Abstract
Increased synchrony within neuroanatomical networks is often observed in neurophysiologic studies of human brain disease. Most often, this phenomenon is ascribed to a compensatory process in the face of injury, though evidence supporting such accounts is limited. Given the known dependence of resting-state functional connectivity (rsFC) on underlying structural connectivity (SC), we examine an alternative hypothesis: that topographical changes in SC, specifically particular patterns of disconnection, contribute to increased network rsFC. We obtain measures of rsFC using fMRI and SC using probabilistic tractography in 50 healthy and 28 multiple sclerosis subjects. Using a computational model of neuronal dynamics, we simulate BOLD using healthy subject SC to couple regions. We find that altering the model by introducing structural disconnection patterns observed in those multiple sclerosis subjects with high network rsFC generates simulations with high rsFC as well, suggesting that disconnection itself plays a role in producing high network functional connectivity. We then examine SC data in individuals. In multiple sclerosis subjects with high network rsFC, we find a preferential disconnection between the relevant network and wider system. We examine the significance of such network isolation by introducing random disconnection into the model. As observed empirically, simulated network rsFC increases with removal of connections bridging a community with the remainder of the brain. We thus show that structural disconnection known to occur in multiple sclerosis contributes to network rsFC changes in multiple sclerosis and further that community isolation is responsible for elevated network functional connectivity.
This is a preview of subscription content, log in via an institution to check access.
References
Breakspear M, Terry JR, Friston KJ (2003) Modulation of excitatory synaptic coupling facilitates synchronization and complex dynamics in a biophysical model of neuronal dynamics. Network 14:703–732
Bressler SL, Menon V (2010) Large-scale brain networks in cognition: emerging methods and principles. Trends Cognit Sci 14:277–290. https://doi.org/10.1016/j.tics.2010.04.004
Dale AM, Fischl B, Sereno MI (1999) Cortical surface-based analysis. I Segmentation surface reconstruction. NeuroImage 9:179–194. https://doi.org/10.1006/nimg.1998.0395
Deco G, Jirsa VK, McIntosh AR (2011) Emerging concepts for the dynamical organization of resting-state activity in the brain. Nat Rev Neurosci 12:43–56. https://doi.org/10.1038/nrn2961
Faivre A et al (2012) Assessing brain connectivity at rest is clinically relevant in early multiple sclerosis. Mult Scler 18:1251–1258. https://doi.org/10.1177/1352458511435930
Fleischer V et al. (2016) Increased structural white and grey matter network connectivity compensates for functional decline in early multiple sclerosis. Mult Scler. https://doi.org/10.1177/1352458516651503
Fox MD, Raichle ME (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci 8:700–711. https://doi.org/10.1038/nrn2201
Friston KJ, Mechelli A, Turner R, Price CJ (2000) Nonlinear responses in fMRI: the Balloon model, Volterra kernels, and other hemodynamics. NeuroImage 12:466–477 https://doi.org/10.1006/nimg.2000.0630
Goni J et al (2014) Resting-brain functional connectivity predicted by analytic measures of network communication. Proc Natl Acad Sci USA 111:833–838. https://doi.org/10.1073/pnas.1315529111
Gonzalez-Castillo J, Hoy CW, Handwerker DA, Robinson ME, Buchanan LC, Saad ZS, Bandettini PA (2015) Tracking ongoing cognition in individuals using brief whole-brain functional connectivity patterns. Proc Natl Acad Sci USA 112:8762–8767. https://doi.org/10.1073/pnas.1501242112
Greicius M (2008) Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol 21:424–430. https://doi.org/10.1097/WCO.0b013e328306f2c5
Handwerker DA, Roopchansingh V, Gonzalez-Castillo J, Bandettini PA (2012) Periodic changes in fMRI connectivity. NeuroImage 63:1712–1719 https://doi.org/10.1016/j.neuroimage.2012.06.078
Hawellek DJ, Hipp JF, Lewis CM, Corbetta M, Engel AK (2011) Increased functional connectivity indicates the severity of cognitive impairment in multiple sclerosis. Proc Natl Acad Sci USA 108:19066–19071. https://doi.org/10.1073/pnas.1110024108
Honey CJ, Kotter R, Breakspear M, Sporns O (2007) Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proc Natl Acad Sci USA 104:10240–10245. https://doi.org/10.1073/pnas.0701519104
Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P (2009) Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci USA 106:2035–2040. https://doi.org/10.1073/pnas.0811168106
Hutchison RM et al. (2013) Dynamic functional connectivity: promise, issues, and interpretations. NeuroImage 80:360–378 https://doi.org/10.1016/j.neuroimage.2013.05.079
Jenkinson M, Beckmann CF, Behrens TE, Woolrich MW, Smith SM (2012) FSL. NeuroImage 62:782–790. https://doi.org/10.1016/j.neuroimage.2011.09.015
Johnston JM et al (2008) Loss of resting interhemispheric functional connectivity after complete section of the corpus callosum. J Neurosci 28:6453–6458. https://doi.org/10.1523/JNEUROSCI.0573-08.2008
Jones DT et al (2016) Cascading network failure across the Alzheimer’s disease spectrum. Brain J Neurol 139:547–562. https://doi.org/10.1093/brain/awv338
Keil B et al (2013) A 64-channel 3T array coil for accelerated brain MRI. Magn Reson Med 70:248–258. https://doi.org/10.1002/mrm.24427
Mesulam MM (1990) Large-scale neurocognitive networks and distributed processing for attention, language, and memory. Ann Neurol 28:597–613. https://doi.org/10.1002/ana.410280502
Misic B, Betzel RF, de Reus MA, van den Heuvel MP, Berman MG, McIntosh AR, Sporns O (2016) Network-level structure-function relationships in human. Neocortex Cereb Cortex 26:3285–3296. https://doi.org/10.1093/cercor/bhw089
Muthuraman M, Fleischer V, Kolber P, Luessi F, Zipp F, Groppa S (2016) Structural brain network characteristics can differentiate CIS from early. RRMS Front Neurosci 10:14. https://doi.org/10.3389/fnins.2016.00014
Park HJ, Friston K (2013) Structural and functional brain networks: from connections to cognition. Science 342:1238411. https://doi.org/10.1126/science.1238411
Power JD et al (2011) Functional network organization of the human brain. Neuron 72:665–678. https://doi.org/10.1016/j.neuron.2011.09.006
Rocca MA et al (2010) Default-mode network dysfunction and cognitive impairment in progressive MS. Neurology 74:1252–1259. https://doi.org/10.1212/WNL.0b013e3181d9ed91
Rocca MA, Valsasina P, Martinelli V, Misci P, Falini A, Comi G, Filippi M (2012) Large-scale neuronal network dysfunction in relapsing-remitting multiple sclerosis. Neurology 79:1449–1457. https://doi.org/10.1212/WNL.0b013e31826d5f10
Roosendaal SD, Schoonheim MM, Hulst HE, Sanz-Arigita EJ, Smith SM, Geurts JJ, Barkhof F (2010) Resting state networks change in clinically isolated syndrome. Brain J Neurol 133:1612–1621. https://doi.org/10.1093/brain/awq058
Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc Natl Acad Sci USA 105:1118–1123. https://doi.org/10.1073/pnas.0706851105
Schoonheim MM, Geurts JJ, Barkhof F (2010) The limits of functional reorganization in multiple sclerosis. Neurology 74:1246–1247. https://doi.org/10.1212/WNL.0b013e3181db9957
Simioni AC, Dagher A, Fellows LK (2016) Compensatory striatal-cerebellar connectivity in mild-moderate Parkinson’s disease. NeuroImage Clin 10:54–62. https://doi.org/10.1016/j.nicl.2015.11.005
Skardal PS, Restrepo JG (2012) Hierarchical synchrony of phase oscillators in modular networks. Phys Rev E Stat Nonlinear Soft Matter Phys 85:016208 https://doi.org/10.1103/PhysRevE.85.016208
Stephan KE, Iglesias S, Heinzle J, Diaconescu AO (2015) Translational perspectives for computational neuroimaging. Neuron 87:716–732. https://doi.org/10.1016/j.neuron.2015.07.008
Valsasina P et al (2011) A multicentre study of motor functional connectivity changes in patients with multiple sclerosis. Eur J Neurosci 33:1256–1263. https://doi.org/10.1111/j.1460-9568.2011.07623.x
van den Heuvel MP, Mandl RC, Kahn RS, Hulshoff Pol HE (2009) Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 30:3127–3141. https://doi.org/10.1002/hbm.20737
Venkatesan UM, Dennis NA, Hillary FG (2015) Chronology and chronicity of altered resting-state functional connectivity after traumatic brain injury. J Neurotrauma 32:252–264. https://doi.org/10.1089/neu.2013.3318
Yeo BT et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165. https://doi.org/10.1152/jn.00338.2011
Yoo SS, Hu PT, Gujar N, Jolesz FA, Walker MP (2007) A deficit in the ability to form new human memories without sleep. Nat Neurosci 10:385–392. https://doi.org/10.1038/nn1851
Zhang D, Raichle ME (2010) Disease and the brain’s dark energy. Nat Rev Neurol 6:15–28. https://doi.org/10.1038/nrneurol.2009.198
Zhou F et al (2014) Altered inter-subregion connectivity of the default mode network in relapsing remitting multiple sclerosis: a functional and structural connectivity study. PloS One 9:e101198. https://doi.org/10.1371/journal.pone.0101198
Acknowledgements
Data collection and sharing for this project was provided by the MGH-USC Human Connectome Project (Principal Investigators: Bruce Rosen, M.D., Ph.D., Arthur W. Toga, Ph.D., Van J. Weeden, MD). HCP funding was provided by the National Institute of Dental and Craniofacial Research (NIDCR), the National Institute of Mental Health (NIMH), and the National Institute of Neurological Disorders and Stroke (NINDS). HCP data are disseminated by the Laboratory of Neuro Imaging at the University of California, Los Angeles.
Funding
The authors disclosed receipt of the following financial support for the research and/or authorship of this article: This work was supported by grants from the National Multiple Sclerosis Society PP1853 (ECK), and FG 20131-A-1 (KRP) and the National Institutes of Health K23NS078044-04 (ECK).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Eric Klawiter has received research grants from Atlas5D, Biogen, EMD Serono and Roche; and consulting fees from Acorda, Atlas5D, Biogen, EMD Serono, Genentech and Shire. The remaining authors declare they have no conflicts of interest.
Research involving human participants
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All subjects provided written informed consent under institutional board approval. This article does not contain any studies with animals performed by any of the authors.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Patel, K.R., Tobyne, S., Porter, D. et al. Structural disconnection is responsible for increased functional connectivity in multiple sclerosis. Brain Struct Funct 223, 2519–2526 (2018). https://doi.org/10.1007/s00429-018-1619-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00429-018-1619-z