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Madlock-Brown C, Lee A, Seltzer J, Solomonides A, Mathews N, Phuong J, Weiskopf N, Adams WG, Lehmann H, Espinoza J. Racial Disparities in Diabetes Care and Outcomes for Patients with Visual Impairment: A Descriptive Analysis of the TriNetX Research Network. Res Sq 2024:rs.3.rs-3901158. [PMID: 38352357 PMCID: PMC10862972 DOI: 10.21203/rs.3.rs-3901158/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/23/2024]
Abstract
Background: This research delves into the confluence of racial disparities and health inequities among individuals with disabilities, with a focus on those contending with both diabetes and visual impairment. Methods: Utilizing data from the TriNetX Research Network, which includes electronic medical records of roughly 115 million patients from 83 anonymous healthcare organizations, this study employs a directed acyclic graph (DAG) to pinpoint confounders and augment interpretation. We identified patients with visual impairments using ICD-10 codes, deliberately excluding diabetes-related ophthalmology complications. Our approach involved multiple race-stratified analyses, comparing co-morbidities like chronic pulmonary disease in visually impaired patients against their counterparts. We assessed healthcare access disparities by examining the frequency of annual visits, instances of two or more A1c measurements, and glomerular filtration rate (GFR) measurements. Additionally, we evaluated diabetes outcomes by comparing the risk ratio of uncontrolled diabetes (A1c > 9.0) and chronic kidney disease in patients with and without visual impairments. Results: The incidence of diabetes was substantially higher (nearly double) in individuals with visual impairments across White, Asian, and African American populations. Higher rates of chronic kidney disease were observed in visually impaired individuals, with a risk ratio of 1.79 for African American, 2.27 for White, and non-significant for the Asian group. A statistically significant difference in the risk ratio for uncontrolled diabetes was found only in the White cohort (0.843). White individuals without visual impairments were more likely to receive two A1c tests, a trend not significant in other racial groups. African Americans with visual impairments had a higher rate of glomerular filtration rate testing. However, White individuals with visual impairments were less likely to undergo GFR testing, indicating a disparity in kidney health monitoring. This pattern of disparity was not observed in the Asian cohort. Conclusions: This study uncovers pronounced disparities in diabetes incidence and management among individuals with visual impairments, particularly among White, Asian, and African American groups. Our DAG analysis illuminates the intricate interplay between SDoH, healthcare access, and frequency of crucial diabetes monitoring practices, highlighting visual impairment as both a medical and social issue.
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Lewis AE, Weiskopf N, Abrams ZB, Foraker R, Lai AM, Payne PRO, Gupta A. Electronic health record data quality assessment and tools: a systematic review. J Am Med Inform Assoc 2023; 30:1730-1740. [PMID: 37390812 PMCID: PMC10531113 DOI: 10.1093/jamia/ocad120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Revised: 05/16/2023] [Accepted: 06/23/2023] [Indexed: 07/02/2023] Open
Abstract
OBJECTIVE We extended a 2013 literature review on electronic health record (EHR) data quality assessment approaches and tools to determine recent improvements or changes in EHR data quality assessment methodologies. MATERIALS AND METHODS We completed a systematic review of PubMed articles from 2013 to April 2023 that discussed the quality assessment of EHR data. We screened and reviewed papers for the dimensions and methods defined in the original 2013 manuscript. We categorized papers as data quality outcomes of interest, tools, or opinion pieces. We abstracted and defined additional themes and methods though an iterative review process. RESULTS We included 103 papers in the review, of which 73 were data quality outcomes of interest papers, 22 were tools, and 8 were opinion pieces. The most common dimension of data quality assessed was completeness, followed by correctness, concordance, plausibility, and currency. We abstracted conformance and bias as 2 additional dimensions of data quality and structural agreement as an additional methodology. DISCUSSION There has been an increase in EHR data quality assessment publications since the original 2013 review. Consistent dimensions of EHR data quality continue to be assessed across applications. Despite consistent patterns of assessment, there still does not exist a standard approach for assessing EHR data quality. CONCLUSION Guidelines are needed for EHR data quality assessment to improve the efficiency, transparency, comparability, and interoperability of data quality assessment. These guidelines must be both scalable and flexible. Automation could be helpful in generalizing this process.
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Affiliation(s)
- Abigail E Lewis
- Division of Computational and Data Sciences, Washington University in St. Louis, St. Louis, Missouri, USA
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Nicole Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Zachary B Abrams
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Randi Foraker
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Albert M Lai
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Philip R O Payne
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Aditi Gupta
- Institute for Informatics, Data Science and Biostatistics, Washington University in St. Louis, St. Louis, Missouri, USA
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Madlock-Brown C, Wilkens K, Weiskopf N, Cesare N, Bhattacharyya S, Riches NO, Espinoza J, Dorr D, Goetz K, Phuong J, Sule A, Kharrazi H, Liu F, Lemon C, Adams WG. Correction: Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC Public Health 2022; 22:1250. [PMID: 35751109 PMCID: PMC9229081 DOI: 10.1186/s12889-022-13562-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Affiliation(s)
- Charisse Madlock-Brown
- grid.267301.10000 0004 0386 9246Health Informatics and Information Management, University of Tennessee Health Science Center, 66 North Pauline St. rm 221, Memphis, TN 38163 USA ,grid.267301.10000 0004 0386 9246Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN USA
| | - Ken Wilkens
- grid.419635.c0000 0001 2203 7304National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD USA
| | - Nicole Weiskopf
- grid.5288.70000 0000 9758 5690Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR USA
| | - Nina Cesare
- grid.189504.10000 0004 1936 7558Biostatistics and Epidemiology Data Analytics Center, Boston University, Boston, MA USA
| | - Sharmodeep Bhattacharyya
- grid.4391.f0000 0001 2112 1969Department of Statistics, Oregon State University, Corvallis, OR USA
| | - Naomi O. Riches
- grid.223827.e0000 0001 2193 0096Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT USA
| | - Juan Espinoza
- grid.239546.f0000 0001 2153 6013Department of Pediatrics, Children’s Hospital Los Angeles, Los Angeles, CA USA
| | - David Dorr
- grid.5288.70000 0000 9758 5690Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR USA
| | - Kerry Goetz
- grid.280030.90000 0001 2150 6316National Eye Institute, Bethesda, MD USA
| | - Jimmy Phuong
- grid.34477.330000000122986657University of Washington Research Information Technologies, Seattle, WA USA ,grid.470890.2Harborview Injury Prevention Research Center, Seattle, WA USA
| | - Anupam Sule
- grid.416708.c0000 0004 0456 8226Internal Medicine, St Joseph Mercy Oakland Hospital, Pontiac, MI USA
| | - Hadi Kharrazi
- grid.21107.350000 0001 2171 9311Johns Hopkins School of Public Health, Baltimore, MD USA
| | - Feifan Liu
- grid.168645.80000 0001 0742 0364Chan Medical School, University of Massachusetts, Worcester, MA USA
| | - Cindy Lemon
- grid.267301.10000 0004 0386 9246Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN USA
| | - William G. Adams
- grid.189504.10000 0004 1936 7558Boston Medical Center/Boston University School of Medicine, Boston, MA USA
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4
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Madlock-Brown C, Wilkens K, Weiskopf N, Cesare N, Bhattacharyya S, Riches NO, Espinoza J, Dorr D, Goetz K, Phuong J, Sule A, Kharrazi H, Liu F, Lemon C, Adams WG. Clinical, social, and policy factors in COVID-19 cases and deaths: methodological considerations for feature selection and modeling in county-level analyses. BMC Public Health 2022; 22:747. [PMID: 35421958 PMCID: PMC9008430 DOI: 10.1186/s12889-022-13168-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 03/28/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND There is a need to evaluate how the choice of time interval contributes to the lack of consistency of SDoH variables that appear as important to COVID-19 disease burden within an analysis for both case counts and death counts. METHODS This study identified SDoH variables associated with U.S county-level COVID-19 cumulative case and death incidence for six different periods: the first 30, 60, 90, 120, 150, and 180 days since each county had COVID-19 one case per 10,000 residents. The set of SDoH variables were in the following domains: resource deprivation, access to care/health resources, population characteristics, traveling behavior, vulnerable populations, and health status. A generalized variance inflation factor (GVIF) analysis was used to identify variables with high multicollinearity. For each dependent variable, a separate model was built for each of the time periods. We used a mixed-effect generalized linear modeling of counts normalized per 100,000 population using negative binomial regression. We performed a Kolmogorov-Smirnov goodness of fit test, an outlier test, and a dispersion test for each model. Sensitivity analysis included altering the county start date to the day each county reached 10 COVID-19 cases per 10,000. RESULTS Ninety-seven percent (3059/3140) of the counties were represented in the final analysis. Six features proved important for both the main and sensitivity analysis: adults-with-college-degree, days-sheltering-in-place-at-start, prior-seven-day-median-time-home, percent-black, percent-foreign-born, over-65-years-of-age, black-white-segregation, and days-since-pandemic-start. These variables belonged to the following categories: COVID-19 related, vulnerable populations, and population characteristics. Our diagnostic results show that across our outcomes, the models of the shorter time periods (30 days, 60 days, and 900 days) have a better fit. CONCLUSION Our findings demonstrate that the set of SDoH features that are significant for COVID-19 outcomes varies based on the time from the start date of the pandemic and when COVID-19 was present in a county. These results could assist researchers with variable selection and inform decision makers when creating public health policy.
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Affiliation(s)
- Charisse Madlock-Brown
- Health Informatics and Information Management, University of Tennessee Health Science Center, 66 North Pauline St. rm 221, Memphis, TN, 38163, USA.
- Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA.
| | - Ken Wilkens
- National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, USA
| | - Nicole Weiskopf
- Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nina Cesare
- Biostatistics and Epidemiology Data Analytics Center, Boston University, Boston, MA, USA
| | | | - Naomi O Riches
- Obstetrics and Gynecology, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Juan Espinoza
- Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - David Dorr
- Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Jimmy Phuong
- University of Washington Research Information Technologies, Seattle, WA, USA
- Harborview Injury Prevention Research Center, Seattle, WA, USA
| | - Anupam Sule
- Internal Medicine, St Joseph Mercy Oakland Hospital, Pontiac, MI, USA
| | - Hadi Kharrazi
- Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Feifan Liu
- Chan Medical School, University of Massachusetts, Worcester, MA, USA
| | - Cindy Lemon
- Health Outcomes and Policy Research Program, University of Tennessee Health Science Center, Memphis, TN, USA
| | - William G Adams
- Boston Medical Center/Boston University School of Medicine, Boston, MA, USA
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5
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Voss RW, Schmidt TD, Weiskopf N, Marino M, Dorr DA, Huguet N, Warren N, Valenzuela S, O’Malley J, Quiñones AR. Comparing ascertainment of chronic condition status with problem lists versus encounter diagnoses from electronic health records. J Am Med Inform Assoc 2022; 29:770-778. [PMID: 35165743 PMCID: PMC9006679 DOI: 10.1093/jamia/ocac016] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 01/18/2022] [Accepted: 01/27/2022] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE To assess and compare electronic health record (EHR) documentation of chronic disease in problem lists and encounter diagnosis records among Community Health Center (CHC) patients. MATERIALS AND METHODS We assessed patient EHR data in a large clinical research network during 2012-2019. We included CHCs who provided outpatient, older adult primary care to patients age ≥45 years, with ≥2 office visits during the study. Our study sample included 1 180 290 patients from 545 CHCs across 22 states. We used diagnosis codes from 39 Chronic Condition Warehouse algorithms to identify chronic conditions from encounter diagnoses only and compared against problem list records. We measured correspondence including agreement, kappa, prevalence index, bias index, and prevalence-adjusted bias-adjusted kappa. RESULTS Overlap of encounter diagnosis and problem list ascertainment was 59.4% among chronic conditions identified, with 12.2% of conditions identified only in encounters and 28.4% identified only in problem lists. Rates of coidentification varied by condition from 7.1% to 84.4%. Greatest agreement was found in diabetes (84.4%), HIV (78.1%), and hypertension (74.7%). Sixteen conditions had <50% agreement, including cancers and substance use disorders. Overlap for mental health conditions ranged from 47.4% for anxiety to 59.8% for depression. DISCUSSION Agreement between the 2 sources varied substantially. Conditions requiring regular management in primary care settings may have a higher agreement than those diagnosed and treated in specialty care. CONCLUSION Relying on EHR encounter data to identify chronic conditions without reference to patient problem lists may under-capture conditions among CHC patients in the United States.
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Affiliation(s)
| | | | - Nicole Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Miguel Marino
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, Oregon, USA
| | - Nathalie Huguet
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Steele Valenzuela
- Department of Family Medicine, Oregon Health & Science University, Portland, Oregon, USA
| | | | - Ana R Quiñones
- Corresponding Author: Ana R. Quiñones, Department of Family Medicine, Oregon Health & Science University, 3181 S.W. Sam Jackson Park Rd., FM, Portland, OR 97239, USA;
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6
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Dorr DA, D'Autremont C, Pizzimenti C, Weiskopf N, Rope R, Kassakian S, Richardson JE, McClure R, Eisenberg F. Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis. Appl Clin Inform 2021; 12:710-720. [PMID: 34348408 DOI: 10.1055/s-0041-1732401] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
OBJECTIVE This study examines guideline-based high blood pressure (HBP) and hypertension recommendations and evaluates the suitability and adequacy of the data and logic required for a Fast Healthcare Interoperable Resources (FHIR)-based, patient-facing clinical decision support (CDS) HBP application. HBP is a major predictor of adverse health events, including stroke, myocardial infarction, and kidney disease. Multiple guidelines recommend interventions to lower blood pressure, but implementation requires patient-centered approaches, including patient-facing CDS tools. METHODS We defined concept sets needed to measure adherence to 71 recommendations drawn from eight HBP guidelines. We measured data quality for these concepts for two cohorts (HBP screening and HBP diagnosed) from electronic health record (EHR) data, including four use cases (screening, nonpharmacologic interventions, pharmacologic interventions, and adverse events) for CDS. RESULTS We identified 102,443 people with diagnosed and 58,990 with undiagnosed HBP. We found that 21/35 (60%) of required concept sets were unused or inaccurate, with only 259 (25.3%) of 1,101 codes used. Use cases showed high inclusion (0.9-11.2%), low exclusion (0-0.1%), and missing patient-specific context (up to 65.6%), leading to data in 2/4 use cases being insufficient for accurate alerting. DISCUSSION Data quality from the EHR required to implement recommendations for HBP is highly inconsistent, reflecting a fragmented health care system and incomplete implementation of standard terminologies and workflows. Although imperfect, data were deemed adequate for two test use cases. CONCLUSION Current data quality allows for further development of patient-facing FHIR HBP tools, but extensive validation and testing is required to assure precision and avoid unintended consequences.
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Affiliation(s)
- David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Christopher D'Autremont
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Christie Pizzimenti
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Nicole Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Robert Rope
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | - Steven Kassakian
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, United States
| | | | - Rob McClure
- MD Partners, Lafayette, Colorado, United States
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7
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Prieto-Alhambra D, Kostka K, Duarte-Salles T, Prats-Uribe A, Sena A, Pistillo A, Khalid S, Lai L, Golozar A, Alshammari TM, Dawoud D, Nyberg F, Wilcox A, Andryc A, Williams A, Ostropolets A, Areia C, Jung CY, Harle C, Reich C, Blacketer C, Morales D, Dorr DA, Burn E, Roel E, Tan EH, Minty E, DeFalco F, de Maeztu G, Lipori G, Alghoul H, Zhu H, Thomas J, Bian J, Park J, Roldán JM, Posada J, Banda JM, Horcajada JP, Kohler J, Shah K, Natarajan K, Lynch K, Liu L, Schilling L, Recalde M, Spotnitz M, Gong M, Matheny M, Valveny N, Weiskopf N, Shah N, Alser O, Casajust P, Park RW, Schuff R, Seager S, DuVall S, You SC, Song S, Fernández-Bertolín S, Fortin S, Magoc T, Falconer T, Subbian V, Huser V, Ahmed WUR, Carter W, Guan Y, Galvan Y, He X, Rijnbeek P, Hripcsak G, Ryan P, Suchard M. Unraveling COVID-19: a large-scale characterization of 4.5 million COVID-19 cases using CHARYBDIS. Res Sq 2021:rs.3.rs-279400. [PMID: 33688639 PMCID: PMC7941629 DOI: 10.21203/rs.3.rs-279400/v1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Background: Routinely collected real world data (RWD) have great utility in aiding the novel coronavirus disease (COVID-19) pandemic response [1,2]. Here we present the international Observational Health Data Sciences and Informatics (OHDSI) [3] Characterizing Health Associated Risks, and Your Baseline Disease In SARS-COV-2 (CHARYBDIS) framework for standardisation and analysis of COVID-19 RWD. Methods: We conducted a descriptive cohort study using a federated network of data partners in the United States, Europe (the Netherlands, Spain, the UK, Germany, France and Italy) and Asia (South Korea and China). The study protocol and analytical package were released on 11 th June 2020 and are iteratively updated via GitHub [4]. Findings: We identified three non-mutually exclusive cohorts of 4,537,153 individuals with a clinical COVID-19 diagnosis or positive test, 886,193 hospitalized with COVID-19 , and 113,627 hospitalized with COVID-19 requiring intensive services . All comorbidities, symptoms, medications, and outcomes are described by cohort in aggregate counts, and are available in an interactive website: https://data.ohdsi.org/Covid19CharacterizationCharybdis/. Interpretation: CHARYBDIS findings provide benchmarks that contribute to our understanding of COVID-19 progression, management and evolution over time. This can enable timely assessment of real-world outcomes of preventative and therapeutic options as they are introduced in clinical practice.
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Affiliation(s)
- Daniel Prieto-Alhambra
- Centre for Statistics in Medicine (CSM), Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences (NDROMS), University of Oxford, UK
| | | | - Talita Duarte-Salles
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Anthony Sena
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Andrea Pistillo
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Sara Khalid
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Lana Lai
- Division of Cancer Sciences, School of Medical Sciences, University of Manchester, UK
| | - Asieh Golozar
- Regeneron Pharmaceuticals, NY USA, Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD USA
| | - Thamir M Alshammari
- Medication Safety Research Chair, King Saud University, Riyadh, Saudi Arabia
| | - Dalia Dawoud
- National Institute for Health and Care Excellence, London, UK
| | - Fredrik Nyberg
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam Wilcox
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA, 2) UW Medicine, Seattle, WA, USA
| | | | - Andrew Williams
- Tufts Institute for Clinical Research and Health Policy Studies, US
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Carlos Areia
- Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Chi Young Jung
- Division of Respiratory and Critical Care Medicine, Department of Internal Medicine, Daegu Catholic University Medical Center, Daegu, Korea
| | | | | | - Clair Blacketer
- Janssen R&D, Titusville NJ, USA, 2) Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - Daniel Morales
- Division of Population Health and Genomics, University of Dundee, UK
| | - David A Dorr
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Edward Burn
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | | | - Eng Hooi Tan
- Centre for Statistics in Medicine, NDORMS, University of Oxford, UK
| | - Evan Minty
- O'Brien Institute for Public Health, Faculty of Medicine, University of Calgary, Canada
| | | | | | | | - Heba Alghoul
- Faculty of Medicine, Islamic University of Gaza, Palestine
| | - Hong Zhu
- Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Jason Thomas
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | | | - Jimyung Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Jordi Martínez Roldán
- Director of Innovation and Digital Transformation, Hospital del Mar, Barcelona, Spain
| | - Jose Posada
- Stanford University School of Medicine, Stanford, California, USA
| | - Juan M Banda
- Georgia State University, Department of Computer Science, Atlanta, GA, USA
| | - Juan P Horcajada
- Department of Infectious Diseases, Hospital del Mar, Institut Hospital del Mar d'Investigació Mèdica (IMIM), Universitat Autònoma de Barcelona. Universitat Pompeu Fabra, Barcelo
| | - Julianna Kohler
- United States Agency for International Development, Washington, DC, USA
| | - Karishma Shah
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | - Kristine Lynch
- VINCI, VA Salt Lake City Health Care System, Salt Lake City, VA, & Division of Epidemiology, University of Utah, Salt Lake City, UT
| | - Li Liu
- Biomedical Big Data Center, Nanfang Hospital, Southern Medical University, Guangzhou, China
| | - Lisa Schilling
- Data Science to Patient Value Program, University of Colorado Anschutz Medical Campus
| | - Martina Recalde
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Matthew Spotnitz
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | | | - Michael Matheny
- VINCI, Tennessee Valley Healthcare System VA, Nashville, TN & Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN
| | | | - Nicole Weiskopf
- Department of Medical Informatics & Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | | | - Osaid Alser
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Rae Woong Park
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea
| | - Robert Schuff
- Knight Cancer Institute, Oregon Health & Science University
| | | | - Scott DuVall
- VA Informatics and Computing Infrastructure, VA Salt Lake City Health Care System, Salt Lake City, UT, USA
| | - Seng Chan You
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea
| | - Seokyoung Song
- Department of Anesthesiology and Pain Medicine, Catholic University of Daegu, School of Medicine, Daegu, Korea
| | - Sergio Fernández-Bertolín
- Fundació Institut Universitari per a la recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, Spain
| | - Stephen Fortin
- Observational Health Data Analytics, Janssen Research and Development, Raritan, NJ, USA
| | | | - Thomas Falconer
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Vignesh Subbian
- College of Engineering, The University of Arizona, Tucson, Arizona, USA
| | - Vojtech Huser
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Waheed-Ul-Rahman Ahmed
- Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK, 2) College of Medicine and Health, University of Exeter, St Luke's Campus, E
| | - William Carter
- Data Science to Patient Value Program, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Yin Guan
- DHC Technologies Co. Ltd, Beijing, China
| | | | - Xing He
- University of Florida Health
| | - Peter Rijnbeek
- Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA, 2) New York-Presbyterian Hospital, 622 W 168 St, PH20 New York, NY 10032 USA
| | | | - Marc Suchard
- Department of Biostatistics, UCLA Fielding School of Public Health, University of California, Los Angeles
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8
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Huber E, Patel R, Hupp M, Weiskopf N, Chakravarty MM, Freund P. Extrapyramidal plasticity predicts recovery after spinal cord injury. Sci Rep 2020; 10:14102. [PMID: 32839540 PMCID: PMC7445170 DOI: 10.1038/s41598-020-70805-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2019] [Accepted: 08/05/2020] [Indexed: 11/09/2022] Open
Abstract
Spinal cord injury (SCI) leads to wide-spread neurodegeneration across the neuroaxis. We explored trajectories of surface morphology, demyelination and iron concentration within the basal ganglia-thalamic circuit over 2 years post-SCI. This allowed us to explore the predictive value of neuroimaging biomarkers and determine their suitability as surrogate markers for interventional trials. Changes in markers of surface morphology, myelin and iron concentration of the basal ganglia and thalamus were estimated from 182 MRI datasets acquired in 17 SCI patients and 21 healthy controls at baseline (1-month post injury for patients), after 3, 6, 12, and 24 months. Using regression models, we investigated group difference in linear and non-linear trajectories of these markers. Baseline quantitative MRI parameters were used to predict 24-month clinical outcome. Surface area contracted in the motor (i.e. lower extremity) and pulvinar thalamus, and striatum; and expanded in the motor thalamus and striatum in patients compared to controls over 2-years. In parallel, myelin-sensitive markers decreased in the thalamus, striatum, and globus pallidus, while iron-sensitive markers decreased within the left caudate. Baseline surface area expansions within the striatum (i.e. motor caudate) predicted better lower extremity motor score at 2-years. Extensive extrapyramidal neurodegenerative and reorganizational changes across the basal ganglia-thalamic circuitry occur early after SCI and progress over time; their magnitude being predictive of functional recovery. These results demonstrate a potential role of extrapyramidal plasticity during functional recovery after SCI.
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Affiliation(s)
- E Huber
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland
| | - R Patel
- Computational Brain Anatomy Laboratory (CoBrA Lab), Douglas Research Centre, Montreal, QC, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - M Hupp
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland
| | - N Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Linnéstraße 5, 04103, Leipzig, Germany
| | - M M Chakravarty
- Computational Brain Anatomy Laboratory (CoBrA Lab), Douglas Research Centre, Montreal, QC, Canada.,Department of Biological and Biomedical Engineering, McGill University, Montreal, QC, Canada.,Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - P Freund
- Spinal Cord Injury Center Balgrist, University Hospital Zurich, Zurich, Switzerland. .,Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK. .,Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, University College London, London, UK. .,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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9
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Grigoryan K, Nikulin V, Anwander A, Pine K, Weiskopf N, Villringer A, Sehm B. P29 Neural correlates of post-stroke rehabilitation based on brain-computer interfaces: Preliminary outlook. Clin Neurophysiol 2020. [DOI: 10.1016/j.clinph.2019.12.035] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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10
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Kopel R, Sladky R, Laub P, Koush Y, Robineau F, Hutton C, Weiskopf N, Vuilleumier P, Van De Ville D, Scharnowski F. No time for drifting: Comparing performance and applicability of signal detrending algorithms for real-time fMRI. Neuroimage 2019; 191:421-429. [PMID: 30818024 PMCID: PMC6503944 DOI: 10.1016/j.neuroimage.2019.02.058] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2018] [Revised: 02/19/2019] [Accepted: 02/22/2019] [Indexed: 01/15/2023] Open
Abstract
As a consequence of recent technological advances in the field of functional magnetic resonance imaging (fMRI), results can now be made available in real-time. This allows for novel applications such as online quality assurance of the acquisition, intra-operative fMRI, brain-computer-interfaces, and neurofeedback. To that aim, signal processing algorithms for real-time fMRI must reliably correct signal contaminations due to physiological noise, head motion, and scanner drift. The aim of this study was to compare performance of the commonly used online detrending algorithms exponential moving average (EMA), incremental general linear model (iGLM) and sliding window iGLM (iGLMwindow). For comparison, we also included offline detrending algorithms (i.e., MATLAB's and SPM8's native detrending functions). Additionally, we optimized the EMA control parameter, by assessing the algorithm's performance on a simulated data set with an exhaustive set of realistic experimental design parameters. First, we optimized the free parameters of the online and offline detrending algorithms. Next, using simulated data, we systematically compared the performance of the algorithms with respect to varying levels of Gaussian and colored noise, linear and non-linear drifts, spikes, and step function artifacts. Additionally, using in vivo data from an actual rt-fMRI experiment, we validated our results in a post hoc offline comparison of the different detrending algorithms. Quantitative measures show that all algorithms perform well, even though they are differently affected by the different artifact types. The iGLM approach outperforms the other online algorithms and achieves online detrending performance that is as good as that of offline procedures. These results may guide developers and users of real-time fMRI analyses tools to best account for the problem of signal drifts in real-time fMRI. fMRI time series are almost always affected by signal drifts. Signal drifts can be reduced in real-time using different correction methods. Robustness of these methods was tested in the presence of different artifacts. Incremental GLM (iGLM, iGLMwindow) were found optimal in most cases.
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Affiliation(s)
- R Kopel
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - R Sladky
- Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Social, Cognitive and Affective Neuroscience Unit, Department of Basic Psychological Research and Research Methods, Faculty of Psychology, University of Vienna, Vienna, Austria.
| | - P Laub
- Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Y Koush
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Radiology and Medical Imaging, Yale University, New Haven, USA
| | - F Robineau
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland
| | - C Hutton
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, UK
| | - N Weiskopf
- Geneva Neuroscience Center, Geneva, Switzerland; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - P Vuilleumier
- Laboratory for Behavioral Neurology and Imaging of Cognition, Department of Neuroscience, University Medical Center, Geneva, Switzerland; Geneva Neuroscience Center, Geneva, Switzerland
| | - D Van De Ville
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - F Scharnowski
- Department of Radiology and Medical Informatics, CIBM, University of Geneva, Geneva, Switzerland; Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Psychiatric, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zürich, Zürich, Switzerland; Neuroscience Center Zürich, University of Zürich and Swiss Federal Institute of Technology, Winterthurerstr. 190, 8057, Zürich, Switzerland; Zürich Center for Integrative Human Physiology (ZIHP), University of Zürich, Winterthurerstr. 190, 8057, Zürich, Switzerland
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11
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Veazie S, Winchell K, Gilbert J, Paynter R, Ivlev I, Eden KB, Nussbaum K, Weiskopf N, Guise JM, Helfand M. Rapid Evidence Review of Mobile Applications for Self-management of Diabetes. J Gen Intern Med 2018; 33:1167-1176. [PMID: 29740786 PMCID: PMC6025680 DOI: 10.1007/s11606-018-4410-1] [Citation(s) in RCA: 86] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2018] [Revised: 02/13/2018] [Accepted: 03/07/2018] [Indexed: 12/16/2022]
Abstract
BACKGROUND Patients with diabetes lack information on which commercially available applications (apps) improve diabetes-related outcomes. We conducted a rapid evidence review to examine features, clinical efficacy, and usability of apps for self-management of type 1 and type 2 diabetes in adults. METHODS Ovid/Medline and the Cochrane Database of Systematic Reviews were searched for systematic reviews and technology assessments. Reference lists of relevant systematic reviews were examined for primary studies. Additional searches for primary studies were conducted online, through Ovid/Medline, Embase, CINAHL, and ClinicalTrials.gov . Studies were evaluated for eligibility based on predetermined criteria, data were extracted, study quality was assessed using a risk of bias tool, information on app features was collected, and app usability was assessed. Results are summarized qualitatively. RESULTS Fifteen articles evaluating 11 apps were identified: six apps for type 1 and five apps for type 2 diabetes. Common features of apps included setting reminders and tracking blood glucose and hemoglobin A1c (HbA1c), medication use, physical activity, and weight. Compared with controls, use of eight apps, when paired with support from a healthcare provider or study staff, improved at least one outcome, most often HbA1c. Patients did not experience improvements in quality of life, blood pressure, or weight, regardless of app used or type of diabetes. Study quality was variable. Of the eight apps available for usability testing, two were scored "acceptable," three were "marginal," and three were "not acceptable." DISCUSSION Limited evidence suggests that use of some commercially available apps, when combined with additional support from a healthcare provider or study staff, may improve some short-term diabetes-related outcomes. The impact of these apps on longer-term outcomes is unclear. More rigorous and longer-term studies of apps are needed. REGISTRATION This review was funded by the Agency for Healthcare Research and Quality (AHRQ). The protocol is available at: http://www.effectivehealthcare.ahrq.gov/topics/diabetes-mobile-devices/research-protocol .
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Affiliation(s)
- Stephanie Veazie
- Scientific Resource Center, Portland VA Research Foundation, Portland, OR, USA.
| | - Kara Winchell
- Scientific Resource Center, Portland VA Research Foundation, Portland, OR, USA
| | - Jennifer Gilbert
- Scientific Resource Center, Portland VA Research Foundation, Portland, OR, USA
| | - Robin Paynter
- Scientific Resource Center, Portland VA Research Foundation, Portland, OR, USA
| | - Ilya Ivlev
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Karen B Eden
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Kerri Nussbaum
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Nicole Weiskopf
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA
| | - Jeanne-Marie Guise
- Scientific Resource Center, Portland VA Research Foundation, Portland, OR, USA.,Obstetrics and Gynecology, School of Medicine, Oregon Health & Science University, Portland, OR, USA
| | - Mark Helfand
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA.,VA Portland Health Care System, Portland, OR, USA
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12
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Lee K, Weiskopf N, Pathak J. A Framework for Data Quality Assessment in Clinical Research Datasets. AMIA Annu Symp Proc 2018; 2017:1080-1089. [PMID: 29854176 PMCID: PMC5977591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The wide availability of electronic health record (EHR) data for multi-institutional clinical research relies on accurately defined patient cohorts to ensure validity, especially when used in conjunction with open-access research data. There is a growing need to utilize a consensus-driven approach to assess data quality. To achieve this goal, we modified an existing data quality assessment (DQA) framework by re-operationalizing dimensions of quality for a clinical domain of interest - heart failure. We then created an inventory of common phenotype data elements (CPDEs) derived from open-access datasets and evaluated it against the modified DQA framework. We measured our inventory of CPDEs for Conformance, Completeness, and Plausibility. DQA scores were high on Completeness, Value Conformance, and Atemporal and Temporal Plausibility. Our work exhibits a generalizable approach to DQA for clinical research. Future work will 1) map datasets to standard terminologies and 2) create a quantitative DQA tool for research datasets.
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13
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Abstract
BACKGROUND Current computational neuroanatomy focuses on morphological measurements of the brain using standard magnetic resonance imaging (MRI) techniques. In comparison quantitative MRI (qMRI) typically provides a better tissue contrast and also greatly improves the sensitivity and specificity with respect to the microstructural characteristics of tissue. OBJECTIVE Current methodological developments in qMRI are presented, which go beyond morphology because this provides standardized measurements of the microstructure of the brain. The concept of in-vivo histology is introduced, based on biophysical modelling of qMRI data (hMRI) for determination of quantitative histology-like markers of the microstructure. RESULTS The qMRI metrics can be used as direct biomarkers of the microstructural mechanisms driving observed morphological findings. The hMRI metrics utilize biophysical models of the MRI signal in order to determine 3‑dimensional maps of histology-like measurements in the white matter. CONCLUSION Non-invasive brain tissue characterization using qMRI or hMRI has significant implications for both scientific and clinical applications. Both approaches improve the comparability across sites and time points, facilitate multicenter and longitudinal studies as well as standardized diagnostics. The hMRI is expected to shed new light on the relationship between brain microstructure, function and behavior both in health and disease. In the future hMRI will play an indispensable role in the field of computational neuroanatomy.
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Affiliation(s)
- S Mohammadi
- Institut für systemische Neurowissenschaften, Universitätsklinikum Hamburg-Eppendorf, Hamburg, Deutschland
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien
| | - N Weiskopf
- Max-Planck-Institut für Kognitions- und Neurowissenschaften, Stephanstr. 1a, 04103, Leipzig, Deutschland.
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London, Großbritannien.
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14
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Lorio S, Lutti A, Kherif F, Ruef A, Dukart J, Chowdhury R, Frackowiak RS, Ashburner J, Helms G, Weiskopf N, Draganski B. Disentangling in vivo the effects of iron content and atrophy on the ageing human brain. Neuroimage 2014; 103:280-289. [PMID: 25264230 PMCID: PMC4263529 DOI: 10.1016/j.neuroimage.2014.09.044] [Citation(s) in RCA: 54] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2014] [Revised: 09/15/2014] [Accepted: 09/17/2014] [Indexed: 01/06/2023] Open
Abstract
Evidence from magnetic resonance imaging (MRI) studies shows that healthy aging is associated with profound changes in cortical and subcortical brain structures. The reliable delineation of cortex and basal ganglia using automated computational anatomy methods based on T1-weighted images remains challenging, which results in controversies in the literature. In this study we use quantitative MRI (qMRI) to gain an insight into the microstructural mechanisms underlying tissue ageing and look for potential interactions between ageing and brain tissue properties to assess their impact on automated tissue classification. To this end we acquired maps of longitudinal relaxation rate R1, effective transverse relaxation rate R2* and magnetization transfer - MT, from healthy subjects (n=96, aged 21-88 years) using a well-established multi-parameter mapping qMRI protocol. Within the framework of voxel-based quantification we find higher grey matter volume in basal ganglia, cerebellar dentate and prefrontal cortex when tissue classification is based on MT maps compared with T1 maps. These discrepancies between grey matter volume estimates can be attributed to R2* - a surrogate marker of iron concentration, and further modulation by an interaction between R2* and age, both in cortical and subcortical areas. We interpret our findings as direct evidence for the impact of ageing-related brain tissue property changes on automated tissue classification of brain structures using SPM12. Computational anatomy studies of ageing and neurodegeneration should acknowledge these effects, particularly when inferring about underlying pathophysiology from regional cortex and basal ganglia volume changes.
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Affiliation(s)
- S Lorio
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - A Lutti
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - F Kherif
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - A Ruef
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - J Dukart
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - R Chowdhury
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, UK
| | - R S Frackowiak
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland
| | - J Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, UK
| | - G Helms
- University Medical Centre, UMG, Dept. of Cognitive Neurology, Göttingen, Germany
| | - N Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, UCL, London, UK
| | - B Draganski
- LREN, Dept. of Clinical Neurosciences, CHUV, University of Lausanne, Lausanne Switzerland; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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15
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Papoutsi M, Weiskopf N, Langbehn D, Reilmann R, Rees G, Tabrizi S. J02 Brain Training in HD: Enhancing Neural Plasticity using Real-time FMRI Neurofeedback Training. J Neurol Psychiatry 2014. [DOI: 10.1136/jnnp-2014-309032.185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
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16
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Lawson RP, Nord CL, Seymour B, Thomas DL, Dolan RJ, Dayan P, Weiskopf N, Roiser JP. HABENULA RESPONSES DURING APPETITIVE AND AVERSIVE CONDITIONING IN MAJOR DEPRESSIVE DISORDER. Journal of Neurology, Neurosurgery & Psychiatry 2014. [DOI: 10.1136/jnnp-2014-308883.31] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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17
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Becker SMA, Tabelow K, Mohammadi S, Weiskopf N, Polzehl J. Adaptive smoothing of multi-shell diffusion weighted magnetic resonance data by msPOAS. Neuroimage 2014; 95:90-105. [PMID: 24680711 PMCID: PMC4073655 DOI: 10.1016/j.neuroimage.2014.03.053] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2013] [Revised: 01/06/2014] [Accepted: 03/18/2014] [Indexed: 11/08/2022] Open
Abstract
We present a novel multi-shell position-orientation adaptive smoothing (msPOAS) method for diffusion weighted magnetic resonance data. Smoothing in voxel and diffusion gradient space is embedded in an iterative adaptive multiscale approach. The adaptive character avoids blurring of the inherent structures and preserves discontinuities. The simultaneous treatment of all q-shells improves the stability compared to single-shell approaches such as the original POAS method. The msPOAS implementation simplifies and speeds up calculations, compared to POAS, facilitating its practical application. Simulations and heuristics support the face validity of the technique and its rigorousness. The characteristics of msPOAS were evaluated on single and multi-shell diffusion data of the human brain. Significant reduction in noise while preserving the fine structure was demonstrated for diffusion weighted images, standard DTI analysis and advanced diffusion models such as NODDI. MsPOAS effectively improves the poor signal-to-noise ratio in highly diffusion weighted multi-shell diffusion data, which is required by recent advanced diffusion micro-structure models. We demonstrate the superiority of the new method compared to other advanced denoising methods. Method for structure preserving smoothing multi-shell dMRI data Does not rely on any dMRI diffusion model Outperforms naive single-shell POAS and other approaches Feasible for real data application Implemented within a freely available package dti for the R Language
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Affiliation(s)
- S M A Becker
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
| | - K Tabelow
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany.
| | - S Mohammadi
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom
| | - N Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, United Kingdom
| | - J Polzehl
- Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany
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18
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Perotte A, Pivovarov R, Natarajan K, Weiskopf N, Wood F, Elhadad N. Diagnosis code assignment: models and evaluation metrics. J Am Med Inform Assoc 2014; 21:231-7. [PMID: 24296907 PMCID: PMC3932472 DOI: 10.1136/amiajnl-2013-002159] [Citation(s) in RCA: 96] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2013] [Revised: 11/11/2013] [Accepted: 11/12/2013] [Indexed: 12/04/2022] Open
Abstract
BACKGROUND AND OBJECTIVE The volume of healthcare data is growing rapidly with the adoption of health information technology. We focus on automated ICD9 code assignment from discharge summary content and methods for evaluating such assignments. METHODS We study ICD9 diagnosis codes and discharge summaries from the publicly available Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC II) repository. We experiment with two coding approaches: one that treats each ICD9 code independently of each other (flat classifier), and one that leverages the hierarchical nature of ICD9 codes into its modeling (hierarchy-based classifier). We propose novel evaluation metrics, which reflect the distances among gold-standard and predicted codes and their locations in the ICD9 tree. Experimental setup, code for modeling, and evaluation scripts are made available to the research community. RESULTS The hierarchy-based classifier outperforms the flat classifier with F-measures of 39.5% and 27.6%, respectively, when trained on 20,533 documents and tested on 2282 documents. While recall is improved at the expense of precision, our novel evaluation metrics show a more refined assessment: for instance, the hierarchy-based classifier identifies the correct sub-tree of gold-standard codes more often than the flat classifier. Error analysis reveals that gold-standard codes are not perfect, and as such the recall and precision are likely underestimated. CONCLUSIONS Hierarchy-based classification yields better ICD9 coding than flat classification for MIMIC patients. Automated ICD9 coding is an example of a task for which data and tools can be shared and for which the research community can work together to build on shared models and advance the state of the art.
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Affiliation(s)
- Adler Perotte
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Rimma Pivovarov
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Karthik Natarajan
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
- NewYork Presbyterian Hospital, New York, New York, USA
| | - Nicole Weiskopf
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Frank Wood
- Department of Engineering, University of Oxford, Oxford, UK
| | - Noémie Elhadad
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
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19
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Samson RS, Ciccarelli O, Kachramanoglou C, Brightman L, Lutti A, Thomas DL, Weiskopf N, Wheeler-Kingshott CAM. Tissue- and column-specific measurements from multi-parameter mapping of the human cervical spinal cord at 3 T. NMR Biomed 2013; 26:1823-30. [PMID: 24105923 PMCID: PMC4034603 DOI: 10.1002/nbm.3022] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/16/2012] [Revised: 06/25/2013] [Accepted: 08/09/2013] [Indexed: 05/05/2023]
Abstract
The aim of this study was to quantify a range of MR parameters [apparent proton density, longitudinal relaxation time T1, magnetisation transfer (MT) ratio, MT saturation (which represents the additional percentage MT saturation of the longitudinal magnetisation caused by a single MT pulse) and apparent transverse relaxation rate R2*] in the white matter columns and grey matter of the healthy cervical spinal cord. The cervical cords of 13 healthy volunteers were scanned at 3 T using a protocol optimised for multi-parameter mapping. Intra-subject co-registration was performed using linear registration, and tissue- and column-specific parameter values were calculated. Cervical cord parameter values measured from levels C1-C5 in 13 subjects are: apparent proton density, 4822 ± 718 a.u.; MT ratio, 40.4 ± 1.53 p.u.; MT saturation, 1.40 ± 0.12 p.u.; T1 = 1848 ± 143 ms; R2* = 22.6 ± 1.53 s(-1). Inter-subject coefficients of variation were low in both the cervical cord and tissue- and column-specific measurements, illustrating the potential of this method for the investigation of changes in these parameters caused by pathology. In summary, an optimised cervical cord multi-parameter mapping protocol was developed, enabling tissue- and column-specific measurements to be made. This technique has the potential to provide insight into the pathological processes occurring in the cervical cord affected by neurological disorders.
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Affiliation(s)
- RS Samson
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of NeurologyQueen Square, London, UK
- *Correspondence to: R. Samson, UCL Institute of Neurology, Queen Square House, Queen Square, London WC1N 3BG, UK., E-mail:
| | - O Ciccarelli
- NMR Research Unit, Queen Square MS Centre, Department of Brain Repair and Rehabilitation, UCL Institute of NeurologyQueen Square, London, UK
| | - C Kachramanoglou
- NMR Research Unit, Queen Square MS Centre, Department of Brain Repair and Rehabilitation, UCL Institute of NeurologyQueen Square, London, UK
| | | | - A Lutti
- Wellcome Trust Centre for Neuroimaging, UCL Institute of NeurologyQueen Square, London, UK
| | - DL Thomas
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of NeurologyQueen Square, London, UK
| | - N Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of NeurologyQueen Square, London, UK
| | - CAM Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of NeurologyQueen Square, London, UK
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20
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Sulzer J, Haller S, Scharnowski F, Weiskopf N, Birbaumer N, Blefari M, Bruehl A, Cohen L, deCharms R, Gassert R, Goebel R, Herwig U, LaConte S, Linden D, Luft A, Seifritz E, Sitaram R. Real-time fMRI neurofeedback: progress and challenges. Neuroimage 2013; 76:386-99. [PMID: 23541800 PMCID: PMC4878436 DOI: 10.1016/j.neuroimage.2013.03.033] [Citation(s) in RCA: 289] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2012] [Revised: 03/14/2013] [Accepted: 03/19/2013] [Indexed: 01/30/2023] Open
Abstract
In February of 2012, the first international conference on real time functional magnetic resonance imaging (rtfMRI) neurofeedback was held at the Swiss Federal Institute of Technology Zurich (ETHZ), Switzerland. This review summarizes progress in the field, introduces current debates, elucidates open questions, and offers viewpoints derived from the conference. The review offers perspectives on study design, scientific and clinical applications, rtfMRI learning mechanisms and future outlook.
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Affiliation(s)
- J. Sulzer
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, (ETH), Zurich CH-8092, Switzerland
| | - S. Haller
- University of Geneva, Geneva University Hospital CH-1211, Switzerland
| | - F. Scharnowski
- Department of Radiology and Medical Informatics - CIBM, University of Geneva, Switzerland
- Institute of Bioengineering, Swiss Institute of Technology Lausanne (EPFL) CH-1015, Switzerland
| | - N. Weiskopf
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London WC1E 6BT, UK
| | - N. Birbaumer
- The Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen 72074, Germany
- Ospedale San Camillo, IRCCS, Venice 30126, Italy
| | - M.L. Blefari
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, (ETH), Zurich CH-8092, Switzerland
| | - A.B. Bruehl
- Department of Psychiatry, Psychotherapy and Psychosomatica, Zürich University Hospital for Psychiatry, Zurich CH-8032, Switzerland
| | - L.G. Cohen
- National Institutes of Health, Bethesda 20892, USA
| | | | - R. Gassert
- Department of Health Sciences and Technology, Swiss Federal Institute of Technology, (ETH), Zurich CH-8092, Switzerland
| | - R. Goebel
- Department of Neurocognition, University of Maastricht 6200, The Netherlands
| | - U. Herwig
- Department of Psychiatry, Psychotherapy and Psychosomatica, Zürich University Hospital for Psychiatry, Zurich CH-8032, Switzerland
- Department of Psychiatry and Psychotherapy III, University of Ulm, Germany
| | - S. LaConte
- Virginia Tech Carilion Research Institute 24016, USA
| | | | - A. Luft
- Department of Neurology, University Hospital Zurich, Switzerland
- University of Zurich CH-8008, Switzerland
| | - E. Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatica, Zürich University Hospital for Psychiatry, Zurich CH-8032, Switzerland
| | - R. Sitaram
- The Institute of Medical Psychology and Behavioral Neurobiology, University of Tuebingen 72074, Germany
- Department of Biomedical Engineering, University of Florida, Gainesville 32611, USA
- Sri Chitra Tirunal Institute of Medical Science and Technology, Trivandrum, India
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Ekanayake J, Ridgway G, Scharnowski F, Winston J, Yury K, Weiskopf N, Rees G. Altered perceptual bistability in binocular rivalry through neurofeedback training of high order visual areas. J Vis 2013. [DOI: 10.1167/13.9.938] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
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Holve E, Kahn M, Nahm M, Ryan P, Weiskopf N. A comprehensive framework for data quality assessment in CER. AMIA Jt Summits Transl Sci Proc 2013; 2013:86-88. [PMID: 24303241 PMCID: PMC3845781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
The panel addresses the urgent need to ensure that comparative effectiveness research (CER) findings derived from diverse and distributed data sources are based on credible, high-quality data; and that the methods used to assess and report data quality are consistent, comprehensive, and available to data consumers. The panel consists of representatives from four teams leveraging electronic clinical data for CER, patient centered outcomes research (PCOR), and quality improvement (QI) and seeks to change the current paradigm where data quality assessment (DQA) is performed "behind the scenes" using one-off project specific methods. The panelists will present their process of harmonizing existing models for describing and measuring clinical data quality and will describe a comprehensive integrated framework for assessing and reporting DQA findings. The collaborative project is supported by the Electronic Data Methods (EDM) Forum, a three-year grant from the Agency for Healthcare Research and Quality (AHRQ) to facilitate learning and foster collaboration across a set of CER, PCOR, and QI projects designed to build infrastructure and methods for collecting and analyzing prospective data from electronic clinical data .
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Breman H, Peters J, Weiskopf N, Ashburner J, Goebel R. Fast fieldmap-based EPI distortion correction with anatabacus: a plugin for BrainVoyager. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70572-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
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Hutton C, Draganski B, Frackowiak R, Ashburner J, Helms G, Weiskopf N. Grey Matter Morphometry in Magnetization Transfer and T1-weighted Images. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)71890-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
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Lutti A, Tan G, Hutton C, Draganski B, Frackowiak R, Ashburner J, Helms G, Weiskopf N. Fast Whole-Brain T1 Mapping at 1 mm Resolution with RF Bias Correction. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70120-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
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Weiskopf N, Draganski B, Frackowiak R, Ashburner J, Helms G. Improved Segmentation of Subcortical Gray Matter Structures Using Magnetization Transfer (MT) Maps. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)70118-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
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Josephs O, Dick F, Sereno M, Weiskopf N. An Isodynamic High Fidelity MRI Compatible Headphone. Neuroimage 2009. [DOI: 10.1016/s1053-8119(09)72068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
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Harrison LM, Penny W, Flandin G, Ruff CC, Weiskopf N, Friston KJ. Graph-partitioned spatial priors for functional magnetic resonance images. Neuroimage 2008; 43:694-707. [PMID: 18790064 DOI: 10.1016/j.neuroimage.2008.08.012] [Citation(s) in RCA: 15] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2008] [Revised: 08/04/2008] [Accepted: 08/14/2008] [Indexed: 11/27/2022] Open
Abstract
Spatial models of functional magnetic resonance imaging (fMRI) data allow one to estimate the spatial smoothness of general linear model (GLM) parameters and eschew pre-process smoothing of data entailed by conventional mass-univariate analyses. Recently diffusion-based spatial priors [Harrison, L.M., Penny, W., Daunizeau, J., and Friston, K.J. (2008). Diffusion-based spatial priors for functional magnetic resonance images. NeuroImage.] were proposed, which provide a way to formulate an adaptive spatial basis, where the diffusion kernel of a weighted graph-Laplacian (WGL) is used as the prior covariance matrix over GLM parameters. An advantage of these is that they can be used to relax the assumption of isotropy and stationarity implicit in smoothing data with a fixed Gaussian kernel. The limitation of diffusion-based models is purely computational, due to the large number of voxels in a brain volume. One solution is to partition a brain volume into slices, using a spatial model for each slice. This reduces computational burden by approximating the full WGL with a block diagonal form, where each block can be analysed separately. While fMRI data are collected in slices, the functional structures exhibiting spatial coherence and continuity are generally three-dimensional, calling for a more informed partition. We address this using the graph-Laplacian to divide a brain volume into sub-graphs, whose shape can be arbitrary. Their shape depends crucially on edge weights of the graph, which can be based on the Euclidean distance between voxels (isotropic) or on GLM parameters (anisotropic) encoding functional responses. The result is an approximation the full WGL that retains its 3D form and also has potential for parallelism. We applied the method to high-resolution (1 mm(3)) fMRI data and compared models where a volume was divided into either slices or graph-partitions. Models were optimized using Expectation-Maximization and the approximate log-evidence computed to compare these different ways to partition a spatial prior. The high-resolution fMRI data presented here had greatest evidence for the graph partitioned anisotropic model, which was best able to preserve fine functional detail.
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Affiliation(s)
- L M Harrison
- Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3BG UK.
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Moehler M, Blechacz B, Weiskopf N, Zeidler M, Stremmel W, Rommelaere J, Galle PR, Cornelis JJ. Effective infection, apoptotic cell killing and gene transfer of human hepatoma cells but not primary hepatocytes by parvovirus H1 and derived vectors. Cancer Gene Ther 2001; 8:158-67. [PMID: 11332986 DOI: 10.1038/sj.cgt.7700288] [Citation(s) in RCA: 61] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Autonomous parvoviruses preferentially replicate in and kill in vitro-transformed cells and reduce the incidence of spontaneous and implanted tumors in animals. Because of these natural oncotropic and oncolytic properties, parvoviruses deserve to be considered as potential antitumor vectors. Here, we assessed whether parvovirus H1 is able to kill human hepatoma cells by induction of apoptosis but spares primary human liver cells, and whether the former cells can efficiently be transduced by H1 virus-based vectors. Cell death, infectivity, and transgene transduction were investigated in Hep3B, HepG2, and Huh7 cells and in primary human hepatocytes with natural and recombinant H1 virus. All hepatoma cells were susceptible to H1 virus-induced cytolyis. Cell death correlated with H1 virus DNA replication, nonstructural protein expression, and with morphological features of apoptosis. H1 virus-induced apoptosis was more pronounced in p53-deleted Hep3B and p53-mutated Huh7 cells than in HepG2 cells which express wild-type p53. In Hep3B cells, apoptosis was partially inhibited by DEVD-CHO, a caspase-3 inhibitor. In contrast, H1 virus-infected primary hepatocytes were neither positive for nonstructural protein expression nor susceptible to H1 virus-induced killing. Infection with a recombinant parvovirus vector carrying the luciferase gene under control of parvovirus promoter P38 led to higher transgene activities in hepatoma cells than in the hepatocytes. Taken together, H1 virus kills human hepatoma cells at low virus multiplicity but not primary hepatocytes. Thus, recombinant H1 viruses carrying antitumor transgenes may be considered as potential therapeutic options for the treatment of hepatocellular carcinomas.
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Affiliation(s)
- M Moehler
- Department of Internal Medicine, University of Mainz, Germany.
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