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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.
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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
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2
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Ji Q, Jiang X, Wang M, Xin Z, Zhang W, Qu J, Liu GH. Multimodal Omics Approaches to Aging and Age-Related Diseases. PHENOMICS (CHAM, SWITZERLAND) 2024; 4:56-71. [PMID: 38605908 PMCID: PMC11003952 DOI: 10.1007/s43657-023-00125-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 08/09/2023] [Accepted: 08/16/2023] [Indexed: 04/13/2024]
Abstract
Aging is associated with a progressive decline in physiological capacities and an increased risk of aging-associated disorders. An increasing body of experimental evidence shows that aging is a complex biological process coordinately regulated by multiple factors at different molecular layers. Thus, it is difficult to delineate the overall systematic aging changes based on single-layer data. Instead, multimodal omics approaches, in which data are acquired and analyzed using complementary omics technologies, such as genomics, transcriptomics, and epigenomics, are needed for gaining insights into the precise molecular regulatory mechanisms that trigger aging. In recent years, multimodal omics sequencing technologies that can reveal complex regulatory networks and specific phenotypic changes have been developed and widely applied to decode aging and age-related diseases. This review summarizes the classification and progress of multimodal omics approaches, as well as the rapidly growing number of articles reporting on their application in the field of aging research, and outlines new developments in the clinical treatment of age-related diseases based on omics technologies.
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Affiliation(s)
- Qianzhao Ji
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Xiaoyu Jiang
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Minxian Wang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
| | - Zijuan Xin
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Weiqi Zhang
- CAS Key Laboratory of Genomic and Precision Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
| | - Jing Qu
- State Key Laboratory of Stem Cell and Reproductive Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
| | - Guang-Hui Liu
- State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- Key Laboratory of Organ Regeneration and Reconstruction, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101 China
- University of Chinese Academy of Sciences, Beijing, 100049 China
- Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, 100101 China
- Beijing Institute for Stem Cell and Regenerative Medicine, Beijing, 100101 China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing, 100190 China
- Advanced Innovation Center for Human Brain Protection, National Clinical Research Center for Geriatric Disorders, Xuanwu Hospital Capital Medical University, Beijing, 100053 China
- Aging Translational Medicine Center, International Center for Aging and Cancer, Xuanwu Hospital, Capital Medical University, Beijing, 100053 China
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3
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Butler ER, Samia N, White S, Gratton C, Nusslock R. Neuroimmune mechanisms connecting violence with internalizing symptoms: A high-dimensional multimodal mediation analysis. Hum Brain Mapp 2024; 45:e26615. [PMID: 38339956 DOI: 10.1002/hbm.26615] [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: 09/05/2023] [Revised: 12/27/2023] [Accepted: 01/21/2024] [Indexed: 02/12/2024] Open
Abstract
Violence exposure is associated with worsening anxiety and depression symptoms among adolescents. Mechanistically, social defeat stress models in mice indicate that violence increases peripherally derived macrophages in threat appraisal regions of the brain, which have been causally linked to anxious behavior. In the present study, we investigate if there is a path connecting violence exposure with internalizing symptom severity through peripheral inflammation and amygdala connectivity. Two hundred and thirty-three adolescents, ages 12-15, from the Chicago area completed clinical assessments, immune assays and neuroimaging. A high-dimensional multimodal mediation model was fit, using violence exposure as the predictor, 12 immune variables as the first set of mediators and 288 amygdala connectivity variables as the second set, and internalizing symptoms as the primary outcome measure. 56.2% of the sample had been exposed to violence in their lifetime. Amygdala-hippocampus connectivity mediated the association between violence exposure and internalizing symptoms (ζ ̂ Hipp π ̂ Hipp = 0.059 $$ {\hat{\zeta}}_{\mathrm{Hipp}}{\hat{\pi}}_{\mathrm{Hipp}}=0.059 $$ ,95 % CI boot = 0.009,0.134 $$ 95\%{\mathrm{CI}}_{\mathrm{boot}}=\left[\mathrm{0.009,0.134}\right] $$ ). There was no evidence that inflammation or inflammation and amygdala connectivity in tandem mediated the association. Considering the amygdala and the hippocampus work together to encode, consolidate, and retrieve contextual fear memories, violence exposure may be associated with greater connectivity between the amygdala and the hippocampus because it could be adaptive for the amygdala and the hippocampus to be in greater communication following violence exposure to facilitate evaluation of contextual threat cues. Therefore, chronic elevations of amygdala-hippocampal connectivity may indicate persistent vigilance that leads to internalizing symptoms.
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Affiliation(s)
- Ellyn R Butler
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
| | - Noelle Samia
- Department of Statistics and Data Science, Northwestern University, Evanston, Illinois, USA
| | - Stuart White
- Nebraska Children and Families Foundation, Lincoln, Nebraska, USA
| | - Caterina Gratton
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Department of Psychology, Florida State University, Tallahassee, Florida, USA
| | - Robin Nusslock
- Department of Psychology, Northwestern University, Evanston, Illinois, USA
- Institute for Policy Research, Northwestern University, Evanston, Illinois, USA
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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.
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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
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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.
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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
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Li L, Shi C, Guo T, Jagust WJ. Sequential Pathway Inference for Multimodal Neuroimaging Analysis. Stat (Int Stat Inst) 2022; 11:e433. [PMID: 35450402 PMCID: PMC9017676 DOI: 10.1002/sta4.433] [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: 05/29/2021] [Accepted: 09/12/2021] [Indexed: 12/03/2023]
Abstract
Motivated by a multimodal neuroimaging study for Alzheimer's disease, in this article, we study the inference problem, i.e., hypothesis testing, of sequential mediation analysis. The existing sequential mediation solutions mostly focus on sparse estimation, while hypothesis testing is an utterly different and more challenging problem. Meanwhile, the few mediation testing solutions often ignore the potential dependency among the mediators, or cannot be applied to the sequential problem directly. We propose a statistical inference procedure to test mediation pathways when there are sequentially ordered multiple data modalities and each modality involves multiple mediators. We allow the mediators to be conditionally dependent, and the number of mediators within each modality to diverge with the sample size. We produce the explicit significance quantification and establish the theoretical guarantees in terms of asymptotic size, power, and false discovery control. We demonstrate the efficacy of the method through both simulations and an application to a multimodal neuroimaging pathway analysis of Alzheimer's disease.
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Affiliation(s)
- Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, CA, USA
| | - Chengchun Shi
- Department of Statistics, London School of Economics and Political Science, London, UK
| | - Tengfei Guo
- Institute of Biomedical Engineering, Shenzhen Bay Laboratory, Shenzhen, China
| | - William J. Jagust
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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Mejia AF. Discussion on "distributional independent component analysis for diverse neuroimaging modalities" by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo. Biometrics 2022; 78:1109-1112. [PMID: 34897649 PMCID: PMC9188627 DOI: 10.1111/biom.13592] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Revised: 06/21/2021] [Accepted: 06/28/2021] [Indexed: 11/30/2022]
Abstract
I applaud the authors on their innovative generalized independent component analysis (ICA) framework for neuroimaging data. Although ICA has enjoyed great popularity for the analysis of functional magnetic resonance imaging (fMRI) data, its applicability to other modalities has been limited because standard ICA algorithms may not be directly applicable to a diversity of data representations. This is particularly true for single-subject structural neuroimaging, where only a single measurement is collected at each location in the brain. The ingenious idea of Wu et al. (2021) is to transform the data to a vector of probabilities via a mixture distribution with K components, which (following a simple transformation toR K - 1 $\mathbb {R}^{K-1}$ ) can be directly analyzed with standard ICA algorithms, such as infomax (Bell and Sejnowski, 1995) or fastICA (Hyvarinen, 1999). The underlying distribution forming the basis of the mixture is customized to the particular modality being analyzed. This framework, termed distributional ICA (DICA), is applicable in theory to nearly any neuroimaging modality. This has substantial implications for ICA as a general tool for neuroimaging analysis, with particular promise for structural modalities and multimodal studies. This invited commentary focuses on the applicability and potential of DICA for different neuroimaging modalities, questions around details of implementation and performance, and limitations of the validation study presented in the paper.
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Affiliation(s)
- Amanda F. Mejia
- Department of Statistics, Indiana University, Myles Brand Hall E104 901
E. 10th Street Bloomington, IN, 47408, USA
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Zhao Y, Li L. Multimodal data integration via mediation analysis with high-dimensional exposures and mediators. Hum Brain Mapp 2022; 43:2519-2533. [PMID: 35129252 PMCID: PMC9057105 DOI: 10.1002/hbm.25800] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 01/06/2022] [Accepted: 01/23/2022] [Indexed: 12/28/2022] Open
Abstract
Motivated by an imaging proteomics study for Alzheimer's disease (AD), in this article, we propose a mediation analysis approach with high‐dimensional exposures and high‐dimensional mediators to integrate data collected from multiple platforms. The proposed method combines principal component analysis with penalized least squares estimation for a set of linear structural equation models. The former reduces the dimensionality and produces uncorrelated linear combinations of the exposure variables, whereas the latter achieves simultaneous path selection and effect estimation while allowing the mediators to be correlated. Applying the method to the AD data identifies numerous interesting protein peptides, brain regions, and protein–structure–memory paths, which are in accordance with and also supplement existing findings of AD research. Additional simulations further demonstrate the effective empirical performance of the method.
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Affiliation(s)
- Yi Zhao
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lexin Li
- Department of Biostatistics and Epidemiology, University of California, Berkeley, Berkeley, California, USA
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