients completed the full 6-week intervention. One patient discontinued after week 2, one patient was excluded after a revised diagnosis, and two patients discontinued because nutritional ketosis was not achieved. Nutritional ketosis was documented in all completers and was accompanied by marked symptomatic improvement in three of the four completers. Among the completers, two patients met the remission criteria at week 6, whereas one showed partial improvement and one showed no relevant antidepressant benefit despite sustained nutritional ketosis. The intervention was generally feasible, but recruitment and retention were challenging.

In this small prospective pilot trial, a KD was feasible in a subset of patients with TRD and was associated with improvement in depressive symptoms in three of the four completers. However, recruitment difficulties, early discontinuations, and heterogeneity of clinical responses highlight the need for larger controlled studies.

Data passively obtained from smartphones and wearables can provide nearly continuous objective information that enables quantification of states and traits across broad physiological, behavioral, and emotional domains impacted in mental health conditions, including depression and anxiety. Widespread application of such digital phenotyping could transform the assessment of depression and anxiety in research and clinical care, but the field has lacked well-powered longitudinal studies demonstrating the utility of this approach. This paper describes the design and implementation of the Digital Mental Health Study (DMHS), which collected up to 12 months of sensor data from iPhone and Apple Watch in over 4000 consenting participants, a sample diverse by age, sex at birth, ethnicity, and depression symptom severity. To enable the use of these digital phenotypes to assay the complexity and heterogeneity of depression and anxiety, we designed a protocol of periodic self-report and interview-based scales optimized to assess elements of depression, anxiety, and perceived stress as broadly as possible while minimizing participant measurement burden. We report here the strategies used to recruit and enroll the DMHS sample, the process employed to develop study methods and protocols, and initial findings describing longitudinal symptom trajectories and demonstrating high participant engagement over 12 months.

Mental health care has long been a leading domain for the application of clinical virtual reality (VR) technologies. The recent proliferation of affordable all-in-one VR devices has significantly increased accessibility and created new opportunities for innovative mental health therapies. As immersive technologies become more integrated into daily life, scientific evidence and clinical insights can be synthesized into actionable recommendations. The American Medical Extended Reality Association, in collaboration with the Mental Health Virtual Reality International Coalition, presents these comprehensive recommendations for the use of VR in clinical mental health care. This work summarizes the current state of the field, examines the supporting scientific literature, outlines ethical considerations and their practical applications, and offers research-based and experience-informed recommendations. Additionally, it identifies key collaborative directions to advance the responsible and effective use of VR in mental health care. These recommendations may serve as a foundational resource for clinicians, researchers, developers, and policymakers for the integration of VR in mental health practice.

Cannabis use in the United States has become increasingly diverse following widespread legalization, with multiple consumption modalities differing in potency, pharmacokinetics, and health effects. Understanding how psychological distress relates to specific cannabis use modalities is essential for targeted public health surveillance.

To examine the association between past-month serious psychological distress (SPD) and cannabis use modalities among U.S. adults.

We analyzed 33,404 adults aged ≥18 years from the 2023 National Survey on Drug Use and Health (NSDUH). Survey-weighted logistic regression models were used to examine associations between SPD and past-month cannabis use modalities, adjusting for sociodemographic characteristics, substance use behaviors, and mental health treatment. Each cannabis modality was modeled separately as a binary outcome.

Overall, 8.9% of adults reported SPD. Cannabis use was more prevalent among adults with SPD than those without (26.8 vs. 12.3%). In adjusted analyses, SPD was associated with higher odds of multiple cannabis modalities, including smoking (AOR = 1.72, 95% CI: 1.47-2.01), vaping (AOR = 1.84, 95% CI: 1.44-2.34), dabbing (AOR = 1.68, 95% CI: 1.29-2.20), blunt use (AOR = 2.06, 95% CI: 1.69-2.50), topical use (AOR = 1.99, 95% CI: 1.26-3.13), and drops/strips (AOR = 2.41, 95% CI: 1.48-3.93). Edible cannabis use was also significantly associated with SPD (AOR = 1.45, 95% CI: 1.14-1.84), whereas associations with pill use were not statistically significant.

Psychological distress is associated with increased likelihood of using multiple cannabis modalities among U.S. adults, with variation across product types. These findings highlight the importance of considering mental health status in cannabis surveillance and the need for modality-specific approaches in prevention and clinical care.

Depressive and anxiety symptoms (depression and anxiety hereafter), and suicidality are common among psychiatric patients. This study examined the network structure of depressive and anxiety symptoms and suicidality among psychiatric patients across Asia.

Data were drawn from the Research on Asian Psychotropic Prescription Patterns for Antidepressants Phase 3 study (REAP-AD3), which included 2,455 psychiatric patients from 11 Asian countries and territories. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality (i.e., suicidal thoughts or acts) was assessed by a clinical interview. Expected Influence (EI) and Bridge EI were used as centrality indices in the symptom network to characterize the structure of the symptoms.

The point prevalence of suicidality was 30.1% (95% confidence interval [CI] = 28.2, 31.9) among the psychiatric patients. The network analysis identified PHQ2 ("Sad mood") as the most central symptom, followed by GAD2 ("Uncontrollable worry"). Additionally, PHQ8 ("Motor disturbances") and S ("Suicidality") were identified as bridge nodes linking depression and anxiety with suicidality. The flow network indicated that PHQ6 ("Guilt") and PHQ2 ("Sad mood") had the strongest positive associations with suicidality.

Suicidality was common among psychiatric patients across Asia. The central and bridge symptoms might represent potential clinical markers and generate hypotheses for longitudinal and interventional research on depression, anxiety, and suicidality in the future.

The authors aimed to examine the relationship between characteristics of patients and health systems and receipt of mental health treatment among patients assigned to collaborative care (CC) for opioid use disorder co-occurring with depression, posttraumatic stress disorder (PTSD), or both.

This secondary analysis used data from the intervention arm (N=400 participants) of a randomized clinical trial of CC, implemented in 18 low-resourced clinics, for patients with opioid use disorder co-occurring with depression, PTSD, or both. Outcomes were receipt of psychiatric medication or counseling during the 6-month intervention, as obtained from the electronic health records data or a caseload tracking tool. Baseline characteristics were assessed by using t tests for continuous variables and chi-square tests for categorical and binary measures. Adjusted logistic regression was used to identify associations between treatment receipt and characteristics; predicted probabilities for covariates were used to demonstrate effect size.

Of the patients, 51% received psychiatric medication and 44% received counseling. In the adjusted models, co-use of stimulants was associated with less likelihood of receiving psychiatric medication (adjusted odds ratio [AOR]=0.95) or counseling (AOR=0.92), as was lower education. Male sex and health system characteristics were associated with a lower likelihood of receiving counseling; non-Hispanic ethnicity and more severe depression and PTSD symptoms were associated with a greater likelihood of receiving counseling.

Even within CC settings, substantial unmet need for mental health treatment remained, particularly among individuals co-using stimulants. Future research should examine additional support to increase mental health treatment among individuals with co-occurring disorders.