Brain stimulation with 40 Hz heterochromatic flicker extended beyond red, green, and blue.

Alzheimer's disease (AD) is associated with electrophysiological changes in the brain. Pre-clinical and early clinical trials have shown promising results for the possible therapy of AD with 40 Hz neurostimulation. The most notable findings used stroboscopic flicker, but this technique poses an inherent barrier for human applications due to its visible flickering and resulting high level of perceived discomfort. Therefore, alternative options should be investigated for entraining 40 Hz brain activity with light sources that appear less flickering. Previously, chromatic flicker based on red, green, and blue (RGB) have been studied in the context of brain-computer interfaces, but this is an incomplete representation of the colours in the visual spectrum. This study introduces a new kind of heterochromatic flicker based on spectral combinations of blue, cyan, green, lime, amber, and red (BCGLAR). These combinations are investigated by the steady-state visually evoked potential (SSVEP) response from the flicker with an aim of optimising the choice of 40 Hz light stimulation with spectrally similar colour combinations in BCGLAR space. Thirty healthy young volunteers were stimulated with heterochromatic flicker in an electroencephalography experiment with randomised complete block design. Responses were quantified as the 40 Hz signal-to-noise ratio and analysed using mixed linear models. The size of the SSVEP response to heterochromatic flicker is dependent on colour combinations and influenced by both visual and non-visual effects. The amber-red flicker combination evoked the highest SSVEP, and combinations that included blue and/or red consistently evoked higher SSVEP than combinations only with mid-spectrum colours. Including a colour from either extreme of the visual spectrum (blue and/or red) in at least one of the dyadic phases appears to be more important than choosing pairs of colours that are far from each other on the visual spectrum. Spectrally adjacent colour pairs appear less flickering to the perceiver, and thus the results motivate investigations into the limits for how alike the two phases can be and still evoke a 40 Hz response. Specifically, combining a colour on either extreme of the visual spectrum with another proximal colour might provide the best trade-off between flickering sensation and SSVEP magnitude.

Predicting Obsessive-Compulsive Disorder Events in Children and Adolescents in the Wild using a Wearable Biosensor (Wrist Angel): Protocol for the Analysis Plan of a Nonrandomized Pilot Study.

BACKGROUND: Physiological signals such as heart rate and electrodermal activity can provide insight into an individual's mental state, which are invaluable information for mental health care. Using recordings of physiological signals from wearable devices in the wild can facilitate objective monitoring of symptom severity and evaluation of treatment progress. OBJECTIVE: We designed a study to evaluate the feasibility of predicting obsessive-compulsive disorder (OCD) events from physiological signals recorded using wrist-worn devices in the wild. Here, we present an analysis plan for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. METHODS: In total, 18 children and adolescents aged between 8 and 16 years were included in this study. Nine outpatients with an OCD diagnosis were recruited from a child and adolescent mental health center. Nine youths without a psychiatric diagnosis were recruited from the catchment area. Patients completed a clinical interview to assess OCD severity, types of OCD, and number of OCD symptoms in the clinic. Participants wore a biosensor on their wrist for up to 8 weeks in their everyday lives. Patients were asked to press an event tag button on the biosensor when they were stressed by OCD symptoms. Participants without a psychiatric diagnosis were asked to press this button whenever they felt really scared. Before and after the 8-week observation period, participants wore the biosensor under controlled conditions of rest and stress in the clinic. Features are extracted from 4 different physiological signals within sliding windows to predict the distress event logged by participants during data collection. We will test the prediction models within participants across time and multiple participants. Model selection and estimation using 2-layer cross-validation are outlined for both scenarios. RESULTS: Participants were included between December 2021 and December 2022. Participants included 10 female and 8 male participants with an even sex distribution between groups. Patients were aged between 10 and 16 years, and adolescents without a psychiatric diagnosis were between the ages of 8 and 16 years. Most patients had moderate to moderate to severe OCD, except for 1 patient with mild OCD. CONCLUSIONS: The strength of the planned study is the investigation of predictions of OCD events in the wild. Major challenges of the study are the inherent noise of in-the-wild data and the lack of contextual knowledge associated with the recorded signals. This preregistered analysis plan discusses in detail how we plan to address these challenges and may help reduce interpretation bias of the upcoming results. If the obtained results from this study are promising, we will be closer to automated detection of OCD events outside of clinical experiments. This is an important tool for the assessment and treatment of OCD in youth. TRIAL REGISTRATION: ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/study/NCT05064527. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/48571.

Predicting obsessive-compulsive disorder episodes in adolescents using a wearable biosensor-A wrist angel feasibility study.

Introduction: Obsessive-compulsive disorders (OCD) are marked by distress, negative emotions, mental processes and behaviors that are reflected in physiological signals such as heart rate, electrodermal activity, and skin temperature. Continuous monitoring of physiological signals associated with OCD symptoms may make measures of OCD more objective and facilitate close monitoring of prodromal symptoms, treatment progress and risk of relapse. Thus, we explored the feasibility of capturing OCD events in the real world using an unobtrusive wrist worn biosensor and machine learning models. Methods: Nine adolescents (ages 10-17 years) with mild to moderate-severe OCD were recruited from child and adolescent mental health services. Participants were asked to wear the biosensor in the lab during conditions of rest and exposure to OCD symptom-triggering stimuli and for up to 8 weeks in their everyday lives and register OCD events. We explored the relationships among physiological data, registered OCD events, age, OCD symptom severity and symptom types. In the machine learning models, we considered detection of OCD events as a binary classification problem. A nested cross-validation strategy with either random 10-folds, leave-one-subject-out, or leave-week(s)-out in both layers was used. We compared the performance of four models: logistic regression, random forest (RF), feedforward neural networks, and mixed-effect random forest (MERF). To explore the ability of the models to detect OCD events in new patients, we assessed the performance of participant-based generalized models. To explore the ability of models to detect OCD events in future, unseen data from the same patients, we compared the performance of temporal generalized models trained on multiple patients with personalized models trained on single patients. Results: Eight of the nine participants collected biosensor signals totaling 2, 405 h and registered 1, 639 OCD events. Better performance was obtained when generalizing across time compared to across patients. Generalized temporal models trained on multiple patients were found to perform better than personalized models trained on single patients. RF and MERF models outperformed the other models in terms of accuracy in all cross-validation strategies, reaching 70% accuracy in random and participant cross-validation. Conclusion: Our pilot results suggest that it is possible to detect OCD episodes in the everyday lives of adolescents using physiological signals captured with a wearable biosensor. Large scale studies are needed to train and test models capable of detecting and predicting episodes. Clinical trial registration: ClinicalTrials.gov: NCT05064527, registered October 1, 2021.

Adverse events in cognitive behavioral therapy and relaxation training for children and adolescents with obsessive-compulsive disorder: A mixed methods study and analysis plan for the TECTO trial.

Background: Knowledge on adverse events in psychotherapy for youth with OCD is sparse. No official guidelines exist for defining or monitoring adverse events in psychotherapy. Recent recommendations call for more qualitative and quantitative assessment of adverse events in psychotherapy trials. This mixed methods study aims to expand knowledge on adverse events in psychotherapy for youth with OCD. Methods: This is an analysis plan for a convergent mixed methods study within a randomized clinical trial (the TECTO trial). We include at least 128 youth aged 8-17 years with obsessive-compulsive disorder (OCD). Participants are randomized to either family-based cognitive behavioral therapy (FCBT) or family-based psychoeducation and relaxation training (FPRT). Adverse events are monitored quantitatively with the Negative Effects Questionnaire. Furthermore, we assess psychiatric symptoms, global functioning, quality of life, and family factors to investigate predictors for adverse events. We conduct semi-structured qualitative interviews with all youths and their parents on their experience of adverse events in FCBT or FPRT. For the mixed methods analysis, we will merge 1) a qualitative content analysis with descriptive statistics comparing the types, frequencies, and severity of adverse events; 2) a qualitative content analysis of the perceived causes for adverse events with prediction models for adverse events; and 3) a thematic analysis of the participants' treatment evaluation with a correlational analysis of adverse events and OCD severity. Discussion: The in-depth mixed methods analysis can inform 1) safer and more effective psychotherapy for OCD; 2) instruments and guidelines for monitoring adverse events; and 3) patient information on potential adverse events. The main limitation is risk of missing data. Trial registration: ClinicalTrials.gov identifier: NCT03595098. Registered on July 23, 2018.

A Wearable Artificial Intelligence Feedback Tool (Wrist Angel) for Treatment and Research of Obsessive Compulsive Disorder: Protocol for a Nonrandomized Pilot Study.

BACKGROUND: Obsessive compulsive disorder (OCD) in youth is characterized by behaviors, emotions, physiological reactions, and family interaction patterns. An essential component of therapy involves increasing awareness of the links among thoughts, emotions, behaviors, bodily sensations, and family interactions. An automatic assessment tool using physiological signals from a wearable biosensor may enable continuous symptom monitoring inside and outside of the clinic and support cognitive behavioral therapy for OCD. OBJECTIVE: The primary aim of this study is to evaluate the feasibility and acceptability of using a wearable biosensor to monitor OCD symptoms. The secondary aim is to explore the feasibility of developing clinical and research tools that can detect and predict OCD-relevant internal states and interpersonal processes with the use of speech and behavioral signals. METHODS: Eligibility criteria for the study include children and adolescents between 8 and 17 years of age diagnosed with OCD, controls with no psychiatric diagnoses, and one parent of the participating youths. Youths and parents wear biosensors on their wrists that measure pulse, electrodermal activity, skin temperature, and acceleration. Patients and their parents mark OCD episodes, while control youths and their parents mark youth fear episodes. Continuous, in-the-wild data collection will last for 8 weeks. Controlled experiments designed to link physiological, speech, behavioral, and biochemical signals to mental states are performed at baseline and after 8 weeks. Interpersonal interactions in the experiments are filmed and coded for behavior. The films are also processed with computer vision and for speech signals. Participants complete clinical interviews and questionnaires at baseline, and at weeks 4, 7, and 8. Feasibility criteria were set for recruitment, retention, biosensor functionality and acceptability, adherence to wearing the biosensor, and safety related to the biosensor. As a first step in learning the associations between signals and OCD-related parameters, we will use paired t tests and mixed effects models with repeated measures to assess associations between oxytocin, individual biosignal features, and outcomes such as stress-rest and case-control comparisons. RESULTS: The first participant was enrolled on December 3, 2021, and recruitment closed on December 31, 2022. Nine patient dyads and nine control dyads were recruited. Sixteen participating dyads completed follow-up assessments. CONCLUSIONS: The results of this study will provide preliminary evidence for the extent to which a wearable biosensor that collects physiological signals can be used to monitor OCD severity and events in youths. If we find the study to be feasible, further studies will be conducted to integrate biosensor signals output into machine learning algorithms that can provide patients, parents, and therapists with actionable insights into OCD symptoms and treatment progress. Future definitive studies will be tasked with testing the accuracy of machine learning models to detect and predict OCD episodes and classify clinical severity. TRIAL REGISTRATION: ClinicalTrials.gov NCT05064527; https://clinicaltrials.gov/ct2/show/NCT05064527. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45123.

Associations Between the Severity of Obsessive-Compulsive Disorder and Vocal Features in Children and Adolescents: Protocol for a Statistical and Machine Learning Analysis.

BACKGROUND: Artificial intelligence tools have the potential to objectively identify youth in need of mental health care. Speech signals have shown promise as a source for predicting various psychiatric conditions and transdiagnostic symptoms. OBJECTIVE: We designed a study testing the association between obsessive-compulsive disorder (OCD) diagnosis and symptom severity on vocal features in children and adolescents. Here, we present an analysis plan and statistical report for the study to document our a priori hypotheses and increase the robustness of the findings of our planned study. METHODS: Audio recordings of clinical interviews of 47 children and adolescents with OCD and 17 children and adolescents without a psychiatric diagnosis will be analyzed. Youths were between 8 and 17 years old. We will test the effect of OCD diagnosis on computationally derived scores of vocal activation using ANOVA. To test the effect of OCD severity classifications on the same computationally derived vocal scores, we will perform a logistic regression. Finally, we will attempt to create an improved indicator of OCD severity by refining the model with more relevant labels. Models will be adjusted for age and gender. Model validation strategies are outlined. RESULTS: Simulated results are presented. The actual results using real data will be presented in future publications. CONCLUSIONS: A major strength of this study is that we will include age and gender in our models to increase classification accuracy. A major challenge is the suboptimal quality of the audio recordings, which are representative of in-the-wild data and a large body of recordings collected during other clinical trials. This preregistered analysis plan and statistical report will increase the validity of the interpretations of the upcoming results. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/39613.

Family-based cognitive behavioural therapy versus family-based relaxation therapy for obsessive-compulsive disorder in children and adolescents: protocol for a randomised clinical trial (the TECTO trial).

BACKGROUND: Cognitive behavioural therapy (CBT) is the recommended first-line treatment for children and adolescents with obsessive-compulsive disorder (OCD), but evidence concerning treatment-specific benefits and harms compared with other interventions is limited. Furthermore, high risk-of-bias in most trials prevent firm conclusions regarding the efficacy of CBT. We investigate the benefits and harms of family-based CBT (FCBT) versus family-based psychoeducation and relaxation training (FPRT) in youth with OCD in a trial designed to reduce risk-of-bias. METHODS: This is an investigator-initiated, independently funded, single-centre, parallel group superiority randomised clinical trial (RCT). Outcome assessors, data managers, statisticians, and conclusion drawers are blinded. From child and adolescent mental health services we include patients aged 8-17 years with a primary OCD diagnosis and an entry score of ≥16 on the Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS). We exclude patients with comorbid illness contraindicating trial participation; intelligence quotient < 70; or treatment with CBT, PRT, antidepressant or antipsychotic medication within the last 6 months prior to trial entry. Participants are randomised 1:1 to the experimental intervention (FCBT) versus the control intervention (FPRT) each consisting of 14 75-min sessions. All therapists deliver both interventions. Follow-up assessments occur in week 4, 8 and 16 (end-of-treatment). The primary outcome is OCD symptom severity assessed with CY-BOCS at end-of-trial. Secondary outcomes are quality-of-life and adverse events. Based on sample size estimation, a minimum of 128 participants (64 in each intervention group) are included. DISCUSSION: In our trial design we aim to reduce risk-of-bias, enhance generalisability, and broaden the outcome measures by: 1) conducting an investigator-initiated, independently funded RCT; 2) blinding investigators; 3) investigating a representative sample of OCD patients; 3) using an active control intervention (FPRT) to tease apart general and specific therapy effects; 4) using equal dosing of interventions and therapist supervision in both intervention groups; 5) having therapists perform both interventions decided by randomisation; 6) rating fidelity of both interventions; 7) assessing a broad range of benefits and harms with repeated measures. The primary study limitations are the risk of missing data and the inability to blind participants and therapists to the intervention. TRIAL REGISTRATION: ClinicalTrials.gov : NCT03595098, registered July 23, 2018.

Does correlated color temperature affect the ability of humans to identify veins?

In the present study we provide empirical evidence and demonstrate statistically that white illumination settings can affect the human ability to identify veins in the inner hand vasculature. A special light-emitting diode lamp with high color rendering index (CRI 84-95) was developed and the effect of correlated color temperature was evaluated, in the range between 2600 and 5700 K at an illuminance of 40±9 lx on the ability of adult humans to identify veins. It is shown that the ability to identify veins can, on average, be increased up to 24% when white illumination settings that do not resemble incandescent light are applied. The illuminance reported together with the effect of white illumination settings on direct visual perception of biosamples are relevant for clinical investigations during the night.