2,792 resources related to Neurology
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI 2020)
The IEEE International Symposium on Biomedical Imaging (ISBI) is the premier forum for the presentation of technological advances in theoretical and applied biomedical imaging. ISBI 2020 will be the 17th meeting in this series. The previous meetings have played a leading role in facilitating interaction between researchers in medical and biological imaging. The 2020 meeting will continue this tradition of fostering cross-fertilization among different imaging communities and contributing to an integrative approach to biomedical imaging across all scales of observation.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Design and analysis of algorithms, computer systems, and digital networks; methods for specifying, measuring, and modeling the performance of computers and computer systems; design of computer components, such as arithmetic units, data storage devices, and interface devices; design of reliable and testable digital devices and systems; computer networks and distributed computer systems; new computer organizations and architectures; applications of VLSI ...
Serves as a compendium for papers on the technological advances in control engineering and as an archival publication which will bridge the gap between theory and practice. Papers will highlight the latest knowledge, exploratory developments, and practical applications in all aspects of the technology needed to implement control systems from analysis and design through simulation and hardware.
IEEE Journal of Biomedical and Health Informatics, 2019
The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are ...
2010 International Conference on Networking and Information Technology, 2010
The purpose of this paper is to present an overview of existing automatics medical image registration methods in neurology with a view to application BrainView, which can provide a general overview of image data sets and allow the operator to visually compare images in the medical domain. The aim of the developed application is a registration of a brain slices ...
2015 International Conference on Electronic Design, Computer Networks & Automated Verification (EDCAV), 2015
Acetylcholine sensitive field effect transistor (AchFET) has been modeled and simulated to use as circuit element in a neuron circuit. AchFET has been modeled using enzyme kinetics and site binding theory. Based on the simulated characteristics, it is used as an analog of variable conductance in the Hodgkin-Huxley electrical model of neuron that was used to study the resistance and ...
1998 IEEE Nuclear Science Symposium Conference Record. 1998 IEEE Nuclear Science Symposium and Medical Imaging Conference (Cat. No.98CH36255), 1998
In order to characterize physiological cerebral activation or tissue response to a treatment, at least two FDG-PET studies are mandatory. In this work, a study of one hour duration with two injections at 30 min apart is reported. The separation of the two input curves (IC) consisted of fitting the blood curve corresponding to the first injection using spectral analysis, ...
Multimodal Imaging in Neurology: Special Focus on MRI Applications and MEG, None
The field of brain imaging is developing at a rapid pace and has greatly advanced the areas of cognitive and clinical neuroscience. The availability of neuroimaging techniques, especially magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG) and magnetic source imaging (MSI) has brought about breakthroughs in neuroscience. To obtain comprehensive information about the activity ...
Scientific Discovery & Deep Brain Stimulation: Jerrold Vitek, MD, PhD
Q&A with Sri Sarma: IEEE Brain Podcast, Episode 2
Q&A with Dr. Elisa Konofagou: IEEE Brain Podcast, Episode 10
Learning with Memristive Neural Networks: Neuromorphic Computing - Joshua Yang at INC 2019
Laura Specker Sullivan: Neuroscience & Brain Panel - Forecasting the Future by Looking at the Past - TTM 2018
Connecting Silicon & Brain Neurons: Neuromorphic Computing - Stefano Vassanelli at INC 2019
Q&A with Jack Gallant: IEEE Brain Podcast, Episode 11
Q&A with Eric Perreault: IEEE Brain Podcast, Episode 1
Neuropriming: What Are The Ethical Implications? - IEEE TechEthics Virtual Panel
Brave New Brain-Tech | IEEE TechEthics Panel
The advancement of scientific and medical research over the past years has generated a wealth of experimental data from multiple technologies, including genomics, transcriptomics, proteomics, and other forms of -omics data, which are available for a number of diseases. The integration of such multisource data is a key component toward the success of precision medicine. In this paper, we are investigating a multisource data integration method developed by our group, regarding its ability to drive to clusters of connected pathways under two different approaches: first, a disease-centric approach, where we integrate data around a disease, and second, a gene-centric approach, where we integrate data around a gene. We have used as a paradigm for the first approach Huntington's disease (HD), a disease with a plethora of available data, whereas for the second approach the GBA2, a gene that is related to spastic ataxia (SA), a phenotype with sparse availability of data. Our paper shows that valuable information at the level of disease-related pathway clusters can be obtained for both HD and SA. New pathways that classical pathway analysis methods were unable to reveal, emerged as necessary “connectors” to build connected pathway stories formed as pathway clusters. The capability to integrate multisource molecular data, concluding to something more than the sum of the existing information, empowers precision and personalized medicine approaches.
The purpose of this paper is to present an overview of existing automatics medical image registration methods in neurology with a view to application BrainView, which can provide a general overview of image data sets and allow the operator to visually compare images in the medical domain. The aim of the developed application is a registration of a brain slices from computer tomography slides where the algorithm transforms the intensities of a source images and after it provides geometrical transformation with the minimisation of the sum of squared difference criterion.
Acetylcholine sensitive field effect transistor (AchFET) has been modeled and simulated to use as circuit element in a neuron circuit. AchFET has been modeled using enzyme kinetics and site binding theory. Based on the simulated characteristics, it is used as an analog of variable conductance in the Hodgkin-Huxley electrical model of neuron that was used to study the resistance and capacitance properties of a patch of squid axon membrane. PSPICE simulation of the circuit using AchFET gives satisfactory reproduction of sodium and potassium currents and simulated electronic action potential is very similar to the experimentally recorded one. Due to the simplicity of this circuit, it may be used as an ideal unit in neurology to study the receptor function and electrical activity of neuron.
In order to characterize physiological cerebral activation or tissue response to a treatment, at least two FDG-PET studies are mandatory. In this work, a study of one hour duration with two injections at 30 min apart is reported. The separation of the two input curves (IC) consisted of fitting the blood curve corresponding to the first injection using spectral analysis, then estimating the second blood curve by removing the remnant of the first. Tissue time activity curves (tTAC) were fitted for the first 30 min using the first IC and were extrapolated till 60 min. This extrapolated part was removed from the tissue response to the second injection before being fitted using the second IC. Other data were obtained for simple injection from which regional cerebral metabolic rates for glucose (rCMRGlu) estimated from 0-30 min were compared to those obtained from 0-60 min. Maximal rCMRGlu differences in double injection with activation were found to be on the average four times higher than those from 0-30 and 0-60 min baseline simple injection. The method is expected to be more accurate to observe drug uptake or tumor response to a treatment.
The field of brain imaging is developing at a rapid pace and has greatly advanced the areas of cognitive and clinical neuroscience. The availability of neuroimaging techniques, especially magnetic resonance imaging (MRI), functional MRI (fMRI), diffusion tensor imaging (DTI) and magnetoencephalography (MEG) and magnetic source imaging (MSI) has brought about breakthroughs in neuroscience. To obtain comprehensive information about the activity of the human brain, different analytical approaches should be complemented. Thus, in "intermodal multimodality" imaging, great efforts have been made to combine the highest spatial resolution (MRI, fMRI) with the best temporal resolution (MEG or EEG). "Intramodal multimodality" imaging combines various functional MRI techniques (e.g., fMRI, DTI, and/or morphometric/volumetric analysis). The multimodal approach is conceptually based on the combination of different noninvasive functional neuroimaging tools, their registration and cointegration. In particular, the combination of imaging applications that map different functional systems is useful, such as fMRI as a technique for the localization of cortical function and DTI as a technique for mapping of white matter fiber bundles or tracts. This booklet gives an insight into the wide field of multimodal imaging with respect to concepts, data acquisition, and postprocessing. Examples for intermodal and intramodal multimodality imaging are also demonstrated. Table of Contents: Introduction / Neurological Measurement Techniques and First Steps of Postprocessing / Coordinate Transformation / Examples for Multimodal Imaging / Clinical Aspects of Multimodal Imaging / References / Biography
Hyperperfusion detected on arterial spin labeling (ASL) images acquired after acute stroke onset has been shown to correlate with development of subsequent intracerebral hemorrhage. We present in this study a quantitative hyperperfusion detection model that can provide an objective decision support for the interpretation of ASL cerebral blood flow (CBF) maps and rapidly delineate hyperperfusion regions. The detection problem is solved using Deep Learning such that the model relates ASL image patches to the corresponding label (normal or hyperperfused). Our method takes into account the regional intensity values of contralateral hemisphere during the labeling of a pixel. Each input vector is associated to a label corresponding to the presence of hyperperfusion that was manually established by a clinical researcher in Neurology. When compared to the manually established hyperperfusion, the predicted maps reached an accuracy of 97.45 ± 2.49% after crossvalidation. Pattern recognition based on deep learning can provide an accurate and objective measure of hyperperfusion on ASL CBF images and could therefore improve the detection of hemorrhagic transformation in acute stroke patients.
Brain stimulation has emerged as an effective treatment for a wide range of neurological and psychiatric diseases. Parkinson's disease, epilepsy, and essential tremor have FDA indications for electrical brain stimulation using intracranially implanted electrodes. Interfacing implantable brain devices with local and cloud computing resources have the potential to improve electrical stimulation efficacy, disease tracking, and management. Epilepsy, in particular, is a neurological disease that might benefit from the integration of brain implants with off-the-body computing for tracking disease and therapy. Recent clinical trials have demonstrated seizure forecasting, seizure detection, and therapeutic electrical stimulation in patients with drug-resistant focal epilepsy. In this paper, we describe a next-generation epilepsy management system that integrates local handheld and cloud-computing resources wirelessly coupled to an implanted device with embedded payloads (sensors, intracranial EEG telemetry, electrical stimulation, classifiers, and control policy implementation). The handheld device and cloud computing resources can provide a seamless interface between patients and physicians, and realtime intracranial EEG can be used to classify brain state (wake/sleep, preseizure, and seizure), implement control policies for electrical stimulation, and track patient health. This system creates a flexible platform in which low demand analytics requiring fast response times are embedded in the implanted device and more complex algorithms are implemented in offthebody local and distributed cloud computing environments. The system enables tracking and management of epileptic neural networks operating over time scales ranging from milliseconds to months.
The basal ganglia are intimately connected to the frontal cortex via five fronto-striatal circuits. While the role of the frontal cortex in cognition has been extensively studied, the contribution of the basal ganglia to cognition has remained less clear. In Parkinson's disease, posteroventral pallidotomy (PVP) involves surgical lesioning of the internal section of the globus pallidus (GPi, the final output pathway from the basal ganglia) to relieve the motor symptoms of the disorder. PVP in Parkinson's disease provides a unique opportunity to investigate the impact of disruption of striatal outflow to the frontal cortex on cognition. We assessed executive function and working memory after withdrawal of medication in 13 patients with Parkinson's disease before and 3 months after unilateral PVP compared to 12 age-and IQ-matched normals assessed twice with an interval of 3 months. The tests used were: Wisconsin Card Sorting (WCST), Self-Ordered Random Number Sequences, Missing Digit Test, Paced Visual Serial Addition Test (PVSAT), and Visual Conditional Associative Learning Test (VCALT). After PVP, the patients performed significantly better on the Self-Ordered Random Number Sequences and the WCST, an improvement that was also observed in the normals across the two assessment and is therefore likely to reflect practice effects. Relative to the normals, the patients showed significant differential change following PVP on the Missing Digit Test and PVSAT, on which they performed worse after compared to before surgery, while the controls performed better on the second assessment. For the patients, performance on the VCALT also indicated deterioration after PVP, but the changes approached significance. The side of PVP had no effect on the results. The pattern of change observed 3 months after PVP was maintained at 15-month follow-up. The results suggest that striatal outflow to the frontal cortex may be essential for those aspects of executive function that showed deterioration after PVP. &
The needs and importance of the simple screening examination for dementia have risen recently. In the present study, we corrected the patients of Alzheimer's disease, and performed the standard examination for dementia and a simple computerized touch panel-type screening test for the early diagnosis of dementia (touch panel-type screening test), and compared the scores. There is a good correlation MMSE score and a simple computerized touch panel-type screening test by single regression analysis. And so, a simple computerized touch panel-type screening test is very useful for assessment of dementia.
Insight is a Semantic Web technology-based platform to support large-scale secondary analysis of healthcare data for neurology clinical research. Insight features the novel use of: (1) provenance metadata, which describes the history or origin of patient data, in clinical research analysis, and (2) support for patient cohort queries across multiple institutions conducting research in epilepsy, which is the one of the most common neurological disorders affecting 50 million persons worldwide. Insight is being developed as a healthcare informatics infrastructure to support a national network of eight epilepsy research centers across the U.S. funded by the U.S. Centers for Disease Control and Prevention (CDC). This paper describes the use of the World Wide Web Consortium (W3C) PROV recommendation for provenance metadata that allows researchers to create patient cohorts based on the provenance of the research studies. In addition, the paper describes the use of descriptive logic-based OWL2 epilepsy ontology for cohort queries with “expansion of query expression” using ontology reasoning. Finally, the evaluation results for the data integration and query performance are described using data from three research studies with 180 epilepsy patients. The experiment results demonstrate that Insight is a scalable approach to use Semantic provenance metadata for context-based data analysis in healthcare informatics.
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Assistant Professor -- Computational Multi-omics Diagnostics (Tenure Track)
The Department of Pathology and Laboratory Medicine at the Perelman School of Medicine at the University of Pennsylvania