Biology subjects

Wehrle, F.

Publications and source records attributed to Wehrle, F..

2 recordsLinked to original sources

bioRxiv2025

The Infraslow Fluctuation of Sigma Power During Sleep in Young Individuals with Schizophrenia

A reduction in sleep spindles, a major electrophysiological characteristic of Non-Rapid Eye Movement sleep, has been suggested as a potential biomarker of schizophrenia. While research has primarily focused on the spindle quantity, recent studies have begun to explore their temporal dynamics throughout the night. In healthy individuals, sleep spindles fluctuate on an infraslow [~]50-second timescale, alternating between phases of high and low spindle activity. This fluctuation is referred to as the infraslow fluctuation of sigma power (ISFS), which is modulated by noradrenergic activity from the locus coeruleus and linked to the organization of arousal and memory reactivation processes during sleep. Given the known deficit in sleep spindles, dysregulation of noradrenergic activity, and impairments in sleep maintenance and memory in schizophrenia, this study investigates the ISFS in sleep electroencephalography data from individuals with either Childhood-Onset Schizophrenia (COS; N = 17) or Early-Onset Schizophrenia (EOS; N = 11), aged 9 to 21 years, alongside age- and sex-matched healthy controls (N = 56). The presence and strength of the ISFS were reduced in both COS and EOS groups compared to controls, particularly in central-parietal electrodes. No significant differences in these features of the ISFS were found between the two clinical groups, despite group differences in sleep spindle density and clinical characteristics. These findings suggest that the ISFS is observable but reduced in young patients with schizophrenia and support the notion that the timing of sleep spindles may inform pathomechanistic models of the disorder, as well as future diagnostic approaches and interventions.

neuroscience↗

bioRxiv2022

The AI Neuropsychologist: Automatic scoring of memory deficits with deep learning

BackgroundMemory deficits are a hallmark of many different neurological and psychiatric conditions. The Rey-Osterrieth complex figure (ROCF) is the state-of-the-art assessment tool for neuropsychologists across the globe to assess the degree of non-verbal visual memory deterioration. To obtain a score, a trained clinician inspects a patients ROCF drawing and quantifies deviations from the original figure. This manual procedure is time-consuming, slow and scores vary depending on the clinicians experience, motivation and tiredness. MethodsHere, we leverage novel deep learning architectures to automatize the rating of memory deficits. For this, we collected more than 20k hand-drawn ROCF drawings from patients with various neurological and psychiatric disorders as well as healthy participants. Unbiased ground truth ROCF scores were obtained from crowdsourced human intelligence. This dataset was used to train and evaluate a multi-head convolutional neural network. ResultsThe model performs highly unbiased as it yielded predictions very close to the ground truth and the error was similarly distributed around zero. The neural network outperforms both online raters and clinicians. The scoring system can reliably identify and accurately score individual figure elements in previously unseen ROCF drawings, which facilitates explainability of the AI-scoring system. To ensure generalizability and clinical utility, the model performance was successfully replicated in a large independent prospective validation study that was pre-registered prior to data collection. ConclusionsOur AI-powered scoring system provides healthcare institutions worldwide with a digital tool to assess objectively, reliably and time-efficiently the performance in the ROCF test from hand-drawn images.

neuroscience↗