Gold, N. | bioRxiv Science

bioRxiv2023

Self-reported effects of classic psychedelics on stuttering

Stuttering is a neurodevelopmental communication disorder that can lead to significant social, occupational, and educational challenges. Traditional behavioral interventions for stuttering can be helpful, but effects are often limited. Classic psychedelics hold promise as a complement to traditional interventions, but their impact on stuttering is unknown. We conducted a qualitative content analysis to explore potential benefits and negative effects of psychedelics on stuttering using publicly available Reddit posts. A combined inductive-deductive approach was used whereby meaningful units were extracted and codes were initially assigned inductively. We then deductively applied an established framework to organize the effects (i.e., codes) into five subthemes (Behavioral, Emotional, Cognitive, Belief, and Social Connection), each of which was grouped under an organizing theme (positive, negative, neutral). Results indicated that the effects of psychedelics spanned all subthemes. Nearly 75% of participants reported overall positive effects. Nearly 60% of participants indicated positive behavioral change (e.g., reduced stuttering, increased speech control), 40% reported positive emotional benefit, 15% reported positive cognitive changes, 12% reported positive effects on beliefs, and 7% indicated positive social effects. Approximately 10% of participants reported negative behavioral effects (e.g., increased stuttering, reduced speech control). Psychedelics may help many stutterers improve communication, cultivate a healthier outlook, and promote psychological well-being. These preliminary results indicate that future clinical trials investigating psychedelic-assisted speech therapy for stuttering are warranted.

pathology↗

bioRxiv2017

Real-time changepoint detection for noisy biological time series

Experimentally and clinically collected time series data are often contaminated with significant confounding noise, creating short, noisy time series. This noise, due to natural variability and measurement error, poses a challenge to conventional change point detection methods.\n\nWe propose a novel and robust statistical method for change point detection for noisy biological time sequences. Our method is a significant improvement over traditional change point detection methods, which only examine a potential anomaly at a single time point. In contrast, our method considers all suspected anomaly points and considers the joint probability distribution of the number of change points and the elapsed time between two consecutive anomalies. We validate our method with three simulated time series, a widely accepted benchmark data set, two geological time series, a data set of ECG recordings, and a physiological data set of heart rate variability measurements of fetal sheep model of human labour, comparing it to three existing methods. Our method demonstrates significantly improved performance over the existing pointwise detection methods.

bioinformatics↗