Large-scale DCMs for resting-state fMRI

Abstract

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of Bayesian model reduction to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM—with functional connectivity priors—is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

References 34

1. 1.

   Biswal B. B., Mennes M., Zuo X.-N., Gohel S., Kelly C., Smith S. M., … Milham M. P. (2010 ). Toward discovery science of human brain function. Proceedings of the National Academy of Sciences, 107, 4734–4739.

   Not in our library10.1073/pnas.0911855107

2. 2.

   Breakspear M. (2004 ). “Dynamic” connectivity in neural systems: Theoretical and empirical considerations. Neuroinformatics, 2(2), 205–226.

   Not in our library10.1385/ni:2:2:205

3. 3.

   Bullmore E., & Sporns O. (2009 ). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nat Rev Neurosci, 10(3), 186–198.

   Not in our library10.1038/nrn2575

4. 4.

   Carlson R. (1999 ). Inverse eigenvalue problems on directed graphs. Transactions of the American Mathematical Society, 351(10), 4069–4088.

   Not in our library10.1090/s0002-9947-99-02175-3

5. 5.

   Chung F. (2005 ). Laplacians and the Cheeger inequality for directed graphs. Annals of Combinatorics, 9(1), 1–19.

   Not in our library10.1007/s00026-005-0237-z

6. 6.

   Donetti L., Neri F., & Munoz M. A. (2006 ). Optimal network topologies: Expanders, cages, Ramanujan graphs entangled networks and all that. Journal of Statistical Mechanics.

   Not in our library10.1088/1742-5468/2006/08/p08007

7. 7.

   Fornito A., Zalesky A., & Breakspear M. (2015 ). The connectomics of brain disorders. Nature Reviews Neuroscience, 16(3), 159–172.

   Not in our library10.1038/nrn3901

8. 8.

   Fransson P. (2005 ). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26(1), 15–29.

   Not in our library10.1002/hbm.20113

9. 9.
   Analysing connectivity with Granger causality and dynamic causal modelling · 2013

   No notice
10. 10.

    Friston K. J. (2009 ). Modalities, modes, and models in functional neuroimaging. Science, 326(5951), 399–403.

    Not in our library10.1126/science.1174521

11. 11.

    Friston K. J., Frith C. D., Liddle P. F., & Frackowiak R. S. (1993 ). Functional connectivity: The principal-component analysis of large (PET) data sets. Journal of Cerebral Blood Flow and Metabolism, 13(1), 5–14.

    Not in our library10.1038/jcbfm.1993.4

12. 12.

    Friston K. J., Harrison L., & Penny W. (2003 ). Dynamic causal modelling. Neuroimage, 19(4), 1273–1302.

    Not in our library10.1016/s1053-8119(03)00202-7

13. 13.

    Friston K. J., Kahan J., Biswal B., & Razi A. (2014 ). A DCM for resting state fMRI. NeuroImage, 94, 396–407.

    Not in our library10.1016/j.neuroimage.2013.12.009

14. 14.

    Friston K. J., Kahan J., Razi A., Stephan K. E., & Sporns O. (2014 ). On nodes and modes in resting state fMRI. NeuroImage.

    Not in our library10.1016/j.neuroimage.2014.05.056

15. 15.

    Friston K. J., Li B., Daunizeau J., & Stephan K. E. (2011 ). Network discovery with DCM. NeuroImage, 56(3), 1202–1221.

    Not in our library10.1016/j.neuroimage.2010.12.039

16. 16.
    Bayesian model reduction and empirical Bayes for group (DCM) studies · 2016

    No notice
17. 17.

    Friston K. J., Stephan K., Li B. J., & Daunizeau J. (2010 ). Generalised filtering. Mathematical Problems in Engineering, Article ID 621670

    Not in our library10.1155/2010/621670

18. 18.

    Kahan J., Urner M., Moran R., Flandin G., Marreiros A., Mancini L., … Foltynie T. (2014 ). Resting state functional MRI in Parkinson’s disease: The impact of deep brain stimulation on “effective” connectivity. Brain, 137(4), 1130–1144.

    Not in our library10.1093/brain/awu027

19. 19.

    Li B., Daunizeau J., Stephan K. E., Penny W., Hu D., & Friston K. (2011 ). Generalised filtering and stochastic DCM for fMRI. NeuroImage, 58(2), 442–457.

    Not in our library10.1016/j.neuroimage.2011.01.085

20. 20.

    Margulies D. S., Ghosh S. S., Goulas A., Falkiewicz M., Huntenburg J. M., Langs G., … Smallwood J. (2016 ). Situating the default-mode network along a principal gradient of macro scale cortical organization. Proceedings of the National Acad emy of Sciences.

    Not in our library10.1073/pnas.1608282113

21. 21.

    Nakagawa T. T., Jirsa V. K., Spiegler A., McIntosh A. R., & Deco G. (2013 ). Bottom up modeling of the connectome: Linking structure and function in the resting brain and their changes in aging. NeuroImage, 80, 318–329.

    Not in our library10.1016/j.neuroimage.2013.04.055

22. 22.

    Penny W. D., Friston K. J., Ashburner J. T., Kiebel S. J., & Nichols T. E. (2011 ). Statistical parametric mapping: The analysis of functional brain images. London: Academic Press.

    Not in our library

23. 23.

    Qi S. L., Meesters S., Nicolay K., Romeny B. M. T., & Ossenblok P. (2015 ). The influence of construction methodology on structural brain network measures: A review. Journal of Neuroscience Methods, 253, 170–182.

    Not in our library10.1016/j.jneumeth.2015.06.016

24. 24.

    Raichle M. E. (2011 ). The restless brain. Brain Connect, 1(1), 3–12.

    Not in our library10.1089/brain.2011.0019

25. 25.

    Raj A., Kuceyeski A., & Weiner M. (2012 ). A network diffusion model of disease progression in dementia. Neuron, 73(6), 1204–1215.

    Not in our library10.1016/j.neuron.2011.12.040

26. 26.

    Razi A., & Friston K. J. (2016 ). The connected brain causality, models, and intrinsic dynamics. IEEE Signal Processing Magazine, 33(3), 14–35.

    Not in our library10.1109/msp.2015.2482121

27. 27.
    Construct validation of a DCM for resting state fMRI · 2015

    No notice
28. 28.

    Rubinov M., & Sporns O. (2010 ). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52(3), 1059–1069.

    Not in our library10.1016/j.neuroimage.2009.10.003

29. 29.

    Rubinov M., & Sporns O. (2011 ). Weight-conserving characterization of complex functional brain networks. NeuroImage, (56), 4 2068–2079.

    Not in our library10.1016/j.neuroimage.2011.03.069

30. 30.

    Seghier M. L., & Friston K. J. (2013 ). Network discovery with large DCMs. NeuroImage, 68, 181–191.

    Not in our library10.1016/j.neuroimage.2011.03.069

31. 31.

    Simpson S. L., Bowman F. D., & Laurienti P. J. (2013 ). Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain. Statistics Surveys, 7, 1–36.

    Not in our library10.1214/13-ss103

32. 32.

    Smith S. M., Vidaurre D., Beckmann C. F., Glasser M. F., Jenkinson M., Miller K. L., … Van Essen D. C. (2013 ). Functional connectomics from resting-state fMRI. Trends Cognitive Sciences, 17(12), 666–682.

    Not in our library10.1016/j.tics.2013.09.016

33. 33.

    Sporns O. (2014 ). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17(5), 652–6660.

    Not in our library10.1038/nn.3690

34. 34.

    Sporns O., & Betzel R. F. (2016 ). Modular brain networks. Annual Review of Psychology, 67, 613–640.

    Not in our library10.1146/annurev-psych-122414-033634

Cited by papers we hold 2

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• The Hierarchical Organization of the Default, Dorsal Attention and Salience Networks in Adolescents and Young Adults · 2017
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