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A paradigm for longitudinal complex network analysis over patient cohorts in neuroscience

Published online by Cambridge University Press:  05 August 2019

Heather Shappell,
Yorghos Tripodis,
Ronald J. Killiany and
Eric D. Kolaczyk
Heather Shappell*
Affiliation:
Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
Yorghos Tripodis
Affiliation:
Department of Biostatistics, Boston University, Boston, MA, USA (email: Yorghos@bu.edu)
Ronald J. Killiany
Affiliation:
Department of Anatomy and Neurobiology, Boston University, Boston, MA, USA (email: killiany@bu.edu)
Eric D. Kolaczyk
Affiliation:
Department of Mathematics and Statistics, Boston University, Boston, MA, USA (email: kolaczyk@bu.edu)
*
*Corresponding author. Email: hshappe1@jh.edu
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Abstract

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The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Many of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs and an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models to the neuroscience setting. Stochastic Actor-Oriented Models are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e. network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state functional magnetic resonance imaging setting with data from the Alzheimer’s Disease Neuroimaging Initiative study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with Alzheimer’s disease.

Keywords

network analysislongitudinal network analysiscomplex brain networksStochastic Actor-Oriented ModelsAlzheimer’s diseaseresting-state fMRIfunctional connectivity

Information

Type
Original Article
Information
Network Science , Volume 7 , Issue 2 , June 2019, pp. 196 - 214
Copyright
© Cambridge University Press 2019 

Footnotes

†

Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf

References

Andrews-Hanna, J. R., Snyder, A. Z., Vincent, J. L, Lustig, C, Head, D., Raichle, M. E, & Buckner, R. L. (2007). Disruption of large-scale brain systems in advanced aging. Neuron, 56(5), 924–935.CrossRefGoogle ScholarPubMed
Bai, F., Watson, D. R., Shi, Y., Wang, Y., Yue, C., Wu, D., Yuan, Y., Zhang, Z., (2011). Specifically progressive deficits of brain functional marker in amnestic type mild cognitive impairment. PLoS One, 6(9), e24271.CrossRefGoogle ScholarPubMed
Beekly, D. L., Ramos, E. M., van Belle, G., Deitrich, W., Clark, A. D., Jacka, M. E., Kukull, W. A.; NIA-Alzheimer’s Disease euation’s. (2004). The national Alzheimer’s coordinating center (nacc) database: An Alzheimer disease database. Alzheimer Disease & Associated Disorders, 18(4), 270–277.Google ScholarPubMed
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(10), 4734–4739.CrossRefGoogle ScholarPubMed
Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: Emerging methods and principles. Trends in Cognitive Sciences, 14(6), 277–290.CrossRefGoogle ScholarPubMed
Buckner, R. L., Andrews-Hanna, J. R., & Schacter, D. L. (2008). The brain’s default network. Annals of the New York Academy of Sciences, 1124(1), 1–38.CrossRefGoogle ScholarPubMed
Cole, M. W., Reynolds, J. R., Power, J. D., Repovs, G., Anticevic, A., & Braver, T. S. (2013). Multi-task connectivity reveals flexible hubs for adaptive task control. Nature Neuroscience, 16(9), 1348–1355.CrossRefGoogle ScholarPubMed
Damoiseaux, J. S., Beckmann, C. F., Sanz Arigita, E. J., Barkhof, F., Scheltens, Ph., Stam, C. J., … Rombouts, S. A. R. B. (2008). Reduced resting-state brain activity in the default network in normal aging. Cerebral Cortex, 18(8), 1856–1864.CrossRefGoogle ScholarPubMed
Damoiseaux, J. S., Prater, K. E., Miller, B. L., & Greicius, M. D. (2012). Functional connectivity tracks clinical deterioration in Alzheimer’s disease. Neurobiology of Aging, 33(4), 828.e19–828.e30.CrossRefGoogle ScholarPubMed
Danon, L., Diaz-Guilera, A., Duch, J., & Arenas, A. (2005). Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 2005(9), P09008.CrossRefGoogle Scholar
DerSimonian, R., & Laird, N. (1986). Meta-analysis in clinical trials. Controlled Clinical Trials, 7(3), 177–188.CrossRefGoogle ScholarPubMed
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C, Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage, 31(3), 968–980.CrossRefGoogle ScholarPubMed
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., … Milham, M. P. (2014). The autism brain imaging data exchange: Towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular Psychiatry, 19(6), 659–667.CrossRefGoogle ScholarPubMed
Etkin, A., Prater, K. E., Schatzberg, A. F., Menon, V., & Greicius, M. D. (2009). Disrupted Amygdalar subregion functional connectivity and evidence of a compensatory network in generalized anxiety disorder. Archives of General Psychiatry, 66(12), 1361–1372.CrossRefGoogle ScholarPubMed
Fleisher, A. S., Sherzai, A., Taylor, C., Langbaum, J. B. S, Chen, K., & Buxton, R. B. (2009). Resting-state BOLD networks versus task-associated functional MRI for distinguishing Alzheimer’s disease risk groups. Neuroimage, 47(4), 1678–1690.CrossRefGoogle ScholarPubMed
Gardini, S., Venneri, A., Sambataro, F., Cuetos, F., Fasano, F., Marchi, M., … Caffarra, P. (2015). Increased functional connectivity in the default mode network in mild cognitive impairment: A maladaptive compensatory mechanism associated with poor semantic memory performance. Journal of Alzheimer’s Disease, 45(2), 457–470.CrossRefGoogle ScholarPubMed
Greicius, M. D., Krasnow, B., Reiss, A. L, & Menon, V. (2003). Functional connectivity in the resting brain: A network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences, 100(1), 253–258.CrossRefGoogle ScholarPubMed
Greicius, M. D., Srivastava, G., Reiss, A. L, & Menon, V. (2004). Default-mode network activity distinguishes Alzheimer’s disease from healthy aging: Evidence from functional MRI. Proceedings of the National Academy of Sciences of the United States of America, 101(13), 4637–4642.CrossRefGoogle ScholarPubMed
Hanneke, S., Fu, W., & Xing, E. P. (2010). Discrete temporal models of social networks. Electronic Journal of Statistics, 4, 585–605.CrossRefGoogle Scholar
Ishak, K. J., Platt, R. W., Joseph, L., Hanley, J. A., & Caro, J. J. (2007). Meta-analysis of longitudinal studies. Clinical Trials, 4(5), 525–539.CrossRefGoogle ScholarPubMed
Jackson, D., Riley, R., & White, I. R. (2011). Multivariate meta-analysis: Potential and promise. Statistics in Medicine, 30(20), 2481–2498.CrossRefGoogle ScholarPubMed
Kolaczyk, E. D. (2009). Statistical analysis of network data. New York, Springer.CrossRefGoogle Scholar
Laird, A. R., Eickhoff, S. B., Li, K., Robin, D. A., Glahn, D. C., & Fox, P. T. (2009). Investigating the functional heterogeneity of the default mode network using coordinate-based meta-analytic modeling. The Journal of Neuroscience, 29(46), 14496–14505.CrossRefGoogle ScholarPubMed
Lee, M. H., Smyser, C. D., & Shimony, J. S. (2013). Resting-state fMRI: A review of methods and clinical applications. American Journal of Neuroradiology, 34(10), 1866–1872.CrossRefGoogle ScholarPubMed
Leifeld, P., & Cranmer, S. J. (2015). A theoretical and empirical comparison of the temporal exponential random graph model and the stochastic actor-oriented model. arxiv preprint arxiv:1506.06696.Google Scholar
Liang, X., Wang, J., Yan, C., Shu, N., Xu, K., Gong, G., & He, Y. (2012). Effects of different correlation metrics and preprocessing factors on small-world brain functional networks: A resting-state functional MRI study. PLoS One, 7(3), e32766.CrossRefGoogle ScholarPubMed
Lospinoso, J. A. (2012). Statistical models for social network dynamics. Ph.D. thesis, University of Oxford, UK.Please provide the location of the Institution for the reference “Lospinoso (2012)”.Google Scholar
Mathys, C., Hoffstaedter, F., Caspers, J., Caspers, S., Südmeyer, M., Grefkes, C., … Langner, R. (2014). An age-related shift of resting-state functional connectivity of the subthalamic nucleus: A potential mechanism for compensating motor performance decline in older adults. Frontiers in Aging Neuroscience, 6, 178.CrossRefGoogle ScholarPubMed
Mesulam, M. (1990). Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Annals of Neurology, 28(5), 597–613.CrossRefGoogle Scholar
Milham, M. P., Fair, D., Mennes, M., & Mostofsky, S. H. (2012). The adhd-200 consortium: A model to advance the translational potential of neuroimaging in clinical neuroscience. Frontiers in Systems Neuroscience, 6, 62.Google Scholar
Moussa, M. N., Steen, M. R., Laurienti, P. J., & Hayasaka, S. (2012). Consistency of network modules in resting-state fMRI connectome data. PLoS One, 7(8), e44428.CrossRefGoogle ScholarPubMed
Mueller, S. G., Weiner, M. W., Thal, L. J., Petersen, R. C., Jack, C. R., Jagust, W., … Beckett, L. (2005). Ways toward an early diagnosis in Alzheimer’s disease: The Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia, 1(1), 55–66.CrossRefGoogle Scholar
Normand, S. L. T. (1999). Meta-analysis: Formulating, evaluating, combining and reporting. Statistics in Medicine, 18, 321–359.3.0.CO;2-P>CrossRefGoogle ScholarPubMed
Reuter, M., Schmansky, N. J., Rosas, H. D., & Fischl, B. (2012). Within-subject template estimation for unbiased longitudinal image analysis. Neuroimage, 61(4), 1402–1418.CrossRefGoogle ScholarPubMed
Ripley, R. M., Snijders, T. A. B., & Preciado, P. (2011). Manual for RSiena. University of Oxford, Department of Statistics, Nuffield College, 1 May.Google Scholar
Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. Neuroimage, 52(3), 1059–1069.CrossRefGoogle ScholarPubMed
Salvador, R., Suckling, J., Coleman, M. R., Pickard, J. D., Menon, D., & Bullmore, E. D. (2005). Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex, 15(9), 1332–1342.CrossRefGoogle ScholarPubMed
Sanz-Arigita, E. J., Schoonheim, M. M., Damoiseaux, J. S., Rombouts, S. A., Maris, E., Barkhof, F., … Stam, C. J. (2010). Loss of “small-world” networks in Alzheimer’s disease: Graph analysis of fMRI resting-state functional connectivity. PLoS One, 5(11), e13788.CrossRefGoogle ScholarPubMed
Schulz, K. P., Bédard, A.-C. V., Fan, J., Clerkin, S. M., Dima, D., Newcorn, J. H., & Halperin, J. M. (2014). Emotional bias of cognitive control in adults with childhood attention-deficit/hyperactivity disorder. Neuroimage. Clinical, 5, 1–9.CrossRefGoogle ScholarPubMed
Schwarz, A. J., & McGonigle, J. (2011). Negative edges and soft thresholding in complex network analysis of resting state functional connectivity data. Neuroimage, 55(3), 1132–1146.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Simpson, S. L., & Laurienti, P. J. (2016). Disentangling brain graphs: A note on the conflation of network and connectivity analyses. Brain Connectivity, 6(2), 95–98.CrossRefGoogle ScholarPubMed
Singer, J. D. (1998). Using SAS PROC MIXED to fit multilevel models, hierarchical models, and individual growth models. Journal of Educational and Behavioral Statistics, 23(4), 323–355.CrossRefGoogle Scholar
Snijders, T. A. B., Koskinen, J., & Schweinberger, M. (2010a). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, 4(2), 567.CrossRefGoogle Scholar
Snijders, T. A. B, Van de Bunt, G. G., & Steglich, C. E. G. (2010b). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.CrossRefGoogle Scholar
Sorg, C., Riedl, V., Mühlau, M., Calhoun, V. D., Eichele, T., Läer, L., … Wohlschläger, A. M. (2007). Selective changes of resting-state networks in individuals at risk for Alzheimer’s disease. Proceedings of the National Academy of Sciences, 104(47), 18760–18765.CrossRefGoogle ScholarPubMed
Sporns, O. (2011). Networks of the brain. Cambridge, MA: MIT Press.Google Scholar
Sporns, O. (2013). Structure and function of complex brain networks. Dialogues in Clinical Neuroscience, 15(3), 247–262.Google ScholarPubMed
Sporns, O. (2014). Contributions and challenges for network models in cognitive neuroscience. Nature Neuroscience, 17(5), 652–660.CrossRefGoogle ScholarPubMed
Stam, C. J. (2014). Modern network science of neurological disorders. Nature Reviews Neuroscience, 15(10), 683–695.CrossRefGoogle ScholarPubMed
Steglich, C., Snijders, T. A. B, & West, P. (2006). Applying SIENA. Methodology, 2(1), 48–56.CrossRefGoogle Scholar
Supekar, K., Menon, V., Rubin, D., Musen, M., & Greicius, M. D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer’s disease. Public Library of Science International Society for Computational Biology, 4(6), e1000100.Google ScholarPubMed
Telesford, Q. K., Simpson, S. L., Burdette, J. H., Hayasaka, S., & Laurienti, P. J. (2011). The brain as a complex system: Using network science as a tool for understanding the brain. Brain Connectivity, 1(4), 295–308.CrossRefGoogle Scholar
van den Heuvel, M. P., Mandl, R. C. W., Kahn, R. S., & Hulshoff Pol, H. E. (2009). Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Human Brain Mapping, 30(10), 3127–3141.CrossRefGoogle ScholarPubMed
Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., … Yacoub, E.; WU-Minn HCP Consortium. (2012). The human connectome project: A data acquisition perspective. Neuroimage, 62(4), 2222–2231.CrossRefGoogle ScholarPubMed
Van Wijk, B. C. M., Stam, C. J., & Daffertshofer, A. (2010). Comparing brain networks of different size and connectivity density using graph theory. PLoS One, 5(10), e13701.CrossRefGoogle ScholarPubMed
Varoquaux, G., Gramfort, A., Poline, J.-B., & Thirion, B. (2010). Brain covariance selection: better individual functional connectivity models using population prior. In Advances in neural information processing systems (pp. 2334–2342).Google Scholar
Wang, J., Zuo, X., & He, Y. (2010). Graph-based network analysis of resting-state functional MRI. Frontiers in Systems Neuroscience, 4, 16.Google ScholarPubMed
Wicker, B., Ruby, P., Royet, J.-P., & Fonlupt, P. (2003). A relation between rest and the self in the brain? Brain Research Reviews, 43(2), 224–230.CrossRefGoogle Scholar
Won, S., Morris, N., Lu, Q., & Elston, R. C. (2009). Choosing an optimal method to combine p-values. Statistics in Medicine, 28(11), 1537–1553.CrossRefGoogle ScholarPubMed
Wu, X., Li, R., Fleisher, A. S., Reiman, E. M., Guan, X., Zhang, Y., … Yao, L. (2011). Altered default mode network connectivity in Alzheimer’s diseasea resting functional MRI and Bayesian network study. Human Brain Mapping, 32(11), 1868–1881.CrossRefGoogle Scholar
Xiang, J., Guo, H., Cao, R., Liang, H., & Chen, J. (2013). An abnormal resting-state functional brain network indicates progression towards Alzheimer’s disease. Neural Regeneration Research, 8(30), 2789.Google ScholarPubMed
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