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Yang Z, Zhuang X, Sreenivasan K, Mishra V, Curran T, Byrd R, Nandy R, Cordes D. 3D spatially-adaptive canonical correlation analysis: Local and global methods. Neuroimage 2018; 169:240-255. [PMID: 29248697 PMCID: PMC5856611 DOI: 10.1016/j.neuroimage.2017.12.025] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Revised: 12/07/2017] [Accepted: 12/11/2017] [Indexed: 01/13/2023] Open
Abstract
Local spatially-adaptive canonical correlation analysis (local CCA) with spatial constraints has been introduced to fMRI multivariate analysis for improved modeling of activation patterns. However, current algorithms require complicated spatial constraints that have only been applied to 2D local neighborhoods because the computational time would be exponentially increased if the same method is applied to 3D spatial neighborhoods. In this study, an efficient and accurate line search sequential quadratic programming (SQP) algorithm has been developed to efficiently solve the 3D local CCA problem with spatial constraints. In addition, a spatially-adaptive kernel CCA (KCCA) method is proposed to increase accuracy of fMRI activation maps. With oriented 3D spatial filters anisotropic shapes can be estimated during the KCCA analysis of fMRI time courses. These filters are orientation-adaptive leading to rotational invariance to better match arbitrary oriented fMRI activation patterns, resulting in improved sensitivity of activation detection while significantly reducing spatial blurring artifacts. The kernel method in its basic form does not require any spatial constraints and analyzes the whole-brain fMRI time series to construct an activation map. Finally, we have developed a penalized kernel CCA model that involves spatial low-pass filter constraints to increase the specificity of the method. The kernel CCA methods are compared with the standard univariate method and with two different local CCA methods that were solved by the SQP algorithm. Results show that SQP is the most efficient algorithm to solve the local constrained CCA problem, and the proposed kernel CCA methods outperformed univariate and local CCA methods in detecting activations for both simulated and real fMRI episodic memory data.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Xiaowei Zhuang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | | | - Virendra Mishra
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA
| | - Tim Curran
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA
| | - Richard Byrd
- Department of Computer Science, University of Colorado, Boulder, CO 80309, USA
| | - Rajesh Nandy
- School of Public Health, University of North Texas, Fort Worth, TX 76107, USA
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV 89106, USA; Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, USA.
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Lee A, Särkkä A, Madhyastha TM, Grabowski TJ. Characterizing cross-subject spatial interaction patterns in functional magnetic resonance imaging studies: A two-stage point-process model. Biom J 2017; 59:1352-1381. [PMID: 28699334 DOI: 10.1002/bimj.201600212] [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: 10/20/2016] [Revised: 05/16/2017] [Accepted: 05/18/2017] [Indexed: 11/08/2022]
Abstract
We develop a two-stage spatial point process model that introduces new characterizations of activation patterns in multisubject functional Magnetic Resonance Imaging (fMRI) studies. Conventionally multisubject fMRI methods rely on combining information across subjects one voxel at a time in order to identify locations of peak activation in the brain. The two-stage model that we develop here addresses shortcomings of standard methods by explicitly modeling the spatial structure of functional signals and recognizing that corresponding cross-subject functional signals can be spatially misaligned. In our first stage analysis, we introduce a marked spatial point process model that captures the spatial features of the functional response and identifies a configuration of activation units for each subject. The locations of these activation units are used as input for the second stage model. The point process model of the second stage analysis is developed to characterize multisubject activation patterns by estimating the strength of cross-subject interactions at different spatial ranges. The model uses spatial neighborhoods to account for the cross-subject spatial misalignment in corresponding functional units. We applied our methods to an fMRI study of 21 individuals who performed an attention test. We identified four brain regions that are involved in the test and found that our model results agree well with our understanding of how these regions engage with the tasks performed during the attention test. Our results highlighted that cross-subject interactions are stronger in brain areas that have a more specific function in performing the experimental tasks than in other areas.
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Affiliation(s)
- Adél Lee
- Etosha Business and Research Consulting, Mount Berry, GA, 30149, USA
| | - Aila Särkkä
- Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, 41296, Gothenburg, Sweden
| | - Tara M Madhyastha
- Department of Radiology, University of Washington, Seattle, WA, 98185, USA
| | - Thomas J Grabowski
- Department of Neurology and Department of Radiology, University of Washington, Seattle, WA, 98185, USA
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Pircalabelu E, Claeskens G, Jahfari S, Waldorp LJ. A focused information criterion for graphical models in fMRI connectivity with high-dimensional data. Ann Appl Stat 2015. [DOI: 10.1214/15-aoas882] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Nichols TE. Multiple testing corrections, nonparametric methods, and random field theory. Neuroimage 2012; 62:811-5. [PMID: 22521256 DOI: 10.1016/j.neuroimage.2012.04.014] [Citation(s) in RCA: 134] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2012] [Revised: 04/03/2012] [Accepted: 04/09/2012] [Indexed: 11/16/2022] Open
Abstract
I provide a selective review of the literature on the multiple testing problem in fMRI. By drawing connections with the older modalities, PET in particular, and how software implementations have tracked (or lagged behind) theoretical developments, my narrative aims to give the methodological researcher a historical perspective on this important aspect of fMRI data analysis.
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Affiliation(s)
- Thomas E Nichols
- Warwick Manufacturing Group & Department of Statistics, University of Warwick, Coventry CV4 7AL, UK.
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Cordes D, Jin M, Curran T, Nandy R. Optimizing the performance of local canonical correlation analysis in fMRI using spatial constraints. Hum Brain Mapp 2011; 33:2611-26. [PMID: 23074078 DOI: 10.1002/hbm.21388] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2011] [Revised: 04/23/2011] [Accepted: 05/19/2011] [Indexed: 11/06/2022] Open
Abstract
The benefits of locally adaptive statistical methods for fMRI research have been shown in recent years, as these methods are more proficient in detecting brain activations in a noisy environment. One such method is local canonical correlation analysis (CCA), which investigates a group of neighboring voxels instead of looking at the single voxel time course. The value of a suitable test statistic is used as a measure of activation. It is customary to assign the value to the center voxel for convenience. The method without constraints is prone to artifacts, especially in a region of localized strong activation. To compensate for these deficiencies, the impact of different spatial constraints in CCA on sensitivity and specificity are investigated. The ability of constrained CCA (cCCA) to detect activation patterns in an episodic memory task has been studied. This research shows how any arbitrary contrast of interest can be analyzed by cCCA and how accurate P-values optimized for the contrast of interest can be computed using nonparametric methods. Results indicate an increase of up to 20% in detecting activation patterns for some of the advanced cCCA methods, as measured by ROC curves derived from simulated and real fMRI data.
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Affiliation(s)
- Dietmar Cordes
- Department of Radiology, School of Medicine, University of Colorado-Denver, Colorado, USA.
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6
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Effective connectivity of fMRI data using ancestral graph theory: Dealing with missing regions. Neuroimage 2011; 54:2695-705. [DOI: 10.1016/j.neuroimage.2010.10.054] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2009] [Revised: 08/21/2010] [Accepted: 10/18/2010] [Indexed: 11/22/2022] Open
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Tabelow K, Clayden JD, de Micheaux PL, Polzehl J, Schmid VJ, Whitcher B. Image analysis and statistical inference in neuroimaging with R. Neuroimage 2011; 55:1686-93. [PMID: 21238596 DOI: 10.1016/j.neuroimage.2011.01.013] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2010] [Accepted: 01/07/2011] [Indexed: 10/18/2022] Open
Abstract
R is a language and environment for statistical computing and graphics. It can be considered an alternative implementation of the S language developed in the 1970s and 1980s for data analysis and graphics (Becker and Chambers, 1984; Becker et al., 1988). The R language is part of the GNU project and offers versions that compile and run on almost every major operating system currently available. We highlight several R packages built specifically for the analysis of neuroimaging data in the context of functional MRI, diffusion tensor imaging, and dynamic contrast-enhanced MRI. We review their methodology and give an overview of their capabilities for neuroimaging. In addition we summarize some of the current activities in the area of neuroimaging software development in R.
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Affiliation(s)
- K Tabelow
- Weierstrass Institute, Berlin, Germany.
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Weeda WD, Waldorp LJ, Grasman RPPP, van Gaal S, Huizenga HM. Functional connectivity analysis of fMRI data using parameterized regions-of-interest. Neuroimage 2010; 54:410-6. [PMID: 20637877 DOI: 10.1016/j.neuroimage.2010.07.022] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2010] [Revised: 07/07/2010] [Accepted: 07/09/2010] [Indexed: 11/30/2022] Open
Abstract
Connectivity analysis of fMRI data requires correct specification of regions-of-interest (ROIs). Selection of ROIs based on outcomes of a GLM analysis may be hindered by conservativeness of the multiple comparison correction, while selection based on brain anatomy may be biased due to inconsistent structure-to-function mapping. To alleviate these problems we propose a method to define functional ROIs without the need for a stringent multiple comparison correction. We extend a flexible framework for fMRI analysis (Activated Region Fitting, Weeda et al. 2009) to connectivity analysis of fMRI data. This method describes an entire fMRI data volume by regions of activation defined by a limited number of parameters. Therefore a less stringent multiple comparison procedure is required. The regions of activation from this analysis can be directly used to estimate functional connectivity. Simulations show that Activated Region Fitting can recover the connectivity of brain regions. An application to real data of a Go/No-Go experiment highlights the advantages of the method.
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Affiliation(s)
- Wouter D Weeda
- University of Amsterdam, Department of Psychology, Amsterdam, The Netherlands.
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Robust and unbiased variance of GLM coefficients for misspecified autocorrelation and hemodynamic response models in fMRI. Int J Biomed Imaging 2009; 2009:723912. [PMID: 19746181 PMCID: PMC2738954 DOI: 10.1155/2009/723912] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2008] [Revised: 04/03/2009] [Accepted: 06/21/2009] [Indexed: 11/18/2022] Open
Abstract
As a consequence of misspecification of the hemodynamic response and noise variance models, tests on general linear model coe cients are not valid. Robust estimation of the variance of the general linear model (GLM) coecients in fMRI time series is therefore essential. In this paper an alternative method to estimate the variance of the GLM coe cients accurately is suggested and compared to other methods. The alternative, referred to as the sandwich, is based primarily on the fact that the time series are obtained from multiple exchangeable stimulus presentations. The analytic results show that the sandwich is unbiased. Using this result, it is possible to obtain an exact statistic which keeps the 5% false positive rate. Extensive Monte Carlo simulations show that the sandwich is robust against misspeci cation of the autocorrelations and of the hemodynamic response model. The sandwich is seen to be in many circumstances robust, computationally efficient, and flexible with respect to correlation structures across the brain. In contrast, the smoothing approach can be robust to a certain extent but only with specific knowledge of the circumstances for the smoothing parameter.
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