nach oben

Nuclear Medicine and Molecular Imaging

Erschienen in:

15.01.2019 | Perspective

Clinical Personal Connectomics Using Hybrid PET/MRI

verfasst von: Dong Soo Lee

Erschienen in: Nuclear Medicine and Molecular Imaging | Ausgabe 3/2019

Einloggen, um Zugang zu erhalten

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.

Matsuda H. Voxel-based morphometry of brain MRI in normal aging and Alzheimer’s disease. Aging Dis. 2013;4:29–37.PubMed

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.CrossRefPubMedPubMedCentral

Lee Y, Bjørnstad JF. Extended likelihood approach to large-scale multiple testing. J R Stat Soc Ser B Stat Methodol. 2013;75:553–75.CrossRef

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.CrossRefPubMedPubMedCentral

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.CrossRef

Friston KJ. Functional and effective connectivity: a review. Brain Connect. 2011;1:13–36.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

10.

Deco G, Kringelbach ML. Great expectations: using whole-brain computational connectomics for understanding neuropsychiatric disorders. Neuron. 2014;84:892–905.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

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.

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMedPubMedCentral

19.

Caron F, Fox EB. Sparse graphs using exchangeable random measures. J R Stat Soc Ser B Stat Methodol. 2017;79:1295–366.CrossRef

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.PubMedPubMedCentral

21.

Allard A, Serrano MÁ, García-Pérez G, Boguñá M. The geometric nature of weights in real complex networks. Nat Commun. 2017;8:14103.CrossRefPubMedPubMedCentral

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.CrossRef

23.

Latora V, Marchiori M. Efficient behavior of small-world networks. Phys Rev Lett. 2001;87:198701.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

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.

27.

Santoro A, Faulkner R, Raposo D, Rae J, Chrzanowski M, Weber T, et al. Relational recurrent neural networks. arXiv:1806.01822. 2018.

28.

Kipf T, Fetaya E, Wang KC, Welling M, Zemel R. Neural relational inference for interacting systems. arXiv:1802.04687. 2018.

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.

30.

Nichols TE, Holmes AP. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum Brain Mapp. 2002;15:1–25.CrossRefPubMed

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.

32.

Chung MK, Lee H, Gritsenko A, DiChristofano A, Pluta D, Ombao H, et al. Topological brain network distances. arXiv:1809.03878. 2018.

33.

Krioukov D, Papadopoulos F, Kitsak M, Vahdat A, Boguná M. Hyperbolic geometry of complex networks. Phys Rev E. 2010;82:036106.CrossRef

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.CrossRefPubMedPubMedCentral

35.

Tadić B, Andjelković M, Šuvakov M. Origin of hyperbolicity in brain-to-brain coordination networks. Front Phys. 2018;6:7.CrossRef

36.

Kaiser A, Schreiber T. Information transfer in continuous processes. Physica D. 2002;166:43–62.

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.CrossRefPubMed

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.CrossRefPubMedPubMedCentral

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.

40.

Yue T, Wang H. Deep learning for genomics: a concise overview. arXiv:1802.00810. 2018.

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.CrossRefPubMed

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.

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.CrossRefPubMedPubMedCentral

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.CrossRefPubMed

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.

46.

Lee H, Ma Z, Wang Y, Chung MK. Topological distances between networks and its application to brain imaging. arXiv:1701.04171. 2017.

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.

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.CrossRefPubMed

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.

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.CrossRefPubMed

51.

Logothetis NK. What we can do and what we cannot do with fMRI. Nature. 2008;453:869.CrossRefPubMed

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.CrossRefPubMedCentral

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.CrossRefPubMed

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.CrossRefPubMed

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.CrossRefPubMed

56.

Berthelot D, Schumm T, Metz L. BEGAN: boundary equilibrium generative adversarial networks. arXiv:1703.10717. 2017.

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.CrossRefPubMed

Titel: Clinical Personal Connectomics Using Hybrid PET/MRI
verfasst von: Dong Soo Lee
Publikationsdatum: 15.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Nuclear Medicine and Molecular Imaging / Ausgabe 3/2019
Print ISSN: 1869-3474
Elektronische ISSN: 1869-3482
DOI: https://doi.org/10.1007/s13139-019-00572-3

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Clinical Personal Connectomics Using Hybrid PET/MRI

Abstract

Die Highlights vom Kongress des American College of Cardiology 2024

Springer Medizin

Abstract

Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten

Weitere Artikel der Ausgabe 3/2019

Perspectives for Concepts of Individualized Radionuclide Therapy, Molecular Radiotherapy, and Theranostic Approaches

Eruption of Metastatic Paraganglioma After Successful Therapy with 177Lu/90Y-DOTATOC and 177Lu-DOTATATE

Quantitative Imaging of Alpha-Emitting Therapeutic Radiopharmaceuticals

Lesion-Wise Comparison of Pre-Therapy and Post-Therapy Effective Half-Life of Iodine-131 in Pediatric and Young Adult Patients with Differentiated Thyroid Cancer Undergoing Radioiodine Therapy

How Do the More Recent Reconstruction Algorithms Affect the Interpretation Criteria of PET/CT Images?

Perspectives in Radiomics for Personalized Medicine and Theranostics