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Harmonized-Multinational qEEG norms (HarMNqEEG)

NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

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On the run-length of the structural change in time series data

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AIP Conference Proceedings 2019年第1期2194卷

作者： Wiwik Prihartanti Dwilaksana Abdullah Rasyid Nur Iriawan 1Department of Business Administration Faculty of Social and Political ScienceUniversitas WR Supratman 60111 Surabaya Indonesia 2Department of Statistics Faculty of Mathematics Computing and Data ScienceInstitut Teknologi Sepuluh Nopember 60111 Surabaya Indonesia

The movement of data changes in time series often cannot be seen as a single model throughout the time the serial data is recorded. The occurrence of model structure changes often must be accommodated in time series data modeling. This paper aims to study the Markov Switching models in capturing the structural changes the closing price stocks data of three companies (PT. Indofood Sukses Makmur Tbk. (***), PT. Indofood CBP Sukses Makmur Tbk. (***), and PT. Mustika Ratu Tbk. (***)) from the member and not members of LQ45, estimating the run-length for each model structure, and forecasting the one-step-ahead of stocks. The parameters in the Markov Switching models are estimated using the Expectation Maximization (EM). The result shows that the three companies had more than one structural model. The best stock for investment is *** share which have the longest Average Run Length (ARL) that provides the bigger probability of the forecasting regime and the stock values.

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A new machine learning framework for understanding the link etween cannabis use and first-episode psychosis 12

A new machine learning framework for understanding the link ...

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12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018

作者： Alghamdi, Wajdi Stamate, Daniel Stahl, Daniel Zamyatin, Alexander Murray, Robin Di Forti, Marta Data Science and Soft Computing Lab Department of Computing Goldsmiths University of London United Kingdom Department of Biostatistics and Health Informatics Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom Faculty of Informatics Department of Applied Informatics National Research Tomsk State University Russia Department of Psychosis Studies Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom MRC Social Genetic and Developmental Psychiatry Centre Institute of Psychiatry Psychology and Neuroscience King's College London United Kingdom

ISBN: (纸本)9781614998570

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes. © 2018 The authors and IOS Press.

关键词： eHealth First-episode psychosis Gaussian processes Machine learning Neural networks Support vector machine

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Virtual reality learning environments for vocational education: a comparative study with conventional instructional media on two-stroke engine

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IOP Conference Series: Materials science and Engineering 2020年第1期979卷

作者： F Sholichin N M Suaib D A Irawati Sutiman M Solikin A Yudantoko A Yudianto I W Adiyasa A J Sihes H A Sulaiman School of Computing Faculty of Engineering Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. UTM Big Data Center Ibnu Sina Institute of Scientific and Industrial Research Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Automotive Engineering of Education Faculty of Engineering Universitas Negeri Yogyakarta School of Education Faculty of Social Science and Humanities Universiti Teknologi Malaysia 81310 UTM Skudai Johor Malaysia. Universiti Teknikal Malaysia Melaka 76100 Durian Tunggal Melaka

Virtual reality technology has been adopted as a modern learning media in vocational education. Nowadays, the practical learning needs complete equipment and tools to achieve the goal study. However, the conventional media requirement is a challenge for academician for learning process, specifically in automotive and machine areas. Virtual reality provides a solution for this problem, but still needs proper analysis on the impact of usability between conventional and VR media to which each learning objective is achieved. The case study used in this research is a two-stroke engine, which is currently a rare item in the real world. We replicated this two-stroke engine in the developed VR system using Unity game engine and Oculus quest. A comparison on the usability effectiveness on conventional and virtual reality methods was conducted using cognitive walkthrough, which follows the four steps of learning objective training. The result shows that three out of four learning objectives are similar with conventional objective training. One of the process on preparation for disassembly machine has slight difference with conventional training; faulty engine due from environment elements (liquid, rusty iron) was not included.

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Environment scan of generative AI infrastructure for clinical and translational science

Npj health systems

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Npj health systems 2025年第1期2卷 4页

作者： Betina Idnay Zihan Xu William G Adams Mohammad Adibuzzaman Nicholas R Anderson Neil Bahroos Douglas S Bell Cody Bumgardner Thomas Campion Mario Castro James J Cimino I Glenn Cohen David Dorr Peter L Elkin Jungwei W Fan Todd Ferris David J Foran David Hanauer Mike Hogarth Kun Huang Jayashree Kalpathy-Cramer Manoj Kandpal Niranjan S Karnik Avnish Katoch Albert M Lai Christophe G Lambert Lang Li Christopher Lindsell Jinze Liu Zhiyong Lu Yuan Luo Peter McGarvey Eneida A Mendonca Parsa Mirhaji Shawn Murphy John D Osborne Ioannis C Paschalidis Paul A Harris Fred Prior Nicholas J Shaheen Nawar Shara Ida Sim Umberto Tachinardi Lemuel R Waitman Rosalind J Wright Adrian H Zai Kai Zheng Sandra Soo-Jin Lee Bradley A Malin Karthik Natarajan W Nicholson Price Ii Rui Zhang Yiye Zhang Hua Xu Jiang Bian Chunhua Weng Yifan Peng Department of Biomedical Informatics Columbia University Irving Medical Center New York NY USA. Department of Population Health Sciences Weill Cornell Medicine New York NY USA. Department of Pediatrics Boston Medical Center Boston MA USA Chobanian & Avedisian School of Medicine Boston University Boston MA USA. Oregon Clinical and Translational Research Institute Oregon Health and Science University Portland OR USA. Department of Public Health Sciences University of California Davis Davis CA USA. Keck School of Medicine University of Southern California Los Angeles CA USA. Department of Medicine David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA. Department of Pathology and Laboratory Medicine University of Kentucky College of Medicine Lexington KY USA. Clinical and Translational Science Center Weill Cornell Medicine New York NY USA. Division of Pulmonary Critical Care and Sleep Medicine University of Kansas School of Medicine Kansas City KS USA. Department of Biomedical Informatics and Data Science Heersink School of Medicine University of Alabama Birmingham AL USA. Harvard Law School Petrie-Flom Center for Health Law Policy Biotechnology and Bioethics Harvard University Cambridge MA USA. Department of Biomedical Informatics University at Buffalo Buffalo NY USA. Center for Clinical and Translational Science Mayo Clinic Rochester MN USA. Technology and Digital Solutions Stanford Medicine Stanford University Stanford CA USA. Center for Biomedical Informatics Rutgers Cancer Institute New Brunswick NJ USA. Department of Learning Health Sciences University of Michigan Medical School Ann Arbor MI USA. Altman Clinical and Translational Research Institute (ACTRI) University of California San Diego La Jolla CA USA. Department of Biostatistics and Health Data Science School of Medicine Indiana University Indianapolis IN USA. Department of Ophthalmology CCTSI University of Colorado Aurora CO USA. Center for Clinic

This study reports a comprehensive environmental scan of the generative AI (GenAI) infrastructure in the national network for clinical and translational science across 36 institutions supported by the CTSA Program led by the National Center for Advancing Translational sciences (NCATS) of the National Institutes of Health (NIH) at the United States. Key findings indicate a diverse range of institutional strategies, with most organizations in the experimental phase of GenAI deployment. The results underscore the need for a more coordinated approach to GenAI governance, emphasizing collaboration among senior leaders, clinicians, information technology staff, and researchers. Our analysis reveals that 53% of institutions identified data security as a primary concern, followed by lack of clinician trust (50%) and AI bias (44%), which must be addressed to ensure the ethical and effective implementation of GenAI technologies.

关键词： Health services Public health

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Multivariate generalized Poisson regression model with exposure and correlation as a function of covariates: Parameter estimation and hypothesis testing

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AIP Conference Proceedings 2019年第1期2192卷

作者： Sarni Maniar Berliana Purhadi Sutikno Santi Puteri Rahayu 1Doctoral Candidate at Department of Statistics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya Indonesia 2Politeknik Statistika STIS Jakarta Indonesia

This paper presents the parameter estimation and the simultaneous testing for the parameters of a modified multivariate generalized Poisson regression (MGPR) model that takes into account a measure of exposure and defines the correlation as a function of covariates. An exposure is included in the model to account for population size difference of the analysis units in the study where the exposure is not necessarily the same for each response variable. The correlations between the response variable are defined as a function of the covariates with the assumption that each response variable and their correlations are affected by the same covariates. The Newton method with BHHH algorithm is used to obtain maximum likelihood estimators of the modified MGPR model. The test statistic G2 for simultaneous hypothesis testing is achieved using the likelihood ratio method which is asymptotically chi-square distributed with v degrees of freedom.

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A comparison of some link functions for binomial regression models with application to school drop-out rates in East Java

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AIP Conference Proceedings 2019年第1期2194卷

作者： Rindang Bangun Prasetyo Heri Kuswanto Nur Iriawan Brodjol Sutijo Suprih Ulama 1Doctoral Student at Department of Statistic Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Surabaya 60111 Indonesia 2BPS - Statistics Indonesia Jl. Dr. Sutomo 6-8 Jakarta 10710 Indonesia 3Department of Statistic Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Jl. Arif Rahman Hakim Surabaya 60111 Indonesia

Classical linear regression is inadequate when the response variable is the number of success in a sequence of experiments. However, the binomial regression is considered more suitable. Binomial regression can be analyzed through the Generalized Linear Model (GLM) with a specifics link functions. Some of link functions usually used in binomial regressions are logit, probit, and complimentary log-log (cloglog). Both logit and probit are symmetrical links functions, while cloglog is asymmetrical. This study aims to compare all three link functions and evaluate them by employing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) index. All three link function will be applied for binomial regression models using simulation and real data on school drop-out rates in East Java Indonesia. Based on the AIC and BIC index, it is evidenced that the cloglog gives the best performance than the logit and probit link function for the school drop-out rates model. Therefore, this case is considered appropriate using the asymmetrical assumptions.

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Minimizing cost and backorder risk through customer priority on spares inventory

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Journal of Physics: Conference Series 2019年第1期1218卷

作者： V Lukitosari A P Subriadi I Siyamah Mathematics Department Faculty of Mathematics Computing and Data Science Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia Department of Information System Sepuluh Nopember Institute of Technology (ITS) Surabaya 60111 Indonesia

Spare parts demands usually occur due to the need to replace spare parts on damaged equipment. Uncertain demands and many types of spare parts require complex management. Therefore, the management of spare parts is very challenging. In this study, we developed a spare part inventory method by considering priorities based on customer class. Customers will be grouped according to the number of demands. The number of requests becomes a fill rate priority. Any request that cannot be fulfilled will have backorder risk. This customer prioritization aims to minimize costs and backorder risks. The difference in customer class aims to minimize total inventory costs. Spare parts will be issued based on the queue discipline which is first in first out. The inventory model is solved by the Lagrange multiplier method. Model verification is carried out in real practice in illustrative examples. The results of the research show that customer priorities that are carried out in the provision of the fulfilment of spare parts can reduce costs and backorder risks.

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Complete edge-colored permutation graphs

arXiv

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arXiv 2020年

作者： Hartmann, Tom Bannach, Max Middendorf, Martin Stadler, Peter F. Wieseke, Nicolas Hellmuth, Marc Bioinformatics Group Department of Computer Science & Interdisciplinary Center for Bioinformatics Universität Leipzig Härtelstraße 16–18 LeipzigD-04107 Germany Institute for Theoretical Computer Science Universität zu Lübeck Ratzeburger Allee 160 LübeckD-23562 Germany Swarm Intelligence and Complex Systems Group Faculty of Mathematics and Computer Science University of Leipzig Augustusplatz 10 LeipzigD-04109 Germany Halle-Jena-Leipzig Competence Center for Scalable Data Services and Solutions Dresden-Leipzig Leipzig Research Center for Civilization Diseases Centre for Biotechnology and Biomedicine at Leipzig University Universität Leipzig Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Institute for Theoretical Chemistry University of Vienna Währingerstrasse 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Insitute 1399 Hyde Park Rd. Santa FeNM87501 United States School of Computing University of Leeds EC Stoner Building LeedsLS2 9JT United Kingdom

We introduce the concept of complete edge-colored permutation graphs as complete graphs that are the edge-disjoint union of "classical" permutation graphs. We show that a graph G = (V, E) is a complete edge-colored permutation graph if and only if each monochromatic subgraph of G is a "classical" permutation graph and G does not contain a triangle with 3 different colors. Using the modular decomposition as a framework we demonstrate that complete edge-colored permutation graphs are characterized in terms of their strong prime modules, which induce also complete edge-colored permutation graphs. This leads to an O(|V |2)-time recognition algorithm. We show, moreover, that complete edge-colored permutation graphs form a superclass of so-called symbolic ultrametrics and that the coloring of such graphs is always a Gallai coloring. Copyright © 2020, The Authors. All rights reserved.

关键词： Graphic methods

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Blood pressure trajectories from early life and their association with cognitive function changes in midlife

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Alzheimer's & Dementia 2021年第S10期17卷

作者： Ileana De Anda-Duran Camilo Fernandez-Alonso Owen T Carmichael Rhoda Au Lydia Bazzano Vijaya B Kolachalama Tulane School of Public Health and Tropical Medicine New Orleans LA USA Tulane University New Orleans LA USA Pennington Biomedical Research Center Baton Rouge LA USA Boston University Alzheimer’s Disease Center Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Boston University Schools of Medicine & Public Health Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA College of Arts & Sciences Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA Whitaker Cardiovascular Institute Boston University School of Medicine Boston MA USA

Background Hypertension (HTN) is a well-established risk factor for cognitive impairment and dementia. HTN-induced vascular endothelium and smooth muscle damage have been described as pivotal components of the pathophysiological cascade over the life-course. While the impact on cognitive function (CF) changes has been studied from midlife- to old age, the impact from childhood to middle age exposure remains poorly understood. We aimed to explore this relationship in a community-based cohort of middle-age adults. Methods The analysis included a total of 342 adult participants (67.3% Woman, 38.9% African American) from the Bogalusa Heart Study, with CF assessments at two time points, baseline (age 48.8 ± 4.9 years) and follow-up (age 51.7 ± 5.2 years), and at least 3 observations of BP since childhood. CF change was assessed by computing the difference of the two timepoints standardized global cognitive score (GCS) obtained from a battery of tests covering major neuropsychological domains. Systolic BP (SBP) trajectories were created using latent class trajectory modeling. Mixed linear models with unstructured covariance were used to determine the association between SBP trajectories and CF changes in midlife. Results Mean CF assessment follow-up was 2.9 ± 0.6 years, and mean follow-up for BP and other traditional cardiovascular (CV) risk factors was 41.8 ± 3.4 years. Three distinct trajectory classes of SBP were identified: increasing to plateau (n=226 [66.1%]), moderate- (n=54 [15.8%]) and high- (n=62 [18.1%]) increasing (see Figure 1). With the increasing to plateau SBP trajectory as reference, and after adjustment for modifiable dementia risk factors and traditional CV risk factors, the high-increasing SBP trajectory was associated with greater decrease in GCS (β= -1.22 [95%CI -2.39, -0.07]; p =0.038). The moderate-increasing SBP trajectory showed no statistically significant association with GCS change (β= -1.00 [95%CI -2.23, - 0.22]; p =0.109). (see Table 1) Conc

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