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Cappello L, Madrid Padilla OH, Palacios JA. Bayesian change point detection with spike and slab priors. J Comput Graph Stat 2023. [DOI: 10.1080/10618600.2023.2182312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
Affiliation(s)
| | | | - Julia A. Palacios
- Departments of Statistics and Biomedical Data Science, Stanford University
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Jiang F, Jin H, Gao Y, Xie X, Cummings J, Raj A, Nagarajan S. Time-varying dynamic network model for dynamic resting state functional connectivity in fMRI and MEG imaging. Neuroimage 2022; 254:119131. [PMID: 35337963 PMCID: PMC9942947 DOI: 10.1016/j.neuroimage.2022.119131] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2021] [Revised: 02/04/2022] [Accepted: 03/21/2022] [Indexed: 01/26/2023] Open
Abstract
Dynamic resting state functional connectivity (RSFC) characterizes fluctuations that occur over time in functional brain networks. Existing methods to extract dynamic RSFCs, such as sliding-window and clustering methods that are inherently non-adaptive, have various limitations such as high-dimensionality, an inability to reconstruct brain signals, insufficiency of data for reliable estimation, insensitivity to rapid changes in dynamics, and a lack of generalizability across multiply functional imaging modalities. To overcome these deficiencies, we develop a novel and unifying time-varying dynamic network (TVDN) framework for examining dynamic resting state functional connectivity. TVDN includes a generative model that describes the relation between a low-dimensional dynamic RSFC and the brain signals, and an inference algorithm that automatically and adaptively learns the low-dimensional manifold of dynamic RSFC and detects dynamic state transitions in data. TVDN is applicable to multiple modalities of functional neuroimaging such as fMRI and MEG/EEG. The estimated low-dimensional dynamic RSFCs manifold directly links to the frequency content of brain signals. Hence we can evaluate TVDN performance by examining whether learnt features can reconstruct observed brain signals. We conduct comprehensive simulations to evaluate TVDN under hypothetical settings. We then demonstrate the application of TVDN with real fMRI and MEG data, and compare the results with existing benchmarks. Results demonstrate that TVDN is able to correctly capture the dynamics of brain activity and more robustly detect brain state switching both in resting state fMRI and MEG data.
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Affiliation(s)
- Fei Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA 94158, USA.
| | - Huaqing Jin
- Department of Statistics and Actuarial Science, the University of Hong Kong, CN, Hong Kong
| | - Yijing Gao
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Xihe Xie
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Jennifer Cummings
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA
| | - Ashish Raj
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
| | - Srikantan Nagarajan
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94158, USA.
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3
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Shaochuan L. Scalable Bayesian Multiple Changepoint Detection via Auxiliary Uniformisation. Int Stat Rev 2022. [DOI: 10.1111/insr.12511] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Lu Shaochuan
- School of Statistics Beijing Normal University Xin Jie Kou Wai Da Jie 19 Beijing P.R. China
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4
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Jin H, Yin G, Yuan B, Jiang F. Bayesian Hierarchical Model for Change Point Detection in Multivariate Sequences. Technometrics 2021. [DOI: 10.1080/00401706.2021.1927848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Huaqing Jin
- Department of Statistics and Actuarial Science, the University of Hong Kong, Hong Kong, Hong Kong
| | - Guosheng Yin
- Department of Statistics and Actuarial Science, the University of Hong Kong, Hong Kong, Hong Kong
| | - Binhang Yuan
- Computer Science Department, Rice University, Houston, TX
| | - Fei Jiang
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA
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5
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Jula Vanegas L, Behr M, Munk A. Multiscale Quantile Segmentation. J Am Stat Assoc 2021. [DOI: 10.1080/01621459.2020.1859380] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Affiliation(s)
- Laura Jula Vanegas
- Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany
| | - Merle Behr
- Department of Statistics, University of California at Berkeley, Berkeley, CA
| | - Axel Munk
- Institute for Mathematical Stochastics, University of Göttingen, Göttingen, Germany;
- Max Planck Institute for Biophysical Chemistry, Göttingen, Germany
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6
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Kovács S, Li H, Bühlmann P. Seeded intervals and noise level estimation in change point detection: a discussion of Fryzlewicz (2020). J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00077-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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7
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Fang X, Li J, Siegmund D. Segmentation and estimation of change-point models: False positive control and confidence regions. Ann Stat 2020. [DOI: 10.1214/19-aos1861] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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8
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Abstract
Tree-like structures are abundant in the empirical sciences as they can summarize high-dimensional data and show latent structure among many samples in a single framework. Prominent examples include phylogenetic trees or hierarchical clustering derived from genetic data. Currently, users employ ad hoc methods to test for association between a given tree and a response variable, which reduces reproducibility and robustness. In this paper, we introduce treeSeg, a simple to use and widely applicable methodology with high power for testing between all levels of hierarchy for a given tree and the response while accounting for the overall false positive rate. Our method allows for precise uncertainty quantification and therefore, increases interpretability and reproducibility of such studies across many fields of science. Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.
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9
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Wang W, He X, Zhu Z. Statistical inference for multiple change‐point models. Scand Stat Theory Appl 2020. [DOI: 10.1111/sjos.12456] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Wu Wang
- Statistics Program King Abdullah University of Science and Technology Saudi Arabia
| | - Xuming He
- Department of Statistics University of Michigan USA
| | - Zhongyi Zhu
- Department of Statistics Fudan University China
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10
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Fryzlewicz P. Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection. J Korean Stat Soc 2020. [DOI: 10.1007/s42952-020-00060-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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12
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Li Y, Lund R, Hewaarachchi A. Multiple changepoint detection with partial information on changepoint times. Electron J Stat 2019. [DOI: 10.1214/19-ejs1568] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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13
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Li H, Guo Q, Munk A. Multiscale change-point segmentation: beyond step functions. Electron J Stat 2019. [DOI: 10.1214/19-ejs1608] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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14
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Fryzlewicz P. Tail-greedy bottom-up data decompositions and fast multiple change-point detection. Ann Stat 2018. [DOI: 10.1214/17-aos1662] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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15
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Yi T, Wang Z, Yi D. Bayesian sieve methods: approximation rates and adaptive posterior contraction rates. J Nonparametr Stat 2018. [DOI: 10.1080/10485252.2018.1470241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
- Taihe Yi
- College of System Engineering, National University of Defense Technology, Changsha, People's Republic of China
| | - Zhengming Wang
- College of System Engineering, National University of Defense Technology, Changsha, People's Republic of China
| | - Dongyun Yi
- College of Liberal Arts and Science, National University of Defense Technology, Changsha, People's Republic of China
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18
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Yi T, Wang Z. Bayesian sieve method for piece-wise smooth regression. Stat Probab Lett 2017. [DOI: 10.1016/j.spl.2017.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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19
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Tecuapetla-Gómez I, Munk A. Autocovariance Estimation in Regression with a Discontinuous Signal and m-Dependent Errors: A Difference-Based Approach. Scand Stat Theory Appl 2016. [DOI: 10.1111/sjos.12256] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | - Axel Munk
- Institute for Mathematical Stochastics; University of Goettingen
- Max Planck Institute for Biophysical Chemistry at Goettingen
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20
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Pein F, Sieling H, Munk A. Heterogeneous change point inference. J R Stat Soc Series B Stat Methodol 2016. [DOI: 10.1111/rssb.12202] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | | | - Axel Munk
- Georg-August-Universität Göttingen and Max Planck Institute for Biophysical Chemistry; Göttingen Germany
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22
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Qian H, Kou SC. Statistics and Related Topics in Single-Molecule Biophysics. ANNUAL REVIEW OF STATISTICS AND ITS APPLICATION 2014; 1:465-492. [PMID: 25009825 PMCID: PMC4084599 DOI: 10.1146/annurev-statistics-022513-115535] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Since the universal acceptance of atoms and molecules as the fundamental constituents of matter in the early twentieth century, molecular physics, chemistry and molecular biology have all experienced major theoretical breakthroughs. To be able to actually "see" biological macromolecules, one at a time in action, one has to wait until the 1970s. Since then the field of single-molecule biophysics has witnessed extensive growth both in experiments and theory. A distinct feature of single-molecule biophysics is that the motions and interactions of molecules and the transformation of molecular species are necessarily described in the language of stochastic processes, whether one investigates equilibrium or nonequilibrium living behavior. For laboratory measurements following a biological process, if it is sampled over time on individual participating molecules, then the analysis of experimental data naturally calls for the inference of stochastic processes. The theoretical and experimental developments of single-molecule biophysics thus present interesting questions and unique opportunity for applied statisticians and probabilists. In this article, we review some important statistical developments in connection to single-molecule biophysics, emphasizing the application of stochastic-process theory and the statistical questions arising from modeling and analyzing experimental data.
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Affiliation(s)
- Hong Qian
- Department of Applied Mathematics, University of Washington Seattle, WA 98195
| | - S C Kou
- Department of Statistics, Harvard University, MA 02138
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