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Zhao Y, Caffo BS, Luo X. Longitudinal regression of covariance matrix outcomes. Biostatistics 2024; 25:385-401. [PMID: 36451549 DOI: 10.1093/biostatistics/kxac045] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 10/25/2022] [Accepted: 11/02/2022] [Indexed: 02/17/2024] Open
Abstract
In this study, a longitudinal regression model for covariance matrix outcomes is introduced. The proposal considers a multilevel generalized linear model for regressing covariance matrices on (time-varying) predictors. This model simultaneously identifies covariate-associated components from covariance matrices, estimates regression coefficients, and captures the within-subject variation in the covariance matrices. Optimal estimators are proposed for both low-dimensional and high-dimensional cases by maximizing the (approximated) hierarchical-likelihood function. These estimators are proved to be asymptotically consistent, where the proposed covariance matrix estimator is the most efficient under the low-dimensional case and achieves the uniformly minimum quadratic loss among all linear combinations of the identity matrix and the sample covariance matrix under the high-dimensional case. Through extensive simulation studies, the proposed approach achieves good performance in identifying the covariate-related components and estimating the model parameters. Applying to a longitudinal resting-state functional magnetic resonance imaging data set from the Alzheimer's Disease (AD) Neuroimaging Initiative, the proposed approach identifies brain networks that demonstrate the difference between males and females at different disease stages. The findings are in line with existing knowledge of AD and the method improves the statistical power over the analysis of cross-sectional data.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, 410 W 10th Street, Indianapolis, IN 46202, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX 77030, USA
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2
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Steinway SN, Tang B, Telezing J, Ashok A, Kamal A, Yu CY, Jagtap N, Buxbaum JL, Elmunzer J, Wani SB, Khashab MA, Caffo BS, Akshintala VS. A machine learning-based choledocholithiasis prediction tool to improve ERCP decision making: a proof-of-concept study. Endoscopy 2024; 56:165-171. [PMID: 37699524 DOI: 10.1055/a-2174-0534] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
BACKGROUND Previous studies demonstrated limited accuracy of existing guidelines for predicting choledocholithiasis, leading to overutilization of endoscopic retrograde cholangiopancreatography (ERCP). More accurate stratification may improve patient selection for ERCP and allow use of lower-risk modalities. METHODS A machine learning model was developed using patient information from two published cohort studies that evaluated performance of guidelines in predicting choledocholithiasis. Prediction models were developed using the gradient boosting model (GBM) machine learning method. GBM performance was evaluated using 10-fold cross-validation and area under the receiver operating characteristic curve (AUC). Important predictors of choledocholithiasis were identified based on relative importance in the GBM. RESULTS 1378 patients (mean age 43.3 years; 61.2% female) were included in the GBM and 59.4% had choledocholithiasis. Eight variables were identified as predictors of choledocholithiasis. The GBM had accuracy of 71.5% (SD 2.5%) (AUC 0.79 [SD 0.06]) and performed better than the 2019 American Society for Gastrointestinal Endoscopy (ASGE) guidelines (accuracy 62.4% [SD 2.6%]; AUC 0.63 [SD 0.03]) and European Society of Gastrointestinal Endoscopy (ESGE) guidelines (accuracy 62.8% [SD 2.6%]; AUC 0.67 [SD 0.02]). The GBM correctly categorized 22% of patients directed to unnecessary ERCP by ASGE guidelines, and appropriately recommended as the next management step 48% of ERCPs incorrectly rejected by ESGE guidelines. CONCLUSIONS A machine learning-based tool was created, providing real-time, personalized, objective probability of choledocholithiasis and ERCP recommendations. This more accurately directed ERCP use than existing ASGE and ESGE guidelines, and has the potential to reduce morbidity associated with ERCP or missed choledocholithiasis.
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Affiliation(s)
- Steven N Steinway
- Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Bohao Tang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Jeremy Telezing
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Aditya Ashok
- Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Ayesha Kamal
- Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Chung Yao Yu
- Division of Gastroenterology, University of Southern California Keck School of Medicine, Los Angeles, United States
| | - Nitin Jagtap
- Department of Gastroenterology, Asian Institute of Gastroenterology, Hyderabad, India
| | - James L Buxbaum
- Division of Gastroenterology, University of Southern California Keck School of Medicine, San Francisco, United States
| | - Joseph Elmunzer
- Division of Gastroenterology and Hepatology, Medical University of South Carolina, Charleston, United States
| | - Sachin B Wani
- Division of Gastroenterology, University of Colorado Anschutz Medical Campus, Aurora, United States
| | - Mouen A Khashab
- Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, United States
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, United States
| | - Venkata S Akshintala
- Division of Gastroenterology and Hepatology, Johns Hopkins Medical Institutions, Baltimore, United States
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Aston SA, Caffo BS, Bhasin H, Moran TH, Tamashiro KL. Timing matters: The contribution of running during different periods of the light/dark cycle to susceptibility to activity-based anorexia in rats. Physiol Behav 2023; 271:114349. [PMID: 37709000 DOI: 10.1016/j.physbeh.2023.114349] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [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: 05/04/2023] [Revised: 08/16/2023] [Accepted: 09/11/2023] [Indexed: 09/16/2023]
Abstract
Individuals with anorexia nervosa (AN) exhibit dangerous weight loss due to restricted eating and hyperactivity. Those with AN are predominantly women and most cases have an age of onset during adolescence. Activity-based anorexia (ABA) is a rodent behavioral paradigm that recapitulates many of the features of AN including restricted food intake and hyperactivity, resulting in precipitous weight loss. In addition, there is enhanced sensitivity to the paradigm during adolescence. In ABA, animals are given time-restricted access to food and unlimited access to a running wheel. Under these conditions, most animals increase their running and decrease their food intake resulting in precipitous weight loss until they either die or researchers discontinue the paradigm. Some animals learn to balance their food intake and energy expenditure and are able to stabilize and eventually reverse their weight loss. For these studies, adolescent (postnatal day 33-42), female Sprague Dawley (n = 68) rats were placed under ABA conditions (unlimited access to a running wheel and 1.5 hrs access to food) until they either reached 25% body weight loss or for 7 days. 70.6% of subjects reached 25% body weight loss before 7 days and were designated susceptible to ABA while 29.4% animals were resistant to the paradigm and did not achieve the weight loss criterion. We used discrete time survival analysis to investigate the contribution of food intake and running behavior during distinct time periods both prior to and during ABA to the likelihood of reaching the weight loss criterion and dropping out of ABA. Our analyses revealed risk factors, including total running and dark cycle running, that increased the likelihood of dropping out of the paradigm, as well as protective factors, including age at the start of ABA, the percent of total running exhibited as food anticipatory activity (FAA), and food intake, that reduced the likelihood of dropping out. These measures had predictive value whether taken before or during exposure to ABA conditions. Our findings suggest that certain running and food intake behaviors may be indicative of a phenotype that predisposes animals to susceptibility to ABA. They also provide evidence that running during distinct time periods may reflect functioning of distinct neural circuitry and differentially influence susceptibility and resistance to the paradigm.
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Affiliation(s)
- S Andrew Aston
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America.
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States of America
| | - Harshit Bhasin
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America; Currently: Cardiovascular Disease Initiative, The Broad Institute of MIT and Harvard, Cambridge, MA, United States of America
| | - Timothy H Moran
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Kellie L Tamashiro
- Department of Psychiatry & Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
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4
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Levine MA, Chen H, Wodka EL, Deronda AC, Caffo BS, Ewen JB. A Multi-Trait Multi-Method Examination of Psychometric Instrument Performance in Autism Spectrum Disorder. Assessment 2023:10731911231198205. [PMID: 37694841 DOI: 10.1177/10731911231198205] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Anecdotal evidence has suggested that rater-based measures (e.g., parent report) may have strong across-trait/within-individual covariance that detracts from trait-specific measurement precision; rater measurement-related bias may help explain poor correlation within Autism Spectrum Disorder (ASD) samples between rater-based and performance-based measures of the same trait. We used a multi-trait, multi-method approach to examine method-associated bias within an ASD sample (n = 83). We examined performance/rater-instrument pairs for attention, inhibition, working memory, motor coordination, and core ASD features. Rater-based scores showed an overall greater methodology bias (57% of variance in score explained by method), while performance-based scores showed a weaker methodology bias (22%). The degree of inter-individual variance explained by method alone substantiates an anecdotal concern associated with the use of rater measures in ASD.
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Affiliation(s)
| | - Huan Chen
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | - Brian S Caffo
- Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joshua B Ewen
- Kennedy Krieger Institute, Baltimore, MD, USA
- Johns Hopkins University, Baltimore, MD, USA
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5
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Zhao Y, Wang B, Liu CF, Faria AV, Miller MI, Caffo BS, Luo X. Identifying brain hierarchical structures associated with Alzheimer's disease using a regularized regression method with tree predictors. Biometrics 2023; 79:2333-2345. [PMID: 36263865 PMCID: PMC10115907 DOI: 10.1111/biom.13775] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 10/03/2022] [Indexed: 11/30/2022]
Abstract
Brain segmentation at different levels is generally represented as hierarchical trees. Brain regional atrophy at specific levels was found to be marginally associated with Alzheimer's disease outcomes. In this study, we propose an ℓ1 -type regularization for predictors that follow a hierarchical tree structure. Considering a tree as a directed acyclic graph, we interpret the model parameters from a path analysis perspective. Under this concept, the proposed penalty regulates the total effect of each predictor on the outcome. With regularity conditions, it is shown that under the proposed regularization, the estimator of the model coefficient is consistent in ℓ2 -norm and the model selection is also consistent. When applied to a brain sMRI dataset acquired from the Alzheimer's Disease Neuroimaging Initiative (ADNI), the proposed approach identifies brain regions where atrophy in these regions demonstrates the declination in memory. With regularization on the total effects, the findings suggest that the impact of atrophy on memory deficits is localized from small brain regions, but at various levels of brain segmentation. Data used in preparation of this paper were obtained from the ADNI database.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Chin-Fu Liu
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Andreia V. Faria
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas, USA
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Crasta JE, Nebel MB, Svingos A, Tucker RN, Chen HW, Busch T, Caffo BS, Stephens J, Suskauer SJ. Rethinking recovery in adolescent concussions: Network-level functional connectivity alterations associated with motor deficits. Hum Brain Mapp 2023; 44:3271-3282. [PMID: 36999674 PMCID: PMC10171516 DOI: 10.1002/hbm.26280] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [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: 06/15/2022] [Revised: 02/10/2023] [Accepted: 03/06/2023] [Indexed: 04/01/2023] Open
Abstract
Adolescents who are clinically recovered from concussion continue to show subtle motor impairment on neurophysiological and behavioral measures. However, there is limited information on brain-behavior relationships of persistent motor impairment following clinical recovery from concussion. We examined the relationship between subtle motor performance and functional connectivity of the brain in adolescents with a history of concussion, status post-symptom resolution, and subjective return to baseline. Participants included 27 adolescents who were clinically recovered from concussion and 29 never-concussed, typically developing controls (10-17 years); all participants were examined using the Physical and Neurologic Examination of Subtle Signs (PANESS). Functional connectivity between the default mode network (DMN) or dorsal attention network (DAN) and regions of interest within the motor network was assessed using resting-state functional magnetic resonance imaging (rsfMRI). Compared to controls, adolescents clinically recovered from concussion showed greater subtle motor deficits as evaluated by the PANESS and increased connectivity between the DMN and left lateral premotor cortex. DMN to left lateral premotor cortex connectivity was significantly correlated with the total PANESS score, with more atypical connectivity associated with more motor abnormalities. This suggests that altered functional connectivity of the brain may underlie subtle motor deficits in adolescents who have clinically recovered from concussion. More investigation is required to understand the persistence and longer-term clinical relevance of altered functional connectivity and associated subtle motor deficits to inform whether functional connectivity may serve as an important biomarker related to longer-term outcomes after clinical recovery from concussion.
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Affiliation(s)
- Jewel E. Crasta
- Occupational Therapy DivisionThe Ohio State UniversityColumbusOhioUSA
| | - Mary Beth Nebel
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Adrian Svingos
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Robert N. Tucker
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
- Carle Illinois College of MedicineUniversity of Illinois at Urbana‐ChampaignChampaignILUSA
| | - Hsuan Wei Chen
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Tyler Busch
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
| | - Brian S. Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreMarylandUSA
| | - Jaclyn Stephens
- Department of Occupational TherapyColorado State UniversityFort CollinsColoradoUSA
| | - Stacy J. Suskauer
- Brain Injury Clinical Research CenterKennedy Krieger InstituteBaltimoreMarylandUSA
- Department of Physical Medicine and RehabilitationJohns Hopkins University School of MedicineBaltimoreMarylandUSA
- Department of PediatricsJohns Hopkins University School of MedicineBaltimoreMarylandUSA
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7
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Levine MA, Chen H, Wodka EL, Caffo BS, Ewen JB. Autism and Hierarchical Models of Intelligence. J Autism Dev Disord 2023:10.1007/s10803-023-05984-x. [PMID: 37118644 DOI: 10.1007/s10803-023-05984-x] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/02/2023] [Indexed: 04/30/2023]
Abstract
BACKGROUND The Wechsler Intelligence Scale for Children (WISC) employs a hierarchical model of general intelligence in which index scores separate out different clinically-relevant aspects of intelligence; the test is designed such that index scores are statistically independent from one another within the normative sample. Whether or not the existing index scores meet the desired psychometric property of being statistically independent within autistic samples is unknown. METHOD We conducted a factor analysis on WISC fifth edition (WISC-V) (N = 83) and WISC fourth edition (WISC-IV) (N = 131) subtest data in children with autism. We compared the data-driven exploratory factor analysis with the manual-derived index scores, including in a typically developing (TD) WISC-IV cohort (N = 209). RESULTS The WISC-IV TD cohort showed the expected 1:1 relationship between empirically derived factors and manual-derived index scores. We observed less unique correlations between our data-driven factors and manualized IQ index scores in both ASD samples (WISC-IV and WISC-V). In particular, in both WISC-IV and -V, working memory (WM) influenced index scores in autistic individuals that do not load on WM in the normative sample. CONCLUSIONS WISC index scores do not show the desired statistical independence within autistic samples, as judged against an empirically-derived exploratory factor analysis. In particular, within the currently used WISC-V version, WM influences multiple index scores.
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Affiliation(s)
- Michael A Levine
- Neurology and Developmental Medicine, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, USA
| | - Huan Chen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Ericka L Wodka
- Center for Autism and Related Disorders, Kennedy Krieger Institute, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Joshua B Ewen
- Neurology and Developmental Medicine, Kennedy Krieger Institute, 707 North Broadway, Baltimore, MD, 21205, USA.
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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Morales Pantoja IE, Smirnova L, Muotri AR, Wahlin KJ, Kahn J, Boyd JL, Gracias DH, Harris TD, Cohen-Karni T, Caffo BS, Szalay AS, Han F, Zack DJ, Etienne-Cummings R, Akwaboah A, Romero JC, Alam El Din DM, Plotkin JD, Paulhamus BL, Johnson EC, Gilbert F, Curley JL, Cappiello B, Schwamborn JC, Hill EJ, Roach P, Tornero D, Krall C, Parri R, Sillé F, Levchenko A, Jabbour RE, Kagan BJ, Berlinicke CA, Huang Q, Maertens A, Herrmann K, Tsaioun K, Dastgheyb R, Habela CW, Vogelstein JT, Hartung T. First Organoid Intelligence (OI) workshop to form an OI community. Front Artif Intell 2023; 6:1116870. [PMID: 36925616 PMCID: PMC10013972 DOI: 10.3389/frai.2023.1116870] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 02/08/2023] [Indexed: 03/08/2023] Open
Abstract
The brain is arguably the most powerful computation system known. It is extremely efficient in processing large amounts of information and can discern signals from noise, adapt, and filter faulty information all while running on only 20 watts of power. The human brain's processing efficiency, progressive learning, and plasticity are unmatched by any computer system. Recent advances in stem cell technology have elevated the field of cell culture to higher levels of complexity, such as the development of three-dimensional (3D) brain organoids that recapitulate human brain functionality better than traditional monolayer cell systems. Organoid Intelligence (OI) aims to harness the innate biological capabilities of brain organoids for biocomputing and synthetic intelligence by interfacing them with computer technology. With the latest strides in stem cell technology, bioengineering, and machine learning, we can explore the ability of brain organoids to compute, and store given information (input), execute a task (output), and study how this affects the structural and functional connections in the organoids themselves. Furthermore, understanding how learning generates and changes patterns of connectivity in organoids can shed light on the early stages of cognition in the human brain. Investigating and understanding these concepts is an enormous, multidisciplinary endeavor that necessitates the engagement of both the scientific community and the public. Thus, on Feb 22-24 of 2022, the Johns Hopkins University held the first Organoid Intelligence Workshop to form an OI Community and to lay out the groundwork for the establishment of OI as a new scientific discipline. The potential of OI to revolutionize computing, neurological research, and drug development was discussed, along with a vision and roadmap for its development over the coming decade.
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Affiliation(s)
- Itzy E. Morales Pantoja
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Lena Smirnova
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Alysson R. Muotri
- Department of Pediatrics and Cellular and Molecular Medicine, School of Medicine, University of California, San Diego, San Diego, CA, United States
- Center for Academic Research and Training in Anthropogeny (CARTA), Archealization Center (ArchC), Kavli Institute for Brain and Mind, University of California, San Diego, San Diego, CA, United States
| | - Karl J. Wahlin
- Viterbi Family Department of Ophthalmology & the Shiley Eye Institute, UC San Diego, La Jolla, CA, United States
| | - Jeffrey Kahn
- Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, United States
| | - J. Lomax Boyd
- Berman Institute of Bioethics, Johns Hopkins University, Baltimore, MD, United States
| | - David H. Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Chemistry, Johns Hopkins University, Baltimore, MD, United States
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD, United States
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD, United States
- Center for Microphysiological Systems (MPS), Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Oncology and Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Timothy D. Harris
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, United States
| | - Tzahi Cohen-Karni
- Departments of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
- Department of Materials Science and Engineering, Carnegie Mellon University, Pittsburgh, PA, United States
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Alexander S. Szalay
- Department of Computer Science, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD, United States
- Mark Foundation Center for Advanced Genomics and Imaging, Johns Hopkins University, Baltimore, MD, United States
| | - Fang Han
- Department of Statistics and Economics, University of Washington, Seattle, WA, United States
| | - Donald J. Zack
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Molecular Biology and Genetics, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Ralph Etienne-Cummings
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Akwasi Akwaboah
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - July Carolina Romero
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Dowlette-Mary Alam El Din
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Jesse D. Plotkin
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Barton L. Paulhamus
- Department of Research and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Erik C. Johnson
- Department of Research and Exploratory Development, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Frederic Gilbert
- Philosophy Program, School of Humanities, University of Tasmania, Hobart, TAS, Australia
| | | | | | - Jens C. Schwamborn
- Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, Esch-sur-Alzette, Luxembourg
| | - Eric J. Hill
- School of Biosciences, College of Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Paul Roach
- Department of Chemistry, School of Science, Loughborough University, Loughborough, Leicestershire, United Kingdom
| | - Daniel Tornero
- Department of Biomedical Sciences, Institute of Neuroscience, University of Barcelona, Barcelona, Spain
- Clinic Hospital August Pi i Sunyer Biomedical Research Institute (IDIBAPS), Barcelona, Spain
| | - Caroline Krall
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Molecular and Comparative Pathobiology, Johns Hopkins University, Baltimore, MD, United States
| | - Rheinallt Parri
- Aston Pharmacy School, College of Health and Life Sciences, Aston University, Birmingham, United Kingdom
| | - Fenna Sillé
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Andre Levchenko
- Department of Biomedical Engineering, Yale Systems Biology Institute, Yale University, New Haven, CT, United States
| | - Rabih E. Jabbour
- Department of Bioscience and Biotechnology, University of Maryland Global Campus, Rockville, MD, United States
| | | | - Cynthia A. Berlinicke
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Qi Huang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Alexandra Maertens
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Kathrin Herrmann
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Katya Tsaioun
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Raha Dastgheyb
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Christa Whelan Habela
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - Joshua T. Vogelstein
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Thomas Hartung
- Center for Alternatives to Animal Testing (CAAT), Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
- Center for Alternatives to Animal Testing (CAAT)-Europe, University of Konstanz, Konstanz, Germany
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9
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Tang B, Levine M, Adamek JH, Wodka EL, Caffo BS, Ewen JB. Evaluating causal psychological models: A study of language theories of autism using a large sample. Front Psychol 2023; 14:1060525. [PMID: 36910768 PMCID: PMC9998497 DOI: 10.3389/fpsyg.2023.1060525] [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] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 02/03/2023] [Indexed: 03/14/2023] Open
Abstract
We used a large convenience sample (n = 22,223) from the Simons Powering Autism Research (SPARK) dataset to evaluate causal, explanatory theories of core autism symptoms. In particular, the data-items collected supported the testing of theories that posited altered language abilities as cause of social withdrawal, as well as alternative theories that competed with these language theories. Our results using this large dataset converge with the evolution of the field in the decades since these theories were first proposed, namely supporting primary social withdrawal (in some cases of autism) as a cause of altered language development, rather than vice versa. To accomplish the above empiric goals, we used a highly theory-constrained approach, one which differs from current data-driven modeling trends but is coherent with a very recent resurgence in theory-driven psychology. In addition to careful explication and formalization of theoretical accounts, we propose three principles for future work of this type: specification, quantification, and integration. Specification refers to constraining models with pre-existing data, from both outside and within autism research, with more elaborate models and more veridical measures, and with longitudinal data collection. Quantification refers to using continuous measures of both psychological causes and effects, as well as weighted graphs. This approach avoids "universality and uniqueness" tests that hold that a single cognitive difference could be responsible for a heterogeneous and complex behavioral phenotype. Integration of multiple explanatory paths within a single model helps the field examine for multiple contributors to a single behavioral feature or to multiple behavioral features. It also allows integration of explanatory theories across multiple current-day diagnoses and as well as typical development.
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Affiliation(s)
- Bohao Tang
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | | | - Jack H Adamek
- Kennedy Krieger Institute, Baltimore, MD, United States
| | - Ericka L Wodka
- Kennedy Krieger Institute, Baltimore, MD, United States.,School of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Brian S Caffo
- Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - Joshua B Ewen
- Kennedy Krieger Institute, Baltimore, MD, United States.,School of Medicine, Johns Hopkins University, Baltimore, MD, United States.,Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, MD, United States
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10
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Caffo BS, D'Asaro FA, Garcez A, Raffinetti E. Corrigendum: Editorial: Explainable artificial intelligence models and methods in finance and healthcare. Front Artif Intell 2023; 6:1157762. [PMID: 36890982 PMCID: PMC9987245 DOI: 10.3389/frai.2023.1157762] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 02/22/2023] Open
Abstract
[This corrects the article DOI: 10.3389/frai.2022.970246.].
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Affiliation(s)
- Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
| | - Fabio A D'Asaro
- Department of Human Sciences, University of Verona, Verona, Italy
| | - Artur Garcez
- Department of Computer Science, City University of London, London, United Kingdom
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11
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Mejia AF, Bolin D, Yue YR, Wang J, Caffo BS, Nebel MB. Template independent component analysis with spatial priors for accurate subject-level brain network estimation and inference. J Comput Graph Stat 2022; 32:413-433. [PMID: 37377728 PMCID: PMC10292763 DOI: 10.1080/10618600.2022.2104289] [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] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2021] [Accepted: 06/14/2022] [Indexed: 10/17/2022]
Abstract
Independent component analysis is commonly applied to functional magnetic resonance imaging (fMRI) data to extract independent components (ICs) representing functional brain networks. While ICA produces reliable group-level estimates, single-subject ICA often produces noisy results. Template ICA is a hierarchical ICA model using empirical population priors to produce more reliable subject-level estimates. However, this and other hierarchical ICA models assume unrealistically that subject effects are spatially independent. Here, we propose spatial template ICA (stICA), which incorporates spatial priors into the template ICA framework for greater estimation efficiency. Additionally, the joint posterior distribution can be used to identify brain regions engaged in each network using an excursions set approach. By leveraging spatial dependencies and avoiding massive multiple comparisons, stICA has high power to detect true effects. We derive an efficient expectation-maximization algorithm to obtain maximum likelihood estimates of the model parameters and posterior moments of the latent fields. Based on analysis of simulated data and fMRI data from the Human Connectome Project, we find that stICA produces estimates that are more accurate and reliable than benchmark approaches, and identifies larger and more reliable areas of engagement. The algorithm is computationally tractable, achieving convergence within 12 hours for whole-cortex fMRI analysis.
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Affiliation(s)
- Amanda F. Mejia
- Department of Statistics, Indiana University, Bloomington, IN, 47408
| | - David Bolin
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
| | - Yu Ryan Yue
- Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, New York, NY, 10010
| | - Jiongran Wang
- Department of Statistics, Indiana University, Bloomington, IN, 47408
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, 21205
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, 21205
- Department of Neurology, Johns Hopkins University, Baltimore, MD, 21205
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12
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Huang Q, Tang B, Romero JC, Yang Y, Elsayed SK, Pahapale G, Lee TJ, Morales Pantoja IE, Han F, Berlinicke C, Xiang T, Solazzo M, Hartung T, Qin Z, Caffo BS, Smirnova L, Gracias DH. Shell microelectrode arrays (MEAs) for brain organoids. Sci Adv 2022; 8:eabq5031. [PMID: 35977026 PMCID: PMC9385157 DOI: 10.1126/sciadv.abq5031] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Accepted: 07/06/2022] [Indexed: 05/30/2023]
Abstract
Brain organoids are important models for mimicking some three-dimensional (3D) cytoarchitectural and functional aspects of the brain. Multielectrode arrays (MEAs) that enable recording and stimulation of activity from electrogenic cells offer notable potential for interrogating brain organoids. However, conventional MEAs, initially designed for monolayer cultures, offer limited recording contact area restricted to the bottom of the 3D organoids. Inspired by the shape of electroencephalography caps, we developed miniaturized wafer-integrated MEA caps for organoids. The optically transparent shells are composed of self-folding polymer leaflets with conductive polymer-coated metal electrodes. Tunable folding of the minicaps' polymer leaflets guided by mechanics simulations enables versatile recording from organoids of different sizes, and we validate the feasibility of electrophysiology recording from 400- to 600-μm-sized organoids for up to 4 weeks and in response to glutamate stimulation. Our studies suggest that 3D shell MEAs offer great potential for high signal-to-noise ratio and 3D spatiotemporal brain organoid recording.
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Affiliation(s)
- Qi Huang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bohao Tang
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21287, USA
| | - July Carolina Romero
- Center for Alternatives to Animal Testing, Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Yuqian Yang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Gayatri Pahapale
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Tien-Jung Lee
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Itzy E. Morales Pantoja
- Center for Alternatives to Animal Testing, Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Fang Han
- Department of Statistics, University of Washington, Seattle, WA 98195, USA
| | - Cynthia Berlinicke
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Wilmer Eye Institute, Baltimore, MD 21287, USA
| | - Terry Xiang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Mallory Solazzo
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Thomas Hartung
- Center for Alternatives to Animal Testing, Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
- CAAT-Europe, University of Konstanz, 78464 Konstanz, Germany
- Environmental Metrology & Policy Program, Georgetown University, Washington, DC, 20057, USA
- Department of Molecular Microbiology and Immunology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA
| | - Zhao Qin
- Department of Civil and Environmental Engineering, Syracuse University, Syracuse, NY 13244, USA
| | - Brian S. Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Lena Smirnova
- Center for Alternatives to Animal Testing, Department of Environmental Health and Engineering, Bloomberg School of Public Health and Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
- Environmental Metrology & Policy Program, Georgetown University, Washington, DC, 20057, USA
| | - David H. Gracias
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Materials Science and Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Chemistry, Johns Hopkins University, Baltimore, MD 21218, USA
- Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA
- Laboratory for Computational Sensing and Robotics (LCSR), Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA
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13
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Berardi G, Frey-Law L, Sluka KA, Bayman EO, Coffey CS, Ecklund D, Vance CGT, Dailey DL, Burns J, Buvanendran A, McCarthy RJ, Jacobs J, Zhou XJ, Wixson R, Balach T, Brummett CM, Clauw D, Colquhoun D, Harte SE, Harris RE, Williams DA, Chang AC, Waljee J, Fisch KM, Jepsen K, Laurent LC, Olivier M, Langefeld CD, Howard TD, Fiehn O, Jacobs JM, Dakup P, Qian WJ, Swensen AC, Lokshin A, Lindquist M, Caffo BS, Crainiceanu C, Zeger S, Kahn A, Wager T, Taub M, Ford J, Sutherland SP, Wandner LD. Multi-Site Observational Study to Assess Biomarkers for Susceptibility or Resilience to Chronic Pain: The Acute to Chronic Pain Signatures (A2CPS) Study Protocol. Front Med (Lausanne) 2022; 9:849214. [PMID: 35547202 PMCID: PMC9082267 DOI: 10.3389/fmed.2022.849214] [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] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/17/2022] [Indexed: 11/13/2022] Open
Abstract
Chronic pain has become a global health problem contributing to years lived with disability and reduced quality of life. Advances in the clinical management of chronic pain have been limited due to incomplete understanding of the multiple risk factors and molecular mechanisms that contribute to the development of chronic pain. The Acute to Chronic Pain Signatures (A2CPS) Program aims to characterize the predictive nature of biomarkers (brain imaging, high-throughput molecular screening techniques, or "omics," quantitative sensory testing, patient-reported outcome assessments and functional assessments) to identify individuals who will develop chronic pain following surgical intervention. The A2CPS is a multisite observational study investigating biomarkers and collective biosignatures (a combination of several individual biomarkers) that predict susceptibility or resilience to the development of chronic pain following knee arthroplasty and thoracic surgery. This manuscript provides an overview of data collection methods and procedures designed to standardize data collection across multiple clinical sites and institutions. Pain-related biomarkers are evaluated before surgery and up to 3 months after surgery for use as predictors of patient reported outcomes 6 months after surgery. The dataset from this prospective observational study will be available for researchers internal and external to the A2CPS Consortium to advance understanding of the transition from acute to chronic postsurgical pain.
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Affiliation(s)
- Giovanni Berardi
- Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Laura Frey-Law
- Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Kathleen A. Sluka
- Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Emine O. Bayman
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, United States
| | - Christopher S. Coffey
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, United States
| | - Dixie Ecklund
- Department of Biostatistics, College of Public Health, University of Iowa, Iowa City, IA, United States
| | - Carol G. T. Vance
- Department of Physical Therapy and Rehabilitation Science, Carver College of Medicine, University of Iowa, Iowa City, IA, United States
| | - Dana L. Dailey
- Department of Physical Therapy, St. Ambrose University, Davenport, IA, United States
| | - John Burns
- Department of Psychiatry, Rush University Medical Center, Chicago, IL, United States
| | - Asokumar Buvanendran
- Department of Anesthesiology, Rush University Medical Center, Chicago, IL, United States
| | - Robert J. McCarthy
- Department of Anesthesiology, Rush University Medical Center, Chicago, IL, United States
| | - Joshua Jacobs
- Department of Orthopaedic Surgery, Rush University Medical Center, Chicago, IL, United States
| | - Xiaohong Joe Zhou
- Departments of Radiology, Neurosurgery, and Bioengineering, University of Illinois College of Medicine at Chicago, Chicago, IL, United States
| | - Richard Wixson
- NorthShore Orthopaedic and Spine Institute, NorthShore University HealthSystem, Skokie, IL, United States
| | - Tessa Balach
- Department of Orthopaedic Surgery and Rehabilitation Medicine, The University of Chicago, Chicago, IL, United States
| | - Chad M. Brummett
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Daniel Clauw
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
- Department of Medicine (Rheumatology), University of Michigan, Ann Arbor, MI, United States
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
| | - Douglas Colquhoun
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
| | - Steven E. Harte
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
- Department of Medicine (Rheumatology), University of Michigan, Ann Arbor, MI, United States
| | - Richard E. Harris
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
- Department of Medicine (Rheumatology), University of Michigan, Ann Arbor, MI, United States
| | - David A. Williams
- Department of Anesthesiology, University of Michigan, Ann Arbor, MI, United States
- Department of Medicine (Rheumatology), University of Michigan, Ann Arbor, MI, United States
- Department of Psychiatry, University of Michigan, Ann Arbor, MI, United States
- Department of Psychology, University of Michigan, Ann Arbor, MI, United States
| | - Andrew C. Chang
- Section of Thoracic Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Jennifer Waljee
- Section of Plastic and Reconstructive Surgery, University of Michigan, Ann Arbor, MI, United States
| | - Kathleen M. Fisch
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Kristen Jepsen
- Institute of Genomic Medicine Genomics Center, University of California, San Diego, La Jolla, CA, United States
| | - Louise C. Laurent
- Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San Diego, La Jolla, CA, United States
| | - Michael Olivier
- Department of Internal Medicine, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Carl D. Langefeld
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Timothy D. Howard
- Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California, Davis, Davis, CA, United States
| | - Jon M. Jacobs
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Panshak Dakup
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Wei-Jun Qian
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Adam C. Swensen
- Environmental and Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, United States
| | - Anna Lokshin
- Department of Pathology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Martin Lindquist
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Ciprian Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Scott Zeger
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Ari Kahn
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, United States
| | - Tor Wager
- Presidential Cluster in Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, United States
| | - Margaret Taub
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - James Ford
- Geisel School of Medicine at Dartmouth, Hanover, NH, United States
| | - Stephani P. Sutherland
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Laura D. Wandner
- National Institute of Neurological Disorders and Stroke, The National Institutes of Health, Bethesda, MD, United States
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14
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Wang B, Caffo BS, Luo X, Liu C, Faria AV, Miller MI, Zhao Y. Regularized regression on compositional trees with application to MRI analysis. J R Stat Soc Ser C Appl Stat 2022; 71:541-561. [DOI: 10.1111/rssc.12545] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
Affiliation(s)
- Bingkai Wang
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Brian S. Caffo
- Department of BiostatisticsJohns Hopkins Bloomberg School of Public Health BaltimoreMarylandUSA
| | - Xi Luo
- Department of Biostatistics and Data ScienceThe University of Texas Health Science Center at Houston HoustonTexasUSA
| | - Chin‐Fu Liu
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Andreia V. Faria
- Department of RadiologyJohns Hopkins University School of Medicine BaltimoreMarylandUSA
| | - Michael I. Miller
- Center for Imaging Science, Biomedical EngineeringJohns Hopkins University BaltimoreMarylandUSA
| | - Yi Zhao
- Department of BiostatisticsIndiana University School of Medicine and for the Alzheimer's Disease Neuroimaging Initiative IndianapolisIndianaUSA
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15
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Zhao Y, Caffo BS, Ewen JB. B-value and empirical equivalence bound: A new procedure of hypothesis testing. Stat Med 2022; 41:964-980. [PMID: 35014082 PMCID: PMC8881334 DOI: 10.1002/sim.9298] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 11/09/2021] [Accepted: 12/08/2021] [Indexed: 11/11/2022]
Abstract
In this study, we propose a two-stage procedure for hypothesis testing, where the first stage is conventional hypothesis testing and the second is an equivalence testing procedure using an introduced empirical equivalence bound (EEB). In 2016, the American Statistical Association released a policy statement on P-values to clarify the proper use and interpretation in response to the criticism of reproducibility and replicability in scientific findings. A recent solution to improve reproducibility and transparency in statistical hypothesis testing is to integrate P-values (or confidence intervals) with practical or scientific significance. Similar ideas have been proposed via the equivalence test, where the goal is to infer equality under a presumption (null) of inequality of parameters. However, the definition of scientific significance/equivalence can sometimes be ill-justified and subjective. To circumvent this drawback, we introduce the B-value and the EEB, which are both estimated from the data. Performing a second-stage equivalence test, our procedure offers an opportunity to improve the reproducibility of findings across studies.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Joshua B Ewen
- Kennedy Krieger Institute and Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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Sheppard SM, Meier EL, Kim KT, Breining BL, Keator LM, Tang B, Caffo BS, Hillis AE. Neural correlates of syntactic comprehension: A longitudinal study. Brain Lang 2022; 225:105068. [PMID: 34979477 PMCID: PMC9232253 DOI: 10.1016/j.bandl.2021.105068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [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: 04/07/2021] [Revised: 12/18/2021] [Accepted: 12/20/2021] [Indexed: 06/14/2023]
Abstract
Broca's area is frequently implicated in sentence comprehension but its specific role is debated. Most lesion studies have investigated deficits at the chronic stage. We aimed (1) to use acute imaging to predict which left hemisphere stroke patients will recover sentence comprehension; and (2) to better understand the role of Broca's area in sentence comprehension by investigating acute deficits prior to functional reorganization. We assessed comprehension of canonical and noncanonical sentences in 15 patients with left hemisphere stroke at acute and chronic stages. LASSO regression was used to conduct lesion symptom mapping analyses. Patients with more severe word-level comprehension deficits and a greater proportion of damage to supramarginal gyrus and superior longitudinal fasciculus were likely to experience acute deficits prior to functional reorganization. Broca's area was only implicated in chronic deficits. We propose that when temporoparietal regions are damaged, intact Broca's area can support syntactic processing after functional reorganization occurs.
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Affiliation(s)
- Shannon M Sheppard
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States; Department of Communication Sciences & Disorders, Chapman University, Irvine, CA 92618, United States.
| | - Erin L Meier
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Kevin T Kim
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Bonnie L Breining
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Lynsey M Keator
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States
| | - Bohao Tang
- Department of Biostatics, Johns Hopkins School of Public Health, Baltimore, MD 21287, United States
| | - Brian S Caffo
- Department of Biostatics, Johns Hopkins School of Public Health, Baltimore, MD 21287, United States
| | - Argye E Hillis
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States; Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States; Department of Cognitive Science, Krieger School of Arts and Sciences, Johns Hopkins University, Baltimore, MD 21218, United States
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17
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Caffo BS, D'Asaro FA, Garcez A, Raffinetti E. Editorial: Explainable artificial intelligence models and methods in finance and healthcare. Front Artif Intell 2022; 5:970246. [PMID: 36052290 PMCID: PMC9426026 DOI: 10.3389/frai.2022.970246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 07/29/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, United States
| | - Fabio A D'Asaro
- Department of Human Sciences, University of Verona, Verona, Italy
| | - Artur Garcez
- Department of Computer Science, City University of London, London, United Kingdom
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18
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Zhao Y, Nebel MB, Caffo BS, Mostofsky SH, Rosch KS. Beyond Massive Univariate Tests: Covariance Regression Reveals Complex Patterns of Functional Connectivity Related to Attention-Deficit/Hyperactivity Disorder, Age, Sex, and Response Control. Biological Psychiatry Global Open Science 2022; 2:8-16. [PMID: 35528865 PMCID: PMC9074810 DOI: 10.1016/j.bpsgos.2021.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Background Studies of brain functional connectivity (FC) typically involve massive univariate tests, performing statistical analysis on each individual connection. In this study, we apply a novel whole-matrix regression approach referred to as covariate assisted principal regression to identify resting-state FC brain networks associated with attention-deficit/hyperactivity disorder (ADHD) and response control. Methods Participants included 8- to 12-year-old children with ADHD (n = 115; 29 girls) and typically developing control children (n = 102; 35 girls) who completed a resting-state functional magnetic resonance imaging scan and a Go/NoGo task. We modeled three sets of covariates to identify resting-state networks associated with an ADHD diagnosis, sex, and response inhibition (commission errors) and variability (ex-Gaussian parameter tau). Results The first network includes FC between striatal-cognitive control (CC) network subregions and thalamic-default mode network (DMN) subregions and is positively related to age. The second consists of FC between CC-visual-somatomotor regions and between CC-DMN subregions and is positively associated with response variability in boys with ADHD. The third consists of FC within the DMN and between DMN-CC-visual regions and differs between boys with and without ADHD. The fourth consists of FC between visual-somatomotor regions and between visual-DMN regions and differs between girls and boys with ADHD and is associated with response inhibition and variability in boys with ADHD. Unique networks were also identified in each of the three models, suggesting some specificity to the covariates of interest. Conclusions These findings demonstrate the utility of our novel covariance regression approach to studying functional brain networks relevant for development, behavior, and psychopathology.
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19
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Beheshtian E, Jalilianhasanpour R, Modir Shanechi A, Sethi V, Wang G, Lindquist MA, Caffo BS, Agarwal S, Pillai JJ, Gujar SK, Sair HI. Identification of the Somatomotor Network from Language Task-based fMRI Compared with Resting-State fMRI in Patients with Brain Lesions. Radiology 2021; 301:178-184. [PMID: 34282966 DOI: 10.1148/radiol.2021204594] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Background Resting-state functional MRI (rs-fMRI) is a potential alternative to task-based functional MRI (tb-fMRI) for somatomotor network (SMN) identification. Brain networks can also be generated from tb-fMRI by using independent component analysis (ICA). Purpose To investigate whether the SMN can be identified by using ICA from a language task without a motor component, the sentence completion functional MRI (sc-fMRI) task, compared with rs-fMRI. Materials and Methods The sc-fMRI and rs-fMRI scans in patients who underwent presurgical brain mapping between 2012 and 2016 were analyzed, using the same imaging parameters (other than scanning time) on a 3.0-T MRI scanner. ICA was performed on rs-fMRI and sc-fMRI scans with use of a tool to separate data sets into their spatial and temporal components. Two neuroradiologists independently determined the presence of the dorsal SMN (dSMN) and ventral SMN (vSMN) on each study. Groups were compared by using t tests, and logistic regression was performed to identify predictors of the presence of SMNs. Results One hundred patients (mean age, 40.9 years ± 14.8 [standard deviation]; 61 men) were evaluated. The dSMN and vSMN were identified in 86% (86 of 100) and 76% (76 of 100) of rs-fMRI scans and 85% (85 of 100) and 69% (69 of 100) of sc-fMRI scans, respectively. The concordance between rs-fMRI and sc-fMRI for presence of dSMN and vSMN was 75% (75 of 100 patients) and 53% (53 of 100 patients), respectively. In 10 of 14 patients (71%) where rs-fMRI did not show the dSMN, sc-fMRI demonstrated it. This rate was 67% for the vSMN (16 of 24 patients). Conclusion In the majority of patients, independent component analysis of sentence completion task functional MRI scans reliably demonstrated the somatomotor network compared with resting-state functional MRI scans. Identifying target networks with a single sentence completion scan could reduce overall functional MRI scanning times by eliminating the need for separate motor tasks. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Field and Birn in this issue.
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Affiliation(s)
- Elham Beheshtian
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Rozita Jalilianhasanpour
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Amirali Modir Shanechi
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Varun Sethi
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Guoqing Wang
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Martin A Lindquist
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Brian S Caffo
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Shruti Agarwal
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Jay J Pillai
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Sachin K Gujar
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
| | - Haris I Sair
- From the Division of Neuroradiology, the Russell H. Morgan Department of Radiology and Radiological Science (E.B., R.J., A.M.S., S.A., J.J.P., S.K.G., H.I.S.) and the Department of Neurosurgery (J.J.P.), Johns Hopkins University School of Medicine, 600 N Wolfe St, Baltimore, MD 21287; Division of Neuroradiology, Department of Radiology, Temple University Hospital, Philadelphia, Pa (V.S.); Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Md (G.W., M.A.L., B.S.C.); and the Malone Center for Engineering in Healthcare, the Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (H.I.S.)
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20
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Al Ammary F, Yu S, Muzaale AD, Segev DL, Liyanage L, Crews DC, Brennan DC, El-Meanawy A, Alqahtani S, Atta MG, Levan ML, Caffo BS, Welling PA, Massie AB. Long-term kidney function and survival in recipients of allografts from living kidney donors with hypertension: a national cohort study. Transpl Int 2021; 34:1530-1541. [PMID: 34129713 DOI: 10.1111/tri.13947] [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] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/08/2021] [Accepted: 06/06/2021] [Indexed: 12/27/2022]
Abstract
Allografts from living kidney donors with hypertension may carry subclinical kidney disease from the donor to the recipient and, thus, lead to adverse recipient outcomes. We examined eGFR trajectories and all-cause allograft failure in recipients from donors with versus without hypertension, using mixed-linear and Cox regression models stratified by donor age. We studied a US cohort from 1/1/2005 to 6/30/2017; 49 990 recipients of allografts from younger (<50 years old) donors including 597 with donor hypertension and 21 130 recipients of allografts from older (≥50 years old) donors including 1441 with donor hypertension. Donor hypertension was defined as documented predonation use of antihypertensive therapy. Among recipients from younger donors with versus without hypertension, the annual eGFR decline was -1.03 versus -0.53 ml/min/m2 (P = 0.002); 13-year allograft survival was 49.7% vs. 59.0% (adjusted allograft failure hazard ratio [aHR] 1.23; 95% CI 1.05-1.43; P = 0.009). Among recipients from older donors with versus without hypertension, the annual eGFR decline was -0.67 versus -0.66 ml/min/m2 (P = 0.9); 13-year allograft survival was 48.6% versus 52.6% (aHR 1.05; 95% CI 0.94-1.17; P = 0.4). In secondary analyses, our inferences remained similar for risk of death-censored allograft failure and mortality. Hypertension in younger, but not older, living kidney donors is associated with worse recipient outcomes.
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Affiliation(s)
- Fawaz Al Ammary
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sile Yu
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Abimereki D Muzaale
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Dorry L Segev
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Luckmini Liyanage
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Deidra C Crews
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel C Brennan
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Ashraf El-Meanawy
- Department of Medicine, Medical College of Wisconsin, Milwaukee, WI, USA
| | - Saleh Alqahtani
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mohamed G Atta
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Macey L Levan
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Paul A Welling
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Physiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Allan B Massie
- Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, MD, USA.,Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
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21
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Al-Khouja A, Shieh E, Fuchs EJ, Marzinke MA, Bakshi RP, Hummert P, Ham AS, Buckheit KW, Breakey J, Weld ED, Chen H, Caffo BS, Buckheit RW, Hendrix CW. Examining the Safety, Pharmacokinetics, and Pharmacodynamics of a Rectally Administered IQP-0528 Gel for HIV Pre-Exposure Prophylaxis: A First-In-Human Study. AIDS Res Hum Retroviruses 2021; 37:444-452. [PMID: 33371779 DOI: 10.1089/aid.2020.0188] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
A lubricating microbicide gel designed for rectal and vaginal use would provide a behaviorally congruent strategy to enhance pre-exposure prophylaxis adherence and reduce HIV infection risk. In this study, we report the first-in-human evaluation of such a gel containing 1% IQP-0528, an investigational antiretroviral. Seven HIV-1-negative participants received one 10 mL rectal dose of radiolabeled 1% IQP-0528 gel. We assessed safety; IQP-0528 pharmacokinetics in plasma, and rectal and vaginal tissue; ex vivo local pharmacodynamics (PD); and colorectal distribution. The 1% gel was determined to be safe with one mild event attributed to study product and no effects on rectal tissue histology. All concentrations measured in plasma and vaginal tissue were below the limit of quantitation. Median IQP-0528 concentrations in rectal tissue exceeded the in vitro EC95 against HIV-1 (0.07 ng/mg) by 3-5 h of dosing and remained above this concentration for at least 24 h, despite a 3-log reduction in concentration over this duration of time. Rectal tissue PD-assessed by ex vivo HIV challenge-demonstrated significant p24 antigen reduction 3-5 h postdose compared with baseline (p = .05), but not 24-26 h postdose (p = .75). Single-photon emission computed tomography/computed tomography imaging revealed that product distribution was localized to the rectosigmoid. The IQP-0528 gel possesses desirable features for a topical microbicide including: local safety with no systemic absorption, delivery of locally high IQP-0528 concentrations, and significant reductions in ex vivo HIV infectivity. However, the gel is limited by its rapid clearance and inability to penetrate vaginal tissues following rectal dosing. Clinical Trial Registration number: NCT03082690.
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Affiliation(s)
- Amer Al-Khouja
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Eugenie Shieh
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Edward J. Fuchs
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Mark A. Marzinke
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Rahul P. Bakshi
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Pamela Hummert
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | | | | | - Jennifer Breakey
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Ethel D. Weld
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Huan Chen
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA
| | | | - Craig W. Hendrix
- Division of Clinical Pharmacology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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22
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Crasta JE, Raja AE, Caffo BS, Hluchan CM, Suskauer SJ. The Effect of Age and Competition Level on Subtle Motor Performance in Adolescents Medically Cleared Postconcussion: Preliminary Findings. Am J Phys Med Rehabil 2021; 100:563-569. [PMID: 32932362 PMCID: PMC8744001 DOI: 10.1097/phm.0000000000001589] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The aim of this study was to examine the effect of age and level of competition on subtle motor performance in adolescents who have recently been medically cleared postconcussion and never-injured controls. DESIGN Thirty adolescents who were recently medically cleared postconcussion (12-18 yrs) and 30 never-concussed, typically developing controls were examined using the Revised Physical and Neurological Examination of Subtle Signs (PANESS) and the Immediate Post-Concussion Assessment and Cognitive Testing. RESULTS Older age was associated with better Immediate Post-Concussion Assessment and Cognitive Testing scores in both groups, whereas only the control group showed improved motor performance on the PANESS with increasing age. Adolescents across both groups participating at a higher level of competition (school or travel level) had better motor performance on the PANESS than those participating at a lower level of competition (recreational level or no sports participation). Adolescents medically cleared postconcussion had greater motor deficits on the PANESS than controls did. CONCLUSION After medical clearance, adolescents with a history of recent concussion demonstrate alterations in the relationship between motor function and age. The PANESS merits further exploration as a measure that is sensitive to factors affecting motor performance, such as age and level of athletic competition, as well as to persistent subtle motor deficits in adolescents medically cleared postconcussion.
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Affiliation(s)
- Jewel E Crasta
- Kennedy Krieger Institute, The Ohio State University
- Division of Occupational Therapy, The Ohio State University
| | - Altamash E Raja
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health
| | - Christine M Hluchan
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine
| | - Stacy J Suskauer
- Kennedy Krieger Institute, The Ohio State University
- Department of Physical Medicine and Rehabilitation, Johns Hopkins University School of Medicine
- Department of Pediatrics, Johns Hopkins University School of Medicine
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23
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Shafer AT, Beason-Held L, An Y, Williams OA, Huo Y, Landman BA, Caffo BS, Resnick SM. Default mode network connectivity and cognition in the aging brain: the effects of age, sex, and APOE genotype. Neurobiol Aging 2021; 104:10-23. [PMID: 33957555 DOI: 10.1016/j.neurobiolaging.2021.03.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 09/03/2020] [Revised: 03/04/2021] [Accepted: 03/24/2021] [Indexed: 01/18/2023]
Abstract
The default mode network (DMN) overlaps with regions showing early Alzheimer's Disease (AD) pathology. Age, sex, and apolipoprotein E ɛ4 are the predominant risk factors for developing AD. How these risk factors interact to influence DMN connectivity and connectivity-cognition relationships before the onset of impairment remains unknown. Here, we examined these issues in 475 cognitively normal adults, targeting total DMN connectivity, its anticorrelated network (acDMN), and the DMN-hippocampal component. There were four main findings. First, in the ɛ3 homozygous group, lower DMN and acDMN connectivity was observed with age. Second, sex and ɛ4 modified the relationship between age and connectivity for the DMN and hippocampus with ɛ4 vs. ɛ3 males showing sustained or higher connectivity with age. Third, in the ɛ3 group, age and sex modified connectivity-cognition relationships with the oldest participants having the most differential patterns due to sex. Fourth, ɛ4 carriers with lower connectivity had poorer cognitive performance. Taken together, our results show the three predominant risk factors for AD interact to influence brain function and function-cognition relationships.
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Affiliation(s)
- Andrea T Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
| | - Lori Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yang An
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Owen A Williams
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD
| | - Yuankai Huo
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Bennett A Landman
- Department of Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, Baltimore, MD.
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24
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Zhao Y, Caffo BS, Wang B, Li CSR, Luo X. A whole-brain modeling approach to identify individual and group variations in functional connectivity. Brain Behav 2021; 11:e01942. [PMID: 33210469 PMCID: PMC7821576 DOI: 10.1002/brb3.1942] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 12/28/2022] Open
Abstract
Resting-state functional connectivity is an important and widely used measure of individual and group differences. Yet, extant statistical methods are limited to linking covariates with variations in functional connectivity across subjects, especially at the voxel-wise level of the whole brain. This paper introduces a modeling approach that regresses whole-brain functional connectivity on covariates. Our approach is a mesoscale approach that enables identification of brain subnetworks. These subnetworks are composite of spatially independent components discovered by a dimension reduction approach (such as whole-brain group ICA) and covariate-related projections determined by the covariate-assisted principal regression, a recently introduced covariance matrix regression method. We demonstrate the efficacy of this approach using a resting-state fMRI dataset of a medium-sized cohort of subjects obtained from the Human Connectome Project. The results suggest that the approach may improve statistical power in detecting interaction effects of gender and alcohol on whole-brain functional connectivity, and in identifying the brain areas contributing significantly to the covariate-related differences in functional connectivity.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chiang-Shan R Li
- Department of Psychiatry, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA.,Department of Neuroscience, Yale School of Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA
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25
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Bae S, Massie AB, Caffo BS, Jackson KR, Segev DL. Machine learning to predict transplant outcomes: helpful or hype? A national cohort study. Transpl Int 2020; 33:1472-1480. [PMID: 32996170 PMCID: PMC8269970 DOI: 10.1111/tri.13695] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [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: 10/02/2019] [Revised: 11/30/2019] [Accepted: 06/29/2020] [Indexed: 12/13/2022]
Abstract
An increasing number of studies claim machine learning (ML) predicts transplant outcomes more accurately. However, these claims were possibly confounded by other factors, namely, supplying new variables to ML models. To better understand the prospects of ML in transplantation, we compared ML to conventional regression in a "common" analytic task: predicting kidney transplant outcomes using national registry data. We studied 133 431 adult deceased-donor kidney transplant recipients between 2005 and 2017. Transplant centers were randomly divided into 70% training set (190 centers/97 787 recipients) and 30% validation set (82 centers/35 644 recipients). Using the training set, we performed regression and ML procedures [gradient boosting (GB) and random forests (RF)] to predict delayed graft function, one-year acute rejection, death-censored graft failure C, all-cause graft failure, and death. Their performances were compared on the validation set using -statistics. In predicting rejection, regression (C = 0.601 0.6110.621 ) actually outperformed GB (C = 0.581 0.5910.601 ) and RF (C = 0.569 0.5790.589 ). For all other outcomes, the C-statistics were nearly identical across methods (delayed graft function, 0.717-0.723; death-censored graft failure, 0.637-0.642; all-cause graft failure, 0.633-0.635; and death, 0.705-0.708). Given its shortcomings in model interpretability and hypothesis testing, ML is advantageous only when it clearly outperforms conventional regression; in the case of transplant outcomes prediction, ML seems more hype than helpful.
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Affiliation(s)
- Sunjae Bae
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Allan B Massie
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, MD, USA
| | - Kyle R Jackson
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Dorry L Segev
- Department of Epidemiology, Johns Hopkins School of Public Health, Baltimore, MD, USA
- Department of Surgery, Johns Hopkins School of Medicine, Baltimore, MD, USA
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26
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Wu D, Chang L, Ernst TM, Caffo BS, Oishi K. Developmental score of the infant brain: characterizing diffusion MRI in term- and preterm-born infants. Brain Struct Funct 2020; 225:2431-2445. [PMID: 32804327 DOI: 10.1007/s00429-020-02132-4] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/10/2020] [Indexed: 10/23/2022]
Abstract
Large-scale longitudinal neuroimaging studies of the infant brain allow us to map the spatiotemporal development of the brain in its early phase. While the postmenstrual age (PMA) is commonly used as a time index to analyze longitudinal MRI data, the nonlinear relationship between PMA and MRI data imposes challenges for downstream analyses. We propose a mathematical model that provides a Developmental Score (DevS) as a data-driven time index to characterize the brain development based on MRI features. 319 diffusion tensor imaging (DTI) datasets were collected from 87 term-born and 66 preterm-born infants at multiple visits, which were automatically segmented based on the JHU neonatal atlas. The mean diffusivity (MD) and fractional anisotropy (FA) in 126 brain parcels were used in the model to derive DevS. We demonstrate that transforming the time index from PMA to DevS improves the linearity of the longitudinal changes in MD and FA in both gray and white matter structures. More importantly, regional developmental differences in DTI metrics between preterm- and term-born infants were identified more clearly using DevS, e.g. 79 structures showed significantly different regression patterns in MD between preterm- and term-born infants, compared to only 27 structures that showed group differences using PMA as the index. Therefore, the DevS model facilitates linear analyses of DTI metrics in the infant brain, and provides a useful tool to characterize altered brain development due to preterm-birth.
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Affiliation(s)
- Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA.,Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Ernst
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kenichi Oishi
- Russell H. Morgan Department of Radiology, Johns Hopkins University School of Medicine, Traylor 217, 720 Rutland Ave, Baltimore, MD, 21215, USA.
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27
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Zhao Y, Li L, Caffo BS. Multimodal neuroimaging data integration and pathway analysis. Biometrics 2020; 77:879-889. [PMID: 32789850 DOI: 10.1111/biom.13351] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [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: 08/28/2019] [Revised: 07/15/2020] [Accepted: 06/05/2020] [Indexed: 12/01/2022]
Abstract
With advancements in technology, the collection of multiple types of measurements on a common set of subjects is becoming routine in science. Some notable examples include multimodal neuroimaging studies for the simultaneous investigation of brain structure and function and multi-omics studies for combining genetic and genomic information. Integrative analysis of multimodal data allows scientists to interrogate new mechanistic questions. However, the data collection and generation of integrative hypotheses is outpacing available methodology for joint analysis of multimodal measurements. In this article, we study high-dimensional multimodal data integration in the context of mediation analysis. We aim to understand the roles that different data modalities play as possible mediators in the pathway between an exposure variable and an outcome. We propose a mediation model framework with two data types serving as separate sets of mediators and develop a penalized optimization approach for parameter estimation. We study both the theoretical properties of the estimator through an asymptotic analysis and its finite-sample performance through simulations. We illustrate our method with a multimodal brain pathway analysis having both structural and functional connectivity as mediators in the association between sex and language processing.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Indiana University School of Medicine, Indianapolis, Indiana
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, California
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland
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28
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McAuliffe D, Zhao Y, Pillai AS, Ament K, Adamek J, Caffo BS, Mostofsky SH, Ewen JB. Learning of skilled movements via imitation in ASD. Autism Res 2020; 13:777-784. [PMID: 31876983 DOI: 10.1002/aur.2253] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 06/05/2019] [Accepted: 12/01/2019] [Indexed: 11/07/2022]
Abstract
Autism spectrum disorder (ASD) consists of altered performance of a range of skills, including social/communicative and motor skills. It is unclear whether this altered performance results from atypical acquisition or learning of the skills or from atypical "online" performance of the skills. Atypicalities of skilled actions that require both motor and cognitive resources, such as abnormal gesturing, are highly prevalent in ASD and are easier to study in a laboratory context than are social/communicative skills. Imitation has long been known to be impaired in ASD; because learning via imitation is a prime method by which humans acquire skills, we tested the hypothesis that children with ASD show alterations in learning novel gestures via imitation. Eighteen participants with ASD and IQ > 80, ages 8-12.9 years, and 19 typically developing peers performed a task in which they watched a video of a model performing a novel, meaningless arm/hand gesture and copied the gesture. Each gesture video/copy sequence was repeated 4-6 times. Eight gestures were analyzed. Examination of learning trajectories revealed that while children with ASD made nearly as much progress in learning from repetition 1 to repetition 4, the shape of the learning curves differed. Causal modeling demonstrated the shape of the learning curve influenced both the performance of overlearned gestures and autism severity, suggesting that it is in the index of learning mechanisms relevant both to motor skills and to autism core features. Autism Res 2020, 13: 777-784.. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. LAY SUMMARY: Imitation is a route by which humans learn a wide range of skills, naturally and in therapies. Imitation is known to be altered in autism spectrum disorder (ASD), but learning via imitation has not been rigorously examined. We found that the shape of the learning curve is altered in ASD, in a way that has a significant impact both on measures of autism severity and of other motor skills.
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Affiliation(s)
- Danielle McAuliffe
- Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
| | - Yi Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Ajay S Pillai
- Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Katarina Ament
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
| | - Jack Adamek
- Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland
| | - Stewart H Mostofsky
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Joshua B Ewen
- Department of Neurology and Developmental Medicine, Kennedy Krieger Institute, Baltimore, Maryland
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, Maryland
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29
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Zhang T, Sun Y, Li H, Yan G, Tanabe S, Miao R, Wang Y, Caffo BS, Quigg MS. Bayesian inference of a directional brain network model for intracranial EEG data. Comput Stat Data Anal 2020. [DOI: 10.1016/j.csda.2019.106847] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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30
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Muschelli J, Gherman A, Fortin JP, Avants B, Whitcher B, Clayden JD, Caffo BS, Crainiceanu CM. Corrigendum to: Neuroconductor: an R platform for medical imaging analysis. Biostatistics 2020; 22:685. [PMID: 32065220 DOI: 10.1093/biostatistics/kxaa006] [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] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Affiliation(s)
- John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Adrian Gherman
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Jean-Philippe Fortin
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Brian Avants
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA 19104, USA
| | - Brandon Whitcher
- Klarismo Ltd, London, UK and Department of Mathematics, Imperial College London, London, UK
| | - Jonathan D Clayden
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London WC1N 1EH, UK
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
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31
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Zhao Y, Wang B, Mostofsky SH, Caffo BS, Luo X. Covariate Assisted Principal regression for covariance matrix outcomes. Biostatistics 2019; 22:629-645. [PMID: 31851318 DOI: 10.1093/biostatistics/kxz057] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [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: 04/02/2019] [Revised: 11/26/2019] [Accepted: 11/29/2019] [Indexed: 11/15/2022] Open
Abstract
In this study, we consider the problem of regressing covariance matrices on associated covariates. Our goal is to use covariates to explain variation in covariance matrices across units. As such, we introduce Covariate Assisted Principal (CAP) regression, an optimization-based method for identifying components associated with the covariates using a generalized linear model approach. We develop computationally efficient algorithms to jointly search for common linear projections of the covariance matrices, as well as the regression coefficients. Under the assumption that all the covariance matrices share identical eigencomponents, we establish the asymptotic properties. In simulation studies, our CAP method shows higher accuracy and robustness in coefficient estimation over competing methods. In an example resting-state functional magnetic resonance imaging study of healthy adults, CAP identifies human brain network changes associated with subject demographics.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA and Department of Biostatistics, Indiana University School of Medicine, 410 W 10th St, Indianapolis, IN 46202, USA
| | - Bingkai Wang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Stewart H Mostofsky
- Center for Neurodevelopmental and Imaging Research (CNIR) at Kennedy Krieger Institute, Johns Hopkins University, 707 N Broadway, Baltimore, MD 21205, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD 21205, USA
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, 1200 Pressler St, Houston, TX 77030, USA
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32
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Mejia AF, Nebel MB, Wang Y, Caffo BS, Guo Y. Template Independent Component Analysis: Targeted and Reliable Estimation of Subject-level Brain Networks using Big Data Population Priors. J Am Stat Assoc 2019; 115:1151-1177. [PMID: 33060872 PMCID: PMC7556739 DOI: 10.1080/01621459.2019.1679638] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 09/11/2019] [Accepted: 10/07/2019] [Indexed: 12/17/2022]
Abstract
Large brain imaging databases contain a wealth of information on brain organization in the populations they target, and on individual variability. While such databases have been used to study group-level features of populations directly, they are currently underutilized as a resource to inform single-subject analysis. Here, we propose leveraging the information contained in large functional magnetic resonance imaging (fMRI) databases by establishing population priors to employ in an empirical Bayesian framework. We focus on estimation of brain networks as source signals in independent component analysis (ICA). We formulate a hierarchical "template" ICA model where source signals-including known population brain networks and subject-specific signals-are represented as latent variables. For estimation, we derive an expectation maximization (EM) algorithm having an explicit solution. However, as this solution is computationally intractable, we also consider an approximate subspace algorithm and a faster two-stage approach. Through extensive simulation studies, we assess performance of both methods and compare with dual regression, a popular but ad-hoc method. The two proposed algorithms have similar performance, and both dramatically outperform dual regression. We also conduct a reliability study utilizing the Human Connectome Project and find that template ICA achieves substantially better performance than dual regression, achieving 75-250% higher intra-subject reliability.
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Affiliation(s)
- Amanda F Mejia
- Department of Statistics, Indiana University, Bloomington, IN 47408
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD 21205
- Department of Neurology, Johns Hopkins University, Baltimore, MD 21205
| | - Yikai Wang
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205
| | - Ying Guo
- Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, GA 30322
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33
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Affiliation(s)
- Sean Kross
- Department of Cognitive Science, The University of California San Diego, La Jolla, CA
| | - Roger D. Peng
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Ira Gooding
- Center for Teaching and Learning, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Jeffrey T. Leek
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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34
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Muschelli J, Gherman A, Fortin JP, Avants B, Whitcher B, Clayden JD, Caffo BS, Crainiceanu CM. Neuroconductor: an R platform for medical imaging analysis. Biostatistics 2019; 20:218-239. [PMID: 29325029 DOI: 10.1093/biostatistics/kxx068] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [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: 02/01/2017] [Accepted: 11/12/2017] [Indexed: 11/14/2022] Open
Abstract
Neuroconductor (https://neuroconductor.org) is an open-source platform for rapid testing and dissemination of reproducible computational imaging software. The goals of the project are to: (i) provide a centralized repository of R software dedicated to image analysis, (ii) disseminate software updates quickly, (iii) train a large, diverse community of scientists using detailed tutorials and short courses, (iv) increase software quality via automatic and manual quality controls, and (v) promote reproducibility of image data analysis. Based on the programming language R (https://www.r-project.org/), Neuroconductor starts with 51 inter-operable packages that cover multiple areas of imaging including visualization, data processing and storage, and statistical inference. Neuroconductor accepts new R package submissions, which are subject to a formal review and continuous automated testing. We provide a description of the purpose of Neuroconductor and the user and developer experience.
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Affiliation(s)
- John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, USA
| | - Adrian Gherman
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, USA
| | - Jean-Philippe Fortin
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, USA
| | - Brian Avants
- Department of Biostatistics and Epidemiology, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Drive, Philadelphia, PA, USA
| | - Brandon Whitcher
- Klarismo Ltd, London, UK and Department of Mathematics, Imperial College London, London, UK
| | - Jonathan D Clayden
- Developmental Imaging and Biophysics Section, UCL Great Ormond Street Institute of Child Health, University College London, 30 Guilford Street, London, UK
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, 615 N Wolfe St, Baltimore, MD, USA
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35
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Chén OY, Crainiceanu C, Ogburn EL, Caffo BS, Wager TD, Lindquist MA. High-dimensional multivariate mediation with application to neuroimaging data. Biostatistics 2019. [PMID: 28637279 DOI: 10.1093/biostatistics/kxx027] [Citation(s) in RCA: 52] [Impact Index Per Article: 10.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
Mediation analysis is an important tool in the behavioral sciences for investigating the role of intermediate variables that lie in the path between a treatment and an outcome variable. The influence of the intermediate variable on the outcome is often explored using a linear structural equation model (LSEM), with model coefficients interpreted as possible effects. While there has been significant research on the topic, little work has been done when the intermediate variable (mediator) is a high-dimensional vector. In this work, we introduce a novel method for identifying potential mediators in this setting called the directions of mediation (DMs). DMs linearly combine potential mediators into a smaller number of orthogonal components, with components ranked based on the proportion of the LSEM likelihood each accounts for. This method is well suited for cases when many potential mediators are measured. Examples of high-dimensional potential mediators are brain images composed of hundreds of thousands of voxels, genetic variation measured at millions of single nucleotide polymorphisms (SNPs), or vectors of thousands of variables in large-scale epidemiological studies. We demonstrate the method using a functional magnetic resonance imaging study of thermal pain where we are interested in determining which brain locations mediate the relationship between the application of a thermal stimulus and self-reported pain.
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Affiliation(s)
- Oliver Y Chén
- Department of Biostatistics, Johns Hopkins University, USA
| | | | | | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, USA
| | - Tor D Wager
- Department of Psychology and Neuroscience, University of Colorado Boulder, 345 UCB, Boulder, CO 80309-0345, USA
| | - Martin A Lindquist
- Department of Biostatistics, Johns Hopkins University, 615 N Wolfe St, Baltimore, MD 21205, USA
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36
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Shappell H, Caffo BS, Pekar JJ, Lindquist MA. Improved state change estimation in dynamic functional connectivity using hidden semi-Markov models. Neuroimage 2019; 191:243-257. [PMID: 30753927 DOI: 10.1016/j.neuroimage.2019.02.013] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [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: 10/12/2018] [Revised: 01/13/2019] [Accepted: 02/05/2019] [Indexed: 12/13/2022] Open
Abstract
The study of functional brain networks has grown rapidly over the past decade. While most functional connectivity (FC) analyses estimate one static network structure for the entire length of the functional magnetic resonance imaging (fMRI) time series, recently there has been increased interest in studying time-varying changes in FC. Hidden Markov models (HMMs) have proven to be a useful modeling approach for discovering repeating graphs of interacting brain regions (brain states). However, a limitation lies in HMMs assuming that the sojourn time, the number of consecutive time points in a state, is geometrically distributed. This may encourage inaccurate estimation of the time spent in a state before switching to another state. We propose a hidden semi-Markov model (HSMM) approach for inferring time-varying brain networks from fMRI data, which explicitly models the sojourn distribution. Specifically, we propose using HSMMs to find each subject's most probable series of network states and the graphs associated with each state, while properly estimating and modeling the sojourn distribution for each state. We perform a simulation study, as well as an analysis on both task-based fMRI data from an anxiety-inducing experiment and resting-state fMRI data from the Human Connectome Project. Our results demonstrate the importance of model choice when estimating sojourn times and reveal their potential for understanding healthy and diseased brain mechanisms.
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Affiliation(s)
- Heather Shappell
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA.
| | - Brian S Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
| | - James J Pekar
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Martin A Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA
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37
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Lindquist MA, Geuter S, Wager TD, Caffo BS. Modular preprocessing pipelines can reintroduce artifacts into fMRI data. Hum Brain Mapp 2019; 40:2358-2376. [PMID: 30666750 DOI: 10.1002/hbm.24528] [Citation(s) in RCA: 103] [Impact Index Per Article: 20.6] [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: 09/18/2018] [Revised: 12/06/2018] [Accepted: 01/08/2019] [Indexed: 12/20/2022] Open
Abstract
The preprocessing pipelines typically used in both task and resting-state functional magnetic resonance imaging (rs-fMRI) analysis are modular in nature: They are composed of a number of separate filtering/regression steps, including removal of head motion covariates and band-pass filtering, performed sequentially and in a flexible order. In this article, we illustrate the shortcomings of this approach, as we show how later preprocessing steps can reintroduce artifacts previously removed from the data in prior preprocessing steps. We show that each regression step is a geometric projection of data onto a subspace, and that performing a sequence of projections can move the data into subspaces no longer orthogonal to those previously removed, reintroducing signal related to nuisance covariates. Thus, linear filtering operations are not commutative, and the order in which the preprocessing steps are performed is critical. These issues can arise in practice when any combination of standard preprocessing steps including motion regression, scrubbing, component-based correction, physiological correction, global signal regression, and temporal filtering are performed sequentially. In this work, we focus primarily on rs-fMRI. We illustrate the problem both theoretically and empirically through application to a test-retest rs-fMRI data set, and suggest remedies. These include (a) combining all steps into a single linear filter, or (b) sequential orthogonalization of covariates/linear filters performed in series.
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Affiliation(s)
- Martin A Lindquist
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
| | - Stephan Geuter
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland.,Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Tor D Wager
- Psychology and Neuroscience, University of Colorado Boulder, Boulder, Colorado
| | - Brian S Caffo
- Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
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38
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Ngufor C, Van Houten H, Caffo BS, Shah ND, McCoy RG. Mixed effect machine learning: A framework for predicting longitudinal change in hemoglobin A1c. J Biomed Inform 2018; 89:56-67. [PMID: 30189255 DOI: 10.1016/j.jbi.2018.09.001] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [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: 05/11/2018] [Revised: 08/28/2018] [Accepted: 09/02/2018] [Indexed: 11/26/2022]
Abstract
Accurate and reliable prediction of clinical progression over time has the potential to improve the outcomes of chronic disease. The classical approach to analyzing longitudinal data is to use (generalized) linear mixed-effect models (GLMM). However, linear parametric models are predicated on assumptions, which are often difficult to verify. In contrast, data-driven machine learning methods can be applied to derive insight from the raw data without a priori assumptions. However, the underlying theory of most machine learning algorithms assume that the data is independent and identically distributed, making them inefficient for longitudinal supervised learning. In this study, we formulate an analytic framework, which integrates the random-effects structure of GLMM into non-linear machine learning models capable of exploiting temporal heterogeneous effects, sparse and varying-length patient characteristics inherent in longitudinal data. We applied the derived mixed-effect machine learning (MEml) framework to predict longitudinal change in glycemic control measured by hemoglobin A1c (HbA1c) among well controlled adults with type 2 diabetes. Results show that MEml is competitive with traditional GLMM, but substantially outperformed standard machine learning models that do not account for random-effects. Specifically, the accuracy of MEml in predicting glycemic change at the 1st, 2nd, 3rd, and 4th clinical visits in advanced was 1.04, 1.08, 1.11, and 1.14 times that of the gradient boosted model respectively, with similar results for the other methods. To further demonstrate the general applicability of MEml, a series of experiments were performed using real publicly available and synthetic data sets for accuracy and robustness. These experiments reinforced the superiority of MEml over the other methods. Overall, results from this study highlight the importance of modeling random-effects in machine learning approaches based on longitudinal data. Our MEml method is highly resistant to correlated data, readily accounts for random-effects, and predicts change of a longitudinal clinical outcome in real-world clinical settings with high accuracy.
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Affiliation(s)
- Che Ngufor
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States.
| | - Holly Van Houten
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Nilay D Shah
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Rozalina G McCoy
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
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39
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Mejia AF, Nebel MB, Barber AD, Choe AS, Pekar JJ, Caffo BS, Lindquist MA. Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage. Neuroimage 2018; 172:478-491. [PMID: 29391241 PMCID: PMC5957759 DOI: 10.1016/j.neuroimage.2018.01.029] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [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: 10/09/2017] [Revised: 01/07/2018] [Accepted: 01/12/2018] [Indexed: 02/04/2023] Open
Abstract
Reliability of subject-level resting-state functional connectivity (FC) is determined in part by the statistical techniques employed in its estimation. Methods that pool information across subjects to inform estimation of subject-level effects (e.g., Bayesian approaches) have been shown to enhance reliability of subject-level FC. However, fully Bayesian approaches are computationally demanding, while empirical Bayesian approaches typically rely on using repeated measures to estimate the variance components in the model. Here, we avoid the need for repeated measures by proposing a novel measurement error model for FC describing the different sources of variance and error, which we use to perform empirical Bayes shrinkage of subject-level FC towards the group average. In addition, since the traditional intra-class correlation coefficient (ICC) is inappropriate for biased estimates, we propose a new reliability measure denoted the mean squared error intra-class correlation coefficient (ICCMSE) to properly assess the reliability of the resulting (biased) estimates. We apply the proposed techniques to test-retest resting-state fMRI data on 461 subjects from the Human Connectome Project to estimate connectivity between 100 regions identified through independent components analysis (ICA). We consider both correlation and partial correlation as the measure of FC and assess the benefit of shrinkage for each measure, as well as the effects of scan duration. We find that shrinkage estimates of subject-level FC exhibit substantially greater reliability than traditional estimates across various scan durations, even for the most reliable connections and regardless of connectivity measure. Additionally, we find partial correlation reliability to be highly sensitive to the choice of penalty term, and to be generally worse than that of full correlations except for certain connections and a narrow range of penalty values. This suggests that the penalty needs to be chosen carefully when using partial correlations.
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Affiliation(s)
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, USA; Department of Neurology, Johns Hopkins University, USA
| | - Anita D Barber
- Center for Psychiatric Neuroscience, Feinstein Institute for Medical Research, USA
| | - Ann S Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - James J Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, USA
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, USA
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Abstract
Reaction Time (RT) is associated with increased amplitude of the Blood Oxygen-Level Dependent (BOLD) response in task positive regions. Few studies have focused on whether opposing RT-related suppression of task activity also occurs. The current study used two Go/No-go tasks with different cognitive demands to examine regions that showed greater BOLD suppression for longer RT trials. These RT-related suppression effects occurred within the DMN and were task-specific, localizing to separate regions for the two tasks. In the task requiring working memory, RT-related de-coupling of the DMN occurred. This was reflected by opposing RT-BOLD effects for different DMN regions, as well as by reduced positive RT-related Psycho-Physiological Interaction (PPI) connectivity within the DMN and a lack of negative RT-related PPI connectivity between DMN and task positive regions. The results suggest that RT-related DMN suppression is task-specific. RT-related de-coupling of the DMN with more complex task demands may contribute to lapses of attention and performance decrements that occur during cognitively-demanding tasks.
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Affiliation(s)
- Anita D Barber
- Feinstein Institute for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA.
| | - Brian S Caffo
- Johns Hopkins School of Public Health, 615 North Wolfe Street, Baltimore, MD, 21205, USA
| | - James J Pekar
- Kennedy Krieger Institute, 707 N. Broadway, Baltimore, MD, 21205, USA
- Johns Hopkins School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA
| | - Stewart H Mostofsky
- Kennedy Krieger Institute, 707 N. Broadway, Baltimore, MD, 21205, USA
- Johns Hopkins School of Medicine, 733 N. Broadway, Baltimore, MD, 21205, USA
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Chen S, Huang L, Qiu H, Nebel MB, Mostofsky SH, Pekar JJ, Lindquist MA, Eloyan A, Caffo BS. Parallel group independent component analysis for massive fMRI data sets. PLoS One 2017; 12:e0173496. [PMID: 28278208 PMCID: PMC5344430 DOI: 10.1371/journal.pone.0173496] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [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: 09/06/2016] [Accepted: 02/21/2017] [Indexed: 11/19/2022] Open
Abstract
Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
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Affiliation(s)
- Shaojie Chen
- Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America
- * E-mail:
| | - Lei Huang
- Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America
| | - Huitong Qiu
- Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America
| | - Mary Beth Nebel
- Kennedy Krieger Institute, Baltimore, United States of America
- Department of Neurology, Johns Hopkins University, Baltimore, United States of America
| | - Stewart H. Mostofsky
- School of Medicine, Johns Hopkins University, Baltimore, United States of America
- Kennedy Krieger Institute, Baltimore, United States of America
| | - James J. Pekar
- Kennedy Krieger Institute, Baltimore, United States of America
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, United States of America
| | - Martin A. Lindquist
- Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America
| | - Ani Eloyan
- Department of Biostatistics, Brown University, Providence, Rhode Island, United States of America
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, United States of America
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Affiliation(s)
- Robert E. Kass
- Department of Statistics, Machine Learning Department, and Center for the Neural Basis of Cognition, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America
| | - Brian S. Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, United States of America
| | - Marie Davidian
- Department of Statistics, North Carolina State University, Raleigh, North Carolina, United States of America
| | - Xiao-Li Meng
- Department of Statistics, Harvard University, Cambridge, Massachusetts, United States of America
| | - Bin Yu
- Department of Statistics and Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, California, United States of America
| | - Nancy Reid
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
- * E-mail:
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Sharer E, Crocetti D, Muschelli J, Barber AD, Nebel MB, Caffo BS, Pekar JJ, Mostofsky SH. Neural Correlates of Visuomotor Learning in Autism. J Child Neurol 2015; 30:1877-86. [PMID: 26350725 PMCID: PMC4941625 DOI: 10.1177/0883073815600869] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [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: 07/10/2015] [Accepted: 07/13/2015] [Indexed: 11/17/2022]
Abstract
Motor impairments are prevalent in children with autism spectrum disorder. The Serial Reaction Time Task, a well-established visuomotor sequence learning probe, has produced inconsistent behavioral findings in individuals with autism. Moreover, it remains unclear how underlying neural processes for visuomotor learning in children with autism compare to processes for typically developing children. Neural activity differences were assessed using functional magnetic resonance imaging during a modified version of the Serial Reaction Time Task in children with and without autism. Though there was no group difference in visuomotor sequence learning, underlying patterns of neural activation significantly differed when comparing sequence (i.e., learning) to random (i.e., nonlearning) blocks. Children with autism demonstrated decreased activity in brain regions implicated in visuomotor sequence learning: superior temporal sulcus and posterior cingulate cortex. The findings implicate differences in brain mechanisms that support initial sequence learning in autism and can help explain behavioral observations of autism-associated impairments in skill development (motor, social, communicative) reliant on visuomotor integration.
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Affiliation(s)
- Elizabeth Sharer
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Deana Crocetti
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Anita D. Barber
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland,Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Mary Beth Nebel
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland,Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland
| | - Jim J. Pekar
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland,Department of Radiology, Johns Hopkins School of Medicine, Baltimore, Maryland,FM Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland
| | - Stewart H. Mostofsky
- Center for Neurodevelopmental and Imaging Research, Kennedy Krieger Institute, Baltimore, Maryland,Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, Maryland,Department of Neurology, Johns Hopkins School of Medicine, Baltimore, Maryland
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Hiruy H, Fuchs EJ, Marzinke MA, Bakshi RP, Breakey JC, Aung WS, Manohar M, Yue C, Caffo BS, Du Y, Abebe KZ, Spiegel HM, Rohan LC, McGowan I, Hendrix CW. A Phase 1 Randomized, Blinded Comparison of the Pharmacokinetics and Colonic Distribution of Three Candidate Rectal Microbicide Formulations of Tenofovir 1% Gel with Simulated Unprotected Sex (CHARM-02). AIDS Res Hum Retroviruses 2015; 31:1098-108. [PMID: 26227279 DOI: 10.1089/aid.2015.0098] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/25/2023] Open
Abstract
CHARM-02 is a crossover, double-blind, randomized trial to compare the safety and pharmacokinetics of three rectally applied tenofovir 1% gel candidate rectal microbicides of varying osmolalities: vaginal formulation (VF) (3111 mOsmol/kg), the reduced glycerin vaginal formulation (RGVF) (836 mOsmol/kg), and an isoosmolal rectal-specific formulation (RF) (479 mOsmol/kg). Participants (n = 9) received a single, 4 ml, radiolabeled dose of each gel twice, once with and once without simulated unprotected receptive anal intercourse (RAI). The safety, plasma tenofovir pharmacokinetics, colonic small molecule permeability, and SPECT/CT imaging of lower gastrointestinal distribution of drug and virus surrogate were assessed. There were no Grade 3 or 4 adverse events reported for any of the products. Overall, there were more Grade 2 adverse events in the VF group compared to RF (p = 0.006) and RGVF (p = 0.048). In the absence of simulated unprotected RAI, VF had up to 3.8-fold greater systemic tenofovir exposure, 26- to 234-fold higher colonic permeability of the drug surrogate, and 1.5- to 2-fold greater proximal migration in the colonic lumen, when compared to RF and RGVF. Similar trends were observed with simulated unprotected RAI, but most did not reach statistical significance. SPECT analysis showed 86% (standard deviation 19%) of the drug surrogate colocalized with the virus surrogate in the colonic lumen. There were no significant differences between the RGVF and RF formulation, with the exception of a higher plasma tenofovir concentration of RGVF in the absence of simulated unprotected RAI. VF had the most adverse events, highest plasma tenofovir concentrations, greater mucosal permeability of the drug surrogate, and most proximal colonic luminal migration compared to RF and RGVF formulations. There were no major differences between RF and RGVF formulations. Simultaneous assessment of toxicity, systemic and luminal pharmacokinetics, and colocalization of drug and viral surrogates substantially informs rectal microbicide product development.
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Affiliation(s)
- Hiwot Hiruy
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Edward J. Fuchs
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Mark A. Marzinke
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Rahul P. Bakshi
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Jennifer C. Breakey
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Wutyi S. Aung
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Madhuri Manohar
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
| | - Chen Yue
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
| | - Brian S. Caffo
- Department of Biostatistics, Johns Hopkins School of Public Health, Baltimore, Maryland
| | - Yong Du
- Department of Radiology, Johns Hopkins School of Medicine, Baltimore, Maryland
| | - Kaleab Z. Abebe
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Hans M.L. Spiegel
- HJF-DAIDS, a Division of The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc., Contractor to National Institute of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland
| | - Lisa C. Rohan
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Ian McGowan
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania
- Magee-Womens Research Institute, Pittsburgh, Pennsylvania
| | - Craig W. Hendrix
- Department of Medicine (Clinical Pharmacology), Johns Hopkins University, Baltimore, Maryland
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Choe AS, Jones CK, Joel SE, Muschelli J, Belegu V, Caffo BS, Lindquist MA, van Zijl PCM, Pekar JJ. Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years. PLoS One 2015; 10:e0140134. [PMID: 26517540 PMCID: PMC4627782 DOI: 10.1371/journal.pone.0140134] [Citation(s) in RCA: 83] [Impact Index Per Article: 9.2] [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: 06/19/2015] [Accepted: 09/22/2015] [Indexed: 11/18/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) permits study of the brain’s functional networks without requiring participants to perform tasks. Robust changes in such resting state networks (RSNs) have been observed in neurologic disorders, and rs-fMRI outcome measures are candidate biomarkers for monitoring clinical trials, including trials of extended therapeutic interventions for rehabilitation of patients with chronic conditions. In this study, we aim to present a unique longitudinal dataset reporting on a healthy adult subject scanned weekly over 3.5 years and identify rs-fMRI outcome measures appropriate for clinical trials. Accordingly, we assessed the reproducibility, and characterized the temporal structure of, rs-fMRI outcome measures derived using independent component analysis (ICA). Data was compared to a 21-person dataset acquired on the same scanner in order to confirm that the values of the single-subject RSN measures were within the expected range as assessed from the multi-participant dataset. Fourteen RSNs were identified, and the inter-session reproducibility of outcome measures—network spatial map, temporal signal fluctuation magnitude, and between-network connectivity (BNC)–was high, with executive RSNs showing the highest reproducibility. Analysis of the weekly outcome measures also showed that many rs-fMRI outcome measures had a significant linear trend, annual periodicity, and persistence. Such temporal structure was most prominent in spatial map similarity, and least prominent in BNC. High reproducibility supports the candidacy of rs-fMRI outcome measures as biomarkers, but the presence of significant temporal structure needs to be taken into account when such outcome measures are considered as biomarkers for rehabilitation-style therapeutic interventions in chronic conditions.
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Affiliation(s)
- Ann S. Choe
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States of America
- * E-mail:
| | - Craig K. Jones
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - Suresh E. Joel
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Visar Belegu
- International Center for Spinal Cord Injury, Kennedy Krieger Institute, Baltimore, MD, United States of America
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
| | - Brian S. Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Martin A. Lindquist
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States of America
| | - Peter C. M. van Zijl
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
| | - James J. Pekar
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States of America
- F. M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States of America
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Yue C, Chen S, Sair HI, Airan R, Caffo BS. Estimating a graphical intra-class correlation coefficient (GICC) using multivariate probit-linear mixed models. Comput Stat Data Anal 2015; 89:126-133. [PMID: 26190878 DOI: 10.1016/j.csda.2015.02.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Data reproducibility is a critical issue in all scientific experiments. In this manuscript, the problem of quantifying the reproducibility of graphical measurements is considered. The image intra-class correlation coefficient (I2C2) is generalized and the graphical intra-class correlation coefficient (GICC) is proposed for such purpose. The concept for GICC is based on multivariate probit-linear mixed effect models. A Markov Chain Monte Carlo EM (mcm-cEM) algorithm is used for estimating the GICC. Simulation results with varied settings are demonstrated and our method is applied to the KIRBY21 test-retest dataset.
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Affiliation(s)
- Chen Yue
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205
| | - Shaojie Chen
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205
| | - Haris I Sair
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21205
| | - Raag Airan
- Department of Radiology and Radiological Science, Johns Hopkins University, Baltimore, MD 21205
| | - Brian S Caffo
- Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA, 21205
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Hannawi Y, Lindquist MA, Caffo BS, Sair HI, Stevens RD. Resting brain activity in disorders of consciousness: a systematic review and meta-analysis. Neurology 2015; 84:1272-80. [PMID: 25713001 DOI: 10.1212/wnl.0000000000001404] [Citation(s) in RCA: 98] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
OBJECTIVE To quantitatively synthesize results from neuroimaging studies that evaluated patterns of resting functional activity in patients with disorders of consciousness (DOC). METHODS We performed a systematic review and coordinate-based meta-analysis of studies published up to May 2014. Studies were included if they compared resting-state functional neuroimaging data acquired in patients with DOC (coma, minimally conscious state, emergence from minimally conscious state, or vegetative state) with a group of healthy controls. Coordinate-based meta-analysis was performed in studies that included voxel-based comparisons at the whole-brain level and if analysis was accomplished with data-driven approaches. RESULTS A total of 36 studies (687 patients, 637 healthy controls) were included in the systematic review. Reported DOC were vegetative state (43.2%), coma (23.4%), minimally conscious state (22.8%), and emergence from minimally conscious state (1.6%); the most common etiologies of DOC were traumatic brain injury (37.7%) and anoxic brain injury (36.9%). Functional neuroimaging was accomplished using fMRI (16 studies), PET (15 studies), SPECT (4 studies), and both PET and SPECT in one study. Meta-analysis in 13 studies (272 patients, 259 healthy controls) revealed consistently reduced activity in patients with DOC in bilateral medial dorsal nucleus of the thalamus, left cingulate, posterior cingulate, precuneus, and middle frontal and medial temporal gyri. CONCLUSIONS In patients with DOC evaluated in the resting state, functional neuroimaging indicates markedly reduced activity within midline cortical and subcortical sites, anatomical structures that have been linked to the default-mode network. Studies are needed to determine the relation between activation (and coherence) within these structures and the emergence of conscious awareness.
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Affiliation(s)
- Yousef Hannawi
- From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD.
| | - Martin A Lindquist
- From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Brian S Caffo
- From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Haris I Sair
- From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
| | - Robert D Stevens
- From the Departments of Anesthesiology and Critical Care Medicine (Y.H., R.D.S.), Neurology (Y.H., R.D.S.), Radiology (H.I.S., R.D.S.), and Neurosurgery (R.D.S.), Johns Hopkins University School of Medicine, Baltimore; and Department of Biostatistics (M.A.L., B.S.C.), Johns Hopkins Bloomberg School of Public Health, Baltimore, MD
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Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion-related artifacts in resting state fMRI using aCompCor. Neuroimage 2014; 96:22-35. [PMID: 24657780 PMCID: PMC4043948 DOI: 10.1016/j.neuroimage.2014.03.028] [Citation(s) in RCA: 267] [Impact Index Per Article: 26.7] [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: 10/12/2013] [Revised: 02/28/2014] [Accepted: 03/10/2014] [Indexed: 11/17/2022] Open
Abstract
Recent studies have illustrated that motion-related artifacts remain in resting-state fMRI (rs-fMRI) data even after common corrective processing procedures have been applied, but the extent to which head motion distorts the data may be modulated by the corrective approach taken. We compare two different methods for estimating nuisance signals from tissues not expected to exhibit BOLD fMRI signals of neuronal origin: 1) the more commonly used mean signal method and 2) the principal components analysis approach (aCompCor: Behzadi et al., 2007). Further, we investigate the added benefit of "scrubbing" (Power et al., 2012) following both methods. We demonstrate that the use of aCompCor removes motion artifacts more effectively than tissue-mean signal regression. In addition, inclusion of more components from anatomically defined regions of no interest better mitigates motion-related artifacts and improves the specificity of functional connectivity estimates. While scrubbing further attenuates motion-related artifacts when mean signals are used, scrubbing provides no additional benefit in terms of motion artifact reduction or connectivity specificity when using aCompCor.
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Affiliation(s)
- John Muschelli
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Mary Beth Nebel
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA.
| | - Brian S Caffo
- Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 N. Wolfe Street Baltimore, MD 21205, USA
| | - Anita D Barber
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
| | - James J Pekar
- The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, 601 North Caroline Street Baltimore, MD 21287, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, 707 North Broadway Baltimore, MD 21205, USA
| | - Stewart H Mostofsky
- Laboratory for Neurocognitive and Imaging Research, Kennedy Krieger Institute, 716 North Broadway Baltimore, MD 21205, USA; Department of Neurology, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA; Department of Psychiatry, Johns Hopkins University School of Medicine, 1800 Orleans Street Baltimore, MD 21287, USA
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Schreiber JM, Lanham DC, Trescher WH, Sparks SE, Wassif CA, Caffo BS, Porter FD, Tierney E, Gropman AL, Ewen JB. Variations in EEG discharges predict ADHD severity within individual Smith-Lemli-Opitz patients. Neurology 2014; 83:151-9. [PMID: 24920862 DOI: 10.1212/wnl.0000000000000565] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
OBJECTIVE We sought to examine the prevalence of EEG abnormalities in Smith-Lemli-Opitz syndrome (SLOS) as well as the relationship between interictal epileptiform discharges (IEDs) and within-subject variations in attentional symptom severity. METHODS In the context of a clinical trial for SLOS, we performed cross-sectional and repeated-measure observational studies of the relationship between EEG findings and cognitive/behavioral factors on 23 children (aged 4-17 years). EEGs were reviewed for clinical abnormalities, including IEDs, by readers blinded to participants' behavioral symptoms. Between-group differences in baseline characteristics of participants with and without IEDs were analyzed. Within-subject analyses examined the association between the presence of IEDs and changes in attention-deficit/hyperactivity disorder (ADHD) symptoms. RESULTS Of 85 EEGs, 43 (51%) were abnormal, predominantly because of IEDs. Only one subject had documented clinical seizures. IEDs clustered in 13 subjects (57%), whereas 9 subjects (39%) had EEGs consistently free of IEDs. While there were no significant group differences in sex, age, intellectual disability, language level, or baseline ADHD symptoms, autistic symptoms tended to be more prevalent in the "IED" group (according to Autism Diagnostic Observation Schedule-2 criteria). Within individuals, the presence of IEDs on a particular EEG predicted, on average, a 27% increase in ADHD symptom severity. CONCLUSIONS Epileptiform discharges are common in SLOS, despite a relatively low prevalence of epilepsy. Fluctuations in the presence of epileptiform discharges within individual children with a developmental disability syndrome may be associated with fluctuations in ADHD symptomatology, even in the absence of clinical seizures.
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Affiliation(s)
- John M Schreiber
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Diane C Lanham
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - William H Trescher
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Susan E Sparks
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Christopher A Wassif
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Brian S Caffo
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Forbes D Porter
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Elaine Tierney
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Andrea L Gropman
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC
| | - Joshua B Ewen
- From the EEG Section, NINDS (J.M.S.), Medical Genetics Branch, National Human Genome Research Institute (S.E.S., A.L.G.), and Program on Developmental Endocrinology and Genetics, NICHD (C.A.W.), NIH, Bethesda; Departments of Child and Adolescent Psychiatry (D.C.L., E.T.) and Neurology and Developmental Medicine (W.H.T., J.B.E.), Kennedy Krieger Institute, Baltimore; Departments of Neurology (W.H.T., J.B.E.) and Psychiatry and Behavioral Sciences (E.T.), Johns Hopkins University School of Medicine, Baltimore, MD; Department of Pediatric Neurology (W.H.T., F.D.P.), Penn State Hershey Children's Hospital, Hershey, PA; Department of Biostatistics (B.S.C.), Johns Hopkins University School of Public Health, Baltimore, MD; Department of Neurology (A.L.G.), Children's National Medical Center; and George Washington University of the Health Sciences (A.L.G.), Washington, DC.
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Thom KA, Li S, Custer M, Preas MA, Rew CD, Cafeo C, Leekha S, Caffo BS, Scalea TM, Lissauer ME. Successful implementation of a unit-based quality nurse to reduce central line-associated bloodstream infections. Am J Infect Control 2014; 42:139-43. [PMID: 24360354 DOI: 10.1016/j.ajic.2013.08.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.3] [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: 05/31/2013] [Revised: 08/19/2013] [Accepted: 08/20/2013] [Indexed: 01/12/2023]
Abstract
BACKGROUND Central line (CL)-associated bloodstream infections (CLABSI) are an important cause of patient morbidity and mortality. Novel strategies to prevent CLABSI are needed. METHODS We described a quasiexperimental study to examine the effect of the presence of a unit-based quality nurse (UQN) dedicated to perform patient safety and infection control activities with a focus on CLABSI prevention in a surgical intensive care unit (SICU). RESULTS From July 2008 to March 2012, there were 3,257 SICU admissions; CL utilization ratio was 0.74 (18,193 CL-days/24,576 patient-days). The UQN program began in July 2010; the nurse was present for 30% (193/518) of the days of the intervention period of July 2010 to March 2012. The average CLABSI rate was 5.0 per 1,000 CL-days before the intervention and 1.5 after the intervention and decreased by 5.1% (P = .005) for each additional 1% of days of the month that the UQN was present, even after adjusting for CLABSI rates in other adult intensive care units, time, severity of illness, and Comprehensive Unit-based Safety Program participation (5.1%, P = .004). Approximately 11.4 CLABSIs were prevented. CONCLUSION The presence of a UQN dedicated to perform infection control activities may be an effective strategy for CLABSI reduction.
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Affiliation(s)
- Kerri A Thom
- University of Maryland School of Medicine, Baltimore, MD.
| | - Shanshan Li
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | | | | | - Cindy D Rew
- University of Maryland Medical Center, Baltimore, MD
| | | | - Surbhi Leekha
- University of Maryland School of Medicine, Baltimore, MD
| | - Brian S Caffo
- Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD
| | - Thomas M Scalea
- University of Maryland School of Medicine, Program in Trauma, Baltimore, MD
| | - Matthew E Lissauer
- University of Maryland School of Medicine, Program in Trauma, Baltimore, MD
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