1
|
Zhao Y. Mediation Analysis with Multiple Exposures and Multiple Mediators. Stat Med 2024. [PMID: 39250913 DOI: 10.1002/sim.10215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/25/2024] [Accepted: 08/23/2024] [Indexed: 09/11/2024]
Abstract
A mediation analysis approach is proposed for multiple exposures, multiple mediators, and a continuous scalar outcome under the linear structural equation modeling framework. It assumes that there exist orthogonal components that demonstrate parallel mediation mechanisms on the outcome, and thus is named principal component mediation analysis (PCMA). Likelihood-based estimators are introduced for simultaneous estimation of the component projections and effect parameters. The asymptotic distribution of the estimators is derived for low-dimensional data. A bootstrap procedure is introduced for inference. Simulation studies illustrate the superior performance of the proposed approach. Applied to a proteomics-imaging dataset from the Alzheimer's disease neuroimaging initiative (ADNI), the proposed framework identifies protein deposition - brain atrophy - memory deficit mechanisms consistent with existing knowledge and suggests potential AD pathology by integrating data collected from different modalities.
Collapse
Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana
| |
Collapse
|
2
|
Si Y, Bandoli G, Cole KM, Daniele Fallin M, Stuart EA, Gurka KK, Althoff KN, Thompson WK. Advancing high quality longitudinal data collection: Implications for the HEALthy Brain and Child Development (HBCD) Study design and recruitment. Dev Cogn Neurosci 2024; 69:101432. [PMID: 39213717 PMCID: PMC11402112 DOI: 10.1016/j.dcn.2024.101432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 08/05/2024] [Accepted: 08/06/2024] [Indexed: 09/04/2024] Open
Abstract
The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The HBCD Study aims to reflect the sociodemographic diversity of pregnant individuals in the U.S. The study will also oversample individuals who use substances during pregnancy and enroll similar individuals who do not use to allow for generalizable inferences of the impact of prenatal substance use on trajectories of child development. Without probability sampling or a randomization-based design, the study requires innovation during enrollment, close monitoring of group differences, and rigorous evaluation of external and internal validity across the enrollment period. In this article, we discuss the HBCD Study recruitment and enrollment data collection processes and potential analytic strategies to account for sources of heterogeneity and potential bias. First, we introduce the adaptive design and enrollment monitoring indices to assess and enhance external and internal validity. Second, we describe the visit schedule for in-person and remote data collection where dyads are randomly assigned to visit windows based on a jittered design to optimize longitudinal trajectory estimation. Lastly, we provide an overview of analytic procedures planned for estimating trajectories.
Collapse
Affiliation(s)
- Yajuan Si
- Survey Research Center, Institute for Social Research, University of Michigan, Ann Arbor, United States.
| | - Gretchen Bandoli
- Department of Pediatrics, University of California San Diego, United States
| | - Katherine M Cole
- Division of Extramural Research, the National Institute on Drug Abuse, United States
| | - M Daniele Fallin
- Rollins School of Public Health, Emory University, United States
| | - Elizabeth A Stuart
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, United States
| | - Kelly K Gurka
- Department of Epidemiology, University of Florida, United States
| | - Keri N Althoff
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, United States
| | | |
Collapse
|
3
|
Chen M, Zhou Y. Causal mediation analysis with a three-dimensional image mediator. Stat Med 2024; 43:2869-2893. [PMID: 38733218 DOI: 10.1002/sim.10106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Revised: 03/20/2024] [Accepted: 04/29/2024] [Indexed: 05/13/2024]
Abstract
Causal mediation analysis is increasingly abundant in biology, psychology, and epidemiology studies and so forth. In particular, with the advent of the big data era, the issue of high-dimensional mediators is becoming more prevalent. In neuroscience, with the widespread application of magnetic resonance technology in the field of brain imaging, studies on image being a mediator emerged. In this study, a novel causal mediation analysis method with a three-dimensional image mediator is proposed. We define the average casual effects under the potential outcome framework, explore several sufficient conditions for the valid identification, and develop techniques for estimation and inference. To verify the effectiveness of the proposed method, a series of simulations under various scenarios is performed. Finally, the proposed method is applied to a study on the causal effect of mother's delivery mode on child's IQ development. It is found that cesarean section may have a negative effect on intellectual performance and that this effect is mediated by white matter development. Additional prospective and longitudinal studies may be necessary to validate these emerging findings.
Collapse
Affiliation(s)
- Minghao Chen
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
| | - Yingchun Zhou
- Key Laboratory of Advanced Theory and Application in Statistics and Data Science-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China
- Institute of Brain and Education Innovation, East China Normal University, Shanghai, People's Republic of China
| |
Collapse
|
4
|
Tang TS, Liu Z, Hosni A, Kim J, Saarela O. A marginal structural model for normal tissue complication probability. Biostatistics 2024:kxae019. [PMID: 38981039 DOI: 10.1093/biostatistics/kxae019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 05/23/2024] [Accepted: 05/28/2024] [Indexed: 07/11/2024] Open
Abstract
The goal of radiation therapy for cancer is to deliver prescribed radiation dose to the tumor while minimizing dose to the surrounding healthy tissues. To evaluate treatment plans, the dose distribution to healthy organs is commonly summarized as dose-volume histograms (DVHs). Normal tissue complication probability (NTCP) modeling has centered around making patient-level risk predictions with features extracted from the DVHs, but few have considered adapting a causal framework to evaluate the safety of alternative treatment plans. We propose causal estimands for NTCP based on deterministic and stochastic interventions, as well as propose estimators based on marginal structural models that impose bivariable monotonicity between dose, volume, and toxicity risk. The properties of these estimators are studied through simulations, and their use is illustrated in the context of radiotherapy treatment of anal canal cancer patients.
Collapse
Affiliation(s)
- Thai-Son Tang
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
| | - Zhihui Liu
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
- Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada
| | - Ali Hosni
- Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada
| | - John Kim
- Princess Margaret Cancer Centre, University Health Network, 610 University Avenue, Toronto, Ontario M5G 2M9, Canada
- Department of Radiation Oncology, University of Toronto, 149 College Street, Toronto, Ontario M5T 1P5, Canada
| | - Olli Saarela
- Dalla Lana School of Public Health, University of Toronto, 155 College Street, Toronto, Ontario M5T 3M7, Canada
| |
Collapse
|
5
|
Dai R, Li R, Lee S, Liu Y. Controlling false discovery rate for mediator selection in high-dimensional data. Biometrics 2024; 80:ujae064. [PMID: 39073774 DOI: 10.1093/biomtc/ujae064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Revised: 03/08/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024]
Abstract
The need to select mediators from a high dimensional data source, such as neuroimaging data and genetic data, arises in much scientific research. In this work, we formulate a multiple-hypothesis testing framework for mediator selection from a high-dimensional candidate set, and propose a method, which extends the recent development in false discovery rate (FDR)-controlled variable selection with knockoff to select mediators with FDR control. We show that the proposed method and algorithm achieved finite sample FDR control. We present extensive simulation results to demonstrate the power and finite sample performance compared with the existing method. Lastly, we demonstrate the method for analyzing the Adolescent Brain Cognitive Development (ABCD) study, in which the proposed method selects several resting-state functional magnetic resonance imaging connectivity markers as mediators for the relationship between adverse childhood events and the crystallized composite score in the NIH toolbox.
Collapse
Affiliation(s)
- Ran Dai
- Department of Biostatistics, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Ruiyang Li
- Department of Biostatistics, Columbia University, New York , NY 10032, United States
| | - Seonjoo Lee
- Department of Psychiatry, Columbia University, New York, NY 10032, United States
| | - Ying Liu
- Department of Psychiatry, Columbia University, New York, NY 10032, United States
| |
Collapse
|
6
|
Lopez Naranjo C, Razzaq FA, Li M, Wang Y, Bosch‐Bayard JF, Lindquist MA, Gonzalez Mitjans A, Garcia R, Rabinowitz AG, Anderson SG, Chiarenza GA, Calzada‐Reyes A, Virues‐Alba T, Galler JR, Minati L, Bringas Vega ML, Valdes‐Sosa PA. EEG functional connectivity as a Riemannian mediator: An application to malnutrition and cognition. Hum Brain Mapp 2024; 45:e26698. [PMID: 38726908 PMCID: PMC11082925 DOI: 10.1002/hbm.26698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 04/05/2024] [Accepted: 04/12/2024] [Indexed: 05/13/2024] Open
Abstract
Mediation analysis assesses whether an exposure directly produces changes in cognitive behavior or is influenced by intermediate "mediators". Electroencephalographic (EEG) spectral measurements have been previously used as effective mediators representing diverse aspects of brain function. However, it has been necessary to collapse EEG measures onto a single scalar using standard mediation methods. In this article, we overcome this limitation and examine EEG frequency-resolved functional connectivity measures as a mediator using the full EEG cross-spectral tensor (CST). Since CST samples do not exist in Euclidean space but in the Riemannian manifold of positive-definite tensors, we transform the problem, allowing for the use of classic multivariate statistics. Toward this end, we map the data from the original manifold space to the Euclidean tangent space, eliminating redundant information to conform to a "compressed CST." The resulting object is a matrix with rows corresponding to frequencies and columns to cross spectra between channels. We have developed a novel matrix mediation approach that leverages a nuclear norm regularization to determine the matrix-valued regression parameters. Furthermore, we introduced a global test for the overall CST mediation and a test to determine specific channels and frequencies driving the mediation. We validated the method through simulations and applied it to our well-studied 50+-year Barbados Nutrition Study dataset by comparing EEGs collected in school-age children (5-11 years) who were malnourished in the first year of life with those of healthy classmate controls. We hypothesized that the CST mediates the effect of malnutrition on cognitive performance. We can now explicitly pinpoint the frequencies (delta, theta, alpha, and beta bands) and regions (frontal, central, and occipital) in which functional connectivity was altered in previously malnourished children, an improvement to prior studies. Understanding the specific networks impacted by a history of postnatal malnutrition could pave the way for developing more targeted and personalized therapeutic interventions. Our methods offer a versatile framework applicable to mediation studies encompassing matrix and Hermitian 3D tensor mediators alongside scalar exposures and outcomes, facilitating comprehensive analyses across diverse research domains.
Collapse
Affiliation(s)
- Carlos Lopez Naranjo
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Fuleah Abdul Razzaq
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Min Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Hangzhou Dianzi UniversityZhejiangHangzhouChina
| | - Ying Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | | | - Anisleidy Gonzalez Mitjans
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Montreal Neurological Institute‐HospitalMcGill UniversityMontrealQuebecCanada
| | - Ronaldo Garcia
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Simon G. Anderson
- The George Alleyne Chronic Disease Research Centre, Caribbean Institute for Health ResearchUniversity of the West IndiesCave HillBarbados
| | - Giuseppe A. Chiarenza
- Centro Internazionale Disturbi di Apprendimento, Attenzione, Iperattività (CIDAAI)MilanItaly
| | | | | | - Janina R. Galler
- Division of Pediatric Gastroenterology and NutritionMassachusetts General Hospital for ChildrenBostonMassachusettsUSA
| | - Ludovico Minati
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Center for Mind/Brain Science (CIMeC)University of TrentoTrentoItaly
| | - Maria L. Bringas Vega
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
| | - Pedro A. Valdes‐Sosa
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
- Cuban Center for NeuroscienceLa HabanaCuba
| |
Collapse
|
7
|
Coffman C, Feczko E, Larsen B, Tervo-Clemmens B, Conan G, Lundquist JT, Houghton A, Moore LA, Weldon K, McCollum R, Perrone AJ, Fayzullobekova B, Madison TJ, Earl E, Dominguez OM, Fair DA, Basu S. Heritability estimation of subcortical volumes in a multi-ethnic multi-site cohort study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.11.575231. [PMID: 38260520 PMCID: PMC10802572 DOI: 10.1101/2024.01.11.575231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Heritability of regional subcortical brain volumes (rSBVs) describes the role of genetics in middle and inner brain development. rSBVs are highly heritable in adults but are not characterized well in adolescents. The Adolescent Brain Cognitive Development study (ABCD), taken over 22 US sites, provides data to characterize the heritability of subcortical structures in adolescence. In ABCD, site-specific effects co-occur with genetic effects which can bias heritability estimates. Existing methods adjusting for site effects require additional steps to adjust for site effects and can lead to inconsistent estimation. We propose a random-effect model-based method of moments approach that is a single step estimator and is a theoretically consistent estimator even when sites are imbalanced and performs well under simulations. We compare methods on rSBVs from ABCD. The proposed approach yielded heritability estimates similar to previous results derived from single-site studies. The cerebellum cortex and hippocampus were the most heritable regions (> 50%).
Collapse
Affiliation(s)
- Christian Coffman
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
| | - Eric Feczko
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Bart Larsen
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Brenden Tervo-Clemmens
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Gregory Conan
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Jacob T. Lundquist
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Audrey Houghton
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Lucille A. Moore
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Kimberly Weldon
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Rae McCollum
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Anders J. Perrone
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Begim Fayzullobekova
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Thomas J. Madison
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Eric Earl
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Oscar Miranda Dominguez
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Damien A. Fair
- Department of Pediatrics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| | - Saonli Basu
- Division of Biostatistics, University of Minnesota, 100 Church Street SE, Minneapolis, 55455-0213, MN, USA
- Masonic Institue for the Devloping Brain, University of Minnesota, 2025 East River Parkway, Minneapolis, 55414, MN, USA
| |
Collapse
|
8
|
Zhou F, He K, Wang K, Xu Y, Ni Y. Functional Bayesian networks for discovering causality from multivariate functional data. Biometrics 2023; 79:3279-3293. [PMID: 37635676 PMCID: PMC10840881 DOI: 10.1111/biom.13922] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2022] [Accepted: 08/10/2023] [Indexed: 08/29/2023]
Abstract
Multivariate functional data arise in a wide range of applications. One fundamental task is to understand the causal relationships among these functional objects of interest. In this paper, we develop a novel Bayesian network (BN) model for multivariate functional data where conditional independencies and causal structure are encoded by a directed acyclic graph. Specifically, we allow the functional objects to deviate from Gaussian processes, which is the key to unique causal structure identification even when the functions are measured with noises. A fully Bayesian framework is designed to infer the functional BN model with natural uncertainty quantification through posterior summaries. Simulation studies and real data examples demonstrate the practical utility of the proposed model.
Collapse
Affiliation(s)
- Fangting Zhou
- Department of Statistics, Texas A&M University, College Station, Texas, USA
- Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Kejun He
- Center for Applied Statistics, Institute of Statistics and Big Data, Renmin University of China, Beijing, China
| | - Kunbo Wang
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yanxun Xu
- Department of Applied Mathematics and Statistics, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yang Ni
- Department of Statistics, Texas A&M University, College Station, Texas, USA
| |
Collapse
|
9
|
Jiang S, Colditz GA. Causal mediation analysis using high-dimensional image mediator bounded in irregular domain with an application to breast cancer. Biometrics 2023; 79:3728-3738. [PMID: 36853975 PMCID: PMC10460830 DOI: 10.1111/biom.13847] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 02/16/2023] [Indexed: 03/02/2023]
Abstract
Mammography is the primary breast cancer screening strategy. Recent methods have been developed using the mammogram image to improve breast cancer risk prediction. However, it is unclear on the extent to which the effect of risk factors on breast cancer risk is mediated through tissue features summarized in mammogram images and the extent to which it is through other pathways. While mediation analysis has been conducted using mammographic density (a summary measure within the image), the mammogram image is not necessarily well described by a single summary measure and, in addition, such a measure provides no spatial information about the relationship between the exposure risk factor and the risk of breast cancer. Thus, to better understand the role of the mammogram images that provide spatial information about the state of the breast tissue that is causally predictive of the future occurrence of breast cancer, we propose a novel method of causal mediation analysis using mammogram image mediator while accommodating the irregular shape of the breast. We apply the proposed method to data from the Joanne Knight Breast Health Cohort and leverage new insights on the decomposition of the total association between risk factor and breast cancer risk that was mediated by the texture of the underlying breast tissue summarized in the mammogram image.
Collapse
Affiliation(s)
- Shu Jiang
- Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| | - Graham A Colditz
- Division of Public Health Sciences, Washington University School of Medicine in St. Louis, St. Louis, Missouri, USA
| |
Collapse
|
10
|
Saha E, Ghosal R. Gender difference in the effects of chronic diseases on daily physical activity patterns in older adults: analysis of objectively measured physical activity in NHATS 2021. Ann Epidemiol 2023; 86:110-118.e4. [PMID: 37625499 DOI: 10.1016/j.annepidem.2023.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Revised: 08/14/2023] [Accepted: 08/16/2023] [Indexed: 08/27/2023]
Abstract
PURPOSE Many chronic diseases have detrimental impact on the physical activity (PA) patterns of older adults. Often such diseases have different degrees of severity in males and females. Quantifying this gender difference would not only enhance our understanding of diseases but would also help design individual-specific PA interventions, thereby improving health outcomes for both genders. METHODS PA data for 747 participants from round 11 (2021) of the National Health and Aging Trends Study were analyzed. Multilevel functional regression models were used to study gender difference in the effects of chronic diseases on daily PA patterns while adjusting for confounders. RESULTS Females with dementia (or Alzheimer's disease), hypertension, heart and lung disease had lower PA at different times of day compared to females without these diseases, whereas males with and without these diseases had comparable daily PA. Males with diabetes had higher midnight PA and lower noon PA compared to males without diabetes, while females' PA with and without diabetes were similar. CONCLUSIONS Our analysis demonstrates that although for most diseases, the daily PA patterns of individuals with the disease are negatively altered compared to healthy individuals, the extent of decline varies by gender and time of day. Designing personalized physical activity interventions considering gender and diurnal PA pattern can potentially improve quality of life across both genders.
Collapse
Affiliation(s)
- Enakshi Saha
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA.
| | - Rahul Ghosal
- Department of Epidemiology and Biostatistics, University of South Carolina, Columbia
| |
Collapse
|
11
|
Zhao Z, Chen C, Mani Adhikari B, Hong LE, Kochunov P, Chen S. Mediation Analysis for High-Dimensional Mediators and Outcomes with an Application to Multimodal Imaging Data. Comput Stat Data Anal 2023; 185:107765. [PMID: 37251499 PMCID: PMC10210585 DOI: 10.1016/j.csda.2023.107765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/31/2023]
Abstract
Multimodal neuroimaging data have attracted increasing attention for brain research. An integrated analysis of multimodal neuroimaging data and behavioral or clinical measurements provides a promising approach for comprehensively and systematically investigating the underlying neural mechanisms of different phenotypes. However, such an integrated data analysis is intrinsically challenging due to the complex interactive relationships between the multimodal multivariate imaging variables. To address this challenge, a novel multivariate-mediator and multivariate-outcome mediation model (MMO) is proposed to simultaneously extract the latent systematic mediation patterns and estimate the mediation effects based on a dense bi-cluster graph approach. A computationally efficient algorithm is developed for dense bicluster structure estimation and inference to identify the mediation patterns with multiple testing correction. The performance of the proposed method is evaluated by an extensive simulation analysis with comparison to the existing methods. The results show that MMO performs better in terms of both the false discovery rate and sensitivity compared to existing models. The MMO is applied to a multimodal imaging dataset from the Human Connectome Project to investigate the effect of systolic blood pressure on whole-brain imaging measures for the regional homogeneity of the blood oxygenation level-dependent signal through the cerebral blood flow.
Collapse
Affiliation(s)
- Zhiwei Zhao
- Department of Mathematics, University of Maryland, 4176 Campus Drive, CollegePark, 20742, MD, USA
| | - Chixiang Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and PublicHealth, University of Maryland School of Medicine, 655 W. Baltimore, Street, Baltimore, 21201, MD, USA
| | - Bhim Mani Adhikari
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - L. Elliot Hong
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - Peter Kochunov
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| | - Shuo Chen
- Division of Biostatistics and Bioinformatics, Department of Epidemiology and PublicHealth, University of Maryland School of Medicine, 655 W. Baltimore, Street, Baltimore, 21201, MD, USA
- Maryland Psychiatric Research Center, Department of Psychiatry, University ofMaryland School of Medicine, 655 W. Baltimore Street, Baltimore, 21201, MD, USA
| |
Collapse
|
12
|
Coffman DL, Dziak JJ, Litson K, Chakraborti Y, Piper ME, Li R. A Causal Approach to Functional Mediation Analysis with Application to a Smoking Cessation Intervention. MULTIVARIATE BEHAVIORAL RESEARCH 2023; 58:859-876. [PMID: 36622859 PMCID: PMC10966971 DOI: 10.1080/00273171.2022.2149449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
The increase in the use of mobile and wearable devices now allows dense assessment of mediating processes over time. For example, a pharmacological intervention may have an effect on smoking cessation via reductions in momentary withdrawal symptoms. We define and identify the causal direct and indirect effects in terms of potential outcomes on the mean difference and odds ratio scales, and present a method for estimating and testing the indirect effect of a randomized treatment on a distal binary variable as mediated by the nonparametric trajectory of an intensively measured longitudinal variable (e.g., from ecological momentary assessment). Coverage of a bootstrap test for the indirect effect is demonstrated via simulation. An empirical example is presented based on estimating later smoking abstinence from patterns of craving during smoking cessation treatment. We provide an R package, funmediation, available on CRAN at https://cran.r-project.org/web/packages/funmediation/index.html, to conveniently apply this technique. We conclude by discussing possible extensions to multiple mediators and directions for future research.
Collapse
Affiliation(s)
- Donna L Coffman
- Department of Epidemiology and Biostatistics, Temple University
| | - John J Dziak
- Edna Bennett Pierce Prevention Research Center, The Pennsylvania State University
| | - Kaylee Litson
- Instructional Technology & Learning Sciences Department, Utah State University
| | | | - Megan E Piper
- Center for Tobacco Research Intervention, University of Wisconsin
| | - Runze Li
- Department of Statistics, The Pennsylvania State University
| |
Collapse
|
13
|
Zhu H, Li T, Zhao B. Statistical Learning Methods for Neuroimaging Data Analysis with Applications. Annu Rev Biomed Data Sci 2023; 6:73-104. [PMID: 37127052 DOI: 10.1146/annurev-biodatasci-020722-100353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The aim of this review is to provide a comprehensive survey of statistical challenges in neuroimaging data analysis, from neuroimaging techniques to large-scale neuroimaging studies and statistical learning methods. We briefly review eight popular neuroimaging techniques and their potential applications in neuroscience research and clinical translation. We delineate four themes of neuroimaging data and review major image processing analysis methods for processing neuroimaging data at the individual level. We briefly review four large-scale neuroimaging-related studies and a consortium on imaging genomics and discuss four themes of neuroimaging data analysis at the population level. We review nine major population-based statistical analysis methods and their associated statistical challenges and present recent progress in statistical methodology to address these challenges.
Collapse
Affiliation(s)
- Hongtu Zhu
- Department of Biostatistics, Department of Statistics, Department of Genetics, and Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina, USA;
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Tengfei Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina, USA
- Department of Radiology, University of North Carolina, Chapel Hill, North Carolina, USA
| | - Bingxin Zhao
- Department of Statistics and Data Science, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| |
Collapse
|
14
|
Cohen ZD, Barnes-Horowitz NM, Forbes CN, Craske MG. Measuring the active elements of cognitive-behavioral therapies. Behav Res Ther 2023; 167:104364. [PMID: 37429044 DOI: 10.1016/j.brat.2023.104364] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 06/09/2023] [Accepted: 07/02/2023] [Indexed: 07/12/2023]
Abstract
Understanding how and for whom cognitive-behavioral therapies work is central to the development and improvement of mental health interventions. Suboptimal quantification of the active elements of cognitive-behavioral therapies has hampered progress in elucidating mechanisms of change. To advance process research on cognitive-behavioral therapies, we describe a theoretical measurement framework that focuses on the delivery, receipt, and application of the active elements of these interventions. We then provide recommendations for measuring the active elements of cognitive-behavioral therapies aligned with this framework. Finally, to support measurement harmonization and improve study comparability, we propose the development of a publicly available repository of assessment tools: the Active Elements of Cognitive-Behavioral Therapies Measurement Kit.
Collapse
Affiliation(s)
- Zachary D Cohen
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States.
| | | | - Courtney N Forbes
- Department of Psychology, University of California, Los Angeles, United States
| | - Michelle G Craske
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, United States; Department of Psychology, University of California, Los Angeles, United States
| |
Collapse
|
15
|
Zeng S, Lange EC, Archie EA, Campos FA, Alberts SC, Li F. A Causal Mediation Model for Longitudinal Mediators and Survival Outcomes with an Application to Animal Behavior. JOURNAL OF AGRICULTURAL, BIOLOGICAL, AND ENVIRONMENTAL STATISTICS 2023; 28:197-218. [PMID: 37415781 PMCID: PMC10321498 DOI: 10.1007/s13253-022-00490-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2021] [Revised: 01/26/2022] [Accepted: 01/28/2022] [Indexed: 07/08/2023]
Abstract
In animal behavior studies, a common goal is to investigate the causal pathways between an exposure and outcome, and a mediator that lies in between. Causal mediation analysis provides a principled approach for such studies. Although many applications involve longitudinal data, the existing causal mediation models are not directly applicable to settings where the mediators are measured on irregular time grids. In this paper, we propose a causal mediation model that accommodates longitudinal mediators on arbitrary time grids and survival outcomes simultaneously. We take a functional data analysis perspective and view longitudinal mediators as realizations of underlying smooth stochastic processes. We define causal estimands of direct and indirect effects accordingly and provide corresponding identification assumptions. We employ a functional principal component analysis approach to estimate the mediator process and propose a Cox hazard model for the survival outcome that flexibly adjusts the mediator process. We then derive a g-computation formula to express the causal estimands using the model coefficients. The proposed method is applied to a longitudinal data set from the Amboseli Baboon Research Project to investigate the causal relationships between early adversity, adult physiological stress responses, and survival among wild female baboons. We find that adversity experienced in early life has a significant direct effect on females' life expectancy and survival probability, but find little evidence that these effects were mediated by markers of the stress response in adulthood. We further developed a sensitivity analysis method to assess the impact of potential violation to the key assumption of sequential ignorability. Supplementary materials accompanying this paper appear on-line.
Collapse
Affiliation(s)
| | | | - Elizabeth A Archie
- Department of Biological Sciences, University of Notre Dame, Notre Dame, IN, USA
| | - Fernando A Campos
- Department of Antropology, University of Texas at San Antonio, San Antonio, TX, USA
| | - Susan C Alberts
- Department of Biology, Duke University, Durham, NC, USA.; Department of Evolutionary Anthropology, Duke University, Durham, NC, USA
| | - Fan Li
- Department of Statistical Science, Duke University, 214 Old Chemistry Building, Durham, NC 27708, USA
| |
Collapse
|
16
|
Nath T, Caffo B, Wager T, Lindquist MA. A machine learning based approach towards high-dimensional mediation analysis. Neuroimage 2023; 268:119843. [PMID: 36586543 PMCID: PMC10332048 DOI: 10.1016/j.neuroimage.2022.119843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 12/02/2022] [Accepted: 12/27/2022] [Indexed: 12/30/2022] Open
Abstract
Mediation analysis is used to investigate the role of intermediate variables (mediators) that lie in the path between an exposure and an outcome variable. While significant research has focused on developing methods for assessing the influence of mediators on the exposure-outcome relationship, current approaches do not easily extend to settings where the mediator is high-dimensional. These situations are becoming increasingly common with the rapid increase of new applications measuring massive numbers of variables, including brain imaging, genomics, and metabolomics. In this work, we introduce a novel machine learning based method for identifying high dimensional mediators. The proposed algorithm iterates between using a machine learning model to map the high-dimensional mediators onto a lower-dimensional space, and using the predicted values as input in a standard three-variable mediation model. Hence, the machine learning model is trained to maximize the likelihood of the mediation model. Importantly, the proposed algorithm is agnostic to the machine learning model that is used, providing significant flexibility in the types of situations where it can be used. We illustrate the proposed methodology using data from two functional Magnetic Resonance Imaging (fMRI) studies. First, using data from a task-based fMRI study of thermal pain, we combine the proposed algorithm with a deep learning model to detect distributed, network-level brain patterns mediating the relationship between stimulus intensity (temperature) and reported pain at the single trial level. Second, using resting-state fMRI data from the Human Connectome Project, we combine the proposed algorithm with a connectome-based predictive modeling approach to determine brain functional connectivity measures that mediate the relationship between fluid intelligence and working memory accuracy. In both cases, our multivariate mediation model links exposure variables (thermal pain or fluid intelligence), high dimensional brain measures (single-trial brain activation maps or resting-state brain connectivity) and behavioral outcomes (pain report or working memory accuracy) into a single unified model. Using the proposed approach, we are able to identify brain-based measures that simultaneously encode the exposure variable and correlate with the behavioral outcome.
Collapse
Affiliation(s)
- Tanmay Nath
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
| | - Brian Caffo
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| | - Tor Wager
- The Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Martin A Lindquist
- The Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA
| |
Collapse
|
17
|
Zhao Y, Luo X. Multilevel mediation analysis with structured unmeasured mediator-outcome confounding. Comput Stat Data Anal 2023. [DOI: 10.1016/j.csda.2022.107623] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
18
|
Chen YH, Chen C, Jian HY, Chen YC, Fan YT, Yang CY, Cheng Y. The neural correlates of emotional conflict monitoring as an early manifestation of affective and cognitive declines in persons with Type 2 diabetes. Brain Commun 2023; 5:fcad022. [PMID: 36844149 PMCID: PMC9945846 DOI: 10.1093/braincomms/fcad022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 08/08/2022] [Accepted: 02/03/2023] [Indexed: 02/09/2023] Open
Abstract
Patients with Type 2 diabetes are known to be more susceptible to experience dementia and depression/anxiety. The neural circuits of emotional conflict monitoring, as indicated by a Stroop task, might become altered in terms of cognitive and affective impairments in diabetes. This study investigated alterations in the emotional conflict monitoring and associations of corresponding brain activities with metabolic parameters in persons with Type 2 diabetes. Participants with normal cognitive and affective functioning, including 40 persons with Type 2 diabetes and 30 non-diabetes control subjects, underwent a functional MRI paradigm with the face-word emotional Stroop task and detailed cognitive and affective assessments, including the Montreal Cognitive Assessment and Beck Anxiety Inventory. Compared with the controls, people with diabetes exhibited stronger emotional interference, as indicated by differential reaction times between congruent and incongruent trials (Δcon). Δcon was correlated with Montreal Cognitive Assessment test scores and fasting glucose levels. People with diabetes demonstrated altered brain activation and functional connectivity in the neural network for emotional conflict monitoring. The neural network for emotional conflict monitoring mediated the association of pancreatic function with anxiety scores as well as the relationship between Δcon and Montreal Cognitive Assessment scores. Results suggested that alterations in the neural network underlying emotional conflict monitoring might present before clinically measurable cognitive and affective decrements were apparent, thereby bridging the gap between dementia and anxiety/depression in persons with diabetes.
Collapse
Affiliation(s)
| | | | | | - Yu-Chun Chen
- Department of Physical Education, National Taiwan University of Sport, Taichung 404, Taiwan
| | - Yang-Teng Fan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan 320, Taiwan
| | - Chih-Yung Yang
- Department of Education and Research, Taipei City Hospital, Taipei 103, Taiwan
| | - Yawei Cheng
- Correspondence to: Yawei Cheng, MD Institute of Neuroscience and Brain Research Center, National Yang Ming Chiao Tung University 155, Linong St., Sec. 2, Beitou Dist., Taipei 11241, Taiwan E-mail:
| |
Collapse
|
19
|
Nonequivalence of two least-absolute-deviation estimators for mediation effects. TEST-SPAIN 2022. [DOI: 10.1007/s11749-022-00837-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
|
20
|
Zhao Y, Chen T, Cai J, Lichenstein S, Potenza MN, Yip SW. Bayesian network mediation analysis with application to the brain functional connectome. Stat Med 2022; 41:3991-4005. [PMID: 35795965 PMCID: PMC10131252 DOI: 10.1002/sim.9488] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 04/12/2022] [Accepted: 05/18/2022] [Indexed: 11/10/2022]
Abstract
The brain functional connectome, the collection of interconnected neural circuits along functional networks, facilitates a cutting-edge understanding of brain functioning, and has a potential to play a mediating role within the effect pathway between an exposure and an outcome. While existing mediation analytic approaches are capable of providing insight into complex processes, they mainly focus on a univariate mediator or mediator vector, without considering network-variate mediators. To fill the methodological gap and accomplish this exciting and urgent application, in the article, we propose an integrative mediation analysis under a Bayesian paradigm with networks entailing the mediation effect. To parameterize the network measurements, we introduce individually specified stochastic block models with unknown block allocation, and naturally bridge effect elements through the latent network mediators induced by the connectivity weights across network modules. To enable the identification of truly active mediating components, we simultaneously impose a feature selection across network mediators. We show the superiority of our model in estimating different effect components and selecting active mediating network structures. As a practical illustration of this approach's application to network neuroscience, we characterize the relationship between a therapeutic intervention and opioid abstinence as mediated by brain functional sub-networks.
Collapse
Affiliation(s)
- Yize Zhao
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
- Yale Center for Analytical Sciences, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Tianqi Chen
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Jiachen Cai
- Department of Biostatistics, Yale University School of Public Health, New Haven, Connecticut, USA
| | - Sarah Lichenstein
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Marc N Potenza
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
- Department of Neuroscience, Yale University School of Medicine, New Haven, Connecticut, USA
- Connecticut Mental Health Center, New Haven, Connecticut, USA
- Connecticut Council on Problem Gambling, Wethersfield, Connecticut, USA
- Wu Tsai Institute, Yale University School of Medicine, New Haven, Connecticut, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, USA
- Child Study Center, Yale University School of Medicine, New Haven, Connecticut, USA
| |
Collapse
|
21
|
Cai X, Coffman DL, Piper ME, Li R. Estimation and inference for the mediation effect in a time-varying mediation model. BMC Med Res Methodol 2022; 22:113. [PMID: 35436861 PMCID: PMC9014585 DOI: 10.1186/s12874-022-01585-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Accepted: 03/17/2022] [Indexed: 11/19/2022] Open
Abstract
Background Traditional mediation analysis typically examines the relations among an intervention, a time-invariant mediator, and a time-invariant outcome variable. Although there may be a total effect of the intervention on the outcome, there is a need to understand the process by which the intervention affects the outcome (i.e., the indirect effect through the mediator). This indirect effect is frequently assumed to be time-invariant. With improvements in data collection technology, it is possible to obtain repeated assessments over time resulting in intensive longitudinal data. This calls for an extension of traditional mediation analysis to incorporate time-varying variables as well as time-varying effects. Methods We focus on estimation and inference for the time-varying mediation model, which allows mediation effects to vary as a function of time. We propose a two-step approach to estimate the time-varying mediation effect. Moreover, we use a simulation-based approach to derive the corresponding point-wise confidence band for the time-varying mediation effect. Results Simulation studies show that the proposed procedures perform well when comparing the confidence band and the true underlying model. We further apply the proposed model and the statistical inference procedure to data collected from a smoking cessation study. Conclusions We present a model for estimating time-varying mediation effects that allows both time-varying outcomes and mediators. Simulation-based inference is also proposed and implemented in a user-friendly R package. Supplementary Information The online version contains supplementary material available at (10.1186/s12874-022-01585-x).
Collapse
Affiliation(s)
- Xizhen Cai
- Department of Mathematics and Statistics, Williams College, Williamstown, MA, USA
| | - Donna L Coffman
- Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA, USA.
| | - Megan E Piper
- Center for Tobacco Research and Intervention, School of Medicine and Public Health, University of Wisconsin, Madison, WI., USA.,Department of Medicine, School of Medicine and Public Health, University of Wisconsin, Madison, WI., USA
| | - Runze Li
- Department of Statistics, Pennsylvania State University, University Park, PA, USA
| |
Collapse
|
22
|
Abstract
DNA methylation alterations have been widely studied as mediators of environmentally induced disease risks. With new advances in technique, epigenome-wide DNA methylation data (EWAS) have become the new standard for epigenetic studies in human populations. However, to date most epigenetic studies of mediation effects only involve selected (gene-specific) candidate methylation markers. There is an urgent need for appropriate analytical methods for EWAS mediation analysis. In this chapter, we provide an overview of recent advances on high-dimensional mediation analysis, with application to two DNA methylation data.
Collapse
Affiliation(s)
- Haixiang Zhang
- Center for Applied Mathematics, Tianjin University, Tianjin, China
| | - Lifang Hou
- Department of Preventive Medicine, Northwestern University, Chicago, IL, USA
| | - Lei Liu
- Division of Biostatistics, Washington University in St. Louis, St. Louis, MO, USA.
| |
Collapse
|
23
|
Zhang X, Xue W, Wang Q. Covariate balancing functional propensity score for functional treatments in cross-sectional observational studies. Comput Stat Data Anal 2021. [DOI: 10.1016/j.csda.2021.107303] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
|
24
|
Cheng Y, Chou J, Martínez RM, Fan YT, Chen C. Psychopathic traits mediate guilt-related anterior midcingulate activity under authority pressure. Sci Rep 2021; 11:14856. [PMID: 34290344 PMCID: PMC8295253 DOI: 10.1038/s41598-021-94372-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 07/05/2021] [Indexed: 12/02/2022] Open
Abstract
Coercive power has different effects on individuals, and which were unable to be fully addressed in Milgram’s famous studies on obedience to authority. While some individuals exhibited high levels of guilt-related anxiety and refused orders to harm, others followed coercive orders throughout the whole event. The lack of guilt is a well-known characteristic of psychopathy, and recent evidence portrays psychopathic personalities on a continuum of clustered traits, while being pervasive in a significant proportion in the population. To investigate whether psychopathic traits better explain discrepancies in antisocial behavior under coercion, we applied a virtual obedience paradigm, in which an experimenter ordered subjects to press a handheld button to initiate successive actions that carry different moral consequences, during fMRI scanning. Psychopathic traits modulated the association between harming actions and guilt feelings on both behavioral and brain levels. This study sheds light on the individual variability in response to coercive power.
Collapse
Affiliation(s)
- Yawei Cheng
- Department of Physical Medicine and Rehabilitation, National Yang Ming Chiao Tung University Hospital, Yilan, Taiwan.,Institute of Neuroscience and Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan.,Department of Education and Research, Taipei City Hospital, Taipei, Taiwan
| | - Judith Chou
- Institute of Neuroscience and Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Róger Marcelo Martínez
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan.,School of Psychological Sciences, National Autonomous University of Honduras, Tegucigalpa, Honduras
| | - Yang-Teng Fan
- Graduate Institute of Medicine, Yuan Ze University, Taoyuan City, Taiwan
| | - Chenyi Chen
- Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan. .,Research Center of Brain and Consciousness, Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan. .,Graduate Institute of Mind, Brain and Consciousness, College of Humanities and Social Sciences, Taipei Medical University, Taipei, Taiwan. .,Psychiatric Research Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.
| |
Collapse
|
25
|
Ma W, Xiao L, Liu B. A functional mixed model for scalar on function regression with application to a functional MRI study. Biostatistics 2021; 22:439-454. [PMID: 31631222 PMCID: PMC8286587 DOI: 10.1093/biostatistics/kxz046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2018] [Revised: 09/23/2019] [Accepted: 09/25/2019] [Indexed: 11/12/2022] Open
Abstract
Motivated by a functional magnetic resonance imaging (fMRI) study, we propose a new functional mixed model for scalar on function regression. The model extends the standard scalar on function regression for repeated outcomes by incorporating subject-specific random functional effects. Using functional principal component analysis, the new model can be reformulated as a mixed effects model and thus easily fit. A test is also proposed to assess the existence of the subject-specific random functional effects. We evaluate the performance of the model and test via a simulation study, as well as on data from the motivating fMRI study of thermal pain. The data application indicates significant subject-specific effects of the human brain hemodynamics related to pain and provides insights on how the effects might differ across subjects.
Collapse
Affiliation(s)
- Wanying Ma
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27606, USA
| | - Luo Xiao
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27606, USA
| | - Bowen Liu
- Department of Statistics, North Carolina State University, 2311 Stinson Drive, Raleigh, NC 27606, USA
| |
Collapse
|
26
|
Zeng S, Rosenbaum S, Alberts SC, Archie EA, Li F. Causal mediation analysis for sparse and irregular longitudinal data. Ann Appl Stat 2021. [DOI: 10.1214/20-aoas1427] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Affiliation(s)
- Shuxi Zeng
- Department of Statistical Science, Duke University
| | | | - Susan C. Alberts
- Departments of Biology and Evolutionary Anthropology, Duke University
| | | | - Fan Li
- Department of Statistical Science, Duke University
| |
Collapse
|
27
|
Feltgen Q, Daunizeau J. An Overcomplete Approach to Fitting Drift-Diffusion Decision Models to Trial-By-Trial Data. Front Artif Intell 2021; 4:531316. [PMID: 33898982 PMCID: PMC8064018 DOI: 10.3389/frai.2021.531316] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
Drift-diffusion models or DDMs are becoming a standard in the field of computational neuroscience. They extend models from signal detection theory by proposing a simple mechanistic explanation for the observed relationship between decision outcomes and reaction times (RT). In brief, they assume that decisions are triggered once the accumulated evidence in favor of a particular alternative option has reached a predefined threshold. Fitting a DDM to empirical data then allows one to interpret observed group or condition differences in terms of a change in the underlying model parameters. However, current approaches only yield reliable parameter estimates in specific situations (c.f. fixed drift rates vs drift rates varying over trials). In addition, they become computationally unfeasible when more general DDM variants are considered (e.g., with collapsing bounds). In this note, we propose a fast and efficient approach to parameter estimation that relies on fitting a "self-consistency" equation that RT fulfill under the DDM. This effectively bypasses the computational bottleneck of standard DDM parameter estimation approaches, at the cost of estimating the trial-specific neural noise variables that perturb the underlying evidence accumulation process. For the purpose of behavioral data analysis, these act as nuisance variables and render the model "overcomplete," which is finessed using a variational Bayesian system identification scheme. However, for the purpose of neural data analysis, estimates of neural noise perturbation terms are a desirable (and unique) feature of the approach. Using numerical simulations, we show that this "overcomplete" approach matches the performance of current parameter estimation approaches for simple DDM variants, and outperforms them for more complex DDM variants. Finally, we demonstrate the added-value of the approach, when applied to a recent value-based decision making experiment.
Collapse
Affiliation(s)
- Q. Feltgen
- Paris Brain Institute (ICM), Sorbonne Université, Inserm, CNRS, Hôpital Pitié‐Salpêtrière, Paris, France
| | - J. Daunizeau
- Paris Brain Institute (ICM), Sorbonne Université, Inserm, CNRS, Hôpital Pitié‐Salpêtrière, Paris, France
- ETH, Zurich, Switzerland
| |
Collapse
|
28
|
Aytur SA, Ray KL, Meier SK, Campbell J, Gendron B, Waller N, Robin DA. Neural Mechanisms of Acceptance and Commitment Therapy for Chronic Pain: A Network-Based fMRI Approach. Front Hum Neurosci 2021; 15:587018. [PMID: 33613207 PMCID: PMC7892587 DOI: 10.3389/fnhum.2021.587018] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 01/12/2021] [Indexed: 01/29/2023] Open
Abstract
Over 100 million Americans suffer from chronic pain (CP), which causes more disability than any other medical condition in the United States at a cost of $560-$635 billion per year (Institute of Medicine, 2011). Opioid analgesics are frequently used to treat CP. However, long term use of opioids can cause brain changes such as opioid-induced hyperalgesia that, over time, increase pain sensation. Also, opioids fail to treat complex psychological factors that worsen pain-related disability, including beliefs about and emotional responses to pain. Cognitive behavioral therapy (CBT) can be efficacious for CP. However, CBT generally does not focus on important factors needed for long-term functional improvement, including attainment of personal goals and the psychological flexibility to choose responses to pain. Acceptance and Commitment Therapy (ACT) has been recognized as an effective, non-pharmacologic treatment for a variety of CP conditions (Gutierrez et al., 2004). However, little is known about the neurologic mechanisms underlying ACT. We conducted an ACT intervention in women (n = 9) with chronic musculoskeletal pain. Functional magnetic resonance imaging (fMRI) data were collected pre- and post-ACT, and changes in functional connectivity (FC) were measured using Network-Based Statistics (NBS). Behavioral outcomes were measured using validated assessments such as the Acceptance and Action Questionnaire (AAQ-II), the Chronic Pain Acceptance Questionnaire (CPAQ), the Center for Epidemiologic Studies Depression Scale (CES-D), and the NIH Toolbox Neuro-QoLTM (Quality of Life in Neurological Disorders) scales. Results suggest that, following the 4-week ACT intervention, participants exhibited reductions in brain activation within and between key networks including self-reflection (default mode, DMN), emotion (salience, SN), and cognitive control (frontal parietal, FPN). These changes in connectivity strength were correlated with changes in behavioral outcomes including decreased depression and pain interference, and increased participation in social roles. This study is one of the first to demonstrate that improved function across the DMN, SN, and FPN may drive the positive outcomes associated with ACT. This study contributes to the emerging evidence supporting the use of neurophysiological indices to characterize treatment effects of alternative and complementary mind-body therapies.
Collapse
Affiliation(s)
- Semra A. Aytur
- Department of Health Management and Policy, University of New Hampshire, Durham, NH, United States
| | - Kimberly L. Ray
- Department of Psychology, University of Texas at Austin, Austin, TX, United States
| | - Sarah K. Meier
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Jenna Campbell
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Barry Gendron
- Wentworth Health Partners Seacoast Physiatry, Somersworth, NH, United States
| | - Noah Waller
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| | - Donald A. Robin
- Department of Communication Sciences and Disorders, University of New Hampshire, Durham, NH, United States
| |
Collapse
|
29
|
Identifying neural signatures mediating behavioral symptoms and psychosis onset: High-dimensional whole brain functional mediation analysis. Neuroimage 2020; 226:117508. [PMID: 33157263 PMCID: PMC7836235 DOI: 10.1016/j.neuroimage.2020.117508] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 10/19/2020] [Accepted: 10/22/2020] [Indexed: 11/26/2022] Open
Abstract
Along the pathway from behavioral symptoms to the development of psychotic disorders sits the multivariate mediating brain. The functional organization and structural topography of large-scale multivariate neural mediators among patients with brain disorders, however, are not well understood. Here, we design a high-dimensional brain-wide functional mediation framework to investigate brain regions that intermediate between baseline behavioral symptoms and future conversion to full psychosis among individuals at clinical high risk (CHR). Using resting-state functional magnetic resonance imaging (fMRI) data from 263 CHR subjects, we extract an α brain atlas and a β brain atlas: the former underlines brain areas associated with prodromal symptoms and the latter highlights brain areas associated with disease onset. In parallel, we identify and separate mediators that potentially positively and negatively mediate symptoms and psychosis, respectively, and quantify the effect of each neural mediator on disease development. Taken together, these results paint a brain-wide picture of neural markers that are potentially mediating behavioral symptoms and the development of psychotic disorders; additionally, they underscore a statistical framework that is useful to uncover large-scale intermediating variables in a regulatory biological system.
Collapse
|
30
|
Jo H, Chen CY, Chen DY, Weng MH, Kung CC. A brain network that supports consensus-seeking and conflict-resolving of college couples' shopping interaction. Sci Rep 2020; 10:17601. [PMID: 33077801 PMCID: PMC7573624 DOI: 10.1038/s41598-020-74699-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 10/06/2020] [Indexed: 11/16/2022] Open
Abstract
One of the typical campus scenes is the social interaction between college couples, and the lesson couples must keep learning is to adapt to each other. This fMRI study investigated the shopping interactions of 30 college couples, one lying inside and the other outside the scanner, beholding the same item from two connected PCs, making preference ratings and subsequent buy/not-buy decisions. The behavioral results showed the clear modulation of significant others’ preferences onto one’s own decisions, and the contrast of the “shop-together vs. shop-alone”, and the “congruent (both liked or disliked the item, 68%) vs. incongruent (one liked but the other disliked, and vice versa)” together trials, both revealed bilateral temporal parietal junction (TPJ) among other reward-related regions, likely reflecting mentalizing during preference harmony. Moreover, when contrasting “own-high/other-low vs. own-low/other-high” incongruent trials, left anterior inferior parietal lobule (l-aIPL) was parametrically mapped, and the “yield (e.g., own-high/not-buy) vs. insist (e.g., own-low/not-buy)” modulation further revealed left lateral-IPL (l-lIPL), together with left TPJ forming a local social decision network that was further constrained by the mediation analysis among left TPJ–lIPL–aIPL. In sum, these results exemplify, via the two-person fMRI, the neural substrate of shopping interactions between couples.
Collapse
Affiliation(s)
- HanShin Jo
- Institute of Medical Informatics, National Cheng Kung University (NCKU), Tainan, Taiwan.,Department of Psychology, NCKU, Tainan, Taiwan
| | - Chiu-Yueh Chen
- Department of Psychology, NCKU, Tainan, Taiwan.,KU Leuven, Leuven, Belgium
| | - Der-Yow Chen
- Department of Psychology, NCKU, Tainan, Taiwan.,Mind Research and Imaging (MRI) Center, Tainan, Taiwan
| | | | - Chun-Chia Kung
- Department of Psychology, NCKU, Tainan, Taiwan. .,Mind Research and Imaging (MRI) Center, Tainan, Taiwan.
| |
Collapse
|
31
|
Liu Y, Wang Y, Gozli DG, Xiang YT, Jackson T. Current status of the anger superiority hypothesis: A meta-analytic review of N2pc studies. Psychophysiology 2020; 58:e13700. [PMID: 33040366 DOI: 10.1111/psyp.13700] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 09/04/2020] [Accepted: 09/09/2020] [Indexed: 11/30/2022]
Abstract
Numerous investigators have tested contentions that angry faces capture early attention more completely than happy faces do in the context of other faces. However, syntheses of studies on early event-related potentials related to the anger superiority hypothesis have yet to be conducted, particularly in relation to the N200 posterior-contralateral (N2pc) component which provides a reliable electrophysiological index related to orienting of attention suitable for testing this hypothesis. Fifteen samples (N = 534) from 13 studies featuring the assessment of N2pc amplitudes during exposure to angry-neutral and/or happy-neutral facial expression arrays were included for meta-analysis. Moderating effects of study design features and sample characteristics on effect size variability were also assessed. N2pc amplitudes elicited by affectively valenced expressions (angry and happy) were significantly more pronounced than those elicited by neutral expressions. However, the mean effect size difference between angry and happy expressions was ns. N2pc effect sizes were moderated by sample age, number of trials, and nature of facial images used (schematic vs. real) with larger effect sizes observed when samples were comparatively younger, more task trials were presented and schematic face arrays were used. N2pc results did not support anger superiority hypothesis. Instead, attentional resources allocated to angry versus happy facial expressions were similar in early stages of processing. As such, possible adaptive advantages of biases in orienting toward both anger and happy expressions warrant consideration in revisions of related theory.
Collapse
Affiliation(s)
- Yanci Liu
- Key Laboratory of Cognition & Personality Southwest University, Chongqing, China
| | - Yang Wang
- Department of Pain and Translational Symptom Science, School of Nursing, University of Maryland, Baltimore, MD, USA
| | - Davood G Gozli
- Department of Psychology, University of Macau, Taipa, Macau S.A.R
| | - Yu-Tao Xiang
- Unit of Psychiatry, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macau S.A.R
| | - Todd Jackson
- Department of Psychology, University of Macau, Taipa, Macau S.A.R
| |
Collapse
|
32
|
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: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|
33
|
Poß D, Liebl D, Kneip A, Eisenbarth H, Wager TD, Barrett LF. Superconsistent estimation of points of impact in non‐parametric regression with functional predictors. J R Stat Soc Series B Stat Methodol 2020. [DOI: 10.1111/rssb.12386] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | | | | | | | - Lisa Feldman Barrett
- Northeastern University Boston
- Massachusetts General Hospital and Harvard Medical School Boston
- Massachusetts General Hospital Charlestown USA
| |
Collapse
|
34
|
Abstract
Covariance estimation is essential yet underdeveloped for analyzing multivariate functional data. We propose a fast covariance estimation method for multivariate sparse functional data using bivariate penalized splines. The tensor-product B-spline formulation of the proposed method enables a simple spectral decomposition of the associated covariance operator and explicit expressions of the resulting eigenfunctions as linear combinations of B-spline bases, thereby dramatically facilitating subsequent principal component analysis. We derive a fast algorithm for selecting the smoothing parameters in covariance smoothing using leave-one-subject-out cross-validation. The method is evaluated with extensive numerical studies and applied to an Alzheimer's disease study with multiple longitudinal outcomes.
Collapse
Affiliation(s)
- Cai Li
- Department of Statistics, North Carolina State Univerisy, NC, USA
| | - Luo Xiao
- Department of Statistics, North Carolina State Univerisy, NC, USA
| | - Sheng Luo
- Department of Biostatistics and Bioinformatics, Duke Universitye, NC, USA
| |
Collapse
|
35
|
Sobel ME, Lindquist MA. Estimating causal effects in studies of human brain function: New models, methods and estimands. Ann Appl Stat 2020; 14:452-472. [DOI: 10.1214/19-aoas1316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
|
36
|
Abstract
Causal mediation analysis aims to quantify the intermediate effect of a mediator on the causal pathway from treatment to outcome. When dealing with multiple mediators, which are potentially causally dependent, the possible decomposition of pathway effects grows exponentially with the number of mediators. An existing approach incorporated the principal component analysis (PCA) to address this challenge based on the fact that the transformed mediators are conditionally independent given the orthogonality of the principal components (PCs). However, the transformed mediator PCs, which are linear combinations of original mediators, can be difficult to interpret. A sparse high-dimensional mediation analysis approach is proposed which adopts the sparse PCA method to the mediation setting. The proposed approach is applied to a task-based functional magnetic resonance imaging study, illustrating its ability to detect biologically meaningful results related to an identified mediator.
Collapse
|
37
|
Hu K. Investigations into ventral prefrontal cortex using mediation models. J Neurosci Res 2019; 98:632-642. [PMID: 31420919 DOI: 10.1002/jnr.24512] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2018] [Revised: 07/21/2019] [Accepted: 07/22/2019] [Indexed: 11/11/2022]
Abstract
The ventral prefrontal cortex (vPFC) is a major focus of investigation in neuroscience, particularly in the studies of emotion and emotion-cognition integration. A crucial question concerning the regulatory function of vPFC is how it is recruited, especially how the function maps onto the structure and determines appropriate behavior. In social exclusion studies, mediation model analyses suggest that vPFC regulates distress by disrupting anterior cingulate cortex (ACC) activities, whereas I recently report (Hu, 2018; Neuropsychologia) that ventral medial prefrontal cortex appears to defend the organism from acute stress by activating ACC. In this review, I synthesize and highlight functional imaging research with mediation analysis that over the past decades has begun to offer new insights into the brain mechanisms underlying vPFC. Toward this end, the first section of the paper outlines a model of the processes and neural systems involved in the interaction of emotion and cognition. The second and third sections survey recent research on emotional regulation with negative and positive pathways, respectively, emanating from vPFC. The fourth section summarizes the current dynamic network findings. Functional mediation analysis helps to identify signals within vPFC and others that are common and/or specific to particular information processing. Finally, I provide a personal perspective of the adoption of mediation model analysis in the investigations into vPFC.
Collapse
Affiliation(s)
- Kesong Hu
- Department of Psychology, Lake Superior State University, Sault Ste. Marie, Michigan.,Institute of Mental Health, Nanjing Xiaozhuang University, Nanjing, China
| |
Collapse
|
38
|
Fan YT, Fang YW, Chen YP, Leshikar ED, Lin CP, Tzeng OJL, Huang HW, Huang CM. Aging, cognition, and the brain: effects of age-related variation in white matter integrity on neuropsychological function. Aging Ment Health 2019; 23:831-839. [PMID: 29634290 DOI: 10.1080/13607863.2018.1455804] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Abstract
Alterations in brain structure are viewed as neurobiological indicators which are closely tied to cognitive changes in healthy human aging. The current study used diffusion tensor imaging (DTI) tractography to investigate the relationship between age, brain variation in white matter (WM) integrity, and cognitive function. Sixteen younger adults (aged 20-28 years) and 18 healthy older adults (aged 60-75 years) underwent DTI scanning and a standardized battery of neuropsychological measures. Behaviorally, older adults exhibited poorer performance on multiple cognitive measures compared to younger adults. At the neural level, the effects of aging on theWM integrity were evident within interhemispheric (the anterior portion of corpus callosum) and transverse (the right uncinate fasciculus) fibers of the frontal regions, and the cingulum-angular fibers. Our correlation results showed that age-related WM differentially influenced cognitive function, with increased fractional anisotropy values in both the anterior corpus callosum and the right cingulum/angular fibers positively correlated with performance on the visuospatial task in older adults. Moreover, mediation analysis further revealed that the WM tract integrity of the frontal interhemspheric fibers was a significant mediator of age-visuospatial performance relation in older adults, but not in younger adults. These findings support the vulnerability of the frontal WM fibers to normal aging and push forward our understanding of cognitive aging by providing a more integrative view of the neural basis of linkages among aging, cognition, and brain.
Collapse
Affiliation(s)
- Yang-Teng Fan
- a Department of Biological Science and Technology , National Chiao Tung University , Taiwan.,b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan
| | - Ya-Wen Fang
- a Department of Biological Science and Technology , National Chiao Tung University , Taiwan.,b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan
| | - Ya-Ping Chen
- b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan
| | - Eric D Leshikar
- c Department of Psychology , University of Illinois at Chicago , IL , USA
| | - Ching-Po Lin
- d Institute of Neuroscience , National Yang Ming University , Taiwan
| | - Ovid J L Tzeng
- a Department of Biological Science and Technology , National Chiao Tung University , Taiwan.,b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan.,e College of Humanities and Social Sciences , Taipei Medical University , Taiwan.,f Department of Educational Psychology and Counseling , National Taiwan Normal University , Taiwan
| | - Hsu-Wen Huang
- b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan.,g Department of Linguistics and Translation , City University of Hong Kong , Hong Kong
| | - Chih-Mao Huang
- a Department of Biological Science and Technology , National Chiao Tung University , Taiwan.,b Cognitive Neuroscience Laboratory , Institute of Linguistics , Academia Sinica , Taiwan
| |
Collapse
|
39
|
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: 57] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [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.
Collapse
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
| |
Collapse
|
40
|
Zhao Y, Luo X. Granger mediation analysis of multiple time series with an application to functional magnetic resonance imaging. Biometrics 2019; 75:788-798. [PMID: 31009067 DOI: 10.1111/biom.13056] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2017] [Revised: 03/03/2019] [Accepted: 03/05/2019] [Indexed: 11/30/2022]
Abstract
This paper presents Granger mediation analysis, a new framework for causal mediation analysis of multiple time series. This framework is motivated by a functional magnetic resonance imaging (fMRI) experiment where we are interested in estimating the mediation effects between a randomized stimulus time series and brain activity time series from two brain regions. The independent observation assumption is thus unrealistic for this type of time-series data. To address this challenge, our framework integrates two types of models: causal mediation analysis across the mediation variables, and vector autoregressive (VAR) models across the temporal observations. We use "Granger" to refer to VAR correlations modeled in this paper. We further extend this framework to handle multilevel data, in order to model individual variability and correlated errors between the mediator and the outcome variables. Using Rubin's potential outcome framework, we show that the causal mediation effects are identifiable under our time-series model. We further develop computationally efficient algorithms to maximize our likelihood-based estimation criteria. Simulation studies show that our method reduces the estimation bias and improves statistical power, compared with existing approaches. On a real fMRI data set, our approach quantifies the causal effects through a brain pathway, while capturing the dynamic dependence between two brain regions.
Collapse
Affiliation(s)
- Yi Zhao
- Department of Biostatistics, Brown University, Providence, Rhode Island
| | - Xi Luo
- Department of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, Texas
| |
Collapse
|
41
|
Chen C, Chen YC, Chen KL, Cheng Y. Atypical Anxiety-Related Amygdala Reactivity and Functional Connectivity in Sant Mat Meditation. Front Behav Neurosci 2018; 12:298. [PMID: 30564108 PMCID: PMC6288484 DOI: 10.3389/fnbeh.2018.00298] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 11/19/2018] [Indexed: 12/28/2022] Open
Abstract
While meditation has drawn much attention in cognitive neuroscience, the neural mechanisms underlying its emotional processing remains elusive. Sant Mat meditators were recruited, who adopt a loving-kindness mode of meditation along with a vegetarian diet and an alcohol-restricted lifestyle and novices. We assessed their State-Trait Anxiety Inventory (STAI) and scanned their amygdala reactivity in response to an explicit and implicit (backward masked) perception of fearful and happy faces. In contrast with novices, meditators reported lower STAI scores. Meditators showed stronger amygdala reactivity to explicit happiness than to fear, whereas novices exhibited the opposite pattern. The amygdala reactivity was reduced in meditators regardless of implicit fear or happiness. Those who had more lifetime practice in meditation reported lower STAI and showed a weaker amygdala response to fear. Furthermore, the amygdala in meditators, relative to novices, had a stronger positive functional connectivity with the ventrolateral prefrontal cortex (PFC) to explicit happiness, but a more negative connectivity with the insula and medial orbitofrontal cortex (OFC) to explicit fear. Mediation analysis indicated the amygdala reactivity as the mediator for the linkage between meditation experience and trait anxiety. The findings demonstrate the neural correlates that underpin the beneficial effects of meditation in Sant Mat. Long-term meditation could be functionally coupled with the amygdala reactivity to explicit and implicit emotional processing, which would help reduce anxiety and potentially enhance well-being.
Collapse
Affiliation(s)
- Chenyi Chen
- Department of Physical Medicine & Rehabilitation, National Yang-Ming University Hospital, Yilan, Taiwan.,Graduate Institute of Injury Prevention and Control, College of Public Health, Taipei Medical University, Taipei, Taiwan.,Institute of Humanities in Medicine, Taipei Medical University, Taipei, Taiwan.,Research Center of Brain and Consciousness, Shuang Ho Hospital, Taipei Medical University, Taipei, Taiwan
| | - Yu-Chun Chen
- Institute of Neuroscience and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Kuan-Ling Chen
- Institute of Neuroscience and Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Yawei Cheng
- Department of Physical Medicine & Rehabilitation, National Yang-Ming University Hospital, Yilan, Taiwan.,Institute of Neuroscience and Brain Research Center, National Yang-Ming University, Taipei, Taiwan.,Department of Research and Education, Taipei City Hospital, Taipei, Taiwan
| |
Collapse
|
42
|
Zheng X, Xue L, Qu A. Time-varying correlation structure estimation and local-feature detection for spatio-temporal data. J MULTIVARIATE ANAL 2018. [DOI: 10.1016/j.jmva.2018.07.012] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
|
43
|
Park SY, Xiao L, Willbur JD, Staicu AM, Jumbe NL. A joint design for functional data with application to scheduling ultrasound scans. Comput Stat Data Anal 2018; 122:101-114. [PMID: 29861518 PMCID: PMC5840761 DOI: 10.1016/j.csda.2018.01.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 01/12/2018] [Accepted: 01/13/2018] [Indexed: 11/29/2022]
Abstract
A joint design for sampling functional data is proposed to achieve optimal prediction of both functional data and a scalar outcome. The motivating application is fetal growth, where the objective is to determine the optimal times to collect ultrasound measurements in order to recover fetal growth trajectories and to predict child birth outcomes. The joint design is formulated using an optimization criterion and implemented in a pilot study. Performance of the proposed design is evaluated via simulation study and application to fetal ultrasound data.
Collapse
Affiliation(s)
| | - Luo Xiao
- North Carolina State University, Raleigh, NC, USA
| | | | | | | |
Collapse
|
44
|
Moayedi M, Salomons TV, Atlas LY. Pain Neuroimaging in Humans: A Primer for Beginners and Non-Imagers. THE JOURNAL OF PAIN 2018; 19:961.e1-961.e21. [PMID: 29608974 PMCID: PMC6192705 DOI: 10.1016/j.jpain.2018.03.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/22/2018] [Accepted: 03/19/2018] [Indexed: 01/06/2023]
Abstract
Human pain neuroimaging has exploded in the past 2 decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these reports. Likewise, new trainees must design rigorous and reliable pain imaging experiments. In this article we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural magnetic resonance imaging. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach.
Collapse
Affiliation(s)
- Massieh Moayedi
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada; University of Toronto Centre for the Study of Pain, University of Toronto, Toronto, Ontario, Canada; Department of Dentistry, Mount Sinai Hospital, Toronto, Ontario, Canada.
| | - Tim V Salomons
- School of Psychology and Clinical Language Science, University of Reading, Reading, UK; Centre for Integrated Neuroscience and Neurodynamics, University of Reading, Reading, UK
| | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, Maryland; National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland
| |
Collapse
|
45
|
Webb-Vargas Y, Chen S, Fisher A, Mejia A, Xu Y, Crainiceanu C, Caffo B, Lindquist MA. Big Data and Neuroimaging. STATISTICS IN BIOSCIENCES 2017; 9:543-558. [PMID: 29335670 PMCID: PMC5766007 DOI: 10.1007/s12561-017-9195-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2014] [Accepted: 05/04/2017] [Indexed: 10/19/2022]
Abstract
Big Data are of increasing importance in a variety of areas, especially in the biosciences. There is an emerging critical need for Big Data tools and methods, because of the potential impact of advancements in these areas. Importantly, statisticians and statistical thinking have a major role to play in creating meaningful progress in this arena. We would like to emphasize this point in this special issue, as it highlights both the dramatic need for statistical input for Big Data analysis and for a greater number of statisticians working on Big Data problems. We use the field of statistical neuroimaging to demonstrate these points. As such, this paper covers several applications and novel methodological developments of Big Data tools applied to neuroimaging data.
Collapse
|
46
|
Cheng Y, Chen C, Decety J. How Situational Context Impacts Empathic Responses and Brain Activation Patterns. Front Behav Neurosci 2017; 11:165. [PMID: 28928643 PMCID: PMC5591329 DOI: 10.3389/fnbeh.2017.00165] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Accepted: 08/21/2017] [Indexed: 01/10/2023] Open
Abstract
Clinical empathy, which is defined as the ability to understand the patient’s experience and feelings from the patient’s perspective, is acknowledged to be an important aspect of quality healthcare. However, how work experience modulates the empathic responses and brain activation patterns in medical professions remains elusive. This fMRI study recruited one hundred female nurses, who varied the length of work experience, and examined how their neural response, functional connectivity, and subjective evaluations of valence and arousal to perceiving another individual in physical pain are modulated by the situational context in which they occur (i.e., in a hospital or at home). Participants with longer hospital terms evaluated pain as less negative in valence and arousal when occurring in a hospital context, but not in a home context. Physical pain perceived in a hospital compared to a home context produced stronger activity in the right temporoparietal junction (rTPJ). The reverse comparison resulted in an increased activity in the insula and anterior midcingulate cortex (aMCC). Mediation analysis indicated that reduced personal accomplishment, a symptom of burnout, breaks down the mediation effect of the putamen on context-dependent valence ratings. Overall, the study demonstrates how situational contexts significantly influence individuals’ empathic processing, and that perceiving reward from patient care protects them from burnout. HighlightsDifferences in behavior ratings and brain activations between medical practitioners perceiving others’ pain in a hospital and at home. Situational contexts significantly influence individual’s empathic processing. Perceiving rewards from patient care protects medical practitioners from burnout. Empathy is a flexible phenomenon.
Collapse
Affiliation(s)
- Yawei Cheng
- Institute of Neuroscience, National Yang-Ming UniversityTaipei, Taiwan.,Department of Physical Medicine and Rehabilitation, National Yang-Ming University HospitalYilan, Taiwan
| | - Chenyi Chen
- Institute of Neuroscience, National Yang-Ming UniversityTaipei, Taiwan
| | - Jean Decety
- Department of Psychology and Department of Psychiatry and Behavioral Neuroscience, University of ChicagoChicago, IL, United States
| |
Collapse
|
47
|
Reiss PT, Goldsmith J, Shang HL, Ogden RT. Methods for scalar-on-function regression. Int Stat Rev 2017; 85:228-249. [PMID: 28919663 PMCID: PMC5598560 DOI: 10.1111/insr.12163] [Citation(s) in RCA: 72] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Accepted: 12/28/2015] [Indexed: 01/16/2023]
Abstract
Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.
Collapse
Affiliation(s)
- Philip T. Reiss
- Department of Child and Adolescent Psychiatry and Department of Population Health, New York University School of Medicine
- Department of Statistics, University of Haifa
| | - Jeff Goldsmith
- Department of Biostatistics, Columbia University Mailman School of Public Health
| | - Han Lin Shang
- Research School of Finance, Actuarial Studies and Statistics, Australian National University
| | - R. Todd Ogden
- Department of Biostatistics, Columbia University Mailman School of Public Health
- New York State Psychiatric Institute
| |
Collapse
|
48
|
Huang L, Reiss PT, Xiao L, Zipunnikov V, Lindquist MA, Crainiceanu CM. Two-way principal component analysis for matrix-variate data, with an application to functional magnetic resonance imaging data. Biostatistics 2017; 18:214-229. [PMID: 27578805 DOI: 10.1093/biostatistics/kxw040] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2015] [Accepted: 07/21/2016] [Indexed: 11/13/2022] Open
Abstract
Many modern neuroimaging studies acquire large spatial images of the brain observed sequentially over time. Such data are often stored in the forms of matrices. To model these matrix-variate data we introduce a class of separable processes using explicit latent process modeling. To account for the size and two-way structure of the data, we extend principal component analysis to achieve dimensionality reduction at the individual level. We introduce necessary identifiability conditions for each model and develop scalable estimation procedures. The method is motivated by and applied to a functional magnetic resonance imaging study designed to analyze the relationship between pain and brain activity.
Collapse
|
49
|
Suk HW, Hwang H. Functional Generalized Structured Component Analysis. PSYCHOMETRIKA 2016; 81:940-968. [PMID: 27714543 PMCID: PMC5471146 DOI: 10.1007/s11336-016-9521-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2013] [Revised: 06/15/2016] [Indexed: 06/06/2023]
Abstract
An extension of Generalized Structured Component Analysis (GSCA), called Functional GSCA, is proposed to analyze functional data that are considered to arise from an underlying smooth curve varying over time or other continua. GSCA has been geared for the analysis of multivariate data. Accordingly, it cannot deal with functional data that often involve different measurement occasions across participants and a large number of measurement occasions that exceed the number of participants. Functional GSCA addresses these issues by integrating GSCA with spline basis function expansions that represent infinite-dimensional curves onto a finite-dimensional space. For parameter estimation, functional GSCA minimizes a penalized least squares criterion by using an alternating penalized least squares estimation algorithm. The usefulness of functional GSCA is illustrated with gait data.
Collapse
Affiliation(s)
- Hye Won Suk
- Department of Psychology, Arizona State University, 950 S. McAllister, BOX 871104, Tempe, AZ, 85287-1104 , USA.
| | | |
Collapse
|
50
|
Peters J, Bühlmann P, Meinshausen N. Causal inference by using invariant prediction: identification and confidence intervals. J R Stat Soc Series B Stat Methodol 2016. [DOI: 10.1111/rssb.12167] [Citation(s) in RCA: 149] [Impact Index Per Article: 18.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Affiliation(s)
- Jonas Peters
- Max Planck Institute for Intelligent Systems; Tübingen Germany
- Eidgenössiche Technische Hochschule Zürich; Switzerland
| | | | | |
Collapse
|