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A Bayesian approach for estimating dynamic functional network connectivity in fMRI data. (English) Zbl 1398.62350

Summary: Dynamic functional connectivity, that is, the study of how interactions among brain regions change dynamically over the course of an fMRI experiment, has recently received wide interest in the neuroimaging literature. Current approaches for studying dynamic connectivity often rely on ad hoc approaches for inference, with the fMRI time courses segmented by a sequence of sliding windows. We propose a principled Bayesian approach to dynamic functional connectivity, which is based on the estimation of time varying networks. Our method utilizes a hidden Markov model for classification of latent cognitive states, achieving estimation of the networks in an integrated framework that borrows strength over the entire time course of the experiment. Furthermore, we assume that the graph structures, which define the connectivity states at each time point, are related within a super-graph, to encourage the selection of the same edges among related graphs. We apply our method to simulated task-based fMRI data, where we show how our approach allows the decoupling of the task-related activations and the functional connectivity states. We also analyze data from an fMRI sensorimotor task experiment on an individual healthy subject and obtain results that support the role of particular anatomical regions in modulating interaction between executive control and attention networks.

MSC:

62P10 Applications of statistics to biology and medical sciences; meta analysis
62A09 Graphical methods in statistics
92C20 Neural biology
92C55 Biomedical imaging and signal processing

Software:

HdBCS; SimTB
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Full Text: DOI Link

References:

[1] Airoldi, E. M.; Costa, T.; Bassetti, F.; Leisen, F.; Guindani, M., Generalized species sampling priors with latent beta reinforcements, Journal of the American Statistical Association, 109, 1466-1480, (2014) · Zbl 1368.62125
[2] Albughdadi, M.; Chaari, L.; Tourneret, J.-Y.; Forbes, F.; Ciuciu, P., A Bayesian non-parametric hidden Markov random model for hemodynamic brain parcellation, Signal Processing, 135, 132-146, (2017)
[3] Allen, E. A.; Damaraju, E.; Plis, S. M.; Erhardt, E. B.; Eichele, T.; Calhoun, V. D., Tracking whole-brain connectivity dynamics in the resting state, Cerebral Cortex, 24, 663-676, (2012)
[4] Badillo, S.; Vincent, T.; Ciuciu, P., Group-level impacts of within- and between-subject hemodynamic variability in fMRI, NeuroImage, 82, 433-448, (2013)
[5] Baker, A.; Brookes, M.; Rezek, A.; Smith, S.; Behrens, T.; Penny, J.; Smith, R.; Woolrich, M., Fast transient networks in spontaneous human brain activity, eLife, 3, 1-18, (2014)
[6] Balqis-Samdin, S.; Ting, C.-M.; Ombao, H.; Salleh, S.-H., A unified estimation framework for state-related changes in effective brain connectivity, IEEE Transactions on Biomedical Engineering, 64, 844-858, (2017)
[7] Beal, M. J.; Ghahramani, Z.; Rasmussen, C., The infinite hidden Markov model, NIPS, 14, 577-584, (2002)
[8] Berger, A.; Posner, M. I., Pathologies of brain attentional networks, Neuroscience and Biobehavioral Reviews, 24, 3-5, (2000)
[9] Bowman, F.; Caffo, B.; Bassett, S.; Kilts, C., A Bayesian hierarchical framework for spatial modeling of fMRI data, NeuroImage, 39, 146-156, (2008)
[10] Bowman, F. D., Brain imaging analysis, Annual Review of Statistics and Its Application, 1, 61-85, (2014)
[11] Bullmore, E.; Sporns, O., Complex brain networks: graph theoretical analysis of structural and functional systems, Nature Reviews Neuroscience, 10, 186-198, (2009)
[12] Calhoun, V.; Adali, T.; Pearlson, G.; Pekar, J., A method for making group inferences from functional MRI data using independent component analysis, Human Brain Mapping, 14, 140-151, (2001)
[13] Calhoun, V. D.; Adali, T.; Pekar, J. A., A method for comparing group fMRI data using independent component analysis: application to visual, motor and visuomotor tasks, Magnetic Resonance Imaging, 22, 1181-1191, (2004)
[14] Calhoun, V. D.; Miller, R.; Pearlson, G.; Adali, T., The chronnectome: time-varying connectivity networks as the next frontier in fMRI data discovery, Neuron, 84, 262-274, (2014)
[15] Cao, J.; Worsley, K., The geometry of correlation fields with an application to functional connectivity of the brain, The Annals of Applied Probability, 9, 1021-1057, (1999) · Zbl 0961.60052
[16] Chaari, L.; Vincent, T.; Forbes, F.; Dojat, M.; Ciuciu, P., Fast joint detection-estimation of evoked brain activity in event-related fMRI using a variational approach, IEEE Transactions on Medical Imaging, 32, 821-837, (2013)
[17] Chang, C.; Glover, G., Time-frequency dynamics of resting-state brain connectivity measured with fMRI, NeuroImage, 50, 81-98, (2010)
[18] Chiang, S.; Cassese, A.; Guindani, M.; Vannucci, M.; Yeh, H. J.; Haneef, Z.; Stern, J. M., Time-dependence of graph theory metrics in functional connectivity analysis, NeuroImage, 125, 601-615, (2015)
[19] Cohen, A. L.; Fair, D. A.; Dosenbach, N. U.; Miezin, F. M.; Dierker, D.; Essen, D. C. V.; Schlaggar, B. L.; Petersen, S. E., Defining functional areas in individual human brains using resting functional connectivity MRI, NeuroImage, 41, 45-57, (2008)
[20] Cribben, I.; Haraldsdottir, R.; Atlas, L.; Wager, T. D.; Lindquist, M. A., Dynamic connectivity regression: determining state-related changes in brain connectivity, NeuroImage, 61, 907-920, (2012)
[21] Damaraju, E.; Allen, E. A.; Belgar, A.; Ford, J. M.; Preda, A.; Turner, J. A.; Vaidya, J. G.; van Erp, T. G.; Calhoun, V. D., Dynamic functional connectivity analysis reveals transient states of dysconnectivity in schizophrenia, NeuroImage, 5, 298-308, (2014)
[22] De Pisapia, N.; Turatto, M.; Lin, P.; Jovicich, J.; Caramazza, A., Unconscious priming instructions modulate activity in default and executive networks of the human brain, Cerebral Cortex, 22, 639-649, (2011)
[23] Dobra, A.; Lenkoski, A.; Rodriguez, A., Bayesian inference for general Gaussian graphical models with application to multivariate lattice data, Journal of the American Statistical Association, 106, 1418-1433, (2011) · Zbl 1234.62018
[24] Erhardt, E. B.; Allen, E. A.; Wei, Y.; Eichele, T.; Calhoun, V. D., Simtb, a simulation toolbox for fMRI data under a model of spatiotemporal separability, NeuroImage, 59, 4160-4167, (2012)
[25] Erhardt, E. B.; Rachakonda, S.; Bedrick, E. J.; Allen, E. A.; Adali, T.; Calhoun, V. D., Comparison of multi-subject ICA methods for analysis of fMRI data, Human Brain Mapping, 32, 2075-2095, (2011)
[26] Flandin, G.; Penny, W., Bayesian fMRI data analysis with sparse spatial basis function priors, NeuroImage, 34, 1108-1125, (2007)
[27] Friston, K. J.; Jezzard, P.; Turner, R., Analysis of functional MRI time-series, Human Brain Mapping, 1, 153-171, (1994)
[28] Garrity, A.; Pearlson, G.; McKiernan, K.; Lloyd, D.; Kiehl, K.; Calhoun, V. D., Aberrant ‘default mode’ functional connectivity in schizophrenia, American Journal of Psychiatry, 164, 450-457, (2007)
[29] Geweke, J., Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments, Bayesian Statistics, 4, 169-193, (1992)
[30] Gollub, R. L.; Shoemaker, J. M.; King, M. D.; White, T.; Ehrlich, S.; Sponheim, S. R.; Clark, V. P.; Turner, J. A.; Mueller, B. A.; Magnotta, V., The MCIC collection: A shared repository of multi-modal, multi-site brain image data from a clinical investigation of schizophrenia, Neuroinformatics, 11, 367-388, (2013)
[31] Hinne, M.; Ambrogioni, L.; Janssen, R. J.; Heskes, T.; van Gerven, M. A., Structurally-informed Bayesian functional connectivity analysis, NeuroImage, 86, 294-305, (2014)
[32] Holbrook, A.; Lan, S.; Vandenberg-Rodesa, A.; Shahbaba, B, Geodesic Lagrangian Monte Carlo over the space of positive definite matrices: with application to Bayesian spectral density estimation, (2016)
[33] Hutchison, R. M.; Womelsdorf, T.; Allen, E. A.; Bandettini, P. A.; Calhoun, V. D.; Corbetta, M.; Penna, S. D.; Duyn, J. H.; Glover, G. H.; Gonzalez-Castillo, J.; Handwerker, D. A.; Keilholz, S.; Kiviniemi, V.; Leopold, D. A.; de Pasquale, F.; Sporns, O.; Walter, M.; Chang, C., Dynamic functional connectivity: promise, issues, and interpretations, NeuroImage, 80, 360-378, (2013)
[34] Jackson, S. R.; Marrocco, R.; Posner, M. I., Networks of anatomical areas controlling visuospatial attention, Neural Networks, 7, 925-944, (1994)
[35] Jones, B.; Carvalho, C.; Dobra, A.; Hans, C.; Carter, C.; West, M., Experiments in stochastic computation for high-dimensional graphical models, Statistical Science, 20, 388-400, (2005) · Zbl 1130.62408
[36] Josse, J.; Pages, J.; Husson, F., Testing the significance of the RV coefficient, Computational Statistics and Data Analysis, 53, 82-91, (2008) · Zbl 1452.62399
[37] Kalus, S.; Sämann, P.; Fahrmeir, L., Classification of brain activation via spatial Bayesian variable selection in fMRI regression, Advances in Data Analysis and Classification, 8, 63-83, (2013) · Zbl 1459.62205
[38] Koshino, H.; Minamoto, T.; Ikeda, T.; Osaka, M.; Otsuka, Y.; Osaka, N., Anterior medial prefrontal cortex exhibits activation during task preparation but deactivation during task execution, PLoS ONE, 6, e22909, (2011)
[39] Koshino, H.; Minamoto, T.; Yaoi, K.; Osaka, M.; Osaka, N., Coactivation of the default mode network regions and working memory network regions during task preparation, Scientific Reports, 4, (2014)
[40] Lauritzen, S., Graphical Models, (1996), Clarendon Press, New York · Zbl 0907.62001
[41] Lazar, N. A., The Statistical Analysis of Functional MRI Data, (2008), Springer, New York · Zbl 1312.62004
[42] Lee, K.; Jones, G.; Caffo, B.; Bassett, S., Spatial Bayesian variable selection models on functional magnetic resonance imaging time-series data, Bayesian Analysis, 9, 699-732, (2014) · Zbl 1327.62507
[43] Li, F.; Zhang, N., Bayesian variable selection in structured high-dimensional covariate space with application in genomics, Journal of American Statistical Association, 105, 1202-1214, (2010) · Zbl 1390.62027
[44] Lindquist, M., The statistical analysis of fMRI data, Statistical Science, 23, 439-464, (2008) · Zbl 1329.62296
[45] Lindquist, M.; Loh, J.; Atlas, L.; Wager, T., Modeling the hemodynamic response function in fMRI: efficiency, bias, and mis-modeling, NeuroImage, 45, 187-198, (2009)
[46] Lindquist, M. A.; Xu, Y.; Nebel, M. B.; Caffo, B. S., Evaluating dynamic bivariate correlations in resting-state fMRI: A comparison study and a new approach, NeuroImage, 101, 531-546, (2014)
[47] Liu, C.; Gaetz, W.; Zhu, H, Estimation of time-varying coherence and its application in understanding brain functional connectivity, EURASIP Journal on Advances in Signal Processing, (2010)
[48] Makni, S.; Idier, J.; Vincent, T.; Thirion, B.; Dehaene-Lambertz, G.; Ciuciu, P., A fully Bayesian approach to the parcel-based detection-estimation of brain activity in fMRI, Neuroimage, 41, 941-969, (2008)
[49] McKeown, M.; Makeig, S.; Brown, G.; Jung, T.; Kindermann, S.; Bell, A.; Sejnowski, T., Analysis of fMRI data by blind separation into independent spatial components, Human Brain Mapping, 6, 160-188, (1998)
[50] Messe, A.; Rudrauf, D.; Benali, H.; Marrelec, G., Relating structure and function in the human brain: relative contributions of anatomy, stationary dynamics, and non-stationarities, PLOS Computational Biology, 10, e1003530, (2014)
[51] Neal, R. M., Handbook of Markov Chain Monte Carlo, Mcmc using Hamiltonian dynamics, (2011)
[52] Nee, D. E.; Wager, T. D.; Jonides, J., Interference resolution: insights from a meta-analysis of neuroimaging tasks, Cognitive, Affective, and Behavioral Neuroscience, 7, 1-17, (2007)
[53] Newton, M. A.; Noueiry, A.; Sarkar, D.; Ahlquist, P., Detecting differential gene expression with a semiparametric hierarchical mixture method, Biostatistics, 5, 155-176, (2004) · Zbl 1096.62124
[54] Patel, R.; Bowman, F.; Rilling, J., A Bayesian approach to determining connectivity of the human brain, Human Brain Mapping, 27, 267-276, (2006)
[55] Determining hierarchical functional networks from auditory stimuli fMRI, Human Brain Mapping, 27, 462-470, (2006)
[56] Peterson, C.; Stingo, F. C.; Vannucci, M., Bayesian inference of multiple Gaussian graphical models, Journal of the American Statistical Association, 110, 159-174, (2015) · Zbl 1373.62106
[57] Pircalabelu, E.; Claeskens, G.; Jahfari, S.; Waldorp, L., A focused information criterion for graphical models in fMRI connectivity with high-dimensional data, Annals of Applied Statistics, 9, 2179-2214, (2015) · Zbl 1397.62466
[58] Poldrack, R.; Mumford, J.; Nichols, T, Handbook of fMRI Data Analysis, (2011), New York: Cambridge University Press · Zbl 1321.92015
[59] Quirós, A.; Diez, R.; Gamerman, D., Bayesian spatiotemporal model of fMRI data, NeuroImage, 49, 442-456, (2010)
[60] Ricardo-Sato, J.; Amaro, E.; Yasumasa-Takahashi, D.; de Maria, F.; Brammer, J.; Morettin, A., A method to produce evolving functional connectivity maps during the course of an fMRI experiment using wavelet-based time-varying Granger causality, NeuroImage, 31, 187-196, (2006)
[61] Roverato, A., Hyper inverse Wishart distribution for non-decomposable graphs and its application to Bayesian inference for Gaussian graphical models, Scandinavian Journal of Statistics, 29, 391-411, (2002) · Zbl 1036.62027
[62] Scott, S. L., Bayesian methods for hidden Markov models: recursive computing in the 21st century, Journal of the American Statistical Association, 97, 337-351, (2002) · Zbl 1073.65503
[63] Shi, H.; Wang, X.; Yi, J.; Zhu, X.; Zhang, X.; Yang, J.; Yao, S., Default mode network alterations during implicit emotional faces processing in first-episode, treatment-naive major depression patients, Frontiers in Psychology, 6, 1198, (2015)
[64] Smith, A. M.; Lewis, B. K. Ruttimann; Ye, F. Q.; Sinnwell, T. M.; Yang, Y.; Duyn, J. H.; Frank, J. A., Investigation of low frequency drift in fMRI signal, NeuroImage, 9, 526-533, (1999)
[65] Smith, M.; Fahrmeir, L., Spatial Bayesian variable selection with application to functional magnetic resonance imaging, Journal of the American Statistical Association, 102, 417-431, (2007) · Zbl 1134.62322
[66] Smith, S. M.; Miller, K. L.; Salimi-Khorshidi, G.; Webster, M.; Beckmann, C. F.; Nichols, T. E.; Ramsey, J. D.; Woolrich, M. W., Network modelling methods for fMRI, NeuroImage, 54, 875-891, (2011)
[67] Stingo, F.; Chen, Y.; Tadesse, M.; Vannucci, M., Incorporating biological information into linear models: A Bayesian approach to the selection of pathways and genes, Annals of Applied Statistics, 5, 1978-2002, (2011) · Zbl 1228.62150
[68] Stingo, F.; Guindani, M.; Vannucci, M.; Calhoun, V., An integrative Bayesian modeling approach to imaging genetics, Journal of the American Statistical Association, 108, 876-891, (2013) · Zbl 06224973
[69] Stingo, F.; Swartz, M.; Vannucci, M., A Bayesian approach for the identification of genes and gene-level SNP aggregates in a genetic analysis of cancer data, Statistics and Its Interface, 8, 137-151, (2015) · Zbl 1405.62205
[70] Uddin, L. Q.; Kelly, A.; Biswal, B. B.; Castellanos, F.; Milham, M. P., Functional connectivity of default mode network components: correlation, anticorrelation, and causality, Human Brain Mapping, 30, 625-637, (2009)
[71] Varoquaux, G.; Gramfort, A.; Poline, J.; Thirion, B., Markov models for fMRI correlation structure: Is brain functional connectivity small world, or decomposable into networks?, Journal of Physiology-Paris, 106, 212-221, (2012)
[72] Varoquaux, G.; Gramfort, A.; Poline, J.; Thirion, B.; Zemel, R.; Shawe-Taylor, J., Brain covariance selection: better individual functional connectivity models using population prior, Advances in Neural Information Processing Systems, 2334-2342, (2010)
[73] Wager, T. D.; Sylvester, C.-Y. C.; Lacey, S. C.; Nee, D. E.; Franklin, M.; Jonides, J., Common and unique components of response inhibition revealed by fMRI, NeuroImage, 27, 323-340, (2005)
[74] Wang, H.; Li, S., Efficient Gaussian graphical model determination under {\textitG}-Wishart prior distributions, Electronic Journal of Statistics, 6, 168-198, (2012) · Zbl 1335.62069
[75] Woolrich, M., Bayesian inference in fMRI, NeuroImage, 62, 801-810, (2012)
[76] Xu, Y.; Lindquist, M. A., Dynamic connectivity detection: an algorithm for determining functional connectivity change points in fMRI data, Frontiers in Neuroscience, 9, (2015)
[77] Yu, Q.; Sui, J.; Liu, J.; Plis, S.; Kiehl, K.; Pearlson, G.; Calhoun, V., Disrupted correlation between low frequency power and connectivity strength of resting state brain networks in schizophrenia, Schizophrenia Research, 143, 165-171, (2013)
[78] Yu, Z.; Prado, R.; Quinlan, E.; Cramer, S.; Ombao, H., Understanding the impact of stroke on brain motor function: A hierarchical Bayesian approach, Journal of the American Statistical Association, 111, 549-563, (2016)
[79] Zalesky, A.; Fornito, A.; Bullmore, E., On the use of correlation as a measure of network connectivity, NeuroImage, 60, 2096-2106, (2012)
[80] Zalesky, A.; Fornito, A.; Harding, I. H.; Cocchi, L.; Yucel, M.; Pantelis, C.; Bullmore, E. T., Whole-brain anatomical networks: does the choice of nodes matter?, NeuroImage, 50, 970-983, (2010)
[81] Zhang, L.; Guindani, M.; Vannucci, M., Bayesian models for functional magnetic resonance imaging data analysis, WIREs Computational Statistics, 7, 21-41, (2015)
[82] Zhang, L.; Guindani, M.; Versace, F.; Engelmann, J.; Vannucci, M., A spatio-temporal nonparametric Bayesian model of multi-subject fMRI data, Annals of Applied Statistics, 10, 638-666, (2016) · Zbl 1400.62299
[83] Zhang, L.; Guindani, M.; Versace, F.; Vannucci, M., A spatio-temporal nonparametric Bayesian variable selection model of fMRI data for clustering correlated time courses, NeuroImage, 95, 162-175, (2014)
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