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Abstract This single‐center study administered MIJ821 (onfasprodil) as an intravenous infusion to healthy volunteers and included two parts: a single ascending dose study (Part 1) and a repeated intravenous dose study (Part 2). Primary objective was to evaluate the safety and tolerability of single ascending intravenous doses infused over a 40‐min period and of two repeated doses (1 week apart) of MIJ821 in healthy volunteers. Secondary objectives were to assess the pharmacokinetics of MIJ821 after intravenous infusion in Part 1 and Part 2 of the study. Overall, 43 subjects in Part 1 and 12 subjects in Part 2 were randomized in the study. Median age in Part 1 and Part 2 was 45.0 and 43.5 years, respectively, with the majority being Caucasian (Part 1: 84%; Part 2: 92%). 19 subjects (44.2%) in Part 1 and 8 subjects (66.7%) in Part 2 experienced at least one adverse event (AE). Following single dose in Part 1 and Part 2, the AUCinf values of MIJ821 increased in a dose‐proportional manner across the dose range 0.016–0.48 mg/kg and the Cmax values in a slight overproportional manner across the dose range 0.048–0.48 mg/kg. At the highest dose of 0.48 mg/kg, the geometric mean AUCinf was 708 h ng/mL and the geometric mean Cmax was 462 ng/mL. Inspection of 1‐h post‐dose resting electroencephalography activity across cohorts showed a relationship to administered dose, providing exploratory evidence of distal target engagement. In conclusion, MIJ821 showed a good safety and tolerability profile in healthy volunteers. Dissociative AEs were mild, transient, and dose‐dependent.

Background: Identifying neurological biomarkers to aid with diagnoses and treatment effect evaluation of mood and cognitive symptoms in major depressive disorder (MDD) is an unmet need. This study explores the Brain Network Analytics (BNA) tool, which provides information on cortical patterns compared to a healthy normative group, for improving MDD management. Methods: BNA was used to extract event-related potentials (ERPs) from high-density EEG, and its clinical utility to assess the effects of vortioxetine on depression and cognition was evaluated. Twenty-five MDD subjects (20–65 years old) with cognitive dysfunction underwent an 8-week open-label treatment using vortioxetine. ERP data, generated during cognitive tasks, were collected at pre-treatment, two weeks and eight weeks into treatment. Correlations with MDD symptoms and cognitive functioning were investigated. Exploratory analyses assessed the differences in baseline and endpoint ERP characteristics between MDD subjects and matched controls (N = 41). Results: Compared to controls, MDD subjects exhibited increased baseline latencies of P200 and P3b ERP components, which normalized at treatment endpoint. P200 and P300 in the MDD group were correlated with clinical assessments, and indicated a pro-cognitive effect of vortioxetine independent of its antidepressant effect. Limitations: This study was limited by a small sample size and lack of a placebo condition. Conclusions: Our results suggest that the application of BNA can potentially support MDD diagnosis and monitor treatment efficacy across various symptom and functional domains. Further studies are warranted to explore the role of BNA in clinical research and practice.

This study aimed to evaluate the safety and tolerability of STP1, a combination of ibudilast and bumetanide, tailored for the treatment of a clinically and biologically defined subgroup of patients with Autism Spectrum Disorder (ASD), namely ASD Phenotype 1 (ASD-Phen1). We conducted a randomized, double-blind, placebo-controlled, parallel-group phase 1b study with two 14-day treatment phases (registered at clinicaltrials.gov as NCT04644003). Nine ASD-Phen1 patients were administered STP1, while three received a placebo. We assessed safety and tolerability, along with electrophysiological markers, such as EEG, Auditory Habituation, and Auditory Chirp Synchronization, to better understand STP1’s mechanism of action. Additionally, we used several clinical scales to measure treatment outcomes. The results showed that STP1 was well-tolerated, with electrophysiological markers indicating a significant and dose-related reduction of gamma power in the whole brain and in brain areas associated with executive function and memory. Treatment with STP1 also increased alpha 2 power in frontal and occipital regions and improved habituation and neural synchronization to auditory chirps. Although numerical improvements were observed in several clinical scales, they did not reach statistical significance. Overall, this study suggests that STP1 is well-tolerated in ASD-Phen1 patients and shows indirect target engagement in ASD brain regions of interest.

Objective The purpose of this study is to explore the possibility of developing a biomarker that can discriminate early-stage Parkinson’s disease from healthy brain function using electroencephalography (EEG) event-related potentials (ERPs) in combination with Brain Network Analytics (BNA) technology and machine learning (ML) algorithms. Background Currently, diagnosis of PD depends mainly on motor signs and symptoms. However, there is need for biomarkers that detect PD at an earlier stage to allow intervention and monitoring of potential disease-modifying therapies. Cognitive impairment may appear before motor symptoms, and it tends to worsen with disease progression. While ERPs obtained during cognitive tasks performance represent processing stages of cognitive brain functions, they have not yet been established as sensitive or specific markers for early-stage PD. Methods Nineteen PD patients (disease duration of ≤2 years) and 30 healthy controls (HC) underwent EEG recording while performing visual Go/No-Go and auditory Oddball cognitive tasks. ERPs were analyzed by the BNA technology, and a ML algorithm identified a combination of features that distinguish early PD from HC. We used a logistic regression classifier with a 10-fold cross-validation. Results The ML algorithm identified a neuromarker comprising 15 BNA features that discriminated early PD patients from HC. The area-under-the-curve of the receiver-operating characteristic curve was 0.79. Sensitivity and specificity were 0.74 and 0.73, respectively. The five most important features could be classified into three cognitive functions: early sensory processing (P50 amplitude, N100 latency), filtering of information (P200 amplitude and topographic similarity), and response-locked activity (P-200 topographic similarity preceding the motor response in the visual Go/No-Go task). Conclusions This pilot study found that BNA can identify patients with early PD using an advanced analysis of ERPs. These results need to be validated in a larger PD patient sample and assessed for people with premotor phase of PD.

Background: Digital technologies have the potential to provide objective and precise tools to detect depression-related symptoms. Deployment of digital technologies in clinical research can enable collection of large volumes of clinically relevant data that may not be captured using conventional psychometric questionnaires and patient-reported outcomes. Rigorous methodology studies to develop novel digital endpoints in depression are warranted.Objective: We conducted an exploratory, cross-sectional study to evaluate several digital technologies in subjects with major depressive disorder (MDD) and persistent depressive disorder (PDD), and healthy controls. The study aimed at assessing utility and accuracy of the digital technologies as potential diagnostic tools for unipolar depression, as well as correlating digital biomarkers to clinically validated psychometric questionnaires in depression.Methods: A cross-sectional, non-interventional study of 20 participants with unipolar depression (MDD and PDD/dysthymia) and 20 healthy controls was conducted at the Centre for Human Drug Research (CHDR), the Netherlands. Eligible participants attended three in-clinic visits (days 1, 7, and 14), at which they underwent a series of assessments, including conventional clinical psychometric questionnaires and digital technologies. Between the visits, there was at-home collection of data through mobile applications. In all, seven digital technologies were evaluated in this study. Three technologies were administered via mobile applications: an interactive tool for the self-assessment of mood, and a cognitive test; a passive behavioral monitor to assess social interactions and global mobility; and a platform to perform voice recordings and obtain vocal biomarkers. Four technologies were evaluated in the clinic: a neuropsychological test battery; an eye motor tracking system; a standard high-density electroencephalogram (EEG)-based technology to analyze the brain network activity during cognitive testing; and a task quantifying bias in emotion perception.Results: Our data analysis was organized by technology – to better understand individual features of various technologies. In many cases, we obtained simple, parsimonious models that have reasonably high diagnostic accuracy and potential to predict standard clinical outcome in depression.Conclusion: This study generated many useful insights for future methodology studies of digital technologies and proof-of-concept clinical trials in depression and possibly other indications.

15q13.3 microdeletion syndrome causes a spectrum of cognitive disorders, including intellectual disability and autism. We assessed the ability of the EEG analysis algorithm Brain Network Analysis (BNA) to measure cognitive function in 15q13.3 deletion patients, and to differentiate between patient and control groups. EEG data was collected from 10 individuals with 15q13.3 microdeletion syndrome (14–18 years of age), as well as 30 age-matched healthy controls, as the subjects responded to Auditory Oddball (AOB) and Go/NoGo cognitive tasks. It was determined that BNA can be used to evaluate cognitive function in 15q13.3 microdeletion patients. This analysis also significantly differentiates between patient and control groups using 5 scores, all of which are produced from ERP peaks related to late cortical components that represent higher cognitive functions of attention allocation and response inhibition (P < 0.05).

Background: Difficulty in modulating multisensory input, specifically the sensory over-responsive (SOR) type, is linked to pain hypersensitivity and anxiety, impacting daily function and quality of life in children and adults. Reduced cortical activity recorded under resting state has been reported, suggestive of neuromodulation as a potential therapeutic modality. This feasibility study aimed to explore neurofeedback intervention in SOR. Methods: Healthy women with SOR (n = 10) underwent an experimental feasibility study comprising four measurement time points (T1—baseline; T2—preintervention; T3—postintervention; T4—follow-up). Outcome measures included resting-state EEG recording, in addition to behavioral assessments of life satisfaction, attaining functional goals, pain sensitivity, and anxiety. Intervention targeted the upregulation of alpha oscillatory power over ten sessions. Results: No changes were detected in all measures between T1 and T2. Exploring the changes in brain activity between T2 and T4 revealed power enhancement in delta, theta, beta, and gamma oscillatory bands, detected in the frontal region (p = 0.03–d = 0.637–1.126) but not in alpha oscillations. Furthermore, a large effect was found in enhancing life satisfaction and goal attainment (Cohen’s d = 1.18; 1.04, respectively), and reduced pain sensitivity and anxiety trait (Cohen’s d = 0.70). Conclusion: This is the first study demonstrating the feasibility of neurofeedback intervention in SOR.