Advertisement

Clinical Personal Connectomics Using Hybrid PET/MRI

  • Dong Soo LeeEmail author
Perspective
  • 24 Downloads

Abstract

Brain connectivity can now be studied with topological analysis using persistent homology. It overcame the arbitrariness of thresholding to make binary graphs for comparison between disease and normal control groups. Resting-state fMRI can yield personal interregional brain connectivity based on perfusion signal on MRI on individual subject bases and FDG PET produces the topography of glucose metabolism. Assuming metabolism perfusion coupling and disregarding the slight difference of representing time of metabolism (before image acquisition) and representing time of perfusion (during image acquisition), topography of brain metabolism on FDG PET and topologically analyzed brain connectivity on resting-state fMRI might be related to yield personal connectomics of individual subjects and even individual patients. The work of association of FDG PET/resting-state fMRI is yet to be warranted; however, the statistics behind the group comparison of connectivity on FDG PET or resting-state MRI was already developed. Before going further into the connectomics construction using directed weighted brain graphs of FDG PET or resting-state fMRI, I detailed in this review the plausibility of using hybrid PET/MRI to enable the interpretation of personal connectomics which can lead to the clinical use of brain connectivity in the near future.

Keywords

Connectivity PET/MRI Classification Persistent homology Permutation Connectomics 

Notes

Funding Information

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean Government (MSIP) (No. 2015M3C7A1028926 and No. 2017M3C7A1048079) and NRF grant funded by the Korean Government (No. 2016R1D1A1A02937497 and No. 2017R1A5A1015626).

Compliance with Ethical Standards

Conflict of Interest

Dong Soo Lee declares that there is no conflict of interest.

Ethical Approval

All procedures performed in studies 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.

Informed Consent

As a review article, obtaining informed consent was waived.

References

  1. 1.
    Matsuda H. Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease. Aging Dis. 2013;4:29–37.Google Scholar
  2. 2.
    Buchholz HG, Wenzel F, Gartenschläger M, Thiele F, Young S, Reuss S, et al. Construction and comparative evaluation of different activity detection methods in brain FDG-PET. Biomed Eng Online. 2015;14:79.CrossRefGoogle Scholar
  3. 3.
    Lee Y, Bjørnstad JF. Extended likelihood approach to large-scale multiple testing. J R Stat Soc Ser B Stat Methodol. 2013;75:553–75.CrossRefGoogle Scholar
  4. 4.
    Lee D, Kang H, Kim E, Lee H, Kim H, Kim YK, et al. Optimal likelihood-ratio multiple testing with application to Alzheimer’s disease and questionable dementia. BMC Med Res Methodol. 2015;15:9.CrossRefGoogle Scholar
  5. 5.
    Lee D, Lee Y. Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field model. J Multivar Anal. 2016;151:1–3.CrossRefGoogle Scholar
  6. 6.
    Friston KJ. Functional and effective connectivity: a review. Brain Connect. 2011;1:13–36.CrossRefGoogle Scholar
  7. 7.
    Choe AS, Jones CK, Joel SE, Muschelli J, Belegu V, Caffo BS, et al. Reproducibility and temporal structure in weekly resting-state fMRI over a period of 3.5 years. PLoS One. 2015;10:e0140134.CrossRefGoogle Scholar
  8. 8.
    Arganda-Carreras I, Turaga SC, Berger DR, Cireşan D, Giusti A, Gambardella LM, et al. Crowdsourcing the creation of image segmentation algorithms for connectomics. Front Neuroanat. 2015;9:142.CrossRefGoogle Scholar
  9. 9.
    Smith SM, Vidaurre D, Beckmann CF, Glasser MF, Jenkinson M, Miller KL, et al. Functional connectomics from resting-state fMRI. Trends Cogn Sci. 2013;17:666–82.CrossRefGoogle Scholar
  10. 10.
    Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron. 2014;84:892–905.CrossRefGoogle Scholar
  11. 11.
    Kim E, Kang H, Lee H, Lee HJ, Suh MW, Song JJ, et al. Morphological brain network assessed using graph theory and network filtration in deaf adults. Hear Res. 2014;315:88–98.CrossRefGoogle Scholar
  12. 12.
    Lee DS, Kang H, Kim H, Park H, Oh JS, Lee JS, et al. Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur J Nucl Med Mol Imaging. 2008;35:1681–91.CrossRefGoogle Scholar
  13. 13.
    Lee H, Kang H, Chung MK, Kim BN, Lee DS. Persistent brain network homology from the perspective of dendrogram. IEEE Trans Med Imaging. 2012;31:2267–77.CrossRefGoogle Scholar
  14. 14.
    Lee H, Kang H, Chung MK, Kim BN, Lee DS. Weighted functional brain network modeling via network filtration. In NIPS Workshop on Algebraic Topology and Machine Learning 2012 (vol. 3). Citeseer.Google Scholar
  15. 15.
    Kim H, Hahm J, Lee H, Kang E, Kang H, Lee DS. Brain networks engaged in audiovisual integration during speech perception revealed by persistent homology-based network filtration. Brain Connect. 2015;5:245–58.CrossRefGoogle Scholar
  16. 16.
    Hahm J, Lee H, Park H, Kang E, Kim YK, Chung CK, et al. Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homology. Sci Rep. 2017;7:41592.CrossRefGoogle Scholar
  17. 17.
    Choi H, Choi Y, Kim KW, Kang H, Kim EE, Chung JK, et al. Maturation of metabolic connectivity of the adolescent rat brain. elife. 2015;4:e11571.CrossRefGoogle Scholar
  18. 18.
    Im HJ, Hahm J, Kang H, Choi H, Lee H, Kim EE, et al. Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson’s disease examined using persistent homology-based analysis. Sci Rep. 2016;6:33875.CrossRefGoogle Scholar
  19. 19.
    Caron F, Fox EB. Sparse graphs using exchangeable random measures. J R Stat Soc Ser B Stat Methodol. 2017;79:1295–366.CrossRefGoogle Scholar
  20. 20.
    Hallquist MN, Hillary FG. Graph theory approaches to functional network organization in brain disorders: a critique for a brave new small-world. Netw Neurosci. 2018;3:1–26.Google Scholar
  21. 21.
    Allard A, Serrano MÁ, García-Pérez G, Boguñá M. The geometric nature of weights in real complex networks. Nat Commun. 2017;8:14103.CrossRefGoogle Scholar
  22. 22.
    Weber M, Saucan E, Jost J. Characterizing complex networks with Forman-Ricci curvature and associated geometric flows. J Complex Netw. 2017;5:527–50.CrossRefGoogle Scholar
  23. 23.
    Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87:198701.CrossRefGoogle Scholar
  24. 24.
    Choi H, Kang H, Lee DS. Alzheimer’s Disease Neuroimaging Initiative. Predicting aging of brain metabolic topography using variational autoencoder. Front Aging Neurosci. 2018;10:212.CrossRefGoogle Scholar
  25. 25.
    Choi H, Ha S, Kang HJ, Lee H, Lee DS, Alzheimer’s Disease Neuroimaging Initiative. Deep learning only by normal brain PET identify unheralded brain anomalies. 2019. Submitted.Google Scholar
  26. 26.
    Santoro A, Raposo D, Barrett DG, Malinowski M, Pascanu R, Battaglia P, et al. A simple neural network module for relational reasoning. Adv Neural Inf Proces Syst. 2017;30:4967–76.Google Scholar
  27. 27.
    Santoro A, Faulkner R, Raposo D, Rae J, Chrzanowski M, Weber T, et al. Relational recurrent neural networks. arXiv:1806.01822. 2018.Google Scholar
  28. 28.
    Kipf T, Fetaya E, Wang KC, Welling M, Zemel R. Neural relational inference for interacting systems. arXiv:1802.04687. 2018.Google Scholar
  29. 29.
    Ying R, He R, Chen K, Eksombatchai P, Hamilton WL, Leskovec J. Graph convolutional neural networks for web-scale recommender systems. arXiv:1806.01973. 2018.Google Scholar
  30. 30.
    Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1–25.CrossRefGoogle Scholar
  31. 31.
    Lee H, Chung MK, Kang H, Kim BN, Lee DS. Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric, Med Image Comput Comput Assist Interv. Berlin: Springer; 2011. p. 302–9.Google Scholar
  32. 32.
    Chung MK, Lee H, Gritsenko A, DiChristofano A, Pluta D, Ombao H, et al. Topological brain network distances. arXiv:1809.03878. 2018.Google Scholar
  33. 33.
    Krioukov D, Papadopoulos F, Kitsak M, Vahdat A, Boguná M. Hyperbolic geometry of complex networks. Phys Rev E. 2010;82:036106.CrossRefGoogle Scholar
  34. 34.
    Muscoloni A, Thomas JM, Ciucci S, Bianconi G, Cannistraci CV. Machine learning meets complex networks via coalescent embedding in the hyperbolic space. Nat Commun. 2017;8:1615.CrossRefGoogle Scholar
  35. 35.
    Tadić B, Andjelković M, Šuvakov M. Origin of hyperbolicity in brain-to-brain coordination networks. Front Phys. 2018;6:7.CrossRefGoogle Scholar
  36. 36.
    Kaiser A, Schreiber T. Information transfer in continuous processes. Physica D. 2002;166:43–62.Google Scholar
  37. 37.
    Vicente R, Wibral M, Lindner M, Pipa G. Transfer entropy—a model-free measure of effective connectivity for the neurosciences. J Comput Neurosci. 2011;30:45–67.CrossRefGoogle Scholar
  38. 38.
    Lindner M, Vicente R, Priesemann V, Wibral M. TRENTOOL: a Matlab open source toolbox to analyse information flow in time series data with transfer entropy. BMC Neurosci. 2011;12:119.CrossRefGoogle Scholar
  39. 39.
    Lee H, Kim E, Ha S, Kang H, Huh Y, Lee Y, et al. Volume entropy for modeling information flow in a brain graph. Sci Rep. 2019; accepted.Google Scholar
  40. 40.
    Yue T, Wang H. Deep learning for genomics: a concise overview. arXiv:1802.00810. 2018.Google Scholar
  41. 41.
    Park J, Hwang D, Kim KY, Kang SK, Kim YK, Lee JS. Computed tomography super-resolution using deep convolutional neural network. Phys Med Biol. 2018;63:145011.CrossRefGoogle Scholar
  42. 42.
    Kang SK, Seo S, Shin SA, Byun MS, Lee DY, Kim YK, et al. Adaptive template generation for amyloid PET using a deep learning approach. Hum Brain Mapp. 2018.  https://doi.org/10.1002/hbm.24210.
  43. 43.
    Choi H, Lee DS, Alzheimer’s Disease Neuroimaging Initiative. Generation of structural MR images from amyloid PET: application to MR-less quantification. J Nucl Med. 2018;59:1111–7.CrossRefGoogle Scholar
  44. 44.
    Hwang D, Kim KY, Kang SK, Seo S, Paeng JC, Lee DS, et al. Improving the accuracy of simultaneously reconstructed activity and attenuation maps using deep learning. J Nucl Med. 2018;59:1624–9.CrossRefGoogle Scholar
  45. 45.
    Lee H, Chung MK, Kang H, Lee DS. Hole detection in metabolic connectivity of Alzheimer’s disease using k− Laplacian. In Med Image Comput Comput Assist Interv. 2014. pp. 297-304. Springer, Champions.Google Scholar
  46. 46.
    Lee H, Ma Z, Wang Y, Chung MK. Topological distances between networks and its application to brain imaging. arXiv:1701.04171. 2017.Google Scholar
  47. 47.
    Lee H, Chung MK, Kang H, Choi H, Kim YK, Lee DS. Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian. In Biomedical Imaging (ISBI 2018), 2018 IEEE 15th International Symposium on 2018. (pp. 20-23). IEEE.Google Scholar
  48. 48.
    Yu M, Hillebrand A, Gouw AA, Stam CJ. Horizontal visibility graph transfer entropy (HVG-TE): a novel metric to characterize directed connectivity in large-scale brain networks. NeuroImage. 2017;156:249–64.CrossRefGoogle Scholar
  49. 49.
    Bielczyk NZ, Uithol S, van Mourik T, Anderson P, Glennon JC, Buitelaar JK. Disentangling casual webs in the brain using functional magnetic resonance imaging: a review of current approaches. Netw Neurosci. 2018:1–37.Google Scholar
  50. 50.
    Frässle S, Lomakina EI, Kasper L, Manjaly ZM, Leff A, Pruessmann KP, et al. A generative model of whole-brain effective connectivity. NeuroImage. 2018;179:505–29.CrossRefGoogle Scholar
  51. 51.
    Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008;453:869.CrossRefGoogle Scholar
  52. 52.
    Nielsen AN, Lauritzen M. Coupling and uncoupling of activity-dependent increases of neuronal activity and blood flow in rat somatosensory cortex. J Physiol. 2001;533:773–85.CrossRefGoogle Scholar
  53. 53.
    Vafaee MS, Meyer E, Marrett S, Paus T, Evans AC, Gjedde A. Frequency-dependent changes in cerebral metabolic rate of oxygen during activation of human visual cortex. J Cereb Blood Flow Metab. 1999;19:272–7.CrossRefGoogle Scholar
  54. 54.
    Lee DS, Lee JS, Kang KW, Jang MJ, Lee SK, Chung JK, et al. Disparity of perfusion and glucose metabolism of epileptogenic zones in temporal lobe epilepsy demonstrated by SPM/SPAM analysis on 15O water PET,[18F] FDG-PET, and [99mTc]-HMPAO SPECT. Epilepsia. 2001;42:1515–22.CrossRefGoogle Scholar
  55. 55.
    Sheth SA, Nemoto M, Guiou M, Walker M, Pouratian N, Toga AW. Linear and nonlinear relationships between neuronal activity, oxygen metabolism, and hemodynamic responses. Neuron. 2004;42:347–55.CrossRefGoogle Scholar
  56. 56.
    Berthelot D, Schumm T, Metz L. BEGAN: boundary equilibrium generative adversarial networks. arXiv:1703.10717. 2017.Google Scholar
  57. 57.
    Silver D, Schrittwieser J, Simonyan K, Antonoglou I, Huang A, Guez A, et al. Mastering the game of Go without human knowledge. Nature. 2017;550:354.CrossRefGoogle Scholar

Copyright information

© Korean Society of Nuclear Medicine 2019

Authors and Affiliations

  1. 1.Department of Nuclear Medicine, College of MedicineSeoul National UniversitySeoulRepublic of Korea
  2. 2.Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of MedicineSeoul National UniversitySeoulRepublic of Korea

Personalised recommendations