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Connectivity Disturbances in Self-Limited Epilepsy with Centrotemporal Spikes: A Partial Directed Coherence Analysis of Electroencephalogram

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Clinical EEG and Neuroscience 2024年第2期55卷 257-264页

作者： Chiang, Ching-Tai Yang, Rei-Cheng Kao, Yu-Chia Wu, Rong-Ching Ouyang, Chen-Sen Lin, Lung-Chang Department of Computer and Communication National Pingtung University Pingtung Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Departments of Pediatrics E-Da Hospital Kaohsiung Taiwan Department of Electrical Engineering I-Shou University Kaohsiung Taiwan Department of Information Management National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan

Although the remission of self-limited epilepsy with centrotemporal spikes (SeLECTS) usually occurs by adolescence, deficits in cognition and behavior are not uncommon. Several functional magnetic resonance imaging (fMRI) studies have revealed connectivity disturbances in patients with SeLECTS associated with cognitive impairment. However, the disadvantages of fMRI are expensive, time-consuming, and motion sensitive. In the current study, we used a partial directed coherence (PDC) method to analyze electroencephalogram (EEG) for exploring brain connectivity in patients with SeLECTS. This study enrolled 38 participants (19 patients with SeLECTS and 19 healthy controls) for PDC analysis. Our results demonstrated that the controls had significantly higher PDC inflow connectivity in the F7, T3, FP1, and F8 channels than patients with SeLECTS. By contrast, the patients with SeLECTS demonstrated significantly higher PDC inflow connectivity than did the controls in the T5, Pz, and P4 channels. We also compared the PDC connectivity in different Brodmann areas between the patients with SeLECTS and the controls. The results revealed that the inflow connectivity in the BA9_46_L area was significantly higher in the controls than in the patients with SeLECTS, whereas the inflow connectivity in the MIF_L area 4 was significantly higher in the patients with SeLECTS than in the controls. Our proposed approach of combining EEG with PDC provides a convenient and useful tool for investigating functional connectivity in patients with SeLECTS. This approach is time-saving and inexpensive compared with fMRI, but it achieves similar results to fMRI. © EEG and Clinical Neuroscience Society (ECNS) 2023.

关键词： cognition EEG fMRI partial directed coherence self-limited epilepsy with centrotemporal spikes

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Dimensionality reduction in 3D causal deep learning for neuroimage generation: an evaluation study

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Journal of Medical Imaging 2025年第2期12卷

作者： Ohara, Erik Y. Vigneshwaran, Vibujithan Souza, Raissa Vamosi, Finn G. Wilms, Matthias Forkert, Nils D. University of Calgary Biomedical Engineering Graduate Program CalgaryAB Canada University of Calgary Department of Radiology CalgaryAB Canada University of Calgary Hotchkiss Brain Institute CalgaryAB Canada University of Calgary Alberta Children's Hospital Research Institute CalgaryAB Canada University of Calgary Department of Computer Science CalgaryAB Canada University of Calgary Departments of Pediatrics and Community Health Sciences CalgaryAB Canada

Purpose: Causal deep learning (DL) using normalizing flows allows the generation of true counterfactual images, which is relevant for many medical applications such as explainability of decisions, image harmonization, and in-silico studies. However, such models are computationally expensive when applied directly to high-resolution 3D images and, therefore, require image dimensionality reduction (DR) to efficiently process the data. The goal of this work was to compare how different DR methods affect counterfactual neuroimage generation. Approach: Five DR techniques [2D principal component analysis (PCA), 2.5D PCA, 3D PCA, autoencoder, and Vector Quantised-Variational AutoEncoder] were applied to 23,692 3D brain images to create low-dimensional representations for causal DL model training. Convolutional neural networks were used to quantitatively evaluate age and sex changes on the counterfactual neuroimages. Age alterations were measured using the mean absolute error (MAE), whereas sex changes were assessed via classification accuracy. Results: The 2.5D PCA technique achieved the lowest MAE of 4.16 when changing the age variable of an original image. When sex was changed, the autoencoder embedding led to the highest classification accuracy of 97.84% while also significantly impacting the age variable predictions, increasing the MAE to 5.24 years. Overall, 3D PCA provided the best balance, with an age prediction MAE of 4.57 years while maintaining 94.01% sex classification accuracy when altering the age variable and 94.73% sex classification accuracy and the lowest age prediction MAE (3.84 years) when altering the sex variable. Conclusions: 3D PCA appears to be the best-suited DR method for causal neuroimage analysis. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Principal component analysis

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Objective diagnosis of ADHD through movement analysis by using a smart chair with piezoelectric material

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pediatrics and Neonatology 2023年第1期64卷 46-52页

作者： Chang, Tung-Ming Wu, Rong-Ching Yang, Rei-Cheng Chiang, Ching-Tai Chiu, Yi-Hung Ouyang, Chen-Sen Wang, Yun-Ming Wu, Ming-Hsu Lin, Guang-Chung Lin, Lung-Chang Department of Pediatric Neurology Changhua Christian Children's Hospital Changhua Taiwan Department of Electrical Engineering I-Shou University Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University Taiwan Department of Computer and Communication National Pingtung University Taiwan Department of Information Engineering I-Shou University Taiwan Department of Biological Science and Technology Institute of Molecular Medicine and Bioengineering Center for Intelligent Drug Systems and Smart Bio-Devices (IDS2B) National Yang Ming Chiao Tung University Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University Taiwan Department of Biological Science and Technology College of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is the most common neuropsychiatric disorder in schoolchildren. ADHD diagnoses are generally made based on criteria from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. The diagnosis is made clinically based on observation and information provided by parents and teachers, which is highly subjective and can lead to disparate results. Considering that hyperactivity is one of the main symptoms of ADHD, the inaccuracy of ADHD diagnosis based on subjective criteria necessitates the identification of a method to objectively diagnose ADHD. Methods: In this study, a medical chair containing a piezoelectric material was applied to objectively analyze movements of patients with ADHD, which were compared with those of patients without ADHD. This study enrolled 62 patients—31 patients with ADHD and 31 patients without ADHD. During the clinical evaluation, participants’ movements were recorded by the piezoelectric material for analysis. The variance, zero-crossing rate, and high energy rate of movements were subsequently analyzed. Results: The results revealed that the variance, zero-crossing rate, and high energy rate were significantly higher in patients with ADHD than in those without ADHD. Classification performance was excellent in both groups, with the area under the curve as high as 98.00%. Conclusion: Our findings suggest that the use of a smart chair equipped with piezoelectric material is an objective and potentially useful method for supporting the diagnosis of ADHD. © 2022 Taiwan Pediatric Association

关键词： Attention-deficit/hyperactivity disorder piezoelectric material smart chair

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Objective and automatic assessment approach for diagnosing attention-deficit/hyperactivity disorder based on skeleton detection and classification analysis in outpatient videos

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Child and Adolescent Psychiatry and Mental Health 2024年第1期18卷 60页

作者： Ouyang, Chen-Sen Yang, Rei-Cheng Wu, Rong-Ching Chiang, Ching-Tai Chiu, Yi-Hung Lin, Lung-Chang Department of Information Management National Kaohsiung University of Science and Technology No.1 University Rd. Yanchao District Kaohsiung 824005 Taiwan Departments of Pediatrics Kaohsiung Medical University Hospital Kaohsiung Medical University #100 Tzyou 1st Rd. Sanmin District Kaohsiung 80756 Taiwan Department of Electrical Engineering I-Shou University No.1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung 84001 Taiwan Department of Computer and Communication National Pingtung University No.4-18 Minsheng Rd. Pingtung County Pingtung 900391 Taiwan Department of Information Engineering I-Shou University No.1 Sec. 1 Syuecheng Rd. Dashu District Kaohsiung 84001 Taiwan Department of Pediatrics School of Medicine College of Medicine Kaohsiung Medical University No.100 Shih-Chuan 1st Road Sanmin District Kaohsiung 807378 Taiwan

Background: Attention-deficit/hyperactivity disorder (ADHD) is diagnosed in accordance with Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition criteria by using subjective observations and information provided by parents and teachers. However, subjective analysis often leads to overdiagnosis or underdiagnosis. There are two types of motor abnormalities in patients with ADHD. First, hyperactivity with fidgeting and restlessness is the major diagnostic criterium for ADHD. Second, developmental coordination disorder characterized by deficits in the acquisition and execution of coordinated motor skills is not the major criterium for ADHD. In this study, a machine learning-based approach was proposed to evaluate and classify 96 patients into ADHD (48 patients, 26 males and 22 females, with mean age: 7y6m) and non-ADHD (48 patients, 26 males and 22 females, with mean age: 7y8m) objectively and automatically by quantifying their movements and evaluating the restlessness scales. Methods: This approach is mainly based on movement quantization through analysis of variance in patients’ skeletons detected in outpatient videos. The patients’ skeleton sequence in the video was detected using OpenPose and then characterized using 11 values of feature descriptors. A classification analysis based on six machine learning classifiers was performed to evaluate and compare the discriminating power of different feature combinations. Results: The results revealed that compared with the non-ADHD group, the ADHD group had significantly larger means in all cases of single feature descriptors. The single feature descriptor “thigh angle”, with the values of 157.89 ± 32.81 and 15.37 ± 6.62 in ADHD and non-ADHD groups (p < 0.0001), achieved the best result (optimal cutoff, 42.39;accuracy, 91.03%;sensitivity, 90.25%;specificity, 91.86%;and AUC, 94.00%). Conclusions: The proposed approach can be used to evaluate and classify patients into ADHD and non-ADHD objectively and automat

关键词： ADHD Cutoff Machine learning OpenPose Skeleton detection Variance analysis

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Note on the Existence of Minimizers for Variational Geometric Active Contours

arXiv

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arXiv 2022年

作者： Diop, El Hadji S. Burdin, Valérie Prasath, V.B. Surya Department of Mathematics University of Thies BP 967 Thies Senegal Image and Information Department Institut Mines Telecom - Telecom Bretagne Technopole Brest-Iroise CS 83818 Brest Cedex 329238 France Division of Biomedical Informatics Cincinnati Children’s Hospital Medical Center CincinnatiOH45229 United States The Departments of Biomedical Informatics Pediatrics Electrical Engineering and Computer Science University of Cincinnati OH United States

We propose here a proof of existence of a minimizer of a segmentation functional based on a priori information on target shapes, and formulated with level sets. The existence of a minimizer is very important, because it guarantees the convergence of any numerical methods (either gradient descents techniques and variants, or PDE resolutions) used to solve the segmentation model. This work can also be used in many other segmentation models to prove the existence of a *** Codes 49J40 (Primary), 94A08 (Secondary) © 2022, CC BY.

关键词： Image segmentation

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Pseudo-MRI-Guided PET Image Reconstruction Method Based on a Diffusion Probabilistic Model

arXiv

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arXiv 2024年

作者： Gan, Weijie Xie, Huidong von Gall, Carl Platsch, Günther Jurkiewicz, Michael T. Andrade, Andrea Anazodo, Udunna C. Kamilov, Ulugbek S. An, Hongyu Cabello, Jorge Department of Computer Science & Engineering Washington University in St. Louis St. LouisMO United States Department of Radiology and Biomedical Imaging Yale University New HavenCT United States Department of Medical Imaging Department of Medical Biophysics Western University 1151 Richmond St LondonONN6A 5C1 Canada Department of Pediatrics Department of Clinical Neurological Sciences Western University 1151 Richmond St LondonONN6A 5C1 Canada Lawson Health Research Institute 268 Grosvenor St LondonONN6A 4V2 Canada Neurology and Neurosurgery Montreal Neurological Institute McGill University MontrealQCH3A 2B4 Canada Siemens Healthineers AG Erlangen Germany Department of Computer Science & Engineering Department of Electrical & System Engineering Washington University in St. Louis St. LouisMO United States Institute of Radiology The Departments of Neurology Biomedical Engineering Electrical & System Engineering Division of Biology and Biomedical Sciences Washington University St. LouisMO United States Siemens Medical Solutions USA Inc. 810 Innovation Dr KnoxvilleTN37932 United States

Anatomically guided PET reconstruction using MRI information has been shown to have the potential to improve PET image quality. However, these improvements are limited to PET scans with paired MRI information. In this work we employed a diffusion probabilistic model (DPM) to infer T1-weightedMRI (deep-MRI) images from FDG-PET brain images. We then use the DPM-generated T1w-MRI to guide the PET reconstruction. The model was trained with brain FDG scans, and tested in datasets containing multiple levels of counts. Deep-MRI images appeared somewhat degraded than the acquired MRI images. Regarding PET image quality, volume of interest analysis in different brain regions showed that both PET reconstructed images using the acquired and the deep-MRI images improved image quality compared to OSEM. Same conclusions were found analysing the decimated datasets. A subjective evaluation performed by two physicians confirmed that OSEM scored consistently worse than the MRI-guided PET images and no significant differences were observed between the MRI-guided PET images. This proof of concept shows that it is possible to infer DPM-based MRI imagery to guide the PET reconstruction, enabling the possibility of changing reconstruction parameters such as the strength of the prior on anatomically guided PET reconstruction in the absence of MRI. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Wearable Biosensors in Congenital Heart Disease: Needs to Advance the Field

JACC: Advances

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JACC: Advances 2023年第2期2卷 100267-100267页

作者： Tandon, Animesh Nguyen, Hoang H. Avula, Sravani Seshadri, Dhruv R. Patel, Akash Fares, Munes Baloglu, Orkun Amdani, Shahnawaz Jafari, Roozbeh Inan, Omer T. Drummond, Colin K. Department of Pediatric Cardiology Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Cleveland Clinic Children's Center for Artificial Intelligence (C4AI) Cleveland Clinic Children's Cleveland OH United States Department of Biomedical Engineering Lerner Research Institute Cleveland Clinic Cleveland OH United States Department of Biomedical Engineering Case School of Engineering at Case Western Reserve University Cleveland OH United States Division of Cardiology Department of Pediatrics UT Southwestern Medical Center Dallas TX United States Department of Orthopaedics University Hospitals Cleveland Medical Center Cleveland OH United States Department of Critical Care Pediatric Institute Cleveland Clinic Children's Cleveland OH United States Departments of Biomedical Engineering Computer Science and Electrical Engineering Texas A&M University College Station TX United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA United States

Traditional measures of clinical status and physiology have generally been based in health care settings, episodic, short in duration, and performed at rest. Wearable biosensors provide an opportunity to obtain continuous non-invasive physiologic data from patients with congenital heart disease (CHD) in the real-world setting, over longer durations, and across varying levels of activity. However, there are significant technical limitations to the use of wearable biosensors in CHD. Here, we review current applications of wearable biosensors in CHD;how clinical and research uses of wearable biosensors must consider various CHD physiologies;the technical challenges in developing wearable biosensors for CHD;and special considerations for digital biomarkers in CHD. © 2023 The Authors

关键词： artificial intelligence congenital heart disease wearable biosensors wearables

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Proteomic clustering reveals the kinetics of disease biomarkers in bovine and human models of post-traumatic osteoarthritis

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Osteoarthritis and Cartilage Open 2021年第4期3卷 100191-100191页

作者： Black, Rebecca Mae Wang, Yang Struglics, André Lorenzo, Pilar Chubinskaya, Susan Grodzinsky, Alan J. Önnerfjord, Patrik Departments of Biological Engineering Massachusetts Institute of Technology Cambridge MA United States Orthopaedics Department of Clinical Sciences Lund Faculty of Medicine Lund University Lund Sweden Rheumatology and Molecular Skeletal Biology Department of Clinical Sciences Lund Faculty of Medicine Lund University Lund Sweden Departments of Pediatrics Orthopedic Surgery and Medicine (Section of Rheumatology) Rush University Medical Center Chicago IL United States Mechanical Engineering Massachusetts Institute of Technology Cambridge MA United States Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA United States

Objectives: In this study, we apply a clustering method to proteomic data sets from bovine and human models of post-traumatic osteoarthritis (PTOA) to distinguish clusters of proteins based on their kinetics of release from cartilage and examined these groups for PTOA biomarker candidates. We then quantified the effects of dexamethasone (Dex) on the kinetics of release of the cartilage media proteome. Design: Mass spectrometry was performed on sample medium collected from two separate experiments using juvenile bovine and human cartilage explants (3 samples/treatment condition) during 20- or 21-day treatment with inflammatory cytokines (TNF-α, IL-6, sIL-6R) with or without a single compressive mechanical injury. All samples were incubated with or without 100 nM Dex. Clustering was performed on the correlation between normalized averaged release vectors for each protein. Results: Our proteomic method identified the presence of distinct clusters of proteins based on the kinetics of their release over three weeks of culture. Clusters of proteins with peak release after one to two weeks had biomarker candidates with increased release compared to control. Dex rescued some of the changes in protein release kinetics the level of control, and in all conditions except control, there was late release of immune-related proteins. Conclusions: We demonstrate a clustering method applied to proteomic data sets to identify and validate biomarkers of early PTOA progression and explore the relationships between the release of spatially related matrix components. Dex restored the kinetics of release to many matrix components, but not all factors that contribute to cartilage homeostasis. © 2021 The Author(s)

关键词： Cartilage matrix Cytokines Dexamethasone Mass spectrometry Post-traumatic osteoarthritis Proteomics

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The Minimum Information about CLinical Artificial Intelligence Checklist for Generative Modeling Research (MI-CLAIM-GEN)

arXiv

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arXiv 2024年

作者： Miao, Brenda Y. Chen, Irene Y. Williams, Christopher Y.K. Davidson, Jaysón Garcia-Agundez, Augusto Sun, Shenghuan Zack, Travis Saria, Suchi Arnaout, Rima Quer, Giorgio Sadaei, Hossein J. Torkamani, Ali Beaulieu-Jones, Brett Yu, Bin Gianfrancesco, Milena Butte, Atul J. Norgeot, Beau Sushil, Madhumita Bakar Computational Health Sciences Institute University of California San Francisco San FranciscoCA United States UCSF-UC Berkeley Joint Program in Computational Precision Health University of California Berkeley University of California San Francisco BerkeleyCA United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA United States Berkeley AI Research University of California Berkeley BerkeleyCA United States Department of Medicine Division of Rheumatology University of California San Francisco San FranciscoCA United States Helen Diller Family Comprehensive Cancer Center University of California San Francisco San FranciscoCA United States Center for Data-driven Insights and Innovation University of California Office of the President OaklandCA United States Bayesian Health New YorkNY10282 United States Department of Computer Science Johns Hopkins University Whiting School of Engineering BaltimoreMD United States Department of Health Policy & Management Johns Hopkins University Bloomberg School of Public Health BaltimoreMD United States Department of Medicine Johns Hopkins Medicine BaltimoreMD21205 United States Departments of Medicine Radiology and Pediatrics University of California San Francisco San FranciscoCA United States Scripps Research Translational Institute La Jolla CA United States Department of Integrative Structural and Computational Biology Scripps Research La Jolla CA92037 United States Department of Medicine University of Chicago ChicagoIL United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Qualified Health PBC Palo AltoCA United States

Recent advances in generative models, including large language models (LLMs), vision language models (VLMs), and diffusion models, have accelerated the field of natural language and image processing in medicine and marked a significant paradigm shift in how biomedical models can be developed and deployed. While these models are highly adaptable to new tasks, scaling and evaluating their usage presents new challenges not addressed in previous frameworks. In particular, the ability of these models to produce useful outputs with little to no specialized training data ("zero-" or "few-shot" approaches), as well as the open-ended nature of their outputs, necessitate the development of new guidelines for robust reporting of clinical generative model research. In response to gaps in standards and best practices for the development of clinical AI tools identified by US Executive Order 141103 and several emerging national networks for clinical AI evaluation, we begin to formalize some of these guidelines by building on the original MI-CLAIM checklist. The new checklist, MI-CLAIM-GEN (Table 1), aims to address differences in training, evaluation, interpretability, and reproducibility of new generative models compared to non-generative ("predictive") AI models. This MI-CLAIM-GEN checklist also seeks to clarify cohort selection reporting with unstructured clinical data and adds additional items on alignment with ethical standards for clinical AI research. © 2024, CC BY.

关键词： Generative adversarial networks

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Data-Driven Methods for Advancing Precision Oncology

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Current Pharmacology Reports 2018年第2期4卷 145-156页

作者： Nedungadi, Prema Iyer, Akshay Gutjahr, Georg Bhaskar, Jasmine Pillai, Asha B. Center for Research in Analytics & Technology in Education Amrita Vishwa Vidyapeetham Amritapuri Kollam India Department of Computer Science School of Engineering Amrita Vishwa Vidyapeetham Amritapuri Kollam India Division of Pediatric Hematology/Oncology Departments of Pediatrics and Microbiology and Immunology University of Miami Miller School of Medicine Miami FL United States

Purpose of Review: This article discusses the advances, methods, challenges, and future directions of data-driven methods in advancing precision oncology for biomedical research, drug discovery, clinical research, and practice. Recent Findings: Precision oncology provides individually tailored cancer treatment by considering an individual’s genetic makeup, clinical, environmental, social, and lifestyle information. Challenges include voluminous, heterogeneous, and disparate data generated by different technologies with multiple modalities such as Omics, electronic health records, clinical registries and repositories, medical imaging, demographics, wearables, and sensors. Statistical and machine learning methods have been continuously adapting to the ever-increasing size and complexity of data. Precision Oncology supportive analytics have improved turnaround time in biomarker discovery and time-to-application of new and repurposed drugs. Precision oncology additionally seeks to identify target patient populations based on genomic alterations that are sensitive or resistant to conventional or experimental treatments. Predictive models have been developed for cancer progression and survivorship, drug sensitivity and resistance, and identification of the most suitable combination treatments for individual patient scenarios. In the future, clinical decision support systems need to be revamped to better incorporate knowledge from precision oncology, thus enabling clinical practitioners to provide precision cancer care. Summary: Open Omics datasets, machine learning algorithms, and predictive models have enabled the advancement of precision oncology. Clinical decision support systems with integrated electronic health record and Omics data are needed to provide data-driven recommendations to assist clinicians in disease prevention, early identification, and individualized treatment. Additionally, as cancer is a constantly evolving disorder, clinical decision systems will ne

关键词： Artificial intelligence Big data in health Clinical decision support Health analytics Omics Personalized medicine Precision medicine Precision oncology Predictive analytics

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