Measuring robustness of brain networks in autism spectrum disorder with Ricci curvature

Anish K Simhal, Kimberly L H Carpenter, Saad Nadeem, Joanne Kurtzberg, Allen Song, Allen Tannenbaum, Guillermo Sapiro, Geraldine Dawson, Anish K Simhal, Kimberly L H Carpenter, Saad Nadeem, Joanne Kurtzberg, Allen Song, Allen Tannenbaum, Guillermo Sapiro, Geraldine Dawson

Abstract

Ollivier-Ricci curvature is a method for measuring the robustness of connections in a network. In this work, we use curvature to measure changes in robustness of brain networks in children with autism spectrum disorder (ASD). In an open label clinical trials, participants with ASD were administered a single infusion of autologous umbilical cord blood and, as part of their clinical outcome measures, were imaged with diffusion MRI before and after the infusion. By using Ricci curvature to measure changes in robustness, we quantified both local and global changes in the brain networks and their potential relationship with the infusion. Our results find changes in the curvature of the connections between regions associated with ASD that were not detected via traditional brain network analysis.

Trial registration: ClinicalTrials.gov NCT02176317.

Conflict of interest statement

G.D. is on the Scientific Advisory Boards of Janssen Research and Development, Akili, Inc, LabCorp, Inc, Roche Pharmaceutical Company, and Tris Pharma, and is a consultant to Apple, Gerson Lehrman Group, Guidepoint, Inc, Axial Ventures, and Teva Pharmaceutical. GD and JK have the following relevant patent application: #16493754 and have licensed IP related to this project from which Duke and they have benefited financially. J.K. is Director of the Carolinas Cord Blood Bank and Medical Director of Cord: Use Cord Blood Bank. G.S. consults for Apple Inc., and Volvo Cars, and is on the Board of Directors of SIS.

Figures

Figure 1
Figure 1
Overview of the results. Axial projection of pairs where the change in curvature correlated significantly with behavioral exams. Cross hemisphere brain connections were removed for this analysis. The brain graphics were visualized with the BrainNet Viewer (http://www.nitrc.org/projects/bnv/). L: left, R: right.

References

    1. Zablotsky B, et al. Prevalence and trends of developmental disabilities among children in the United States: 2009–2017. Pediatrics. 2019;144:2009–2017. doi: 10.1542/peds.2019-0811.
    1. American Psychiatric Association . Diagnostic and Statistical Manual of Mental Disorders. 5. Washington, DC: American Psychiatric Association; 2013.
    1. Piven J, Elison JT, Zylka MJ. Toward a conceptual framework for early brain and behavior development in autism. Mol. Psychiatry. 2017;22:1385–1394. doi: 10.1038/mp.2017.131.
    1. Solso S, et al. Diffusion tensor imaging provides evidence of possible axonal overconnectivity in frontal lobes in autism spectrum disorder toddlers. Biol. Psychiatry. 2016;79:676–684. doi: 10.1016/j.biopsych.2015.06.029.
    1. Wolff JJ, et al. Neural circuitry at age 6 months associated with later repetitive behavior and sensory responsiveness in autism. Mol. Autism. 2017;8:8. doi: 10.1186/s13229-017-0126-z.
    1. Meltzer A, de Water J. The role of the immune system in autism spectrum disorder. Neuropsychopharmacology. 2017;42:284–298. doi: 10.1038/npp.2016.158.
    1. Jones KL, de Water J. Maternal autoantibody related autism: mechanisms and pathways. Mol. Psychiatry. 2019;24:252–265. doi: 10.1038/s41380-018-0099-0.
    1. McAllister AK. Immune contributions to cause and effect in autism spectrum disorder. Biol. Psychiatry. 2017;81:380–382. doi: 10.1016/j.biopsych.2016.12.024.
    1. Young AMH, et al. From molecules to neural morphology: understanding neuroinflammation in autism spectrum condition. Mol. Autism. 2016;7:9. doi: 10.1186/s13229-016-0068-x.
    1. Ashwood P, Wills S, de Water J. The immune response in autism: a new frontier for autism research. J. Leukoc. Biol. 2006;80:1–15. doi: 10.1189/jlb.1205707.
    1. Pardo CA, Vargas DL, Zimmerman AW. Immunity, neuroglia and neuroinflammation in autism. Int. Rev. Psychiatry. 2005;17:485–495. doi: 10.1080/02646830500381930.
    1. Bachstetter AD, et al. Peripheral injection of human umbilical cord blood stimulates neurogenesis in the aged rat brain. BMC Neurosci. 2008;9:22. doi: 10.1186/1471-2202-9-22.
    1. Shahaduzzaman M, et al. A single administration of human umbilical cord blood T cells produces long-lasting effects in the aging hippocampus. Age. 2013;35:2071–2087. doi: 10.1007/s11357-012-9496-5.
    1. Englander ZA, et al. Brain structural connectivity increases concurrent with functional improvement: evidence from diffusion tensor MRI in children with cerebral palsy during therapy. NeuroImage Clin. 2015;7:315–324. doi: 10.1016/j.nicl.2015.01.002.
    1. Dawson G, et al. Autologous cord blood infusions are safe and feasible in young children with autism spectrum disorder: results of a single-center phase I open-label trial. Stem Cells Transl. Med. 2017;6:1332–1339. doi: 10.1002/sctm.16-0474.
    1. Carpenter KLH, et al. White matter tract changes associated with clinical improvement in an open-label trial assessing autologous umbilical cord blood for treatment of young children with autism. Stem Cells Transl. Med. 2019 doi: 10.1002/sctm.18-0251.
    1. Sandhu RS, et al. Graph curvature for differentiating cancer networks. Sci. Rep. 2015;5:1–13. doi: 10.1038/srep12323.
    1. Pouryahya, M., Mathews, J. & Tannenbaum, A. Comparing three notions of discrete Ricci curvature on biological networks. arXiv preprint (2017).
    1. Ollivier Y. Ricci curvature of Markov chains on metric spaces. J. Funct. Anal. 2009;256:810–864. doi: 10.1016/j.jfa.2008.11.001.
    1. Doyle JC, Francis BA, Tannenbaum AR. Feedback Control Theory. Chelmsford: Courier Corporation; 2013.
    1. Bauer F, Jost J, Liu S. Ollivier-Ricci curvature and the spectrum of the normalized graph Laplace operator. Math. Res. Lett. 2012;19:1185–1205. doi: 10.4310/MRL.2012.v19.n6.a2.
    1. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodol.) 1995;57:289–300.
    1. Uddin LQ, Menon V. The anterior insula in autism: under-connected and under-examined. Neurosci. Biobehav. Rev. 2009;33:1198–1203. doi: 10.1016/j.neubiorev.2009.06.002.
    1. Qian L, et al. Alterations in hub organization in the white matter structural network in toddlers with autism spectrum disorder: a 2-year follow-up study. Autism Res. 2018;11:1218–1228. doi: 10.1002/aur.1983.
    1. van den Heuvel MP, Sporns O. Network hubs in the human brain. Trends Cogn. Sci. 2013;17:683–696. doi: 10.1016/j.tics.2013.09.012.
    1. Sridharan D, Levitin DJ, Menon V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc. Natl. Acad. Sci. USA. 2008;105:12569–12574. doi: 10.1073/pnas.0800005105.
    1. Abbott AE, et al. Patterns of atypical functional connectivity and behavioral links in autism differ between default, salience, and executive networks. Cereb. Cortex. 2016;26:4034–4045. doi: 10.1093/cercor/bhv191.
    1. Catani M, De Schotten MT. A diffusion tensor imaging tractography atlas for virtual in vivo dissections. Cortex. 2008;44:1105–1132. doi: 10.1016/j.cortex.2008.05.004.
    1. Von Der Heide RJ, Skipper LM, Klobusicky E, Olson IR. Dissecting the uncinate fasciculus: disorders, controversies and a hypothesis. Brain. 2013;136:1692–1707. doi: 10.1093/brain/awt094.
    1. Olson IR, McCoy D, Klobusicky E, Ross LA. Social cognition and the anterior temporal lobes: a review and theoretical framework. Soc. Cogn. Affect. Neurosci. 2013;8:123–133. doi: 10.1093/scan/nss119.
    1. Bubb EJ, Metzler-Baddeley C, Aggleton JP. The cingulum bundle: anatomy, function, and dysfunction. Neurosci. Biobehav. Rev. 2018;92:104–127. doi: 10.1016/j.neubiorev.2018.05.008.
    1. Catani M, et al. Frontal networks in adults with autism spectrum disorder. Brain. 2016;139:616–630. doi: 10.1093/brain/awv351.
    1. Hau J, et al. The cingulum and cingulate U-fibers in children and adolescents with autism spectrum disorders. Hum. Brain Mapp. 2019;40:3153–3164. doi: 10.1002/hbm.24586.
    1. Ameis SH, Catani M. Altered white matter connectivity as a neural substrate for social impairment in Autism Spectrum Disorder. Cortex. 2015;62:158–181. doi: 10.1016/j.cortex.2014.10.014.
    1. Poustka L, et al. Fronto-temporal disconnectivity and symptom severity in children with autism spectrum disorder. World J. Biol. Psychiatry. 2012;13:269–280. doi: 10.3109/15622975.2011.591824.
    1. Elison JT, et al. Frontolimbic neural circuitry at 6 months predicts individual differences in joint attention at 9 months. Dev. Sci. 2013;16:186–197. doi: 10.1111/desc.12015.
    1. Cheon K-A, et al. Involvement of the anterior thalamic radiation in boys with high functioning autism spectrum disorders: a diffusion tensor imaging study. Brain Res. 2011;1417:77–86. doi: 10.1016/j.brainres.2011.08.020.
    1. Cheung C, et al. White matter fractional anisotrophy differences and correlates of diagnostic symptoms in autism. J. Child Psychol. Psychiatry. 2009;50:1102–1112. doi: 10.1111/j.1469-7610.2009.02086.x.
    1. Kumar A, et al. Alterations in frontal lobe tracts and corpus callosum in young children with autism spectrum disorder. Cereb. Cortex. 2010;20:2103–2113. doi: 10.1093/cercor/bhp278.
    1. Sahyoun CP, Belliveau JW, Mody M. White matter integrity and pictorial reasoning in high-functioning children with autism. Brain Cogn. 2010;73:180–188. doi: 10.1016/j.bandc.2010.05.002.
    1. Fuccillo MV. Striatal circuits as a common node for autism pathophysiology. Front. Neurosci. 2016;10:27. doi: 10.3389/fnins.2016.00027.
    1. Blakemore SJ. The social brain in adolescence. Nat. Rev. Neurosci. 2008;9:267–277. doi: 10.1038/nrn2353.
    1. Dougherty CC, Evans DW, Katuwal GJ, Michael AM. Asymmetry of fusiform structure in autism spectrum disorder: trajectory and association with symptom severity. Mol. Autism. 2016;7:1–11. doi: 10.1186/s13229-016-0089-5.
    1. Patriquin MA, DeRamus T, Libero LE, Laird A, Kana RK. Neuroanatomical and neurofunctional markers of social cognition in autism spectrum disorder. Hum. Brain Mapp. 2016;37:3957–3978. doi: 10.1002/hbm.23288.
    1. Zielinski BA, et al. Longitudinal changes in cortical thickness in autism and typical development. Brain. 2014;137:1799–1812. doi: 10.1093/brain/awu083.
    1. Jao Keehn RJ. Atypical local and distal patterns of occipito-frontal functional connectivity are related to symptom severity in autism. Cereb. Cortex. 2019;29:3319–3330. doi: 10.1093/cercor/bhy201.
    1. Prigge MD, et al. Longitudinal Heschls Gyrus growth during childhood and adolescence in typical development and autism. Autism Res. 2013;6:78–90. doi: 10.1002/aur.1265.
    1. Eyler LT, Pierce K, Courchesne E. A failure of left temporal cortex to specialize for language is an early emerging and fundamental property of autism. Brain. 2012;135:949–960. doi: 10.1093/brain/awr364.
    1. Xia M, Wang J, He Y. BrainNet Viewer: a network visualization tool for human brain connectomics. PLoS ONE. 2013;8:e68910. doi: 10.1371/journal.pone.0068910.
    1. Murias M, et al. Validation of eye-tracking measures of social attention as a potential biomarker for autism clinical trials. Autism Res. 2018;11:166–174. doi: 10.1002/aur.1894.
    1. Murias M, et al. Electrophysiological biomarkers predict clinical improvement in an open-label trial assessing efficacy of autologous umbilical cord blood for treatment of autism. Stem Cells Transl. Med. 2018;7:783–791. doi: 10.1002/sctm.18-0090.
    1. Sparrow S, Cicchetti D, Balla D. Vineland Adaptive Behavior Scales (Vineland II): Caregiver/Caregiver Rating Form. 2. Minneapolis, MN: NCS Pearson Inc.; 2005.
    1. Martin NA, Brownell R. Expressive One-Word Picture Vocabulary Test (EOWPVT-4) 4. Novato, CA: Academic Therapy Publications Inc.; 2011.
    1. Guy, W. & Bonato, R. E. CGI: clinical global impressions. Manual for the ECDEU assessment battery. Rev. Ed. Chase C (1970).
    1. Smith SM. Fast robust automated brain extraction. Hum. Brain Mapp. 2002;17:143–155. doi: 10.1002/hbm.10062.
    1. Jenkinson, M., Pechaud, M. & Smith, S. BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, vol. 17, 167 (Toronto., 2005).
    1. Jenkinson M, Smith S. A global optimisation method for robust affine registration of brain images. Med. Image Anal. 2001;5:143–156. doi: 10.1016/S1361-8415(01)00036-6.
    1. Jenkinson M, Bannister P, Brady M, Smith S. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage. 2002;17:825–841. doi: 10.1006/nimg.2002.1132.
    1. Avants BB, Tustison N, Song G. Advanced normalization tools (ANTS) Insight J. 2009;2:1–35.
    1. Avants BB, et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage. 2011;54:2033–2044. doi: 10.1016/j.neuroimage.2010.09.025.
    1. Zhang Y, Brady M, Smith S. Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Trans. Med. Imaging. 2001;20:45–57. doi: 10.1109/42.906424.
    1. Gerhard S, et al. The connectome viewer toolkit: an open source framework to manage, analyze, and visualize connectomes. Front. Neuroinform. 2011;5:1–15. doi: 10.3389/fninf.2011.00003.
    1. Do Carmo MP. Riemannian Geometry. Berlin: Birkhauser; 2015.

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