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Gene set correlation enrichment analysis for interpreting and annotating gene expression profiles

Nucleic Acids research 2024年第3期52卷 E17页

作者： Chang, Lan-Yun Lee, Meng-Zhan Wu, Yujia Lee, Wen-Kai Ma, Chia-Liang Chang, Jun-Mao Chen, Ciao-Wen Huang, Tzu-Chun Lee, Chia-Hwa Lee, Jih-Chin Tseng, Yu-Yao Lin, Chun-Yu Institute of Bioinformatics and Systems Biology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Medical Laboratory Science and Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B) National Yang Ming Chiao Tung University Hsinchu 300 Taiwan TMU Research Center of Cancer Translational Medicine Taipei Medical University Taipei 110 Taiwan Ph.D. Program in Medical Biotechnology College of Medical Science and Technology Taipei Medical University New Taipei City 235 Taiwan Department of Otolaryngology-Head and Neck Surgery Tri-Service General Hospital National Defense Medical Center Taipei 110 Taiwan Department of Food Science Nutrition and Nutraceutical Biotechnology Shih Chien University Taipei 104 Taiwan Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 300 Taiwan Cancer and Immunology Research Center National Yang Ming Chiao Tung University Taipei 112 Taiwan Institute of Data Science and Engineering National Yang Ming Chiao Tung University Hsinchu 300 Taiwan School of Dentistry Kaohsiung Medical University Kaohsiung 807 Taiwan

Pathway analysis, including nontopology-based (non-TB) and topology-based (TB) methods, is widely used to interpret the biological phenomena underlying differences in expression data between two phenotypes. By considering dependencies and interactions between genes, TB methods usually perform better than non-TB methods in identifying pathways that include closely relevant or directly causative genes for a given phenotype. However, most TB methods may be limited by incomplete pathway data used as the reference network or by difficulties in selecting appropriate reference networks for different research topics. Here, we propose a gene set correlation enrichment analysis method, Gscore, based on an expression dataset-derived coexpression network to examine whether a differentially expressed gene (DEG) list (or each of its DEGs) is associated with a known gene set. Gscore is better able to identify target pathways in 89 human disease expression datasets than eight other state-of-the-art methods and offers insight into how disease-wide and pathway-wide associations reflect clinical outcomes. When applied to RNA-seq data from COVID-19-related cells and patient samples, Gscore provided a means for studying how DEGs are implicated in COVID-19-related pathways. In summary, Gscore offers a powerful analytical approach for annotating individual DEGs, DEG lists, and genome-wide expression profiles based on existing biological knowledge. © 2024 Oxford University Press. All rights reserved.

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A cellular hierarchy in melanoma uncouples growth and metastasis (vol 610, pg 190, 2022)

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NATURE 2022年第7934期611卷 E4-E4页

作者： Karras, Panagiotis Bordeu, Ignacio Pozniak, Joanna Nowosad, Ada Pazzi, Cecilia Van Raemdonck, Nina Landeloos, Ewout Van Herck, Yannick Pedri, Dennis Bervoets, Greet Makhzami, Samira Khoo, Jia Hui Pavie, Benjamin Lamote, Jochen Marin-Bejar, Oskar Dewaele, Michael Liang, Han Zhang, Xingju Hua, Yichao Wouters, Jasper Browaeys, Robin Bergers, Gabriele Saeys, Yvan Bosisio, Francesca van den Oord, Joost Lambrechts, Diether Rustgi, Anil K. Bechter, Oliver Blanpain, Cedric Simons, Benjamin D. Rambow, Florian Marine, Jean-Christophe Laboratory for Molecular Cancer Biology Center for Cancer Biology VIB Leuven Belgium Department of Oncology KU Leuven Leuven Belgium Laboratory of Tumor Microenvironment and Therapeutic Resistance Center for Cancer Biology VIB Leuven Belgium Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge UK The Wellcome Trust/CRUK Gurdon Institute University of Cambridge Cambridge UK Wellcome Trust-MRC Stem Cell Institute University of Cambridge Cambridge UK Department of General Medical Oncology UZ Leuven Leuven Belgium BGI-Shenzhen Shenzhen China VIB BioImaging Core VIB Center for Brain and Disease Research Leuven Belgium VIB Bioimaging Core VIB Center for Inflammation Research Ghent Belgium Department of Biomedical Molecular Biology Ghent University Ghent Belgium FACS Expertise Center Center for Cancer Biology VIB Leuven Belgium Center for Brain & Disease Research VIB-KU Leuven Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Data Mining and Modeling for Biomedicine Group VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Laboratory for Translational Cell and Tissue Research Department of Imaging and Pathology KU Leuven Leuven Belgium Laboratory of Translational Genetics Center for Cancer Biology VIB Leuven Belgium Laboratory of Translational Genetics Center for Human Genetics KU Leuven Leuven Belgium Herbert Irving Comprehensive Center Columbia University Irving Medical Center New York USA Laboratory of Stem Cells and Cancer Université Libre de Bruxelles (ULB) Brussels Belgium

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STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

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A Study of Generative Large Language Model for Medical research and Healthcare

arXiv

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arXiv 2023年

作者： Peng, Cheng Yang, Xi Chen, Aokun Smith, Kaleb E. PourNejatian, Nima Costa, Anthony B. Martin, Cheryl Flores, Mona G. Zhang, Ying Magoc, Tanja Lipori, Gloria Mitchell, Duane A. Ospina, Naykky S. Ahmed, Mustafa M. Hogan, William R. Shenkman, Elizabeth A. Guo, Yi Bian, Jiang Wu, Yonghui Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Cancer Informatics Shared Resource University of Florida Health Cancer Center GainesvilleFL United States NVIDIA Santa ClaraCA United States Research Computing University of Florida GainesvilleFL United States Integrated Data Repository Research Services University of Florida GainesvilleFL United States Lillian S. Wells Department of Neurosurgery UF Clinical and Translational Science Institute University of Florida United States Division of Endocrinology Division of Cardiovascular Medicine Department of Medicine College of Medicine University of Florida GainesvilleFL United States

There is enormous enthusiasm and concerns in using large language models (LLMs) in healthcare, yet current assumptions are all based on general-purpose LLMs such as ChatGPT. This study develops a clinical generative LLM, GatorTronGPT, using 277 billion words of mixed clinical and English text with a GPT-3 architecture of 20 billion parameters. GatorTronGPT improves biomedical natural language processing for medical research. Synthetic NLP models trained using GatorTronGPT generated text outperform NLP models trained using real-world clinical text. Physicians’ Turing test using 1 (worst) to 9 (best) scale shows that there is no significant difference in linguistic readability (p = 0.22;6.57 of GatorTronGPT compared with 6.93 of human) and clinical relevance (p = 0.91;7.0 of GatorTronGPT compared with 6.97 of human) and that physicians cannot differentiate them (p Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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GatorTron: A Large Clinical Language Model to Unlock Patient Information from Unstructured Electronic Health Records

arXiv

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arXiv 2022年

作者： Yang, Xi Chen, Aokun PourNejatian, Nima Shin, Hoo Chang Smith, Kaleb E. Parisien, Christopher Compas, Colin Martin, Cheryl Costa, Anthony B. Flores, Mona G. Zhang, Ying Magoc, Tanja Harle, Christopher A. Lipori, Gloria Mitchell, Duane A. Hogan, William R. Shenkman, Elizabeth A. Bian, Jiang Wu, Yonghui Department of Health Outcomes and Biomedical Informatics College of Medicine University of Florida GainesvilleFL United States Cancer Informatics and eHealth core University of Florida Health Cancer Center GainesvilleFL United States NVIDIA Santa ClaraCA United States Research Computing University of Florida GainesvilleFL United States Integrated Data Repository Research Services University of Florida GainesvilleFL United States Lillian S. Wells Department of Neurosurgery UF Clinical and Translational Science Institute University of Florida United States

There is an increasing interest in developing artificial intelligence (AI) systems to process and interpret electronic health records (EHRs). Natural language processing (NLP) powered by pretrained language models is the key technology for medical AI systems utilizing clinical narratives. However, there are few clinical language models, the largest of which trained in the clinical domain is comparatively small at 110 million parameters (compared with billions of parameters in the general domain). It is not clear how large clinical language models with billions of parameters can help medical AI systems utilize unstructured EHRs. In this study, we develop from scratch a large clinical language model – GatorTron – using >90 billion words of text (including >82 billion words of de-identified clinical text) and systematically evaluate it on 5 clinical NLP tasks including clinical concept extraction, medical relation extraction, semantic textual similarity, natural language inference (NLI), and medical question answering (MQA). We examine how (1) scaling up the number of parameters and (2) scaling up the size of the training data could benefit these NLP tasks. GatorTron models scale up the clinical language model from 110 million to 8.9 billion parameters and improve 5 clinical NLP tasks (e.g., 9.6% and 9.5% improvement in accuracy for NLI and MQA), which can be applied to medical AI systems to improve healthcare delivery. The GatorTron models are publicly available at: https://***/orgs/nvidia/teams/clara/models/gatortron_og. © 2022, CC BY-NC-ND.

关键词： Semantics

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Deep learning model improves radiologists'performance in detection and classification of breast lesions

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Chinese Journal of Cancer research 2021年第6期33卷 682-693页

作者： Yingshi Sun Yuhong Qu Dong Wang Yi Li Lin Ye Jingbo Du Bing Xu Baoqing Li Xiaoting Li Kexin Zhang Yanjie Shi Ruijia Sun Yichuan Wang Rong Long Dengbo Chen Haijiao Li Liwei Wang Min Cao Key Laboratory of Carcinogenesis and Translational Research(Ministry of Educatdon/Beijing) Department of RadiologyPeking University Cancer Hospital&InstituteBeijing 100142China Department of Radiology Beijing Chaoyang HospitalCapital Medical UniversityBeijing 100020China Key Laboratory of Machine Perception MOESchool of EECSPeking UniversityBeijing 100871China Departnient of Radiology Shunyi Women’s&Children’s Hospital of Beijing Children’s HospitalBeijing 101399China Department of Radiology Beijing Chaoyang Maternal and Child Health CenterBeijing 122099China Department of Radiology Beijing Daxing District Peopled HospitalBeijing 102699China Department of Radiology Shunyi District HospitalBeijing 101312China Department of Medical Imaging Beijing Shijingshan HospitalBeijing 100040China Center for Data Science Peking UniversityBeijing 100871China

Objective:Computer-aided diagnosis using deep learning algorithms has been initially applied in the field of mammography,but there is no large-scale clinical ***:This study proposed to develop and verify an artificial intelligence model based on ***,mammograms retrospectively collected from six centers were randomized to a training dataset and a validation dataset for establishing the ***,the model was tested by comparing 12 radiologists’performance with and without ***,prospectively enrolled women with mammograms from six centers were diagnosed by radiologists with the *** detection and diagnostic capabilities were evaluated using the freeresponse receiver operating characteristic(FROC)curve and ROC ***:The sensitivity of model for detecting lesions after matching was 0.908 for false positive rate of 0.25 in unilateral *** area under ROC curve(AUC)to distinguish the benign lesions from malignant lesions was0.855[95%confidence interval(95%CI):0.830,0.880].The performance of 12 radiologists with the model was higher than that of radiologists alone(AUC:0.852 vs.0.805,P=0.005).The mean reading time of with the model was shorter than that of reading alone(80.18 s vs.62.28 s,P=0.032).In prospective application,the sensitivity of detection reached 0.887 at false positive rate of 0.25;the AUC of radiologists with the model was 0.983(95%CI:0.978,0.988),with sensitivity,specificity,positive predictive value(PPV),and negative predictive value(NPV)of94.36%,98.07%,87.76%,and 99.09%,***:The artificial intelligence model exhibits high accuracy for detecting and diagnosing breast lesions,improves diagnostic accuracy and saves time.

关键词： Breast cancer mammography deep learning artificial intelligence

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A brain-wide risk score of Alzheimer’s disease based on multimodal neuroimaging predicts cognition in large N>37000 population data

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Elaheh Zendehrouh Mohammad S.E. Sendi Vince D Calhoun Georgia Institute of Technology Atlanta GA USA Correspondece Mclean Hospital and Harvard Medical School Boston MA USA School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Georgia State University Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA

Background Alzheimer’s disease (AD) is the most frequent age-related dementia impairing memory, thinking, and social skills. Much research has been conducted in recent years to investigate the genetic risk of AD, but little effort has been made to find a brain imaging-based AD risk evaluation. Here, we proposed a multimodal neuroimaging-based measure of AD risk, called brain-wide risk score (BRS). Method Functional network connectivity (FNC) and voxelwise gray matter (GM) from the Open Access Series of Imaging Studies (OASIS)-3 (N = 1302 samples) cohort was used as a reference dataset to define mild cognitive impairment (MCI) and control (CN)group to evaluate individuals in the UK Biobank (N = 37,784) dataset. We calculated the mean of FNC and GM of MCI and CN. Next, we removed the mean FNC and GM from each sample FNC and GM of the target dataset. Then, we calculated the distance between the target dataset and CN (i.e., dist_CN) and MCI (i.e., dist_MCI) of the reference dataset using Euclidian distance. Finally, we calculated Δdiff = dist_CN-dist_MCI as a BRS of AD, where the positive values are associated with higher AD BRS. Using two BRSs estimated based on FNC and GM for target dataset, we identified four AD biotypes. Samples with two negative Δdist represent low AD risk (Biotype1), samples with two positive Δdist represent high AD risk (Biotype4), and samples with one negative and one positive Δdist represent moderate AD risk (Biotype2/3). Next, we compared cognitive scores including fluid intelligence (FI) and reaction time (RT) across four biotypes. Result Fig.1 shows the 2D histogram of two BRS estimated from FNC and GM. We identified 11704 participants as biotype1, 10910 as biotype2, 7804 as biotype3 and 7366 as biotype4. We also found that Biotype1, with lower AD BRS, significantly shows a higher FI score than Biotype3 and Biotype4 (Fig.2, FI panel). Additionally, Biotype 4, with the highest BRS, had the most increased RT among all four AD biotypes (Fig.2,

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Relationship Between Urinary Uranium and Cardiac Geometry and Left Ventricular Function: The Strong Heart Study

JACC: Advances

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JACC: Advances 2024年第12P1期3卷 101408-101408页

作者： Lieberman-Cribbin, Wil Martinez-Morata, Irene Domingo-Relloso, Arce Umans, Jason G. Cole, Shelley A. Grau-Perez, Maria Pichler, Gernot Devereux, Richard B. Nigra, Anne E. Kupsco, Allison Navas-Acien, Ana Department of Environmental Health Sciences Columbia University Mailman School of Public Health New York NY United States Department of Biostatistics Columbia University Mailman School of Public Health New York NY United States Georgetown-Howard Universities Center for Clinical and Translational Science Washington DC United States MedStar Health Research Institute Hyattsville MD United States Population Health Program Texas Biomedical Research Institute San Antonio TX United States Missouri Breaks Industries Research Cheyenne River Sioux Tribe Eagle Butte SD United States Big data and Artificial Intelligence Unit Biomedical Research Institute INCLIVA Valencia Spain Department of Statistics and Operational Research University of Valencia Valencia Spain Department of Cardiology and Karl Landsteiner Institute for Cardiovascular and Critical Care Research Clinic Floridsdorf Vienna Austria Division of Cardiology Weill Cornell Medical College New York NY United States

Background: Uranium is a potentially cardiotoxic, nonessential element commonly found in drinking water throughout the United States. Objectives: The purpose of this study was to evaluate if urinary uranium concentrations were associated with measures of cardiac geometry and function among American Indian young adults from the Strong Heart Family Study. Methods: Urinary uranium was measured among 1,332 participants free of diabetes, cardiovascular disease, and <50 years of age at baseline (2001-2003). Transthoracic echocardiography and blood pressure were assessed at baseline and at a follow-up visit (2006-2009). We estimated adjusted mean differences in cardiac geometry and function measures at baseline and follow-up using linear mixed-effect models with a random intercept and slope over time. Results: Median (interquartile range) uranium was 0.029 (0.045) μg/g creatinine. In fully adjusted cross-sectional models, a log-doubling of urinary uranium was positively associated with left ventricular (LV) mass index (mean difference: 0.49 g/m², 95% CI: 0.07-0.92 g/m²), left atrial systolic diameter (0.01 cm/m², 0.01-0.02 cm/m²), and stroke volume (0.66 mL, 0.25-1.08 mL) at baseline. Prospectively, uranium was associated with increases in left atrial diameter (0.01 cm/m², 0.01-0.02 cm/m²), pulse pressure (0.28 mm Hg, 0.05-0.52 mm Hg), and incident LV hypertrophy (odds ratio: 1.25, 95% confidence interval: 1.06, 1.48). Conclusions: Urinary uranium levels were adversely associated with measures of cardiac geometry and LV function among American Indian adults, including increases in pulse pressure and LV hypertrophy. These findings support the need to determine the potential long-term subclinical and clinical cardiovascular effects of chronic uranium exposure, and the need for future strategies to reduce exposure. © 2024 The Authors

关键词： American Indian populations cardiac geometry left ventricular function uranium

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Pda-Coated Ti Implants Doped with Cu and Zn Ions: Enhanced Osteogenic and Antibacterial Properties

SSRN

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SSRN 2024年

作者： Kang, Jingyang Liu, Chenhui Meng, Sikun Zhang, Mengge Han, Yibing Qi, Cheng Li, Mei Wang, Huachun Zhang, Tianzhen Cheeloo College of Medicine Shandong University 758 Hefei Road Shandong Qingdao266035 China Key Laboratory for Liquid Solid Structural Evolution and Processing of Materials Ministry of Education School of Materials Science and Engineering Shandong University 17923 Jingshi Road Shandong Jinan250061 China Department of Medical Data Science Center of Medical Innovation and Translational Research Osaka University Graduate School of Medicine Suita Yamadaoka 2-2 Osaka565-0871 Japan Cheeloo College of Medicine Shandong University 758 Hefei Road Shandong Qingdao266035 China Department of Oral and Maxillofacial Surgery Qi Lu Hospital of Shandong University Shandong Jinan250012 China Department of Stomatology Shandong Provincial Third Hospital Shandong University 11 Wuyingshan Middle Road Shandong Jinan250031 China

Titanium (Ti) is extensively utilized in dental implants due to its exceptional biocompatibility, corrosion resistance, and mechanical properties. Nonetheless, its biological inertness impedes its osteogenic and antimicrobial capabilities, which are essential for implant stability and infection prevention. This study explores the improvement of Ti's surface properties by coating it with polydopamine (PDA) and subsequently doping it with Cu²⁺ and Zn²⁺ ions. We first prepared Ti-PDA, followed by the fabrication of Ti-PDA-Cu and Ti-PDA-Cu/Zn samples. Material characterization revealed that Ti-PDA-Cu/Zn exhibits greater wettability compared to Ti and can consistently and steadily release Cu²⁺ and Zn²⁺. In vitro experiments demonstrated that Ti-PDA-Cu/Zn significantly enhanced the adhesion, proliferation, and mineralization of osteoblasts, increased the expression of osteogenic genes, and demonstrated exceptional antibacterial activity against Escherichia coli and Staphylococcus aureus. This dual-functional surface modification presents a promising approach to augment the osteogenic and antimicrobial properties of Ti implants. © 2024, The Authors. All rights reserved.

关键词： Escherichia coli

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Resting-state dynamic functional network connectivity predicts cognition in 37,784 participants of UK Biobank

引用

Alzheimer's & Dementia 2023年第S3期19卷

作者： Mohammad S.E. Sendi Elaheh Zendehrouh Robyn L Miller David H Salat Vince D Calhoun School of Electrical and Computer Engineering (ECE) at the Georgia Institute of Technology Atlanta GA USA Tri-Institutional Georgia State University/Georgia Institute of Technology/Emory University Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Wallace H. Coulter Department of Biomedical Engineering at Emory University and Georgia Tech Atlanta GA USA Georgia State University Atlanta GA USA Neuroimaging Research for Veterans Center VA Boston Healthcare System Jamaica Plain MA USA Massachusetts General Hospital Charlestown MA USA Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Charlestown MA USA

Background Age-related cognitive decline after 65 is a well-known phenomenon, but little is known about how brain functional changes are related to cognitive decline. To this end, previous studies explored the link between functional network connectivity (FNC) estimated from resting-state functional MRI (rs-fMRI) and cognitive scores in healthy adults. FNC is often assumed to be static over time. However, this assumption runs contrary to the dynamic nature of brain FNC, and dynamic FNC (dFNC) has been recently introduced to overcome this limitation. The current study investigated the relationship between dFNC estimated from 36,263 individuals from UK Biobank and cognition. Method We used the resting-state fMRI (duration: 5min) data of 37,784 (20,157 females) adults’ brains, demographic information (age:64.06± 7.51), and cognitive scores from the UK Biobank in which the cognitive scores include fluid intelligence (FI), reaction time (RT), and pairs matching (Pairs). We adapted group independent component analysis to extract 53 data-driven components for the whole brain using a fully automated approach (Fig.1: Step1). Next, we used the sliding window and Pearson correlation to estimate the dFNC among 53 components (Fig.1: Step2). We used k-means clustering to put dFNCs of all individuals into three separated states and calculated individual state vectors (Fig.1: Step3). Then, we estimated 32 dFNC features based on three estimated states and state vectors (Fig.1: Step4). Finally, we trained a two-fold cross-validation support vector regression to predict the cognitive scores (Fig.1: Step5). Result The estimated dFNC feature predicted FI score with high accuracy (the correlation between measure and predicted score is R = 0.043, p = 6.6e −17 , Fig.2A), mean time to correctly identify match in RT task (R = 0.065, p = 1.9 −34 , Fig.2B), and time to complete Pairs task (R = 0.049, p = 1.3e −20 , Fig.2C). Conclusion Here we explored the link between dFNC features and cogniti

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