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arXiv 2021年

作者： Li, Shuangning Sesia, Matteo Romano, Yaniv Candès, Emmanuel Sabatti, Chiara Department of Statistics Stanford University United States Department of Data Sciences and Operations University of Southern California United States Departments of Electrical Engineering and of Computer Science Technion Israel Departments of Statistics and of Mathematics Stanford University United States Departments of Statistics and of Biomedical Data Sciences Stanford University United States

This paper develops a method based on model-X knockoffs to find conditional associations that are consistent across diverse environments, controlling the false discovery rate. The motivation for this problem is that large data sets may contain numerous associations that are statistically significant and yet misleading, as they are induced by confounders or sampling imperfections. However, associations consistently replicated under different conditions may be more interesting. In fact, consistency sometimes provably leads to valid causal inferences even if conditional associations do not. While the proposed method is flexible and can be deployed in a wide range of applications, this paper highlights its relevance to genome-wide association studies, in which consistency across populations with diverse ancestries mitigates confounding due to unmeasured variants. The effectiveness of this approach is demonstrated by simulations and applications to the UK Biobank data. Copyright © 2021, The Authors. All rights reserved.

关键词： Genes

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A Multiplexed Electrochemical Measurement System for Characterization of Implanted Electrodes

A Multiplexed Electrochemical Measurement System for Charact...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： A. T. Gardner Hunter J. Strathman Ross M. Walker Departments of Electrical and Computer Engineering University of Utah Salt Lake City Utah Biomedical Engineering University of Utah Salt Lake City Utah

ISBN: (数字)9781728133201

ISBN: (纸本)9781728133218

Microelectrode arrays are important tools that enable a wide range of neuroscience experiments, as well as emerging medical applications of neuroprosthetics and neuromodulation. Design-oriented characterization of electrodes provides information that is important for system optimization and for improving longevity in applications. Of specific interest is electrode impedance and noise across frequency, because these aspects have direct implications for neural recording performance. The time required to perform electrochemical measurements is a major limitation, because each electrode must be characterized individually. A solution is to use a single multiplexed circuit to simultaneously measure multiple electrodes. This report describes the design and in vivo test results of an electrochemical potentiostat system that rapidly multiplexes through electrodes, reducing measurement time for impedance spectroscopy by ∼8× at frequencies relevant to neural signals (<7.5 kHz). The system was used to gather in vivo characterization data from a microwire array implanted in rodent cortex, and the results are compared to a gold standard bench-top instrument.

关键词： Electrodes Multiplexing Current measurement Frequency measurement Voltage measurement Impedance In vivo

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Deep Learning Analyses of Brain MRI to Identify Sustained Attention Deficit in Treated Obstructive Sleep Apnea: A Pilot Study

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Sleep and Vigilance 2022年第1期6卷 179-184页

作者： Agarwal, Chirag Gupta, Saransh Najjar, Muhammad Weaver, Terri E. Zhou, Xiaohong Joe Schonfeld, Dan Prasad, Bharati Department of Medicine University of Illinois Chicago 840 S. Wood Street MC 719 Chicago 60612 IL United States Jesse Brown VA Medical Center Chicago IL United States Department of Biomedical Informatics Harvard Medical School Boston MA United States Biobehavioral Nursing Science College of Nursing University of Illinois Chicago Chicago IL United States Center for Magnetic Resonance Research University of Illinois Chicago Chicago IL United States Departments of Radiology Neurosurgery and Bioengineering University of Illinois Chicago Chicago IL United States Department of Electrical and Computer Engineering University of Illinois Chicago Chicago IL United States

Purpose: Persistent sustained attention deficit (SAD) after continuous positive airway pressure (CPAP) treatment is a source of quality of life and occupational impairment in obstructive sleep apnea (OSA). However, persistent SAD is difficult to predict in patients initiated on CPAP treatment. We performed secondary analyses of brain magnetic resonance (MR) images in treated OSA participants, using deep learning, to predict SAD. Methods: 26 middle-aged men with CPAP use of more than 6 h daily and MR imaging were included. SAD was defined by psychomotor vigilance task lapses of more than 2. 17 participants had SAD and 9 were without SAD. A Convolutional Neural Network (CNN) model was used for classifying the MR images into + SAD and − SAD categories. Results: The CNN model achieved an accuracy of 97.02 ± 0.80% in classifying MR images into + SAD and − SAD categories. Assuming a threshold of 90% probability for the MR image being correctly classified, the model provided a participant-level accuracy of 99.11 ± 0.55% and a stable image level accuracy of 97.45 ± 0.63%. Conclusion: Deep learning methods, such as the proposed CNN model, can accurately predict persistent SAD based on MR images. Further replication of these findings will allow early initiation of adjunctive pharmacologic treatment in high-risk patients, along with CPAP, to improve quality of life and occupational fitness. Future augmentation of this approach with explainable artificial intelligence methods may elucidate the neuroanatomical areas underlying persistent SAD to provide mechanistic insights and novel therapeutic targets. © 2021, This is a U.S. government work and not under copyright protection in the U.S.;foreign copyright protection may apply.

关键词： Alertness Daytime sleepiness MRI OSA Sleep apnea

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Conceptual design considerations for a wireless intraocular pressure sensor system for effective glaucoma management

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Journal of Medical engineering and Technology 2019年第8期43卷 457-467页

作者： Fink, Wolfgang Brown, Shaun Nuncio Zuniga, Andres Yang, Eui-Hyeok George, Thomas Visual and Autonomous Exploration Systems Research Laboratory Departments of Electrical and Computer Engineering and Biomedical Engineering University of Arizona TucsonAZ United States Department of Mechanical Engineering Stevens Institute of Technology HobokenNJ United States SaraniaSat Inc TujungaCA United States

As a leading form of preventable visual impairment, it is imperative to assess glaucoma treatment as a function of intraocular pressure (IOP). IOP can spike throughout the day. This necessitates a device that can (1) monitor IOP outside of clinical visits by providing a memory when IOP exceeds a set threshold indicating the possibility for glaucomatous damage to occur;and (2) accurately assess IOP. Both requirements point ultimately towards the development of an implantable device. The Wireless Intraocular Pressure Sensor System (WIPSS) devised by our team uses optical technologies and may assist an overseeing clinician with assessing glaucoma treatment efficacy and avoiding irreversible glaucomatous visual field loss downstream. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Conceptual design

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Multi-Resolution Data Fusion for Super Resolution Imaging

arXiv

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arXiv 2021年

作者： Reid, Emma J. Drummy, Lawrence F. Bouman, Charles A. Buzzard, Gregery T. The Department of Mathematics Purdue University West LafayetteIN47907 United States The Materials and Manufacturing Directorate Air Force Research Laboratory Wright-Patterson Air Force Base OH45433 United States The Departments of Electrical and Computer Engineering and Biomedical Engineering Purdue University IN47907 United States

Applications in materials and biological imaging are limited by the ability to collect high-resolution data over large areas in practical amounts of time. One solution to this problem is to collect low-resolution data and interpolate to produce a high-resolution image. However, most existing super-resolution algorithms are designed for natural images, often require aligned pairing of high and low-resolution training data, and may not directly incorporate a model of the imaging sensor. In this paper, we present a Multi-resolution Data Fusion (MDF) algorithm for accurate interpolation of low-resolution electron microscope data at multiple resolutions up to 8x. Our approach uses small quantities of unpaired high-resolution data to train a neural network prior model denoiser and then uses the Multi-Agent Consensus Equilibrium (MACE) problem formulation to balance this denoiser with a forward model agent that promotes fidelity to measured data. A key theoretical novelty is the analysis of mismatched backprojectors, which modify typical forward model updates for computational efficiency or improved image quality. We use MACE to prove that using a mismatched back-projector is equivalent to using a standard back-projector and an appropriately modified prior model. We present electron microscopy results at 4x and 8x interpolation factors that exhibit reduced artifacts relative to existing methods while maintaining fidelity to acquired data and accurately resolving sub-pixel-scale features. © 2021, CC BY.

关键词： Interpolation

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Author Correction: Dynamical flexible inference of nonlinear latent factors and structures in neural population activity

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Nature biomedical engineering 2025年第2期9卷 283页

作者： Hamidreza Abbaspourazad Eray Erturk Bijan Pesaran Maryam M Shanechi Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. Departments of Neurosurgery Neuroscience and Bioengineering University of Pennsylvania Philadelphia PA USA. Ming Hsieh Department of Electrical and Computer Engineering Viterbi School of Engineering University of Southern California Los Angeles CA USA. shanechi@usc.edu. Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los Angeles CA USA. shanechi@usc.edu.

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Deep Learning for Detection and Localization of B-Lines in Lung Ultrasound

arXiv

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arXiv 2023年

作者： Lucassen, Ruben T. Jafari, Mohammad H. Duggan, Nicole M. Jowkar, Nick Mehrtash, Alireza Fischetti, Chanel Bernier, Denie Prentice, Kira Duhaime, Erik P. Jin, Mike Abolmaesumi, Purang Heslinga, Friso G. Veta, Mitko Duran-Mendicuti, Maria A. Frisken, Sarah Shyn, Paul B. Golby, Alexandra J. Boyer, Edward Wells, William M. Goldsmith, Andrew J. Kapur, Tina Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States The Department of Biomedical Engineering Eindhoven University of Technology Eindhoven5612 Netherlands The Department of Emergency Medicine Brigham and Women’s Hospital BostonMA02115 United States Centaur Labs BostonMA02116 United States The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV5T 1Z4 Canada The Departments of Neurosurgery and Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Note on the Existence of Minimizers for Variational Geometric Active Contours

arXiv

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arXiv 2022年

作者： Diop, El Hadji S. Burdin, Valérie Prasath, V.B. Surya Department of Mathematics University of Thies BP 967 Thies Senegal Image and Information Department Institut Mines Telecom - Telecom Bretagne Technopole Brest-Iroise CS 83818 Brest Cedex 329238 France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States The Departments of Biomedical Informatics Pediatrics Electrical Engineering and Computer Science University of Cincinnati OH United States

We propose here a proof of existence of a minimizer of a segmentation functional based on a priori information on target shapes, and formulated with level sets. The existence of a minimizer is very important, because it guarantees the convergence of any numerical methods (either gradient descents techniques and variants, or PDE resolutions) used to solve the segmentation model. This work can also be used in many other segmentation models to prove the existence of a *** Codes 49J40 (Primary), 94A08 (Secondary) © 2022, CC BY.

关键词： Image segmentation

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Towards frequency adaptation for delayed feedback deep brain stimulations

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Neural Regeneration Research 2018年第3期13卷 408-409页

作者： Mohammad Daneshzand Miad Faezipour Buket D.Barkana D-BEST Lab Departments of Computer Science and Engineering and Biomedical Engineering University of Bridgeport Department of Electrical Engineering University of Bridgeport

In neurodegenerative disorders such as Parkinson＇s disease （PD）, deep brain stimulation （DBS） is a desirable approach when the medication is less effective for treating the symptoms. DBS incorporates transferring electrical pulses to a specific tissue of the central nervous system, obtaining therapeutic results by modulating the neuronal activity of that region. DBS has certain advantages such as reversibility and adjustability features over medication, since the neuronal firing patterns can be recorded and used to alter the parameters of the DBS signal （Benabid et al., 2009）. One of the DBS indications is its ability to suppress the abnormal neuronal activity to treat symptoms like tremor, akinesia and dystonia.

关键词： Figure DBS Towards frequency adaptation for delayed feedback deep brain stimulations STN HFS

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A novel near-infrared light responsive 4D printed nanoarchitecture with dynamically and remotely controllable transformation

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Nano Research 2019年第6期12卷 1381-1388页

作者： Haitao Cui Shida Miao Timothy Esworthy Se-jun Lee Xuan Zhou Sung Yun Hann Thomas J.Webster Brent T.Harris Lijie Grace Zhang Department of Mechanical and Aerospace Engineering The George Washington UniversityWashingtonDC 20052USA Department of Chemical Engineering Northeastern UniversityBostonMA 02115USA Department of Neurology and Pathology Georgetown UniversityWashingtonDC 20007USA Departments of Electrical and Computer Engineering The George Washington UniversityWashingtonDC 20052USA Department of Biomedical Engineering The George Washington UniversityWashingtonDC 20052USA Department of Medicine The George Washington UniversityWashingtonDC 20052USA

Four-dimensional (4D) printing is an emerging and highly innovative additive manufacturing process by which to fabricate pre-designed,self-assembly structures with the ability to transform over ***,one of the critical challenges of 4D printing is the lack of advanced 4D printing systems that not only meet all the essential requirements of shape change but also possess smart,dynamic capabilities to spatiotemporally and instantly control the shape-transformation ***,we present a facile 4D printing platform which incorporates nanomaterials into the conventional stimuli-responsive polymer,allowing the 4D printed object to achieve a dynamic and remote controlled,on-time and position shape transformation.A proof-of-concept 4D printed brain model was created using near-infrared light (NIR) responsive nanocomposite to evaluate the capacity for controllable 4D transformation,and the feasibility of photothermal stimulation for modulating neural stem cell *** novel 4D printing strategy can not only be used to create dynamic 3D patterned biological structures that can spatiotemporally control their shapes or behaviors of transformation under a human benign stimulus (NIR),but can also provide a potential method for building complex self-morphing objects for widespread applications.

关键词： near-infrared light responsive four-dimensional (4D) printing dynamically and remotely controllable graphene brain neural stem cell

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