Biology subjects

Guendelman, M.

Publications and source records attributed to Guendelman, M..

3 recordsLinked to original sources

bioRxiv2026

Sleep initiation difficulties involve weaker neural and physiological sleep transitions, particularly in children with neurodevelopmental conditions

The transition from wake to stable sleep is characterized by multiple neural, physiological, and behavioral changes. How these changes may differ in individuals with difficulties falling asleep such as children with neurodevelopmental conditions is poorly understood. Here, we studied sleep initiation in >2000 nights recorded from 186 children who participated in the Simons Sleep Project (SSP). Data included simultaneous, synchronized recordings of actigraphy, electroencephalography (EEG), photoplethysmography (PPG), and skin temperature. We extracted multiple neural, physiological, and behavioral measures that are known to increase/decrease during the sleep initiation period including EEG delta (1-4Hz) power, movement counts, heart rate (HR), and skin temperature. Transitions from 20 minutes before sleep onset to 40 minutes after sleep onset were modeled with a sigmoid function enabling the quantification of transition timing, speed, and magnitude per measure. Individuals with longer sleep onset latencies (SOL) exhibited smaller increases in EEG delta power and skin temperature as well as smaller decreases in HR and activity counts. These findings indicate that difficulties falling asleep are associated with multiple forms of cortical, physiological, and behavioral hyperarousal that can be measured at home with wearable devices. Importantly, transition magnitudes were key to explaining differences in SOL across participants (26% explained variance) in contrast to transition speed or timing within the sleep initiation period (<13% explained variance). Longer SOL and weaker transitions were particularly prominent in children diagnosed with autism and/or attention deficit hyperactivity disorder (ADHD).

neuroscience↗

bioRxiv2025

The Hypno-PC: Uncovering Sleep Dynamics through Principal Component Analysis and Hidden Markov Modeling of Electrophysiological Signals

The conventional approach to sleep analysis relies on pre-defined, visually scored stages derived from electrophysiological signals. This manual method demands substantial effort and is influenced by subjective assessments, implicitly assuming that these categories accurately reflect underlying biological processes. Recent advancements indicate that low-dimensional representations of complex brain activity can provide objective means of identifying brain states. These approaches can potentially uncover inherent patterns within sleep, offering valuable insights into its organization. In this study, we applied Principal Component Analysis (PCA) to spectral features extracted from high-density EEG, EOG, EMG, and ECG recorded overnight at both 30- and 4-second resolutions. Notably, the first principal component--the "Hypno-PC"--strongly aligns with the hypnogram at both time scales. Subsequently, we employed a Gaussian Hidden Markov Model (GHMM) to delineate discrete states in the PCA-transformed data and to quantify their temporal dynamics. Using minimal supervision (less than 0.5% of the data labeled) and a cross-subject approach, the model achieved alignment with standard sleep labels comparable to the typical inter-rater agreement. Finally, independent component analysis (ICA) was applied to the PCA space, decomposing it into an independent set of components that potentially represent distinct physiological processes. The integrated use of PCA, GHMM, and ICA provides a reproducible and scalable methodology that aligns with traditional sleep staging, while offering a more flexible and comprehensive perspective on sleep states. Our findings indicate that these data-driven, unsupervised methods effectively uncover the intrinsic dynamics of sleep, advancing automated sleep analysis and enhancing our understanding of sleep organization. Statement of SignificanceThis study introduces a data-driven framework for sleep analysis designed to objectively identify brain states using low-dimensional representations of electrophysiological signals during sleep. By integrating a suite of unsupervised learning techniques, our methodology offers an alternative to subjective manual scoring, potentially enhancing both efficiency and reproducibility. In addition to aligning with traditional sleep staging, this approach uncovers subtle sleep dynamics across multiple time scales, enabling the discovery of patterns that conventional methods might overlook. These advancements hold promise for automated sleep monitoring and the study of sleep disorders, potentially improving diagnostic accuracy and facilitating large-scale sleep research. By addressing current limitations in sleep analysis techniques, this framework lays the groundwork for more elaborate and scalable assessments.

neuroscience↗

bioRxiv2023

Seizure dynamotype classification using non-invasive recordings

ObjectiveRecently, a seizure classification approach derived from complex systems and nonlinear dynamics has been suggested, termed the "taxonomy of seizure dynamotypes." This framework is based on modeling the dynamical process of the transition in and out of a seizure. It has been examined in computational and animal models in-vitro and recently in human intracranial data. However, its applicability and value in surface EEG remain unclear. This study examined the applicability of dynamotype classification to seizure information extracted from surface EEG and tested how it relates to clinical factors. MethodsSurface EEG recordings from 1,215 seizures were analyzed. We used an automated pre-processing pipeline, resulting in independent components (ICs) for each seizure. Subsequently, we visually identified ICs with clear seizure information and classified them based on the suggested taxonomy. To examine the possibility of automatic classification, we applied a random forest classifier combined with EEG features and evaluated its performance in identifying seizure-related ICs and classifying dynamical types. Lastly, we used a Bayesian estimator to examine the likelihood of the different dynamical types under various clinical conditions. ResultsWe found an apparent onset and offset bifurcation in 49.5% and 40.3% of seizures, respectively. Bifurcation prevalence aligns with that previously reported using intracranial data and computational modeling. The automated classifiers, evaluated with a leave-one-patient-out paradigm, provided good performance. In addition, bifurcation prevalence differed between vigilance states and seizure classes. SignificanceWe demonstrated a method to extract seizure information and classify dynamotypes in non-invasive recordings with a visual as well as an automated framework. Extending this classification to a larger scale and a broader population may provide further insights into seizure dynamics. Key Points BoxO_LIWe characterized the dynamical types of transitions at seizure onset and offset based on seizure information extracted from surface EEG. C_LIO_LIWe classified the dynamical types (dynamotypes) in 49.5% and 40.3% of seizure onsets and offsets, respectively. C_LIO_LIThe dynamotype distribution in surface EEG data aligns with previous findings from intracranial EEG and theoretical expectations. C_LIO_LIThe likelihood of the dynamical type of a seizure exhibits differences across clinical seizure classes and vigilance states. C_LIO_LIAutomated detection and classification of seizure bifurcations are possible using relevant features and pre-existing tools. C_LI

neuroscience↗