Biology subjects

Jenni, O. G.

Publications and source records attributed to Jenni, O. G..

4 recordsLinked to original sources

bioRxiv2024

Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence

1An objective measure of brain maturation is highly insightful for monitoring both typical and atypical development. Slow wave activity, recorded in the sleep electroencephalogram (EEG), reliably indexes age-related changes in sleep pressure as well as deficits related to developmental disorders such as attention-deficit hyperactivity disorder (ADHD). We aimed to determine whether wake EEG measured before and after sleep could index the same developmental changes in sleep pressure, using data collected from 163 participants 3-25 years old. We analyzed ageand sleep-dependent changes in two measures of oscillatory activity, amplitudes and density, as well as two measures of aperiodic activity, offsets and exponents. We then compared these wake measures to sleep slow wave amplitudes and slopes. Finally, we compared wake EEG in children with ADHD (N=58) to neurotypical controls. Of the four wake measures, only oscillation amplitudes consistently exhibited the same changes as sleep slow waves. Wake amplitudes decreased with age, decreased after sleep, and this overnight decrease decreased with age. Furthermore, wake amplitudes were significantly related to both sleep slow wave amplitudes and slopes. Wake oscillation densities decreased overnight in children but increased overnight in adolescents and adults. Aperiodic offsets decreased linearly with age, decreased after sleep, and were significantly related to sleep slow wave amplitudes. Aperiodic exponents also decreased with age, but increased after sleep. No wake measure showed significant effects of ADHD. Overall, our results indicate that wake oscillation amplitudes, and to some extent aperiodic offsets, behave like sleep slow waves across sleep and development. At the same time, overnight changes in oscillation densities independently reflect some yet-unknown shift in neural activity around puberty.

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↗

bioRxiv2021

Bedtime to the brain: How infants sleep habits intertwine with sleep neurophysiology

Adequate sleep is critical for development and facilitates the maturation of the neurophysiological circuitries at the basis of cognitive and behavioral function. Observational research has associated sleep problems in early life with worse later cognitive, psychosocial, and somatic health outcomes. Yet, the extent to which day-to-day sleep habits in early life relate to neurophysiology - acutely and long-term - remains to be explored. Here, we report that sleep habits in 32 healthy 6-month-olds assessed with actimetry are linked to fundamental aspects of their neurophysiology measured with high-density electroencephalography (hdEEG). Our study reveals four key findings: First, daytime sleep habits are linked to EEG slow wave activity (SWA). Second, habits of nighttime movement and awakenings from sleep are connected with spindle density. Third, habitual sleep timing is linked to neurophysiological connectivity quantified as Delta-coherence. And lastly, Delta-coherence at age 6 months predicts nighttime sleep duration at age 12 months. These novel findings widen our understanding that infants sleep habits are closely intertwined with three particular levels of neurophysiology: sleep pressure (determined by SWA), the maturation of the thalamocortical system (spindles), and the maturation of cortical connectivity (coherence). Our companion paper complements this insight in the perspective of later developmental outcomes: early thalamocortical connectivity (spindle density) at age 6 months predicts later behavioural status at 12 and 24 months. The crucial next step is to extend this concept to clinical groups to objectively characterize infants sleep habits "at risk" that foster later neurodevelopmental problems. HighlightsO_LIInfants habitual sleep behavior (actimetry) is linked with their sleep neurophysiology (EEG) C_LIO_LIHabits of daytime sleeping (naps) are related to slow wave activity C_LIO_LIInfants movements and awakenings at nighttime are linked to their sleep spindles C_LIO_LISleep timing (infants bedtimes) is associated with cortical connectivity in the EEG C_LI

neuroscience↗

bioRxiv2018

Actimetry in infant sleep research: an approach to facilitate comparability

Study ObjectivesOnly standardized objective assessments reliably capture the large variability of sleep behavior in infancy, which is the most pronounced during the human lifespan. This is important for clinical as well as basic research. Actimetry is a cost-efficient method to objectively track infant sleep/wake behavior by assessing limb movements. Nevertheless, the standardization of actimetry-based sleep/wake measures is limited by two factors: the use of different computational approaches and the bias towards measuring only nighttime sleep - neglecting [~]20 % of sleep infants obtain during daytime.\n\nMethodsWe used actimetry in 50 infants for 10 continuous days at ages 3, 6 and 12 mo in a longitudinal approach. We analyzed the infants sleep/wake behaviors applying with two commonly used algorithms: Sadeh and Oakley/Respironics. We compared minute-by-minute agreement and Kappa between the two algorithms, as well as the algorithms with sleep/wake measures from a comprehensive 24-hour, parent-reported diary.\n\nResultsAgreement between uncorrected algorithms was moderate (77 - 84%). By introducing a 6-step adjustment, we increased agreement between algorithms (96 - 97%) and with the diary. This decreased the difference between the two algorithms in e.g. Total Sleep Duration from 4.5 h to 0.2 h.\n\nConclusionsThis computational pipeline enhances comparability between infant actimetry studies and the inclusion of parent-reported diaries allows the integration of daytime sleep. Objectively assessed infant sleep that is comparable across different studies supports the establishment of normative developmental trajectories and clinical cutoffs.

neuroscience↗