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Robust fMRI time-varying functional connectivity analysis using multivariate swarm decomposition

Neurocomputing 2025年 642卷

作者： Lamprou, Charalampos Apostolidis, Georgios Alshehhi, Aamna Hadjileontiadis, Leontios J. Seghier, Mohamed L. Biomedical Engineering and Biotechnology Department Khalifa University of Science and Technology Abu Dhabi127788 United Arab Emirates Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece Khalifa University of Science and Technology Abu Dhabi127788 United Arab Emirates

Time-varying functional connectivity (TVFC) measured with functional MRI (fMRI) captures dynamic changes in statistical dependencies among regional time series, which can be studied with instantaneous phase synchronization analyses. Phase extraction requires narrow-banded resting-state fMRI (rs-fMRI) data typically extracted with conventional band-pass filtering or advanced mode decomposition techniques. However, filtering methods often struggle to eliminate noise effectively, require prior knowledge of cutoff frequencies, and fail to account for non-stationarity in the data. Likewise, existing mode decomposition techniques strongly depend on input parameters and are less reliable for multivariate analyses. Here, we introduce multivariate swarm decomposition (MSwD), a bio-inspired signal decomposition technique that combines the iterative nature of empirical methods with a robust mathematical foundation. Using synthetic signals and real rs-fMRI data from the Human Connectome Project and the Autism Brain Imaging Data Exchange I, we showed that MSwD-based PS (MSwD-PS) outperforms four state-of-the-art decomposition techniques in several key areas: (1) being more robust to input parameters and better at detecting true synchronizations, showing a 3–65% lower normalized root mean square error in simulated data and being 15.8-73.1% less prone to identifying short biologically implausible transitions between brain states, and (2) showing a reduced likelihood of false positives, being less affected by spurious synchronizations. Likewise, MSwD-informed functional connectivity analysis improved subject fingerprinting and autism spectrum disorder classification using graph neural networks. Overall, MSwD-PS can reduce the risk of false positives in TVFC, which could be extremely useful for processing rs-fMRI data with unknown ground truth in diverse clinical populations. © 2025 The Authors

关键词： Variational mode decomposition

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A deep learning for automated segmentation of skeletal muscle at the third lumbar vertebra in computed tomography images

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Journal of Medical Imaging and Radiation Sciences 2022年第4期53卷 S37-S37页

作者： Cheewakul, Jongwat Yanaihap, N Yue-roh, T Kepan, N Thiangsook, W Rojanaaumpawan, Th Ina, N Radiology Department Faculty of Medicine Prince of Songkhla University Songkhla Thailand Biomedical Engineering Program Department of Electrical Engineering Faculty of Engineering Prince of Songkhla University Songkhla Thailand

IntroductionThe purpose of this study is to apply deep learning to segment and visualize the skeletal muscle in the middle of third lumbar vertebra on computed tomography images. MethodsBetween July 1 and December 31, 2021, patients aged 20 to 60 who had computed tomography at Songklanagarind Hospital computed tomography center will have their images of the abdomen axial slice at the middle of the third lumbar vertebra gathered. We use 215 images, including computed tomography (original images) and binary images, for data preparation using region growing and manual segmentation in MATLAB by over ten years experienced radiation technologist. Additionally, the dataset is separated into three distinct categories: train, validation, and verification. The deep learning model was developed using python and a 2D dilated residual attention U-Net architecture. And Thresholding-based solutions were desired to prevent over detection of the model's exterior muscle area. ResultOn the validation set, the model's accuracy, Binary Cross-Entropy loss, and similarity Intersection over union (IoU)were 0.9926, 0.0387, and 0.8869, respectively, for the model performance of automated segmentation in the middle of the third lumbar vertebra using computed tomography images. In addition, the rate of validation errors decreases from 0.99 percent to 0.49 percent. ConclusionFinally, when employing deep learning for automated segmentation and visualization of skeletal muscle in the middle of the third lumbar vertebra on computed tomography images was highly effective.

关键词： Deep learning Convolution Neural Network 2D U-net Computed Tomography Image segmentation

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Angel's girl for blind painters: An efficient painting navigation system validated by multimodal evaluation approach

arXiv

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arXiv 2021年

作者： Liu, Hang Hu, Menghan Chen, Yuzhen Li, Qingli Zhai, Guangtao Yang, Simon X. Zhang, Xiao-Ping Yang, Xiaokang The Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University The Key Laboratory of Articial Intelligence Ministry of Education The Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph The Department of Electrical Computer and Biomedical Engineering Ryerson University

For people who ardently love painting but unfortunately have visual impairments, holding a paintbrush to create a work is a very difficult task. People in this special group are eager to pick up the paintbrush, like Leonardo da Vinci, to create and make full use of their own talents. Therefore, to maximally bridge this gap, we propose a painting navigation system to assist blind people in painting and artistic creation. The proposed system is composed of cognitive system and guidance system. The system adopts drawing board positioning based on QR code, brush navigation based on target detection and bush real-time positioning. Meanwhile, this paper uses human-computer interaction on the basis of voice and a simple but efficient position information coding rule. In addition, we design a criterion to efficiently judge whether the brush reaches the target or not. According to the experimental results, the thermal curves extracted from the faces of testers show that it is relatively well accepted by blindfolded and even blind testers. With the prompt frequency of 1s, the painting navigation system performs best with the completion degree of 89% ± 8.37% and overflow degree of 347% ± 162.14%. Meanwhile, the excellent and good types of brush tip trajectory account for 74%, and the relative movement distance is 4.21 ± 2.51. This work demonstrates that it is practicable for the blind people to feel the world through the brush in their hands. In the future, we plan to deploy "Angle's Eyes" on the phone to make it more portable. The demo video of the proposed painting navigation system is available at: https://***/10.6084/***.9760004.v1. © 2021, CC BY.

关键词： Cognitive systems

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Adaptive control for flow and volume regulation in multi-producer district heating systems

arXiv

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arXiv 2021年

作者： Martinez, Juan Eduardo Machado Cucuzzella, Michele Pronk, Nina Scherpen, Jacquelien M.A. The Jan C. Willems Center for Systems and Control ENTEG Faculty of Science and Engineering University of Groningen Nijenborgh 4 Groningen9747 AG Netherlands The Department of Electrical Computer and Biomedical Engineering University of Pavia via Ferrata 5 Pavia27100 Italy

Flow and storage volume regulation is essential for the adequate transport and management of energy resources in district heating systems. In this letter, we propose a novel and suitably tailored-decentralized-adaptive control scheme addressing this problem whilst offering closed-loop stability guarantees. We focus on a system configuration comprising multiple heat producers, consumers and storage tanks exchanging energy through a common distribution network, which are features of modern and prospective district heating installations. The proposed controller is based on passivity, backstepping and (indirect) adaptive control theory. Copyright © 2021, The Authors. All rights reserved.

关键词： District heating

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MR slice profile estimation by learning to match internal patch distributions

arXiv

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arXiv 2021年

作者： Han, Shuo Remedios, Samuel Carass, Aaron Schär, Michael Prince, Jerry L. Department of Biomedical Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Johns Hopkins University BaltimoreMD21218 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Radiology Johns Hopkins University BaltimoreMD21205 United States

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm. Existing super-resolution algorithms make assumptions about the slice selection profile since it is not readily known for a given image. In this work, we estimate a slice selection profile given a specific image by learning to match its internal patch distributions. Specifically, we assume that after applying the correct slice selection profile, the image patch distribution along HR in-plane directions should match the distribution along the LR through-plane direction. Therefore, we incorporate the estimation of a slice selection profile as part of learning a generator in a generative adversarial network (GAN). In this way, the slice selection profile can be learned without any external data. Our algorithm was tested using simulations from isotropic MR images, incorporated in a through-plane super-resolution algorithm to demonstrate its benefits, and also used as a tool to measure image resolution. Our code is at https://***/shuohan/espreso2. © 2021, CC BY-NC-ND.

关键词： Generative adversarial networks

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Retention time trajectory matching for target compound peak identification in chromatographic analysis

arXiv

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arXiv 2021年

作者： Zang, Wenzhe Sharma, Ruchi Li, Maxwell Wei-Hao Fan, Xudong Department of Biomedical Engineering University of Michigan 1101 Beal Avenue Ann ArborMI48109 United States University of Michigan Ann ArborMI48109 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Retention time drift caused by fluctuations in physical factors such as temperature ramping rate and carrier gas flow rate is ubiquitous in chromatographic measurements. Proper peak identification and alignment across different chromatograms is critical prior to any subsequent analysis. This work introduces a peak identification method called retention time trajectory (RTT) matching, which uses chromatographic retention times as the only input and identifies peaks associated with any subset of a predefined set of target compounds. RTT matching is also capable of reporting interferents. An RTT is a 2-dimensional (2D) curve formed uniquely by the retention times of the chromatographic peaks. The RTTs obtained from the chromatogram of a test sample and of pre-characterized library are matched and statistically compared. The best matched pair implies identification. Unlike most existing peak alignment methods, no mathematical warping or transformations are involved. Based on the experimentally characterized RTT, an RTT hybridization method is developed to rapidly generate more RTTs without performing actual time-consuming chromatographic measurements. This enables successful identification even for chromatograms with serious retention time drift. Experimentally obtained gas chromatograms and publicly available fruit metabolomics liquid chromatograms are used to generate over two trillions of tests that validate the proposed method, demonstrating real-time peak/interferent identification. Copyright © 2021, The Authors. All rights reserved.

关键词： Chromatographic analysis

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Ensemble ranking: An aggregation of multiple multicriteria methods and scenarios and its application to power generation planning

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Decision Analytics Journal 2024年 10卷

作者： Alves, Marcos Antonio Oliveira, Bruno Alberto Soares Guimarães, Frederico Gadelha Graduate Program in Electrical Engineering Av. Antônio Carlos 6627 MG Belo Horizonte 31270-901 Brazil FITec - Technological Innovations Av. Cristóvão Colombo 485 Funcionários MG Belo Horizonte 30140-150 Brazil Department of Computer Science Rua Reitor Píres Albuquerque ICEx MG Belo Horizonte 31270-901 Brazil

Evaluating maintenance plans for power generation is a critical task managed by the National System Operator, as it is directly related to criteria such as operational cost, rationing, and availability of natural resources. This study identifies the most suitable maintenance schedule between uncertainties related to solution ranking methods and future scenarios. We investigated the performance of various multicriteria decision-making methods, including the Analytic Hierarchy Process (AHP), TODIM ((an acronym in Portuguese for Interactive Multicriteria decision-making), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR), and Weighted Aggregated Sum Product ASsessment (WASPAS), in prioritizing maintenance plans across three scenarios. These included a scenario derived from an energy generation problem and two others based on variations in hydrology and energy demand parameters. The results analyze and discuss the similarities and differences in the rankings. Due to the lack of consensus among the methods regarding the optimal maintenance plan, we propose an ensemble ranking that considers an important factor for both the method and the scenario. The solution from the ensemble ranking, compared to the lowest-cost solution obtained in the optimization process, showed a slight cost increase of 0.32% and a 9.34% deviation from the base maintenance plan, yet it can deal with different uncertainties. The findings of this study can provide useful and robust information to energy stakeholders and serve as a reference for energy policy. © 2024 The Authors

关键词： Generation maintenance scheduling problems Hydrothermal dispatch Multicriteria decision-making Power generation planning Robust decision-making Uncertainty scenarios

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Weakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification

arXiv

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arXiv 2022年

作者： Chatterjee, Soumick Yassin, Hadya Dubost, Florian Nürnberger, Andreas Speck, Oliver Data and Knowledge Engineering Group Faculty of Computer Science Otto von Guericke University Magdeburg Germany Biomedical Magnetic Resonance Faculty of Nature Sciences Otto von Guericke University Magdeburg Germany Genomics Research Centre Human Technopole Milan Italy Institute for Medical Engineering Faculty of Electrical Engineering and Information Technology Otto von Guericke University Magdeburg Germany Department of Biomedical Data Science Stanford University StanfordCA United States Centre for Behavioural Brain Sciences Magdeburg Germany German Centre for Neurodegenerative Disease Magdeburg Germany

Deep learning models have shown their potential in recent years for several applications. However, most of the models are opaque in nature and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some fields, such as medicine, require a high degree of transparency to accept and adopt such technologies. Consequently, there is a movement towards creating explainable/interpretable models or applying post-hoc explainability/interpretability methods on classifiers to build trust in deep learning models. Moreover, deep learning methods can be used for segmentation tasks, which typically require hard-to-obtain, time-consuming manually-annotated segmentation labels for training. This paper introduces three inherently-explainable classifiers to tackle both of these problems as one. The localisation heatmaps provided by the networks - representing the models' focus areas and being used in classification decision-making - can be directly interpreted, without requiring any post-hoc methods to derive information for model explanation. The models are trained by using the input image and only the classification labels as groundtruth in a supervised fashion - without using any information about the location of the region of interest (i.e. the segmentation labels), making the segmentation training of the models weakly-supervised through classification labels. The final segmentation of the regions of interest is obtained by thresholding these heatmaps. The models were employed for the task of multi-class brain tumour classification using two different datasets, resulting in the best F1-score of 0.93 for the supervised classification task while securing a median Dice score of 0.67 ± 0.08 for the weakly-supervised segmentation task. Furthermore, the obtained accuracy on a subset of tumour-only images outperformed the state-of-the-art glioma tumour grading binary classifiers with the best model achieving 98.7% accuracy. Copyright © 2022, The Autho

关键词： Magnetic resonance imaging

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Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

arXiv

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arXiv 2023年

作者： Zhang, Yuhong Li, Qin Nahata, Sujal Jamal, Tasnia Cheng, Shih-Kuen Cauwenberghs, Gert Jung, Tzyy-Ping The School of Engineering Brown University ProvidenceRI02912 United States Institute for Neural Computation University of California San Diego La Jolla CA92093 United States Department of Radiology and Biomedical Imaging Yale University New HavenCT06520 United States The Department of Bioengineering University of California Los Angeles Los AngelesCA90095 United States The Department of Computer Science and Engineering University of California San Diego La Jolla CA92093 United States The Department of Electrical and Computer Engineering University of California San Diego La Jolla CA92093 United States The Institute of Cognitive Neuroscience National Central University Taoyuan32001 Taiwan The Department of Bioengineering Institute for Neural Computation University of California San Diego La Jolla CA92093 United States The Institute for Neural Computation University of California San Diego La Jolla CA92093 United States

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine comprehension of semantic language meaning. This shift calls for interdisciplinary research that bridges cognitive science and natural language processing (NLP). This pilot study aims to provide insights into individuals’ neural states during a semantic relation reading-comprehension task. We propose jointly analyzing LLMs, eye-gaze, and electroencephalographic (EEG) data to study how the brain processes words with varying degrees of relevance to a keyword during reading. We also use a feature engineering approach to improve the fixation-related EEG data classification while participants read words with high versus low relevance to the keyword. The best validation accuracy in this word-level classification is over 60% across 12 subjects. Words of high relevance to the inference keyword had significantly more eye fixations per word: 1.0584 compared to 0.6576 when excluding no-fixation words, and 1.5126 compared to 1.4026 when including them. This study represents the first attempt to classify brain states at a word level using LLM knowledge. It provides valuable insights into human cognitive abilities and the realm of Artificial General Intelligence (AGI), and offers guidance for developing potential reading-assisted technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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Analytical Estimation of Out-of-plane Strain in Ultrasound Elastography to Improve Axial and Lateral Displacement Fields*

Analytical Estimation of Out-of-plane Strain in Ultrasound E...

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

作者： Niusha Kheirkhah Ali Sadeghi-Naini Abbas Samani School of Biomedical Engineering Western University London Canada Department of Electrical Engineering and Computer Science York University Toronto Canada Departments of Medical Biophysics and Electrical and Computer Engineering Western University London Canada

ISBN: (数字)9781728119908

ISBN: (纸本)9781728119915

Many types of cancers are associated with changes in tissue mechanical properties. This has led to the development of elastography as a clinically viable method where tissue mechanical properties are mapped and visualized for cancer detection and staging. In quasi-static ultrasound elastography, a mechanical stimulation is applied to the tissue using ultrasound probe. Using ultrasound radiofrequency (RF) data acquired before and after the stimulation, the tissue displacement field can be estimated. Elasticity image reconstruction algorithms use this displacement data to generate images of the tissue elasticity properties. The accuracy of the generated elasticity images depends highly on the accuracy of the tissue displacement estimation. Tissue incompressibility can be used as a constraint to improve the estimation of axial and, more importantly, the lateral displacements in 2D ultrasound elastography. Especially in clinical applications, this requires accurate estimation of the out-of-plane strain. Here, we propose a method for providing an accurate estimate of the out-of-plane strain which is incorporated in the incompressibility equation to improve the axial and lateral displacements estimation before elastography image reconstruction. The method was validated using in silico and tissue mimicking phantom studies, leading to significant improvement in the estimated displacement.

关键词： Mathematical model Strain Breast Estimation Elastography Ultrasonic imaging Probes

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