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Ren Y, Osborne N, Peterson CB, DeMaster DM, Ewing‐Cobbs L, Vannucci M. Bayesian varying-effects vector autoregressive models for inference of brain connectivity networks and covariate effects in pediatric traumatic brain injury. Hum Brain Mapp 2024; 45:e26763. [PMID: 38943369 PMCID: PMC11213982 DOI: 10.1002/hbm.26763] [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: 09/21/2023] [Revised: 05/01/2024] [Accepted: 06/08/2024] [Indexed: 07/01/2024] Open
Abstract
In this article, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimates group-specific directed brain connectivity networks and accounts for the effects of covariates on the network edges. We adopt a flexible approach, allowing for (possibly) nonlinear effects of the covariates on edge strength via a novel Bayesian nonparametric prior that employs a weighted mixture of Gaussian processes. For posterior inference, we achieve computational scalability by implementing a variational Bayes scheme. Our approach enables simultaneous estimation of group-specific networks and selection of relevant covariate effects. We show improved performance over competing two-stage approaches on simulated data. We apply our method on resting-state functional magnetic resonance imaging data from children with a history of traumatic brain injury (TBI) and healthy controls to estimate the effects of age and sex on the group-level connectivities. Our results highlight differences in the distribution of parent nodes. They also suggest alteration in the relation of age, with peak edge strength in children with TBI, and differences in effective connectivity strength between males and females.
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Affiliation(s)
- Yangfan Ren
- Department of StatisticsRice UniversityHoustonTexasUSA
| | | | - Christine B. Peterson
- Department of BiostatisticsThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
| | - Dana M. DeMaster
- Department of Pediatrics, Children's Learning InstituteUniversity of Texas Health Science CenterHoustonTexasUSA
| | - Linda Ewing‐Cobbs
- Department of Pediatrics, Children's Learning InstituteUniversity of Texas Health Science CenterHoustonTexasUSA
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Wang Y, Yan G, Wang X, Li S, Peng L, Tudorascu DL, Zhang T. A variational Bayesian approach to identifying whole-brain directed networks with fMRI data. Ann Appl Stat 2023. [DOI: 10.1214/22-aoas1640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Affiliation(s)
- Yaotian Wang
- Department of Statistics, University of Pittsburgh
| | - Guofen Yan
- Department of Public Health Sciences, University of Virginia
| | - Xiaofeng Wang
- Department of Quantitative Health Sciences, Cleveland Clinic
| | - Shuoran Li
- Department of Statistics, University of Pittsburgh
| | - Lingyi Peng
- Department of Biostatistics, University of Pittsburgh
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DeMaster D, Godlewska BR, Liang M, Vannucci M, Bockmann T, Cao B, Selvaraj S. Effective connectivity between resting-state networks in depression. J Affect Disord 2022; 307:79-86. [PMID: 35331822 DOI: 10.1016/j.jad.2022.03.041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 03/12/2022] [Accepted: 03/17/2022] [Indexed: 10/18/2022]
Abstract
RATIONALE Although depression has been widely researched, findings characterizing how brain regions influence each other remains scarce, yet this is critical for research on antidepressant treatments and individual responses to particular treatments. OBJECTIVES To identify pre-treatment resting state effective connectivity (rsEC) patterns in patients with major depressive disorder (MDD) and explore their relationship with treatment response. METHODS Thirty-four drug-free MDD patients had an MRI scan and were subsequently treated for 6 weeks with an SSRI escitalopram 10 mg daily; the response was defined as ≥50% decrease in Hamilton Depression Rating Scale (HAMD) score. RESULTS rsEC networks in default mode, central executive, and salience networks were identified for patients with depression. Exploratory analyses indicated higher connectivity strength related to baseline depression severity and response to treatment. CONCLUSIONS Preliminary analyses revealed widespread dysfunction of rsEC in depression. Functional rsEC may be useful as a predictive tool for antidepressant treatment response. A primary limitation of the current study was the small size; however, the group was carefully chosen, well-characterized, and included only medication-free patients. Further research in large samples of placebo-controlled studies would be required to confirm the results.
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Affiliation(s)
- Dana DeMaster
- Children's Learning Institute, Department of Pediatrics, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA.
| | - Beata R Godlewska
- Department of Psychiatry, Medical Sciences Division, University of Oxford, United Kingdom.; Oxford Health NHS Foundation Trust, Oxford, United Kingdom
| | - Mingrui Liang
- Department of Statistics, Rice University, Houston, TX, USA
| | | | - Taya Bockmann
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
| | - Bo Cao
- University of Alberta, Department of Psychiatry, Edmonton, Canada
| | - Sudhakar Selvaraj
- Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, McGovern Medical School, Houston, TX, USA
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Keil A, Bernat EM, Cohen MX, Ding M, Fabiani M, Gratton G, Kappenman ES, Maris E, Mathewson KE, Ward RT, Weisz N. Recommendations and publication guidelines for studies using frequency domain and time-frequency domain analyses of neural time series. Psychophysiology 2022; 59:e14052. [PMID: 35398913 PMCID: PMC9717489 DOI: 10.1111/psyp.14052] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 03/08/2022] [Indexed: 01/29/2023]
Abstract
Since its beginnings in the early 20th century, the psychophysiological study of human brain function has included research into the spectral properties of electrical and magnetic brain signals. Now, dramatic advances in digital signal processing, biophysics, and computer science have enabled increasingly sophisticated methodology for neural time series analysis. Innovations in hardware and recording techniques have further expanded the range of tools available to researchers interested in measuring, quantifying, modeling, and altering the spectral properties of neural time series. These tools are increasingly used in the field, by a growing number of researchers who vary in their training, background, and research interests. Implementation and reporting standards also vary greatly in the published literature, causing challenges for authors, readers, reviewers, and editors alike. The present report addresses this issue by providing recommendations for the use of these methods, with a focus on foundational aspects of frequency domain and time-frequency analyses. It also provides publication guidelines, which aim to (1) foster replication and scientific rigor, (2) assist new researchers who wish to enter the field of brain oscillations, and (3) facilitate communication among authors, reviewers, and editors.
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Affiliation(s)
- Andreas Keil
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Edward M. Bernat
- Department of Psychology, University of Maryland, College Park, Maryland, USA
| | - Michael X. Cohen
- Radboud University and University Medical Center, Nijmegen, the Netherlands
| | - Mingzhou Ding
- J Crayton Pruitt Family Department of Biomedical Engineering, University of Florida, Gainesville, Florida, USA
| | - Monica Fabiani
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Gabriele Gratton
- Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA,Psychology Department, University of Illinois at Urbana-Champaign, Champaign, Illinois, USA
| | - Emily S. Kappenman
- Department of Psychology, San Diego State University, San Diego, California, USA
| | - Eric Maris
- Donders Institute for Brain, Cognition, and Behaviour & Faculty of Social Sciences Radboud University, Nijmegen, the Netherlands
| | - Kyle E. Mathewson
- Department of Psychology, Faculty of Science, University of Alberta, Edmonton, Alberta, Canada
| | - Richard T. Ward
- Department and Psychology and Center for the Study of Emotion and Attention, University of Florida, Gainesville, Florida, USA
| | - Nathan Weisz
- Psychology, University of Salzburg, Salzburg, Austria,Neuroscience Institute, Christian Doppler University Hospital, Paracelsus Medical University, Salzburg, Austria
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Vaughn KA, DeMaster D, Kook JH, Vannucci M, Ewing-Cobbs L. Effective connectivity in the default mode network after paediatric traumatic brain injury. Eur J Neurosci 2022; 55:318-336. [PMID: 34841600 PMCID: PMC9198945 DOI: 10.1111/ejn.15546] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Revised: 11/17/2021] [Accepted: 11/20/2021] [Indexed: 01/03/2023]
Abstract
Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi-subject vector autoregressive modelling (BVAR-connect), which used white matter integrity as priors to evaluate effective connectivity-the time-dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions-within the default mode network (DMN). In a prospective longitudinal study, children ages 8-15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post-concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting-state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post-concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal-parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post-concussion symptoms and anxiety.
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Affiliation(s)
- Kelly A. Vaughn
- University of Texas Health Science Center at Houston,,Corresponding Author
| | - Dana DeMaster
- University of Texas Health Science Center at Houston
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Osborne N, Peterson CB, Vannucci M. Latent Network Estimation and Variable Selection for Compositional Data Via Variational EM. J Comput Graph Stat 2022; 31:163-175. [PMID: 36776345 PMCID: PMC9909885 DOI: 10.1080/10618600.2021.1935971] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Network estimation and variable selection have been extensively studied in the statistical literature, but only recently have those two challenges been addressed simultaneously. In this article, we seek to develop a novel method to simultaneously estimate network interactions and associations to relevant covariates for count data, and specifically for compositional data, which have a fixed sum constraint. We use a hierarchical Bayesian model with latent layers and employ spike-and-slab priors for both edge and covariate selection. For posterior inference, we develop a novel variational inference scheme with an expectation-maximization step, to enable efficient estimation. Through simulation studies, we demonstrate that the proposed model outperforms existing methods in its accuracy of network recovery. We show the practical utility of our model via an application to microbiome data. The human microbiome has been shown to contribute too many of the functions of the human body, and also to be linked with a number of diseases. In our application, we seek to better understand the interaction between microbes and relevant covariates, as well as the interaction of microbes with each other. We call our algorithm simultaneous inference for networks and covariates and provide a Python implementation, which is available online.
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Affiliation(s)
| | - Christine B. Peterson
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX
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