Biology subjects

Coon, W. G.

Publications and source records attributed to Coon, W. G..

5 recordsLinked to original sources

bioRxiv2025

Tracking Sleep-Linked Brain Fluid Dynamics Using Modified fNIRS: A Novel Noninvasive Window into Glymphatic Function

BackgroundThe glymphatic system, a brain-wide perivascular and interstitial waste and signal transport pathway for cerebrospinal fluid (CSF) to exchange with interstitial fluid (ISF), has pronounced activity during non-rapid eye movement (NREM) sleep and has been implicated in the pathophysiology of traumatic brain injury, Alzheimers disease, and mood disorders. However, direct measurement in humans is limited because current imaging methods rely on intrathecal contrast-enhanced magnetic resonance imaging (MRI), which is unsuitable for routine or naturalistic sleep studies. The absence of real-time, noninvasive monitoring methods that allow for natural sleep poses a major barrier to advancing glymphatic research in clinical settings. MethodsTo address this barrier, we developed a non-invasive functional near-infrared spectroscopy (fNIRS) forehead array in a wearable headband using non-standard wavelengths to allow for better sensitivity for water measurement. We monitored cortical blood and water dynamics during overnight sleep, quantifying oscillatory patterns of oxyhemoglobin (HbO) and water across sleep stages within the outer layers of the frontal cortex, subarachnoid space, and scalp. A component of the extracted water metrics is hypothesized to serve as a proxy for glymphatic transport without the need for contrast agents or surgical intervention. ResultsOur results demonstrate water concentrations were highest in SWS (d = 1.93, p=0.0002), while HbO concentrations also showed a modest elevation (d = 1.06, ns). Additionally, low-frequency oscillations (LFOs) of both water and HbO signals exhibited distinct dynamics of suprathreshold envelope peak (SEP) frequencies during NREM stages (N2 and N3) as compared to REM and wake, with effect size (d= 1.31, p = 0.003) for water. Interestingly, these water-derived metrics correlate with EEG slow-wave activity, linking fluid-sensitive oscillations to established electrophysiological markers of sleep depth. These findings indicate that water-sensitive oscillatory processes are selectively amplified during deep sleep and scale with EEG-defined sleep depth, consistent with a role for glymphatic-related fluid transport in human sleep. ConclusionWe report novel cortical water shift parameters that are robustly sensitive to sleep stage transitions via a wearable, non-invasive, scalable headband, consistent with predicted glymphatic activity. Future work will cross-validate this method with MRI and other techniques. DisclaimerThe views, information or content, and conclusions presented do not represent the official position or policy of, nor should any official endorsement be inferred on the part of, the Uniformed Services University, the Department of War, the U.S. Government, or Walter Reed National Military Medical Center.

neuroscience↗

bioRxiv2025

ezscore-f: A Set of Freely Available, Validated Sleep Stage Classifiers for Forehead EEG

The increasing availability of wearable forehead EEG devices, such as the Hypnodyne ZMax, open-source DCM, CGX PatchEEG, and many others, has significantly expanded opportunities for convenient, at-home sleep monitoring. However, most publicly available sleep classifiers are trained on scalp EEG from traditional polysomnography (PSG) data, and thus generalize poorly to forehead EEG due to differences in electrode placement, referencing montages, and higher susceptibility to artifacts from user movements and electrode displacement. Conventional classifiers typically do not explicitly account for these artifacts, resulting in inaccurate and misleading sleep stage scoring. To address this gap, we developed a suite of artifact-aware sleep stage classifiers trained specifically using forehead EEG data, leveraging two comprehensive datasets--Wearanize+ and Donders2022--that contain concurrent forehead EEG and clinical PSG recordings. We further introduce two classifier variants: one optimized for real-time applications that operates directly on raw EEG amplitudes, and another optimized for offline analysis utilizing normalized EEG signals. Validation results indicate robust and reliable classification performance across standard sleep stages (Wake, N1, N2, N3, REM), along with effective identification of artifact epochs. Importantly, the developed classifiers generalize well to forehead EEG devices beyond the original training platform. These validated classifiers are freely available to the sleep research community through the open-source ezscore-f package, providing versatile and practical tools for forehead EEG-based sleep stage analysis.

neuroscience↗

bioRxiv2024

Self-Supervised Transformer Model Training for a Sleep-EEG Foundation Model

The American Academy of Sleep Medicine (AASM) recognizes five sleep/wake states (Wake, N1, N2, N3, REM), yet this classification schema provides only a high-level summary of sleep and likely overlooks important neurological or health information. New, data-driven approaches are needed to more deeply probe the information content of sleep signals. Here we present a self-supervised approach that learns the structure embedded in large quantities of neurophysiological sleep data. This masked transformer training procedure is inspired by high performing self-supervised methods developed for speech transcription. We show that self-supervised pre-training matches or outperforms supervised sleep stage classification, especially when labeled data or compute-power is limited. Perhaps more importantly, we also show that our pre-trained model is flexible and can be fine-tuned to perform well on new EEG recording montages not seen in training, and for new tasks including distinguishing individuals or quantifying "brain age" (a potential health biomarker). This suggests that modern methods can automatically learn information that is potentially overlooked by the 5-class sleep staging schema, laying the groundwork for new sleep scoring schemas and further data-driven exploration of sleep.

neuroscience↗

bioRxiv2024

Laying the Foundation: Modern Transformers for Gold-Standard Sleep Analysis

Accurate sleep assessment is critical to the practice of sleep medicine and sleep research. The recent availability of large quantities of publicly available sleep data, alongside recent breakthroughs in AI like transformer architectures, present novel opportunities for data-driven discovery efforts. Transformers are flexible neural networks that not only excel at classification tasks, but also can enable data-driven discovery through un-or self-supervised learning, which requires no human annotations to the input data. While transformers have been extensively used in supervised learning scenarios for sleep stage classification, they have not been fully explored or optimized in forms designed from the ground up for use in un-or self-supervised learning tasks in sleep. A necessary first step will be to study these models on a canonical benchmark supervised learning task (5-class sleep stage classification). Hence, to lay the groundwork for future data-driven discovery efforts, we evaluated optimizations of a transformer-based architecture that has already demonstrated substantial success in self-supervised learning in another domain (audio speech recognition), and trained it to perform the canonical 5-class sleep stage classification task, to establish foundational baselines in the sleep domain. We found that small transformer models designed from the start for (later) self-supervised learning can match other state-of-the-art automated sleep scoring techniques, while also providing the basis for future data-driven discovery efforts using large sleep data sets.

neuroscience↗

bioRxiv2021

Revealing the Physiological Origin of Event-Related Potentials using Electrocorticography in Humans

The scientific and clinical value of event-related potentials (ERPs) depends on understanding the contributions to them of three possible mechanisms: (1) additivity of time-locked voltage changes; (2) phase resetting of ongoing oscillations; (3) asymmetrical oscillatory activity. Their relative contributions are currently uncertain. This study uses analysis of human electrocorticographic activity to quantify the origins of movement-related potentials (MRPs) and auditory evoked potentials (AEPs). The results show that MRPs are generated primarily by endogenous additivity (88%). In contrast, P1 and N1 components of AEPs are generated almost entirely by exogenous phase reset (93%). Oscillatory asymmetry contributes very little. By clarifying ERP mechanisms, these results enable creation of ERP models; and they enhance the value of ERPs for understanding the genesis of normal and abnormal auditory or sensorimotor behaviors.

neuroscience↗