Abstract
Brain network analyses have exploded in recent years and hold great potential in helping us understand normal and abnormal brain function. Network science approaches have facilitated these analyses and our understanding of how the brain is structurally and functionally organized. However, the development of statistical methods that allow relating this organization to health outcomes has lagged behind. We have attempted to address this need by developing mixed modeling frameworks that allow relating system-level properties of brain networks to outcomes of interest. These frameworks serve as a synergistic fusion of multivariate statistical approaches with network science, providing a needed analytic (modeling and inferential) foundation for whole-brain network data. In this chapter we delineate these approaches that have been developed for single-task and multitask (longitudinal) brain network data, illustrate their utility with data applications, detail their implementation with a user-friendly Matlab toolbox, and discuss ongoing work to adapt the methods to (within-task) dynamic network analysis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Simpson SL, Bowman FD, Laurienti PJ (2013) Analyzing complex functional brain networks: fusing statistics and network science to understand the brain. Stat Surv 7:1–36
Simpson SL, Laurienti PJ (2016) Disentangling brain graphs: a note on the conflation of network and connectivity analyses. Brain Connect 6(2):95–98
Sporns O (2010) Networks of the brain. The MIT Press, Cambridge
Sporns O (2018) Graph theory methods: applications in brain networks. Dialog Clin Neurosci 20(2):111
Bullmore E, Sporns O (2009) Complex brain networks: graph theoretical analysis of structural and functional systems. Nat Rev Neurosci 10(3):186
Simpson SL, Burdette JH, Laurienti PJ (2015) The brain science interface. Significance 12(4):34–39
Telesford QK, Simpson SL, Burdette JH, Hayasaka S, Laurienti PJ (2011) The brain as a complex system: using network science as a tool for understanding the brain. Brain Connect 1(4):295–308
Bassett DS, Bullmore ET (2009) Human brain networks in health and disease. Curr Opin Neurol 22(4):340
Cao M, Wang JH, Dai ZJ, Cao XY, Jiang LL, Fan FM, et al (2014) Topological organization of the human brain functional connectome across the lifespan. Develop Cognit Neurosci 7:76–93
Simpson SL, Laurienti PJ (2015) A two-part mixed-effects modeling framework for analyzing whole-brain network data. NeuroImage 113:310–319
Simpson SL, Hayasaka S, Laurienti PJ (2011) Exponential random graph modeling for complex brain networks. PLoS One 6(5):e20039
Simpson SL, Moussa MN, Laurienti PJ (2012) An exponential random graph modeling approach to creating group-based representative whole-brain connectivity networks. Neuroimage 60(2):1117–1126
Solo V, Poline JB, Lindquist MA, Simpson SL, Bowman FD, Chung MK, Cassidy B (2018). Connectivity in fMRI: blind spots and breakthroughs. IEEE Trans Med Imag 37(7), 1537–1550
Handcock MS (2003) Statistical models for social networks: inference and degeneracy. In: Breiger R, Carley K, Pattison PE (eds) Dynamic social network modelling and analysis: workshop summary and papers. National Academy Press, Washington, DC, pp 229–240
Rinaldo, A., Fienberg SE, Zhou Y (2009) On the geometry of discrete exponential families with application to exponential random graph models Electron J Stat 3:446–484
O’Malley AJ (2013) The analysis of social network data: an exciting frontier for statisticians. Stat Med 32(4):539–555
Shehzad Z, Kelly C, Reiss PT, Cameron Craddock R, Emerson JW, McMahon K, et al (2014) A multivariate distance-based analytic framework for connectome-wide association studies. NeuroImage 93:74–94
Simpson SL, Lyday RG, Hayasaka S, Marsh AP, Laurienti PJ (2013) A permutation testing framework to compare groups of brain networks. Front Comput Neurosci 7:171
Bahrami M, Laurienti PJ, Quandt SA, Talton J, Pope CN, Summers P, Burdette JH, Chen H, Liu J, Howard TD, Arcury TA, Simpson SL (2017) The impacts of pesticide and nicotine exposures on functional brain networks in Latino immigrant workers. NeuroToxicology 62:138–150
Bahrami M, Laurienti PJ, Simpson SL (2019) A Matlab toolbox for multivariate analysis of brain networks. Hum Brain Mapp 40(1):175–186
Simpson, S. L., Bahrami M, Laurienti PJ (2019) A mixed-modeling framework for analyzing multitask whole-brain network data. Network Neurosci 3(2):307–324
Tzourio-Mazoyer N, Landeau B, Papathanassiou D, Crivello F, Etard O, Delcroix N, Mazoyer B, Joliot M (2002) Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage 15:273–289
Arcury, T. A., Nguyen HT, Summers P, Talton JW, Holbrook LC, Walker FO, …, Quandt SA (2014) Lifetime and current pesticide exposure among Latino farmworkers in comparison to other Latino immigrants. Am J Ind Med 57(7):776–787
Laurienti PJ, Burdette JH, Talton J, Pope CN, Summers P, Walker FO, …Arcury TA (2016) Brain anatomy in Latino farmworkers exposed to pesticides and nicotine. J Occup Environ Med 58(5):436
Schultz DH, Cole MW (2016) Integrated brain network architecture supports cognitive task performance. Neuron 92(2):278–279
Lebedev AV, Westman E., Simmons A, Lebedeva A, Siepel FJ, Pereira JB, Aarsland D (2014) Large-scale resting state network correlates of cognitive impairment in Parkinson’s disease and related dopaminergic deficits. Front Syst Neurosci 8:45
Baggio HC, Sala-Llonch R, Segura B, Marti MJ, Valldeoriola F, Compta Y, …Junqué C (2014) Functional brain networks and cognitive deficits in Parkinson’s disease. Hum Brain Mapp 35(9):4620–4634
Gamboa OL, Tagliazucchi E, von Wegner F, Jurcoane A, Wahl M, Laufs H, Ziemann U (2014). Working memory performance of early MS patients correlates inversely with modularity increases in resting state functional connectivity networks. Neuroimage 94:385–395
Hugenschmidt CE, Mozolic JL, Tan H, Kraft RA, Laurienti PJ (2009) Age-related increase in cross-sensory noise in resting and steady-state cerebral perfusion. Brain Topogr 21(3–4):241–251
Moussa MN, Vechlekar CD, Burdette JH, Steen MR, Hugenschmidt CE, Laurienti PJ (2011) Changes in cognitive state alter human functional brain networks. Front Hum Neurosci 5:103–113
Hunter DR, Goodreau SM, Handcock MS (2008) Goodness of fit of social network models. J Am Stat Assoc 103(481):248–258
Van Den Heuvel, MP, Sporns O (2011) Rich-club organization of the human connectome. J Neurosci 31(44):15775–15786
Edwards LJ (2000). Modern statistical techniques for the analysis of longitudinal data in biomedical research. Pediatr Pulmonol 30(4):330–344
Ginestet CE, Fournel AP, Simmons A (2014) Statistical network analysis for functional MRI: mean networks and group comparisons. Front Comput Neurosci 8:51
Albert PS, Shen J (2005) Modelling longitudinal semicontinuous emesis volume data with serial correlation in an acupuncture clinical trial. J R Stat Soc Ser C (Appl Stat) 54(4):707–720
Liu L, Ma JZ, Johnson BA (2008) A multi-level two-part random effects model, with application to an alcohol-dependence study. Stat Med 27(18):3528–3539
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069
Friedman EJ, Landsberg AS, Owen JP, Li YO, Mukherjee P (2014) Stochastic geometric network models for groups of functional and structural connectomes. NeuroImage 101:473–484
Wolfinger R, O’connell M (1993). Generalized linear mixed models a pseudo-likelihood approach. J Stat Comput Simul 48(3–4):233–243
Bahrami M, Laurienti PJ, Simpson SL (2019) Analysis of brain subnetworks within the context of their whole-brain networks. Hum Brain Mapp 40(17):5123–5141
McIntosh AR (2000) Towards a network theory of cognition. Neural Netw 13(8–9):861–870
Menon V (2011) Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cognit Sci 15(10):483–506
Mokhtari F, Akhlaghi MI, Simpson SL, Wu G, Laurienti PJ (2019). Sliding window correlation analysis: modulating window shape for dynamic brain connectivity in resting state. NeuroImage 189:655–666
Laurienti PJ, Bahrami M, Lyday RG, Casanova R, Burdette JH, Simpson, SL (2019) Using low-dimensional manifolds to map relationships between dynamic brain networks. Front Hum Neurosci 13:430
Chang, C., Keilholz S, Miller R, Woolrich M (2018) Mapping and interpreting the dynamic connectivity of the brain. NeuroImage 180(PB):335–336
Petersen SE, Sporns O (2015) Brain networks and cognitive architectures. Neuron 88(1):207–219
Park HJ, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342(6158):1238411
Bassett DS, Wymbs NF, Porter MA, Mucha PJ, Carlson JM, Grafton ST (2011) Dynamic reconfiguration of human brain networks during learning. Proc Natl Acad Sci 108(18):7641–7646
Rashid B, Arbabshirani MR, Damaraju E, Cetin MS, Miller R, Pearlson GD, Calhoun VD (2016) Classification of schizophrenia and bipolar patients using static and dynamic resting-state fMRI brain connectivity. Neuroimage 134:645–657
Zhang J, Cheng W, Liu Z, Zhang K, Lei X, Yao Y, …, Feng J (2016) Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders. Brain 139(8):2307–2321
Barttfeld P, Uhrig L, Sitt JD, Sigman M, Jarraya B, Dehaene S (2015) Signature of consciousness in the dynamics of resting-state brain activity. Proc Natl Acad Sci 112(3):887–892
Godwin, D., Barry RL, Marois R (2015) Breakdown of the brain’s functional network modularity with awareness. Proc Natl Acad Sci 112(12):3799–3804
Medaglia JD, Lynall ME, Bassett DS (2015). Cognitive network neuroscience. J Cognit Neurosci 27(8):1471–1491
Hutchison RM, Womelsdorf T, Allen EA, Bandettini PA, Calhoun VD, Corbetta M, …, Handwerker DA (2013) Dynamic functional connectivity: promise, issues, and interpretations. Neuroimage 80:360–378
Shine JM, Bissett PG, Bell PT, Koyejo O, Balsters JH, Gorgolewski KJ, …, Poldrack RA (2016) The dynamics of functional brain networks: integrated network states during cognitive task performance. Neuron 92(2):544–554
Fukushima M, Betzel RF, He Y, de Reus MA, van den Heuvel MP, Zuo XN, Sporns O (2018) Fluctuations between high-and low-modularity topology in time-resolved functional connectivity. NeuroImage 180:406–416
Sizemore AE, Bassett DS (2018) Dynamic graph metrics: tutorial, toolbox, and tale. NeuroImage 180:417–427
Elton A, Gao W (2015). Task-related modulation of functional connectivity variability and its behavioral correlations. Hum Brain Mapp 36(8):3260–3272
Edwards LJ, Simpson SL (2014) An analysis of 24-hour ambulatory blood pressure monitoring data using orthonormal polynomials in the linear mixed model. Blood Pressure Monit 19(3):153
Simpson SL, Edwards LJ (2013) A circular LEAR correlation structure for cyclical longitudinal data. Stat Methods Med Res 22(3):296–306
Acknowledgements
This work was supported by National Institute of Biomedical Imaging and Bioengineering R01EB024559, and Wake Forest Clinical and Translational Science Institute (WF CTSI) NCATS UL1TR001420.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Simpson, S.L. (2022). Mixed Modeling Frameworks for Analyzing Whole-Brain Network Data. In: Ossandon, M.R., Baker, H., Rasooly, A. (eds) Biomedical Engineering Technologies. Methods in Molecular Biology, vol 2393. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-1803-5_30
Download citation
DOI: https://doi.org/10.1007/978-1-0716-1803-5_30
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-1802-8
Online ISBN: 978-1-0716-1803-5
eBook Packages: Springer Protocols