Abstract
Autism is increasing in prevalence and is a neurodevelopmental disorder characterised by impairments in communication skills and social behaviour. Connectomes enable a systems-level representation of the brain with recent interests in understanding the distributed nature of higher order cognitive function using modules or subnetworks. By dividing the connectome according to a central component of the brain critical for its function (it’s hub), we investigate network organisation in autism from hub through to peripheral subnetworks. We complement this analysis by extracting features of energy transport computed from heat kernels fitted with increasing time steps. This heat kernel framework is advantageous as it can capture the energy transported in all direct and indirect pathways between pair-wise regions over ’time’, with features that have correspondence to small-world properties. We apply our framework to resting-state functional MRI connectomes from a large, publically available autism dataset, ABIDE. We show that energy propagating through the brain over time are different between subnetworks, and that heat kernel features significantly differ between autism and controls. Furthermore, the hub was functionally preserved and similar to controls, however, increasing statistical significance between groups was found in increasingly peripheral subnetworks. Our results support the increasing opinion of non-hub regions playing an important role in functional organisation. This work shows that analysing autism by subnetworks with the heat kernel reflects the atypical activations in peripheral regions as alterations in energy dispersion and may provide useful features towards understanding the distributed impact of this disorder on the functional connectome.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Abdelnour, F., Voss, H.U., Raj, A.: Network diffusion accurately models the relationship between structural and functional brain connectivity networks. Neuroimage 90, 335–347 (2014)
Baio, J., et al.: Prevalence of autism spectrum disorder among children aged 8 years-autism and developmental disabilities monitoring network, 11 sites, United States, 2014. MMWR Surveill. Summ. 67(6), 1 (2018)
Chung, A.W., Ahtam, B., Grant, P.E., Im, K.: Rich club-based subnetworks in 16p11.2 deletion syndrome reveal differential structural alterations. In: Organization of Human Brain Mapping, Rome, Italy, p. 5204 (2019)
Chung, A.W., Mannix, R., Feldman, H.A., Grant, P.E., Im, K.: Longitudinal structural connectomic and rich-club analysis in adolescent mTBI reveals persistent, distributed brain alterations acutely through to one year post-injury. arXiv:1909.08071 [q-bio.NC], pp. 1–22, September 2019
Chung, A.W., Pesce, E., Monti, R.P., Montana, G.: Classifying HCP task-fMRI networks using heat kernels. In: 2016 International Workshop on Pattern Recognition in NeuroImaging (PRNI), pp. 1–4. IEEE (2016)
Chung, A.W., et al.: Characterising brain network topologies: a dynamic analysis approach using heat kernels. Neuroimage 141, 490–501 (2016)
Chung, F.R., Graham, F.C.: Spectral Graph Theory, vol. 92. American Mathematical Society (1997)
Collin, G., Kahn, R.S., de Reus, M.A., Cahn, W., van den Heuvel, M.P.: Impaired rich club connectivity in unaffected siblings of schizophrenia patients. Schizophr. Bull. 40(2), 438–448 (2013)
Crossley, N.A.: The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain 137(8), 2382–2395 (2014)
Di Martino, A., et al.: The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Mol. Psychiatry 19(6), 659 (2014)
Gollo, L.L., Zalesky, A., Hutchison, R.M., van den Heuvel, M., Breakspear, M.: Dwelling quietly in the rich club: brain network determinants of slow cortical fluctuations. Philos. Trans. R. Soc. B: Biol. Sci. 370(1668), 20140165 (2015)
Grayson, D.S., et al.: Structural and functional rich club organization of the brain in children and adults. PloS One 9(2), e88297 (2014)
Gu, S., et al.: Controllability of structural brain networks. Nature Commun. 6, 8414 (2015)
van den Heuvel, M.I., et al.: Hubs in the human fetal brain network. Dev. Cogn. Neurosci. 30, 108–115 (2018)
van den Heuvel, M.P., Kahn, R.S., Goñi, J., Sporns, O.: High-cost, high-capacity backbone for global brain communication. Proc. Natl. Acad. Sci. 109(28), 11372–11377 (2012)
Hong, S.J., et al.: Atypical functional connectome hierarchy in Autism. Nature Commun. 10(1), 1022 (2019)
Keown, C.L., Datko, M.C., Chen, C.P., Maximo, J.O., Jahedi, A., Müller, R.A.: Network organization is globally atypical in autism: a graph theory study of intrinsic functional connectivity. Biol. Psychiatry: Cogn. Neurosci. Neuroimaging 2(1), 66–75 (2017)
Ktena, S.I., et al.: Brain connectivity measures improve modeling of functional outcome after acute ischemic stroke. Stroke, published online ahead of print 12, September (2019). https://doi.org/10.1161/STROKEAHA.119.025738
Müller, R.A., Fishman, I.: Brain connectivity and neuroimaging of social networks in Autism. Trends Cogn. Sci. 22, 1103–1116 (2018)
Power, J.D., Schlaggar, B.L., Lessov-Schlaggar, C.N., Petersen, S.E.: Evidence for hubs in human functional brain networks. Neuron 79(4), 798–813 (2013)
Raj, A., LoCastro, E., Kuceyeski, A., Tosun, D., Relkin, N., Weiner, M., Initiative ADNI: Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease. Cell Rep. 10(3), 359–369 (2015)
Rudie, J.D., et al.: Altered functional and structural brain network organization in autism. NeuroImage: Clin. 2, 79–94 (2013)
Sato, J.R., et al.: Connectome hubs at resting state in children and adolescents: reproducibility and psychopathological correlation. Dev. Cogn. Neurosci. 20, 2–11 (2016)
Schirmer, M.D., Chung, A.W.: Structural subnetwork evolution across the life-span: rich-club, feeder, seeder. In: Wu, G., Rekik, I., Schirmer, M.D., Chung, A.W., Munsell, B. (eds.) CNI 2018. LNCS, vol. 11083, pp. 136–145. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00755-3_15
Schirmer, M.D., Chung, A.W., Grant, P.E., Rost, N.S.: Network structural dependency in the human connectome across the life span. Netw. Neurosci. 1–15 (2018)
Schirmer, M.D., et al.: Rich-Club organization: an important determinant of functional outcome after acute ischemic stroke. Front. Neurol. 10, 956 (2019). https://doi.org/10.3389/fneur.2019.00956
Van Den Heuvel, M.P., Sporns, O.: Rich-club organization of the human connectome. J. Neurosci. 31(44), 15775–15786 (2011)
Van Den Heuvel, M.P., Sporns, O.: A cross-disorder connectome landscape of brain dysconnectivity. Nature reviews. Neuroscience 20(7), 435–446 (2019). https://doi.org/10.1038/s41583-019-0177-6
Varoquaux, G., Craddock, R.C.: Learning and comparing functional connectomes across subjects. NeuroImage 80, 405–415 (2013)
Verhelst, H., Vander Linden, C., De Pauw, T., Vingerhoets, G., Caeyenberghs, K.: Impaired rich club and increased local connectivity in children with traumatic brain injury: local support for the rich? Hum. Brain Mapp. 39(7), 2800–2811 (2018)
Zhang, F., Hancock, E.R.: Graph spectral image smoothing using the heat kernel. Pattern Recogn. 41(11), 3328–3342 (2008)
Acknowledgments
This project has received funding from the American Heart Association and Children’s Heart Foundation Postdoctoral Fellowship, 19POST34380005 (AWC) and the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 753896 (MDS).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Schirmer, M.D., Chung, A.W. (2019). Heat Kernels with Functional Connectomes Reveal Atypical Energy Transport in Peripheral Subnetworks in Autism. In: Schirmer, M., Venkataraman, A., Rekik, I., Kim, M., Chung, A. (eds) Connectomics in NeuroImaging. CNI 2019. Lecture Notes in Computer Science(), vol 11848. Springer, Cham. https://doi.org/10.1007/978-3-030-32391-2_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-32391-2_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-32390-5
Online ISBN: 978-3-030-32391-2
eBook Packages: Computer ScienceComputer Science (R0)