Biology subjects

Poon, C.-S.

Publications and source records attributed to Poon, C.-S..

3 recordsLinked to original sources

bioRxiv2025

Depth-Sensitive Assessment of Cerebral Blood Flow and Low-Frequency Oscillations After Traumatic Brain Injury in Mice Using Time-Gated Diffuse Correlation Spectroscopy

Traumatic brain injury (TBI) can lead to long-lasting impairments in cerebral perfusion, making early detection of microvascular changes critical for guiding clinical interventions. In this study, we employed time-gated diffuse correlation spectroscopy (TG-DCS) at 1064 nm to non-invasively quantify depth-resolved cerebral blood flow (CBF) and low-frequency oscillations (LFOs) in a mouse model of closed-head injury. By analyzing early (superficial) and late (deeper) photon arrival times, we identified a significant drop in CBF shortly after injury, with a partial recovery observed at 2 hours post-trauma. Power spectral analysis of the blood flow index revealed significant alterations in LFO bands, particularly in slow-5 (0.01-0.027 Hz) and slow-3 (0.073-0.198 Hz) ranges, with p < 0.05 at both early and late gates. These changes were more pronounced than BFI alterations alone, indicating that LFOs may serve as sensitive biomarkers of neurovascular disruption. Our findings demonstrate the feasibility of TG-DCS for depth-specific monitoring of cerebral hemodynamics and oscillatory dynamics after TBI and suggest its potential utility in translational neurotrauma research.

bioengineering↗

bioRxiv2020

Deep learning model for ultrafast quantification of blood flow in diffuse correlation spectroscopy

Diffuse correlation spectroscopy (DCS) is increasingly used in the optical imaging field to assess blood flow in humans due to its non-invasive, real-time characteristics and its ability to provide label-free, bedside monitoring of blood flow changes. Previous DCS studies have utilized a traditional curve fitting of the analytical or Monte Carlo models to extract the blood flow changes, which are computationally demanding and less accurate when the signal to noise ratio decreases. Here, we present a deep learning model that eliminates this bottleneck by solving the inverse problem more than 2300% faster, with equivalent or improved accuracy compared to the nonlinear fitting with an analytical method. The proposed deep learning inverse model will enable real-time and accurate tissue blood flow quantification with the DCS technique.

bioengineering↗

bioRxiv2018

Non-High Frequency Heart Rate Chaos: A Noninvasive Marker of REM Sleep and Obstructive Sleep Apnea Syndrome in Children

Obstructive sleep apnea syndrome (OSAS) is a highly prevalent condition associated with considerable metabolic, cardiovascular, and neurocognitive morbidity. Childhood OSAS is underdiagnosed due to a limited number of sleep laboratories and the lack of a screening test, and the subtlety of daytime symptoms in children compared to adults. A potential marker of OSAS is apnea-induced sympathoexcitation, which is likely to be exacerbated during rapid-eyemovement (REM) sleep. However, traditional methods of assessing sympathetic activity are either too invasive or insensitive/nonspecific for clinical use, particularly as a screening test. Study population comprised pediatric patients with OSAS (16 moderate/severe, 18 mild) and 18 normal non-snoring controls. We show that the chaotic dynamics of heart rate variability (HRV) as assessed by a sensitive noise titration assay is significantly increased during REM compared to non-REM sleep in children, particularly those with OSAS. The increase in heart rate chaos prevails in the face of decreased parasympathetic-mediated high-frequency component of the HRV power spectrum, indicating that the chaos was correlated to sympathetic instead of parasympathetic activity. Receiver operating characteristic analysis shows that such non-high frequency chaos reveals changing sympathetic-parasympathetic activities that are not discernible by conventional HRV metrics such as low- to high-frequency power ratio or sample entropy, with sensitivity and specificity sufficient to detect even mild OSAS in children. Results suggest a possible role for non-high frequency heart rate chaos as a selective noninvasive marker of sympathoexcitation in REM sleep, OSAS and potentially other cardiovascular abnormalities such as congestive heart failure.

physiology↗