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1. Conscious processing of narrative stimuli synchronizes heart rate between individuals

   https://doi.org/10.1101/2020.05.26.116079  

   Pérez, P ; Madsen, J ; Banellis, L ; Türker, B ; Raimondo, F ; Perlbarg, V ; Valente, M ; Niérat, M-C ; Puybasset, L ; Naccache, L ; et al ( May 2020 , bioRxiv)

   null (Ed.)

   Heart rate has natural fluctuations that are typically ascribed to autonomic function. Recent evidence suggests that conscious processing can affect the timing of the heartbeat. We hypothesized that heart rate is modulated by conscious processing and therefore dependent on attentional focus. To test this, we leverage the observation that neural processes can be synchronized between subjects by presenting an identical narrative stimulus. As predicted, we find significant inter-subject correlation of the heartbeat (ISC-HR) when subjects are presented with an auditory or audiovisual narrative. Consistent with the conscious processing hypothesis, we find that ISC-HR is reduced when subjects are distracted from the narrative, and that higher heart rate synchronization predicts better recall of the narrative. Finally, patients with disorders of consciousness who are listening to a story have lower ISC-HR, as compared to healthy individuals, and that individual ISC-HR might predict a patients’ prognosis.. We conclude that heart rate fluctuations are partially driven by conscious processing, depend on attentional state, and may represent a simple metric to assess conscious state in unresponsive patients. 

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2. Seizure occurrence is linked to multiday cycles in diverse physiological signals

   https://doi.org/10.1111/epi.17607  

   Gregg, Nicholas M. ; Pal Attia, Tal ; Nasseri, Mona ; Joseph, Boney ; Karoly, Philippa ; Cui, Jie ; Stirling, Rachel E. ; Viana, Pedro F. ; Richner, Thomas J. ; Nurse, Ewan S. ; et al ( April 2023 , Epilepsia)

   The factors that influence seizure timing are poorly understood, and seizure unpredictability remains a major cause of disability. Work in chronobiology has shown that cyclical physiological phenomena are ubiquitous, with daily and multiday cycles evident in immune, endocrine, metabolic, neurological, and cardiovascular function. Additionally, work with chronic brain recordings has identified that seizure risk is linked to daily and multiday cycles in brain activity. Here, we provide the first characterization of the relationships between the cyclical modulation of a diverse set of physiological signals, brain activity, and seizure timing.

   In this cohort study, 14 subjects underwent chronic ambulatory monitoring with a multimodal wrist‐worn sensor (recording heart rate, accelerometry, electrodermal activity, and temperature) and an implanted responsive neurostimulation system (recording interictal epileptiform abnormalities and electrographic seizures). Wavelet and filter–Hilbert spectral analyses characterized circadian and multiday cycles in brain and wearable recordings. Circular statistics assessed electrographic seizure timing and cycles in physiology.

   Ten subjects met inclusion criteria. The mean recording duration was 232 days. Seven subjects had reliable electroencephalographic seizure detections (mean = 76 seizures). Multiday cycles were present in all wearable device signals across all subjects. Seizure timing was phase locked to multiday cycles in five (temperature), four (heart rate, phasic electrodermal activity), and three (accelerometry, heart rate variability, tonic electrodermal activity) subjects. Notably, after regression of behavioral covariates from heart rate, six of seven subjects had seizure phase locking to the residual heart rate signal.

   Seizure timing is associated with daily and multiday cycles in multiple physiological processes. Chronic multimodal wearable device recordings can situate rare paroxysmal events, like seizures, within a broader chronobiology context of the individual. Wearable devices may advance the understanding of factors that influence seizure risk and enable personalized time‐varying approaches to epilepsy care.

    

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3. Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry

   https://doi.org/10.1002/jmri.28365  

   Schmidt, Andrew M. ; Desai, Arjun D. ; Watkins, Lauren E. ; Crowder, Hollis A. ; Black, Marianne S. ; Mazzoli, Valentina ; Rubin, Elka B. ; Lu, Quin ; MacKay, James W. ; Boutin, Robert D. ; et al ( July 2022 , Journal of Magnetic Resonance Imaging)

   Deep learning (DL)‐based automatic segmentation models can expedite manual segmentation yet require resource‐intensive fine‐tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine‐tuning is not well characterized.

   Evaluate the generalizability of DL‐based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population.

   Retrospective based on prospectively acquired data.

   Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females).

   A 3‐T, quantitative double‐echo steady state (qDESS).

   Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)‐DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage.

   Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank‐sum tests, root‐mean‐squared error‐coefficient‐of‐variation to quantify manual vs. automatic T2 and volume variations. Bland–Altman plots for manual vs. automatic T2 agreement. APvalue < 0.05 was considered statistically significant.

   DSCs for the qDESS‐trained model, 0.79–0.93, were higher than those for the OAI‐DESS‐trained model, 0.59–0.79. T2 and volume CCCs for the qDESS‐trained model, 0.75–0.98 and 0.47–0.95, were higher than respective CCCs for the OAI‐DESS‐trained model, 0.35–0.90 and 0.13–0.84. Bland–Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS‐trained model, ±2.4 msec and ±4.0 msec, than the OAI‐DESS‐trained model, ±4.4 msec and ±5.2 msec.

   The qDESS‐trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population.

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4. Measurement of Post-Exercise Response of Local Arterial Parameters Using an Adjustable Microfluidic Tactile Sensor

   https://doi.org/10.1109/EMBC46164.2021.9630432  

   Rahman, Md Mahfuzur ; Twiddy, Hannah ; Reynolds, Leryn ; Hao, Zhili ( November 2021 , Annual International Conference of the IEEE Engineering in Medicine and Biology Society)

   In this work, we demonstrate an adjustable microfluidic tactile sensor for measurement of post-exercise response of local arterial parameters. The sensor entailed a polydimethylsiloxane (PDMS) microstructure embedded with a 5×1 resistive transducer array. The pulse signal in an artery deflected the microstructure and registered as a resistance change by the transducer aligned at the artery. PDMS layers of different thicknesses were added to adjust the microstructure thickness for achieving good sensor-artery conformity at the radial artery (RA) and the carotid artery (CA). Pulse signals of nine (n=9) young healthy male subjects were measured at-rest and at different times post-exercise, and a medical instrument was used to simultaneously measure their blood pressure and heart rate. Vibration-model-based analysis was conducted on a measured pulse signal to estimate local arterial parameters: elasticity, viscosity, and radius. The arterial elasticity and viscosity increased, and the arterial radius decreased at the two arteries 1min post-exercise, relative to at-rest. The changes in pulse pressure (PP) and mean blood pressure (MAP) between at-rest and 1min post-exercise were not correlated with that of heart rate and arterial parameters. After the large 1min post-exercise response, the arterial parameters and PP all went back to their at-rest values over time post-exercise.Clinical Relevance— The study results show the potential application of an affordable, user-friendly device for a more comprehensive arterial health assessment. 

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5. A wearable multi-modal acoustic system for breathing analysis

   https://doi.org/10.1121/10.0009487  

   Emokpae, Lloyd E. ; Emokpae, Jr., Roland N. ; Bowry, Ese ; Bin Saif, Jaeed ; Mahmud, Muntasir ; Lalouani, Wassila ; Younis, Mohamed ; Joyner, Jr., Robert L. ( February 2022 , The Journal of the Acoustical Society of America)

   Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide with over 3 × 10⁶deaths in 2019. Such an alarming figure becomes frightening when combined with the number of lost lives resulting from COVID-caused respiratory failure. Because COPD exacerbations identified early can commonly be treated at home, early symptom detections may enable a major reduction of COPD patient readmission and associated healthcare costs; this is particularly important during pandemics such as COVID-19 in which healthcare facilities are overwhelmed. The standard adjuncts used to assess lung function (e.g., spirometry, plethysmography, and CT scan) are expensive, time consuming, and cannot be used in remote patient monitoring of an acute exacerbation. In this paper, a wearable multi-modal system for breathing analysis is presented, which can be used in quantifying various airflow obstructions. The wearable multi-modal electroacoustic system employs a body area sensor network with each sensor-node having a multi-modal sensing capability, such as a digital stethoscope, electrocardiogram monitor, thermometer, and goniometer. The signal-to-noise ratio (SNR) of the resulting acoustic spectrum is used as a measure of breathing intensity. The results are shown from data collected from over 35 healthy subjects and 3 COPD subjects, demonstrating a positive correlation of SNR values to the health-scale score.

    

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