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Functional Parcellation of fMRI data using multistage k-means clustering

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arXiv 2022年

作者： Parmar, Harshit Nutter, Brian Long, Rodney Antani, Sameer Mitra, Sunanda Department of Electrical and Computer Engineering Texas Tech University LubbockTX United States Lister Hill National Center for Biomedical Communications National Library of Medicine National Institutes of Health Bethesda MD United States

Purpose: Functional Magnetic Resonance Imaging (fMRI) data acquired through resting-state studies have been used to obtain information about the spontaneous activations inside the brain. One of the approaches for analysis and interpretation of resting-state fMRI data require spatially and functionally homogenous parcellation of the whole brain based on underlying temporal fluctuations. Clustering is often used to generate functional parcellation. However, major clustering algorithms, when used for fMRI data, have their limitations. Among commonly used parcellation schemes, a tradeoff exists between intra-cluster functional similarity and alignment with anatomical regions. Approach: In this work, we present a clustering algorithm for resting state and task fMRI data which is developed to obtain brain parcellations that show high structural and functional homogeneity. The clustering is performed by multistage binary k-means clustering algorithm designed specifically for the 4D fMRI data. The results from this multistage k-means algorithm show that by modifying and combining different algorithms, we can take advantage of the strengths of different techniques while overcoming their limitations. Results: The clustering output for resting state fMRI data using the multistage k-means approach is shown to be better than simple k-means or functional atlas in terms of spatial and functional homogeneity. The clusters also correspond to commonly identifiable brain networks. For task fMRI, the clustering output can identify primary and secondary activation regions and provide information about the varying hemodynamic response across different brain regions. Conclusion: The multistage k-means approach can provide functional parcellations of the brain using resting state fMRI data. The method is model-free and is data driven which can be applied to both resting state and task fMRI. © 2022, CC BY.

关键词： Magnetic resonance imaging

Generative Adversarial Networks for Heterogeneous Unsupervised Domain Adaptation Detection

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Generative Adversarial Networks for Heterogeneous Unsupervis...

IEEE International Conference on Intelligent engineering Systems (INES)

作者： Homayoun Safarpour Motealegh Mahalegi Amirfarhad Farhadi György Molnár Enikő Nagy Institute of Mechatronics and Vehicle Engineering Óbuda University Budapest Hungary Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Kandó Kálmán Faculty of Electrical Engineering Óbuda University Budapest Hungary János Csere Apáczai Faculty Széchenyi University Győr Hungary Antal Bejczy Center for Intelligent Robotics John von Neumann Faculty of Informatics Óbuda University Budapest Hungary

ISBN: (数字)9798350367591

ISBN: (纸本)9798350367607

Domain adaptation, a subset of transfer learning, involves generating examples from two related but differently distributed source and target domains. This paper introduces an incremental adversarial learning method for unsupervised domain adaptation, where the source data is labeled, and the target data remains unlabeled. We employ a discriminative adversarial strategy as the primary technique for unsupervised domain adaptation to minimize the significant distributional variance between the source and target domains. Traditional domain adaptation methods typically train the source domain to align with a fixed target domain distribution. However, in many real-world scenarios, the target domain's distribution continuously evolves, leading to a lack of generalizability in the trained model for subsequent domains. To tackle this issue, we suggest using a continuous sequential domain adaptation method. This involves including instances from several related target domains in the training process in a sequential and continuous manner. This process ensures that all receiving domains are correctly aligned with their respective sources, and to maintain sequence continuity, the source domain is incrementally updated by incorporating the most reliable data from the current target domain. This enhancement aims to improve the network's training for forthcoming domain data. Leveraging the effectiveness of deep neural networks, our method utilizes deep GANs for domain adaptation. The approach enhances domain matching by augmenting the source with selected target data and introduces a novel strategy for continuous sequential domain matching. This involves a progressive update of source domain samples with reliable data from the current target domain, facilitating seamless adaptation to successive domain shifts. The proposed method demonstrated a high classification accuracy, achieving an impressive 99% and 95% in different settings. Furthermore, it outperforms previous studies rega

关键词： Training Adaptation models Machine learning algorithms Accuracy Transfer learning Clustering algorithms Sensitivity and specificity

Battle Royale Optimizer for solving binary optimization problems[Formula presented]

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Software Impacts 2022年 12卷

作者： Akan, Taymaz Agahian, Saeid Dehkharghani, Rahim Istanbul Ayvansaray University Istanbul Turkey Faculty of Electrical Engineering and Informatics University of Pardubice Pardubice Czech Republic Erzurum Technical University Erzurum Turkey Department of Computer Engineering Işik University Istanbul Turkey

Battle Royale Optimizer (BRO) is a recently proposed metaheuristic optimization algorithm used only in continuous problem spaces. The BinBRO is a binary version of BRO. The BinBRO algorithm employs a differential expression, which utilizes a dissimilarity measure between binary vectors instead of a vector subtraction operator, used in the original BRO algorithm to find the nearest neighbor. To evaluate BinBRO, we applied it to two popular benchmark datasets: the uncapacitated facility location problem (UFLP) and the maximum-cut (Max-Cut) graph problems from OR-Library. An open-source MATLAB implementation of BinBRO is available on CodeOcean and GitHub websites. © 2022 The Author(s)

关键词： Battle Royale Optimization Binary Battle Royale Optimization Discrete optimization Optimization

Internet of Bio-Nano Things: A Review of Applications, Enabling Technologies and Key Challenges

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arXiv 2021年

作者： Kuscu, Murat Unluturk, Bige D. The Department of Electrical and Electronics Engineering Koc University Rumelifeneri Yolu Sariyer Istanbul34450 Turkey The Department of Electrical & Computer Engineering and Biomedical Engineering 775 Woodlot Dr. East LansingMI48823 United States

Internet of Bio-Nano Things (IoBNT) is envisioned to be a heterogeneous network of nanoscale and biological devices, so called Bio-Nano Things (BNTs), communicating via non-conventional means, e.g., molecular communications (MC), in non-conventional environments, e.g., inside human body. The main objective of this emerging networking framework is to enable direct and seamless interaction with biological systems for accurate sensing and control of their dynamics in real time. This close interaction between bio and cyber domains with unprecedentedly high spatio-temporal resolution is expected to open up vast opportunities to devise novel applications, especially in healthcare area, such as intrabody continuous health monitoring. There are, however, substantial challenges to be overcome if the enormous potential of the IoBNT is to be realized. These range from developing feasible nanocommunication and energy harvesting techniques for BNTs to handling the big data generated by IoBNT. In this survey, we attempt to provide a comprehensive overview of the IoBNT framework along with its main components and applications. An investigation of key technological challenges is presented together with a detailed review of the state-of-the-art approaches and a discussion of future research directions. Copyright © 2021, The Authors. All rights reserved.

关键词： Heterogeneous networks

Quantum Generative Diffusion Model: A Fully Quantum-Mechanical Model for Generating Quantum State Ensemble

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arXiv 2024年

作者： Chen, Chuangtao Zhao, Qinglin Zhou, MengChu He, Zhimin Sun, Zhili Situ, Haozhen Faculty of Innovation Engineering Macau University of Science and Technology Macao999078 China Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ07102 United States School of Electronic and Information Engineering Foshan University Foshan528000 China Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Mathematics and Informatics South China Agricultural University Guangzhou510642 China

Classical diffusion models have shown superior generative results. Exploring them in the quantum domain can advance the field of quantum generative learning. This work introduces Quantum Generative Diffusion Model (QGDM) as their simple and elegant quantum counterpart. Through a non-unitary forward process, any target quantum state can be transformed into a completely mixed state that has the highest entropy and maximum uncertainty about the system. A trainable backward process is used to recover the former from the latter. The design requirements for its backward process includes non-unitarity and small parameter count. We introduce partial trace operations to enforce non-unitary and reduce the number of trainable parameters by using a parameter-sharing strategy and incorporating temporal information as an input in the backward process. We present QGDM’s resource-efficient version to reduce auxiliary qubits while preserving generative capabilities. QGDM exhibits faster convergence than Quantum Generative Adversarial Network (QGAN) because its adopted convex-based optimization can result in better convergence. The results of comparing it with QGAN demonstrate its effectiveness in generating both pure and mixed quantum states. It can achieve 53.02% higher fidelity in mixed-state generation than QGAN. The results highlight its great potential to tackle challenging quantum generation tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

Synthesising 3D Cardiac CINE-MR Images and Corresponding Segmentation Masks using a Latent Diffusion Model

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Synthesising 3D Cardiac CINE-MR Images and Corresponding Seg...

IEEE International Symposium on biomedical Imaging

作者： Nina Cheng Zhengji Liu Yash Deo Haoran Dou Ning Bi Kun Wu Fengming Lin Zeike A Taylor Nishant Ravikumar Alejandro F Frangi CISTIB Centre for Computational Imaging and Simulation Technologies in Biomedicine University of Leeds School of Optometry The Hong Kong Polytechnic University NIHR Leeds Biomedical Research Centre Leeds UK Alan Turing Institute London UK Division of Informatics Imaging and Data Science Schools of Computer Science and Health Sciences University of Manchester Manchester UK Cardiovascular Sciences Departments Medical Imaging Research Center (MIRC) Electrical Engineering KU Leuven Leuven Belgium

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

We propose a novel pipeline for the generation of synthetic full spatial cine cardiac magnetic resonance (CMR) images via a latent Denoising Diffusion Implicit Models (DDIMs). These synthetic images can be used as viable alternatives to real data in deep learning model training for downstream cardiac image analysis tasks such as cardiac segmentation. To demonstrate the effectiveness of this approach, we generated synthetic CMR images along with their corresponding segmentation masks. We evaluated model performance using a variety of methods, including generated image fidelity, diversity and calculated the volumes of the generated segmentation masks and compare it with the real segmentation masks. The proposed pipeline has the potential to be widely applied to other tasks in various medical imaging modalities. Effective and efficient generation of 3D cine cardiac images with corresponding segmentation masks can supplement real patient datasets and help reduce the burden of manually annotating images.

关键词： Training Image segmentation Solid modeling Three-dimensional displays Biological system modeling Pipelines Noise reduction

Identifying Genes and Their Interactions from Pathway Figures and Text in biomedical Articles

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Identifying Genes and Their Interactions from Pathway Figure...

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： He, Fei Thompson, Joshua Mao, Ziting Ren, Yijie Nussbaum, Yulia Kholod, Olha Shin, Dmitriy Hannink, Mark Popescu, Mihail Xu, Dong Northeast Normal University School of Information Science and Technology Changchun China University of Missouri Department of Electrical Engineering and Computer Science ColumbiaMO United States Institute for Data Science and Informatics University of Missouri ColumbiaMO United States University of Missouri Department of Pathology and Anatomical Sciences ColumbiaMO United States University of Missouri Department of Biochemistry ColumbiaMO United States University of Missouri Department of Health Management and Informatics ColumbiaMO United States

ISBN: (纸本)9781665401265

Many high-quality biological pathways are presented in figures and text in biomedical literature. They are great resources for studies of biological mechanisms and precision medicine practices. These pathways need to be carefully curated, reconciled, and transformed into a computable form. Current manual curation approaches are inadequate in keeping up with the pace of the literature growth. New bio-curation approaches are needed to streamline the identification of gene interactions from pathway figures and text. This paper proposes a pathway curation approach for identifying genes and their interactions using both figures and text of biomedical articles. Our method integrates deep learning-based object detection models with a Google optical character recognition service to extract genes and their interactions from pathway figures. Our pipeline was evaluated on the figures from PubMed publications with manual annotations. The results demonstrated that our model could effectively retrieve genes and their interactions in pathway figures. The proposed pipeline may accelerate various applications of the latest biomedical discoveries. We also developed a web server at http://*** to provide the gene interaction curation on uploaded pathway figures and corresponding articles. © 2021 IEEE.

关键词： Genes

A differential graded approach to the silting theorem

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arXiv 2021年

作者： Xie, Zongzhen Yang, Dong Zhang, Houjun Department of Mathematics and Computer Science School of Biomedical Engineering and Informatics Nanjing Medical University Nanjing211166 China Department of Mathematics Nanjing University Nanjing210093 China

A silting theorem was established by Buan and Zhou as a generalisation of the classical tilting theorem of Brenner and Butler. In this paper, we give an alternative proof of the theorem by using differential graded **... 详细信息

关键词： Algebra

Effectiveness of Repetitive Transcranial Magnetic Stimulation (rTMS) on DLPFC for Enhancing Cognitive Function in Healthy Adults: A Review

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SSRN

SSRN 2023年

作者： Asgarinejad, Mostafa Saviz, Marzieh Sadjadi, Seyyed Mostafa Saliminia, Sarah Kakaei, Amineh Esmaeili, Peyman Hammoud, Ahmad Ebrahimzadeh, Elias Soltanian-Zadeh, Hamid Institute for Cognitive Sciences Studies Tehran Iran Faculty of Psychology and Education University of Tehran Tehran Iran CIPCE School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Tehran Iran Biomedical Engineering Department School of Electrical Engineering Payame Noor University of North Tehran Tehran Iran Department of Clinical Psychology School of Behavioral Sciences and Mental Health Iran University of Medical Sciences Tehran Iran Department of Health Safety and Environment Shahid Beheshti Medical University Tehran Iran Department of Medical and Technical Information Technology Bauman Moscow State Technical University Moscow Russia

As human beings, we have always sought to expand on our abilities, including our cognitive and motor skills. One of the still-underrated tools employed to this end is repetitive Transcranial Magnetic Stimulation (rTMS). Until recently, rTMS was almost exclusively used in studies with rehabilitation purposes. Only a small strand of literature has focused on the application of rTMS on healthy people with the aim of enhancing cognitive abilities such as decision-making, working memory, attention, source memory, cognitive control, learning, computational speed, risk-taking, and impulsive behaviors. It, therefore, seems that the findings in this particular field are the indirect results of rehabilitation research. In this review paper, we have set to investigate such studies and evaluate the rTMS effectuality in terms of how it improves the cognitive skills in healthy subjects. Furthermore, since the most common brain site used for rTMS protocols is the dorsolateral prefrontal cortex (DLPFC), we have added Theta Burst Stimulation (TBS) wave patterns that are similar to brain patterns to increase the effectiveness of this method. The results of this study can help people who have high-risk jobs including firefighters, surgeons, and military officers with their job performance. © 2023, The Authors. All rights reserved.

关键词： Electroencephalography