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arXiv 2023年

作者： Huang, Jianwei Yue, Ziqin Baydin, Andrey Zhu, Hanyu Nojiri, Hiroyuki Kono, Junichiro He, Yu Yi, Ming Department of Physics and Astronomy Rice University HoustonTX77005 United States Applied Physics Graduate Program Smalley-Curl Institute Rice University HoustonTX77005 United States Department of Electrical and Computer Engineering Rice University HoustonTX77005 United States Smalley–Curl Institute Rice University HoustonTX77005 United States Department of Materials Science and NanoEngineering Rice University HoustonTX77005 United States Institute for Materials Research Tohoku University Katahira 2-1-1 Sendai980-8577 Japan Department of Applied Physics Yale University New HavenCT06511 United States

Angle-resolved photoemission spectroscopy (ARPES) is a powerful tool for probing the momentum-resolved single-particle spectral function of materials. Historically, in situ magnetic fields have been carefully avoided as they are detrimental to the control of photoelectron trajectory during the photoelectron detection process. However, magnetic field is an important experimental knob for both probing and tuning symmetry-breaking phases and electronic topology in quantum materials. In this paper, we introduce an easily implementable method for realizing an in situ tunable magnetic field at the sample position in an ARPES experiment and analyze magnetic field induced artifacts in ARPES data. Specifically, we identified and quantified three distinct extrinsic effects of a magnetic field: Constant energy contour rotation, emission angle contraction, and momentum broadening. We examined these effects in three prototypical quantum materials, i.e., a topological insulator (Bi2Se3), an iron-based superconductor (LiFeAs), and a cuprate superconductor (Pb-Bi2Sr2CuO6+x), and demonstrate the feasibility of ARPES measurements in the presence of a controllable magnetic field. Our studies lay the foundation for the future development of the technique and interpretation of ARPES measurements of field-tunable quantum phases. Copyright © 2023, The Authors. All rights reserved.

关键词： Photoelectrons

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Repositioned Drugs for COVID-19-the Impact on Multiple Organs

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SN comprehensive clinical medicine 2021年第7期3卷 1484-1501页

作者： Christina Chan Sean T Foster Kayla G Chan Matthew J Cacace Shay L Ladd Caleb T Sandum Paul T Wright Brett Volmert Weiyang Yang Aitor Aguirre Wen Li Neil T Wright Department of Chemical Engineering and Materials Sciences Michigan State University 428 S. Shaw Lane Room 2100 EB East Lansing MI 48824 USA. Department of Biochemistry and Molecular Biology Michigan State University East Lansing MI USA. Department of Biomedical Engineering Michigan State University East Lansing MI USA. Institute for Quantitative Health Science and Engineering Michigan State University East Lansing MI USA. Integrative Neuroscience Program Binghamton University Binghamton NY USA. Department of Mechanical Engineering The Pennsylvania State University University Park PA USA. Department of Mechanical Engineering Virginia Polytechnic Institute and State University Blacksburg VA USA. Department of Electrical and Computer Engineering Michigan State University East Lansing MI USA. Department of Mechanical Engineering Michigan State University East Lansing MI USA.

This review summarizes published findings of the beneficial and harmful effects on the heart, lungs, immune system, kidney, liver, and central nervous system of 47 drugs that have been proposed to treat COVID-19. Many of the repurposed drugs were chosen for their benefits to the pulmonary system, as well as immunosuppressive and anti-inflammatory effects. However, these drugs have mixed effects on the heart, liver, kidney, and central nervous system. Drug treatments are critical in the fight against COVID-19, along with vaccines and public health protocols. Drug treatments are particularly needed as variants of the SARS-Cov-2 virus emerge with some mutations that could diminish the efficacy of the vaccines. Patients with comorbidities are more likely to require hospitalization and greater interventions. The combination of treating severe COVID-19 symptoms in the presence of comorbidities underscores the importance of understanding the effects of potential COVID-19 treatments on other organs.

关键词： COVID-19 Central nervous system Drug repositioning Heart Immune system Kidneys Liver Lungs

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Nanoplasmonic imaging of extracellular exosomes on undercut arrayed gold nanodisks

Nanoplasmonic imaging of extracellular exosomes on undercut ...

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Optical Sensors, Sensors 2020 - Part of Optical Sensors and Sensing Congress 2020

作者： Kafle, Bijesh Misbah, Ibrahim Gunawardhana, Lkadds Lin, Steven H. Shih, Wei-Chuan Department of Electrical and Computer Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Program of Materials Science and Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of chemistry University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Biomedical Engineering University of Houston 4800 Calhoun Rd HoustonTX77204 United States Department of Radiation Oncology M.D. Anderson Cancer Center 1515 Holcombe Blvd. HoustonTX77030 United States

We present nanoplasmonic imaging of exosome using antibody-functionalized undercut arrayed gold nanodisks. We show a nanoplasmonic shift map that allows the detection and quantitation of exosome binding. © OSA 2020.

ISBN: (纸本)9781557528209

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Artificial intelligence for modelling infectious disease epidemics

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Nature 2025年第8051期638卷 623-635页

作者： Kraemer, Moritz U. G. Tsui, Joseph L.-H. Chang, Serina Y. Lytras, Spyros Khurana, Mark P. Vanderslott, Samantha Bajaj, Sumali Scheidwasser, Neil Curran-Sebastian, Jacob Liam Semenova, Elizaveta Zhang, Mengyan Unwin, H. Juliette T. Watson, Oliver J. Mills, Cathal Dasgupta, Abhishek Ferretti, Luca Scarpino, Samuel V. Koua, Etien Morgan, Oliver Tegally, Houriiyah Paquet, Ulrich Moutsianas, Loukas Fraser, Christophe Ferguson, Neil M. Topol, Eric J. Duchêne, David A. Stadler, Tanja Kingori, Patricia Parker, Michael J. Dominici, Francesca Shadbolt, Nigel Suchard, Marc A. Ratmann, Oliver Flaxman, Seth Holmes, Edward C. Gomez-Rodriguez, Manuel Schölkopf, Bernhard Donnelly, Christl A. Pybus, Oliver G. Cauchemez, Simon Bhatt, Samir Pandemic Sciences Institute University of Oxford Oxford United Kingdom Department of Biology University of Oxford Oxford United Kingdom Department of Electrical Engineering and Computer Science University of California Berkeley Berkeley CA United States UCSF UC Berkeley Joint Program in Computational Precision Health Berkeley CA United States Division of Systems Virology Department of Microbiology and Immunology The Institute of Medical Science The University of Tokyo Tokyo Japan Section of Epidemiology Department of Public Health University of Copenhagen Copenhagen Denmark Oxford Vaccine Group University of Oxford and NIHR Oxford Biomedical Research Centre Oxford United Kingdom Department of Epidemiology and Biostatistics Imperial College London London United Kingdom Department of Computer Science University of Oxford Oxford United Kingdom School of Mathematics University of Bristol Bristol United Kingdom MRC Centre for Global Infectious Disease Analysis School of Public Health Imperial College London London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Doctoral Training Centre University of Oxford Oxford United Kingdom Institute for Experiential AI Northeastern University MA Boston Thailand Santa Fe Institute Santa Fe NM United States World Health Organization Regional Office for Africa Brazzaville Congo WHO Hub for Pandemic and Epidemic Intelligence Health Emergencies Programme World Health Organization Berlin Germany Centre for Epidemic Response and Innovation (CERI) School for Data Science and Computational Thinking Stellenbosch University Stellenbosch South Africa African Institute for Mathematical Sciences (AIMS) South Africa Muizenberg Cape Town South Africa Genomics England London United Kingdom Scripps Research La Jolla CA United States Department of Biosystems Science and Engineering ETH Zürich Basel Switzerland Swiss Institute of Bioinformatics Lausanne Switzerland The Ethox Centre Nuffield

Infectious disease threats to individual and public health are numerous, varied and frequently unexpected. Artificial intelligence (AI) and related technologies, which are already supporting human decision making in economics, medicine and social science, have the potential to transform the scope and power of infectious disease epidemiology. Here we consider the application to infectious disease modelling of AI systems that combine machine learning, computational statistics, information retrieval and data science. We first outline how recent advances in AI can accelerate breakthroughs in answering key epidemiological questions and we discuss specific AI methods that can be applied to routinely collected infectious disease surveillance data. Second, we elaborate on the social context of AI for infectious disease epidemiology, including issues such as explainability, safety, accountability and ethics. Finally, we summarize some limitations of AI applications in this field and provide recommendations for how infectious disease epidemiology can harness most effectively current and future developments in AI. © Springer Nature Limited 2025.

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IMAGING DYNAMICS BENEATH TURBID MEDIA VIA PARALLELIZED SINGLE-PHOTON DETECTION

arXiv

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arXiv 2021年

作者： Xu, Shiqi Yang, Xi Liu, Wenhui Jonsson, Joakim Qian, Ruobing Chandra Konda, Pavan Zhou, Kevin C. Kreiss, Lucas Dai, Qionghai Wang, Haoqian Berrocal, Edouard Horstmeyer, Roarke Department of Biomedical Engineering Duke University DurhamNC27708 United States Department of Automation Tsinghua University Beijing100084 China Division of Combustion Physics Department of Physics Lund University 22100 Sweden Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Duke University DurhamNC27708 United States

Noninvasive optical imaging through dynamic scattering media has numerous important biomedical applications but still remains a challenging task. While standard diffuse imaging methods measure optical absorption or fluorescent emission, it is also well-established that the temporal correlation of scattered coherent light diffuses through tissue much like optical intensity. Few works to date, however, have aimed to experimentally measure and process such temporal correlation data to demonstrate deep-tissue video reconstruction of decorrelation dynamics. In this work, we utilize a single-photon avalanche diode (SPAD) array camera to simultaneously monitor the temporal dynamics of speckle fluctuations at the single-photon level from 12 different phantom tissue surface locations delivered via a customized fiber bundle array. We then apply a deep neural network to convert the acquired single-photon measurements into video of scattering dynamics beneath rapidly decorrelating tissue phantoms. We demonstrate the ability to reconstruct images of transient (0.1-0.4s) dynamic events occurring up to 8 mm beneath a decorrelating tissue phantom with millimeter-scale resolution, and highlight how our model can flexibly extend to monitor flow speed within buried phantom vessels. © 2021, CC BY.

关键词： Dynamics

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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iPhantom: a framework for automated creation of individualized computational phantoms and its application to CT organ dosimetry

arXiv

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arXiv 2020年

作者： Fu, Wanyi Sharma, Shobhit Abadi, Ehsan Iliopoulos, Alexandros-Stavros Wang, Qi Lo, Joseph Y. Sun, Xiaobai Segars, William P. Samei, Ehsan Department of Electrical and Computer Engineering Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Physics Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Department of Computer Science Duke University DurhamNC27708 United States Department of Radiology Fourth Clinical Hospital of Hebei Medical University Heibei050011 China Departments of Electrical and Computer Engineering Biomedical Engineering Medical Physics Graduate Program Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Departments of Biomedical Engineering Medical Physics Graduate Program and Radiology Carl E. Ravin Advanced Imaging Laboratories Duke University DurhamNC27705 United States Carl E. Ravin Advanced Imaging Laboratories Medical Physics Graduate Program Departments of Radiology Electrical and Computer Engineering Biomedical Engineering and Physics Duke University DurhamNC27705 United States

Objective: This study aims to develop and validate a novel framework, iPhantom, for automated creation of patient-specific phantoms or "digital-twins (DT)" using patient medical images. The framework is applied to assess radiation dose to radiosensitive organs in CT imaging of individual patients. Method: From patient CT images, iPhantom segments selected anchor organs (e.g. liver, bones, pancreas) using a learning-based model developed for multi-organ CT segmentation. Organs challenging to segment (e.g. intestines) are incorporated from a matched phantom template, using a diffeomorphic registration model developed for multi-organ phantom-voxels. The resulting full-patient phantoms are used to assess organ doses during routine CT exams. Result: iPhantom was validated on both the XCAT (n=50) and an independent clinical (n=10) dataset with similar accuracy. iPhantom precisely predicted all organ locations with good accuracy of Dice Similarity Coefficients (DSC) >0.6 for anchor organs and DSC of 0.3-0.9 for all other organs. iPhantom showed Copyright © 2020, The Authors. All rights reserved.

关键词： Phantoms

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Extreme electrodynamics in time-varying media

arXiv

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arXiv 2025年

作者： Scalora, M. Vincenti, M.A. de Ceglia, D. Akozbek, N. Ferrera, M. Rizza, C. Alù, A. Litchinitser, N. Cojocaru, C. Trull, J. FCDD-AMT-MGR DEVCOM AvMC C. M. Bowden Research Center Redstone ArsenalAL35898-5000 United States Department of Information Engineering University of Brescia Brescia25123 Italy US Army Space & Missile Defense Command Tech Center Redstone ArsenalAL35898 United States Institute of Photonics and Quantum Sciences Heriot-Watt University SUPA EdinburghEH14 4AS United Kingdom Department of Physical and Chemical Sciences University of L’Aquila L’AquilaI-67100 Italy Photonics Initiative Advanced Science Research Center City University of New York New YorkNY10031 United States Physics Program Graduate Center City University of New York New YorkNY10016 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States Department of Physics Universitat Politècnica de Catalunya Barcelona Terrassa08222 Spain

Abrupt time variations of the properties of optical materials have been at the center of intense research efforts in recent years, with the prospect of enabling extreme wave transformations and of leveraging time as a degree of freedom for wave control. While the most viable approach to yield ultrafast variations of the optical material response is through optical pumping of nonlinear media, the complex dynamics in these systems are not yet fully understood. Here, as a relevant case study, we rigorously investigate the pump-probe dynamics in a 310nm-thick transparent conductive oxide etalon, using a weak 40 femtosecond probe and a pump that displays peak power densities in the TW/cm2 range with a duration of a few femtoseconds. We examine the pump-probe interaction using a hydrodynamic-Maxwell approach that accounts for diffraction, self-focusing and -defocusing, self- and cross-phase modulation, probe gain, and linear and nonlinear material dispersion expanded in the perturbative regime up to 9th order for both pump and probe. By allowing the intricacies of the pump-probe interaction to proceed in time, we can also define an effective spatio-temporal permittivity for a more direct evaluation of the material ultra-broadband optical behavior. The reported results challenge the conventional modeling of this kind of problem, which has so far overlooked pump dynamics, simplistically assigning a local time-dependent refractive index to the probe that may be designed to fit the experimental data, but has no physical connection to the complex pump-probe interaction. Our approach unveils new dynamics, pointing towards the possibility to achieve extreme pulse compression into the attosecond range and nonlinear diffraction over deeply subwavelength propagation distances, thus opening a possible new path towards novel and cost-effective tools for integrated photonics and attosecond science. © 2025, CC BY-NC-ND.

关键词： Probes

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Nonlocality in photonic materials and metamaterials: roadmap

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Optical Materials Express 2025年第7期15卷 1544-1709页

作者： Francesco Monticone N Asger Mortensen Antonio I Fernández-Domínguez Yu Luo Xuezhi Zheng Christos Tserkezis Jacob B Khurgin Tigran V Shahbazyan André J Chaves Nuno M R Peres Gino Wegner Kurt Busch Huatian Hu Fabio Della Sala Pu Zhang Cristian Ciracì Javier Aizpurua Antton Babaze Andrei G Borisov Xue-Wen Chen Thomas Christensen Wei Yan Yi Yang Ulrich Hohenester Lorenz Huber Martijn Wubs Simone De Liberato P A D Gonçalves F Javier García de Abajo Ortwin Hess Illya Tarasenko Joel D Cox Line Jelver Eduardo J C Dias Miguel Sánchez Sánchez Dionisios Margetis Guillermo Gómez-Santos Igor M Vasilevskiy Tobias Stauber Sergei Tretyakov Constantin Simovski Samaneh Pakniyat J Sebastián Gómez-Díaz Igor V Bondarev Svend-Age Biehs Alexra Boltasseva Vladimir M Shalaev Alexey V Krasavin Anatoly V Zayats Andrea Alù Jung-Hwan Song Mark L Brongersma Uriel Levy Olivia Y Long Cheng Guo Shanhui Fan Sergey I Bozhevolnyi Adam Overvig Filipa R Prudêncio Mário G Silveirinha S Ali Hassani Gangaraj Christos Argyropoulos Paloma A Huidobro Emanuele Galiffi Fan Yang John B Pendry David A B Miller School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA POLIMA—Center for Polariton-driven Light-Matter Interactions University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark D-IAS—Danish Institute for Advanced Study University of Southern Denmark Campusvej 55 DK-5230 Odense M Denmark Departamento de Física Teórica de la Materia Condensada Universidad Autónoma de Madrid E-28049 Madrid Spain Condensed Matter Physics Center (IFIMAC) Universidad Autónoma de Madrid E-28049 Madrid Spain School of Electrical and Electronic Engineering Nanyang Technological University Singapore 639798 Singapore Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China WaveCoRE Division Department of Electrical Engineering KU Leuven Leuven B-3001 Belgium Department of Electrical and Computer Engineering Johns Hopkins University Baltimore Maryland 21218 USA Department of Physics Jackson State University Jackson Mississippi 39217 USA Department of Physics Aeronautics Institute of Technology 12228-900 So Jos dos Campos SP Brazil Centro de Física (CF-UM-UP) and Departamento de Física Universidade do Minho P-4710-057 Braga Portugal International Iberian Nanotechnology Laboratory (INL) Av Mestre José Veiga 4715-330 Braga Portugal Max-Born-Institut 12489 Berlin Germany Humboldt-Universität zu Berlin Institut für Physik AG Theoretische Optik and Photonik 12489 Berlin Germany Center for Biomolecular Nanotechnologies Istituto Italiano di Tecnologia Via Barsanti 14 73010 Arnesano Italy Institute for Microelectronics and Microsystems (CNR-IMM) Via Monteroni Campus Unisalento 73100 Lecce Italy School of Physics Huazhong University of Science and Technology Luoyu Road 1037 Wuhan 430074 China Donostia International Physics Center DIPC 20018 Donostia-San Sebastián Spain Ikerbasque Basque Foundation fo

Photonic technologies continue to drive the quest for new optical materials with unprecedented responses. A major frontier in this field is the exploration of nonlocal (spatially dispersive) materials, going beyond the local, wavevector-independent assumption traditionally adopted in optical material modeling. The growing interest in plasmonic, polaritonic, and quantum materials has revealed naturally occurring nonlocalities, emphasizing the need for more accurate models to predict and design their optical responses. This has major implications also for topological, nonreciprocal, and time-varying systems based on these material platforms. Beyond natural materials, artificially structured materials—metamaterials and metasurfaces—can provide even stronger and engineered nonlocal effects, emerging from long-range interactions or multipolar effects. This is a rapidly expanding area in the field of photonic metamaterials, with open frontiers yet to be explored. In metasurfaces, in particular, nonlocality engineering has emerged as a powerful tool for designing strongly wavevector-dependent responses, enabling enhanced wavefront control, spatial compression, multifunctional devices, and wave-based computing. Furthermore, nonlocality and related concepts play a critical role in defining the ultimate limits of what is possible in optics, photonics, and wave physics. This Roadmap aims to survey the most exciting developments in nonlocal photonic materials and metamaterials, highlight new opportunities and open challenges, and chart new pathways that will drive this emerging field forward—toward new scientific discoveries and technological *** by Optica Publishing Group under the terms of the Creative Commons Attribution 4.0 License . Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

关键词： Chemical elements Localized surface plasmon resonance Material properties Near field scanning optical microscopy Optical materials Thin films

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Evaluation of Frequency-Dependent Effects of Deep Brain Stimulation in a Cortex-Basal Ganglia-Thalamus Network Model of Parkinson’s Disease*

Evaluation of Frequency-Dependent Effects of Deep Brain Stim...

引用

Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Marcelo R. Romano Renan C. Moioli Leonardo A. Elias Neural Engineering Research Laboratory School of Electrical and Computer Engineering University of Campinas Campinas SP Brazil Graduate Program in Bioinformatics Digital Metropolis Institute Federal University of Rio Grande do Norte Natal Brazil Center for Biomedical Engineering University of Campinas Campinas SP Brazil

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Parkinson's disease (PD) is a chronic neurodegenerative disease whose motor symptoms are accompanied by an exaggerated power in the alpha-beta (7-35Hz) band and an increased synchronization of neurons encompassing the cortex-basal ganglia-thalamus network. Currently, deep brain stimulation (DBS) is used as an effective therapy for reducing the excessive power and synchrony observed in brain circuits, thereby ameliorating the PD symptoms. In the present study, we used a biologically plausible computational model of cortex-basal ganglia-thalamus network, which represents both healthy and PD conditions, to systematically investigate the effects of DBS frequency on the model outputs. DBS was applied to the subthalamic nucleus (STN) at different stimulation frequencies (40Hz to 300Hz). Spike train variability and spectral power in the 7-35Hz band were measured from the several nuclei represented in the model. In addition, the magnitude squared coherence between the nuclei was assessed. An increased DBS frequency tended to produce interspike intervals (ISIs) with higher variability as compared to PD condition. Also, DBS significantly reduced the alpha-beta power for almost all brain nuclei. The median of the magnitude-squared coherence matrix (which is a metric of global network synchronization) decreased significantly with the increase of DBS frequency.

关键词： Satellite broadcasting Brain modeling Neurons Computational modeling Coherence Partial discharges Synchronization

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