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Brain Modeling for Control: A Review

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arXiv 2022年

作者： Acharya, Gagan Ruf, Sebastian F. Nozari, Erfan Department of Electrical and Computer Engineering University of California RiversideCA United States Sustainability and Data Sciences Lab Department of Civil and Environmental Engineering Northeastern University BostonMA United States Department of Mechanical Engineering University of California RiversideCA United States Department of Bioengineering University of California RiversideCA United States

Neurostimulation technologies have seen a recent surge in interest from the neuroscience and controls communities alike due to their proven potential to treat conditions such as epilepsy, Parkinson’s Disease, and depression. The provided stimulation can be of different types, such as electric, magnetic, and optogenetic, and is generally applied to a specific region of the brain in order to drive the local and/or global neural dynamics to a desired state of (in)activity. For most neurostimulation techniques, however, an underlying theoretical understanding of their efficacy is still lacking. From a control-theoretic perspective, it is important to understand how each stimulus modality interacts with the inherent complex network dynamics of the brain in order to assess the controllability of the system and develop neurophysiologically relevant computational models that can be used to design the stimulation profile systematically and in closed loop. In this paper, we review the computational modeling studies of (i) deep brain stimulation, (ii) transcranial magnetic stimulation, (iii) direct current stimulation, (iv) transcranial electrical stimulation, and (v) optogenetics as five of the most popular and commonly used neurostimulation technologies in research and clinical settings. For each technology, we split the reviewed studies into (a) theory-driven biophysical models capturing the low-level physics of the interactions between the stimulation source and neuronal tissue, (b) data-driven stimulus-response models which capture the end-to-end effects of stimulation on various biomarkers of interest, and (c) data-driven dynamical system models that extract the precise dynamics of the brain’s response to neurostimulation from neural data. While our focus is particularly on the latter category due to their greater utility in control design, we review key works in the former two categories as the basis and context in which dynamical system models have been and will be dev

关键词： Complex networks

Assessing the capacity of a denoising diffusion probabilistic model to reproduce spatial context

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arXiv 2023年

作者： Deshpande, Rucha Özbey, Muzaffer Li, Hua Anastasio, Mark A. Brooks, Frank J. The Department of Biomedical Engineering Washington University in St. Louis St. LouisMO63130 United States The Department of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States The Department of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States The University of Illinois Beckman Institute UrbanaIL61801 United States

Diffusion models have emerged as a popular family of deep generative models (DGMs). In the literature, it has been claimed that one class of diffusion models-denoising diffusion probabilistic models (DDPMs)-demonstrate superior image synthesis performance as compared to generative adversarial networks (GANs). To date, these claims have been evaluated using either ensemble-based methods designed for natural images, or conventional measures of image quality such as structural similarity. However, there remains an important need to understand the extent to which DDPMs can reliably learn medical imaging domain-relevant information, which is referred to as 'spatial context' in this work. To address this, a systematic assessment of the ability of DDPMs to learn spatial context relevant to medical imaging applications is reported for the first time. A key aspect of the studies is the use of stochastic context models (SCMs) to produce training data. In this way, the ability of the DDPMs to reliably reproduce spatial context can be quantitatively assessed by use of post-hoc image analyses. Error-rates in DDPM-generated ensembles are reported, and compared to those corresponding to a modern GAN. The studies reveal new and important insights regarding the capacity of DDPMs to learn spatial context. Notably, the results demonstrate that DDPMs hold significant capacity for generating contextually correct images that are 'interpolated' between training samples, which may benefit data-augmentation tasks in ways that GANs cannot. Copyright © 2023, The Authors. All rights reserved.

关键词： Diffusion

Label-free imaging flow cytometry for phenotypic analysis of microalgae populations using deep learning

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Label-free imaging flow cytometry for phenotypic analysis of...

Frontiers in Optics + Laser Science 2021, FiO+LS 2021 - Part of Frontiers in Optics, FiO 2021

作者： Işıl, Çağatay de Haan, Kevin Gӧrӧcs, Zoltán Koydemir, Hatice Ceylan Peterman, Spencer Baum, David Song, Fang Skandakumar, Thamira Gumustekin, Esin Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States Neuroscience Department University of California Los AngelesCA90095 United States

We report a field-portable and high-throughput imaging flow-cytometer to perform label-free phenotypic analysis of microalgae populations by extracting and processing the spatial and spectral features of their reconst... 详细信息

ISBN: (纸本)9781557528209

关键词： Algae

New advances in the automation of context-aware information selection and guided model assembly

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arXiv 2021年

作者： Ahmed, Yasmine Butchy, Adam A. Sayed, Khaled Telmer, Cheryl Miskov-Zivanov, Natasa Department of Electrical and Computer Engineering Department of Bioengineering Department of Computational and Systems Biology University of Pittsburgh Molecular Biosensor and Imaging Center Carnegie Mellon University

3D differential phase contrast microscopy with axial motion deblurring

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3D differential phase contrast microscopy with axial motion ...

Computational Optical Sensing and Imaging, COSI 2020 - Part of Imaging and Applied Optics Congress 2020

作者： Cao, Ruiming Kellman, Michael Ren, David Waller, Laura Department of Bioengineering University of California BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States

We demonstrate 3D phase imaging using asymmetric illumination patterns and defocused intensity measurements taken with continuous axial motion. The sample’s 3D refractive index is reconstructed with a motion-correcte... 详细信息

ISBN: (纸本)9781557528209

关键词： Refractive index

A Magnetic Particle Imaging Approach for Minimally Invasive Imaging and Sensing with Implantable Bioelectronic Circuits

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TechRxiv

TechRxiv 2023年

作者： Tay, Zhiwei Kim, Han-Joon Ho, John S. Olivo, Malini Singapore National Institute of Advanced Industrial Science and Technology 1-2-1 Namiki Ibaraki Tsukuba305-8564 Japan National University of Singapore Department of Bioengineering Singapore Department of Electrical and Computer Engineering The Institute for Health Technology National University of Singapore Singapore 2 Fusionopolis Way Innovis 08-03 138634 Singapore

Minimally-invasive and biocompatible implantable bioelectronic circuits are used for long-term monitoring of physiological processes in the body. However, there is a lack of methods that can cheaply and conveniently image the device within the body while simultaneously extracting sensor information. Magnetic Particle Imaging (MPI) with zero background signal, high contrast, and high sensitivity with quantitative images is ideal for this challenge because the magnetic signal is not absorbed with increasing tissue depth and incurs no radiation dose. We show how to easily modify common implantable devices to be imaged by MPI by encapsulating and magnetically-coupling magnetic nanoparticles (SPIOs) to the device circuit. These modified implantable devices not only provide spatial information via MPI, but also couple to our handheld MPI reader to transmit sensor information by modulating harmonic signals from magnetic nanoparticles via switching or frequency-shifting with resistive or capacitive sensors. This paper provides proof-of-concept of an optimized MPI imaging technique for implantable devices to extract spatial information as well as other information transmitted by the implanted circuit (such as biosensing) via encoding in the magnetic particle spectrum. The 4D images present 3D position and a changing color tone in response to a variable biometric. Biophysical sensing via bioelectronic circuits that take advantage of the unique imaging properties of MPI may enable a wide range of minimally invasive applications in biomedicine and diagnosis. © 2023, CC BY.

关键词： Timing circuits

Study of ASIC Readout Used for Detectors in an MR Compatible, RF-Penetrable TOF-PET Insert

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Study of ASIC Readout Used for Detectors in an MR Compatible...

IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Qian Dong Salar Sajedi Ilaria Sacco Peter Fischer Craig S. Levin Departments of Electrical Engineering and Radiology Stanford University Stanford CA USA Departments of Radiology Stanford University Stanford CA USA Department of Computer Science Heidelberg University Baden-Württemberg Germany Department of Radiology Electrical Engineering Bioengineering and Physics Stanford University Molecular Imaging Program at Stanford (MIPS) Stanford CA USA

Developing positron emission tomography (PET) systems with high channel number has driven the need for application-specific integrated circuits (ASICs) owing to their compactness, multi-channel readout capability, and excellent time and energy resolution performance. They can also be quite flexible by employing field-programmable gate arrays (FPGAs) in the readout circuit. In particular, the ASIC+FPGA approach provides a high performance and compact solution for TOF-PET applications. ASICs serve functions of extracting time and energy information from analog SiPM signals, and an FPGA is employed for other application-specific requirements like on-board chip configuration, communication with data acquisition (DAQ) systems, and system monitoring. Based on this foundation, we developed TOF-capable detector modules for our second generation RF-penetrable PET insert. During our previous experiments, we found that different trigger and readout methods lead to different noise pattern and intensity in MR environment, and also have different singles count loss ratio. This paper aims to study and compare different readout configurations for our MR-compatible detectors. We studied four trigger and readout methods: hit-standard, frequent-standard, hit-bypass, and frequent-bypass, and compared their singles count loss ratios with both theoretical models and experimental results. For all four methods, the theoretical results matched the experimental results well. The two methods which used bypass mode performed better than those using the standard mode approach. With the most optimized trigger and readout method (hit-bypass), we achieved 1.2% singles count loss ratio at a 20 kcps count rate per channel.

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Information-Theoretic Analysis of Multimodal Image Translation

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Liu, Ruihao Li, Yudu Li, Yao Du, Yiping P. Liang, Zhi-Pei Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States University of Illinois at Urbana-Champaign National Center for Supercomputing Applications Department of Bioengineering Grainger College of Engineering Beckman Institute for Advanced Science and Technology UrbanaIL61801 United States Shanghai Jiao Tong University School of Biomedical Engineering Institute for Medical Imaging Technology Shanghai200030 China University of Illinois at Urbana-Champaign Beckman Institute for Advanced Science and Technology Department of Electrical and Computer Engineering Grainger College of Engineering UrbanaIL61801 United States

Multimodal image translation has found useful applications in solving several medical imaging problems. In this paper, we presented a systematic analysis of multimodal images and machine learning-based image translation from an information-theoretic perspective. Specifically, we analyzed the amount of mutual information that exists in some commonly used multimodal images. This analysis revealed varying structural correlation across modalities and tissue-dependence of mutual information. We also analyzed the amount of information transferred and gained in multimodal image translation and provided an upper bound on the information gain. Information-theoretic measures were also proposed to assess the effectiveness of an image translator, and the uncertainty associated with image translation. Numerical results were presented to demonstrate the information gain in practical multimodal image translation, and to validate the proposed upper bound on information gain and the translation error predictor. Finally, several potential applications of our analysis results were discussed, including the image denoising and reconstruction using side information generated by image translation. The findings from this study may prove useful for guiding the further development and application of multimodal image translation. © 1982-2012 IEEE.

关键词： Machine translation

Analysis of Diffractive Neural Networks for Seeing Through Random Diffusers

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arXiv 2022年

作者： Li, Yuhang Luo, Yi Bai, Bijie Ozcan, Aydogan Electrical and Computer Engineering Department University of California Los AngelesCA90095 United States Bioengineering Department University of California Los AngelesCA90095 United States University of California Los AngelesCA90095 United States

Imaging through diffusive media is a challenging problem, where the existing solutions heavily rely on digital computers to reconstruct distorted images. We provide a detailed analysis of a computer-free, all-optical imaging method for seeing through random, unknown phase diffusers using diffractive neural networks, covering different deep learning-based training strategies. By analyzing various diffractive networks designed to image through random diffusers with different correlation lengths, a trade-off between the image reconstruction fidelity and distortion reduction capability of the diffractive network was observed. During its training, random diffusers with a range of correlation lengths were used to improve the diffractive network's generalization performance. Increasing the number of random diffusers used in each epoch reduced the overfitting of the diffractive network's imaging performance to known diffusers. We also demonstrated that the use of additional diffractive layers improved the generalization capability to see through new, random diffusers. Finally, we introduced deliberate misalignments in training to "vaccinate" the network against random layer-to-layer shifts that might arise due to the imperfect assembly of the diffractive networks. These analyses provide a comprehensive guide in designing diffractive networks to see through random diffusers, which might profoundly impact many fields, such as biomedical imaging, atmospheric physics, and autonomous driving. Copyright © 2022, The Authors. All rights reserved.

关键词： Digital computers