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M. Hallquist, F. Hillary (2018)
Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-worldNetwork Neuroscience, 3
N. Logothetis (2008)
What we can do and what we cannot do with fMRINature, 453
Michael Lindner, Raul Vicente, V. Priesemann, M. Wibral (2011)
TRENTOOL: A Matlab open source toolbox to analyse information flow in time series data with transfer entropyBMC Neuroscience, 12
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William Hamilton, J. Leskovec (2018)
Graph Convolutional Neural Networks for Web-Scale Recommender SystemsProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
Hongyoon Choi, Yoori Choi, Kyu Kim, Hyejin Kang, D. Hwang, E. Kim, June-Key Chung, D. Lee (2015)
Maturation of metabolic connectivity of the adolescent rat braineLife, 4
Hyekyoung Lee, M. Chung, Hyejin Kang, Dong Lee (2014)
Hole Detection in Metabolic Connectivity of Alzheimer's Disease Using k -LaplacianMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 17 Pt 3
Dong Lee, J. Lee, K. Kang, M. Jang, Sang Lee, June-Key Chung, M. Lee (2001)
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 SPECTEpilepsia, 42
Hongyoon Choi, D. Lee (2017)
Generation of Structural MR Images from Amyloid PET: Application to MR-Less QuantificationThe Journal of Nuclear Medicine, 59
M. Vafaee, E. Meyer, Sean Marrett, T. Paus, Alan Evans, A. Gjedde (1999)
Frequency-Dependent Changes in Cerebral Metabolic Rate of Oxygen during Activation of Human Visual CortexJournal of Cerebral Blood Flow & Metabolism, 19
H. Im, Jarang Hahm, Hyejin Kang, Hongyoon Choi, Hyekyoung Lee, D. Hwang, E. Kim, June-Key Chung, D. Lee (2016)
Disrupted brain metabolic connectivity in a 6-OHDA-induced mouse model of Parkinson’s disease examined using persistent homology-based analysisScientific Reports, 6
A. Nielsen, M. Lauritzen (2001)
Coupling and uncoupling of activity‐dependent increases of neuronal activity and blood flow in rat somatosensory cortexThe Journal of Physiology, 533
H. Matsuda (2013)
Voxel-based Morphometry of Brain MRI in Normal Aging and Alzheimer's Disease.Aging and disease, 4 1
S. Kang, Seongho Seo, S. Shin, M. Byun, D. Lee, Yu Kim, D. Lee, J. Lee (2018)
Adaptive template generation for amyloid PET using a deep learning approachHuman Brain Mapping, 39
Ann Choe, Craig Jones, S. Joel, J. Muschelli, V. Belegu, B. Caffo, M. Lindquist, Peter Zijl, J. Pekar (2015)
Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 YearsPLoS ONE, 10
Hongyoon Choi, Seunggyun Ha, Hyejin Kang, Hyekyoung Lee, D. Lee (2019)
Deep learning only by normal brain PET identify unheralded brain anomaliesEBioMedicine, 43
A. Barrat, M. Barthelemy, Alessandro Weighted (2016)
The geometric nature of weights in real complex networksNature Communications, 8
G. Deco, M. Kringelbach (2014)
Great Expectations: Using Whole-Brain Computational Connectomics for Understanding Neuropsychiatric DisordersNeuron, 84
Adam Santoro, Ryan Faulkner, David Raposo, Jack Rae, Mike Chrzanowski, T. Weber, Daan Wierstra, O. Vinyals, Razvan Pascanu, T. Lillicrap (2018)
Relational recurrent neural networks
D. Lee, Hyejin Kang, Heejung Kim, Hyojin Park, Jungsu Oh, J. Lee, M. Lee (2008)
Metabolic connectivity by interregional correlation analysis using statistical parametric mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adultsEuropean Journal of Nuclear Medicine and Molecular Imaging, 35
Hyekyoung Lee, Hyejin Kang, M. Chung, Bung-Nyun Kim, Dong Lee (2012)
Persistent Brain Network Homology From the Perspective of DendrogramIEEE Transactions on Medical Imaging, 31
S. Frässle, Ekaterina Lomakina, L. Kasper, Zina-Mary Manjaly, A. Leff, K. Pruessmann, J. Buhmann, K. Stephan (2018)
A generative model of whole-brain effective connectivityNeuroImage, 179
Junyoung Park, Donghwi Hwang, K. Kim, S. Kang, Yu Kim, J. Lee (2018)
Computed tomography super-resolution using deep convolutional neural networkPhysics in Medicine & Biology, 63
Hyekyoung Lee, M. Chung, Hyejin Kang, Hongyoon Choi, Yu Kim, Dong Lee (2018)
Abnormal hole detection in brain connectivity by kernel density of persistence diagram and Hodge Laplacian2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)
Eunkyung Kim, Hyejin Kang, Hyekyoung Lee, H. Lee, Myung-Whan Suh, Jae-Jin Song, Seung-ha Oh, Dong Lee (2014)
Morphological brain network assessed using graph theory and network filtration in deaf adultsHearing Research, 315
Donghwan Lee, Hyejin Kang, Eunkyung Kim, Hyekyoung Lee, Heejung Kim, Yu Kim, Youngjo Lee, Dong Lee (2015)
Optimal likelihood-ratio multiple testing with application to Alzheimer’s disease and questionable dementiaBMC Medical Research Methodology, 15
Tianwei Yue, Haohan Wang (2018)
Deep Learning for Genomics: A Concise OverviewArXiv, abs/1802.00810
Donghwan Lee, Youngjo Lee (2016)
Extended likelihood approach to multiple testing with directional error control under a hidden Markov random field modelJ. Multivar. Anal., 151
Heejung Kim, Jarang Hahm, Hyekyoung Lee, E. Kang, Hyejin Kang, Dong Lee (2015)
Brain Networks Engaged in Audiovisual Integration During Speech Perception Revealed by Persistent Homology-Based Network FiltrationBrain connectivity, 5 4
Hyekyoung Lee, Zhiwei Ma, Y. Wang, M. Chung (2017)
Topological Distances between Networks and Its Application to Brain ImagingarXiv: Quantitative Methods
S. Sheth, M. Nemoto, M. Guiou, M. Walker, N. Pouratian, A. Toga (2004)
Linear and Nonlinear Relationships between Neuronal Activity, Oxygen Metabolism, and Hemodynamic ResponsesNeuron, 42
Thomas Nichols, A. Holmes (2002)
Nonparametric permutation tests for functional neuroimaging: A primer with examplesHuman Brain Mapping, 15
H Choi, H Kang, DS Lee (2018)
Alzheimer’s Disease Neuroimaging Initiative. Predicting aging of brain metabolic topography using variational autoencoderFront Aging Neurosci, 10
A. Kaiser, T. Schreiber (2002)
Information transfer in continuous processesPhysica D: Nonlinear Phenomena, 166
B. Tadić, M. Andjelković, M. Šuvakov (2018)
Origin of Hyperbolicity in Brain-to-Brain Coordination NetworksFrontiers in Physics, 6
T. Fetaya, E. Wang, K.-C. Welling, M. Zemel, Thomas Kipf, Ethan Fetaya, Kuan-Chieh Wang, M. Welling, R. Zemel (2018)
Neural Relational Inference for Interacting Systems
V. Latora, Massimo Marchiori (2001)
Efficient behavior of small-world networks.Physical review letters, 87 19
A. Muscoloni, J. Thomas, S. Ciucci, G. Bianconi, C. Cannistraci (2016)
Machine learning meets complex networks via coalescent embedding in the hyperbolic spaceNature Communications, 8
Ignacio Arganda-Carreras, Srinivas Turaga, D. Berger, D. Ciresan, A. Giusti, L. Gambardella, J. Schmidhuber, D. Laptev, Sarvesh Dwivedi, J. Buhmann, Ting Liu, Mojtaba Seyedhosseini, T. Tasdizen, L. Kamentsky, Radim Burget, V. Uher, Xiao Tan, Changming Sun, T. Pham, Erhan Bas, M. Uzunbas, A. Cardona, J. Schindelin, H. Seung (2015)
Crowdsourcing the creation of image segmentation algorithms for connectomicsFrontiers in Neuroanatomy, 9
Hyekyoung Lee, Eunkyung Kim, Seunggyun Ha, Hyejin Kang, Y. Huh, Youngjo Lee, Seonhee Lim, Dong Lee (2019)
Volume entropy for modeling information flow in a brain graphScientific Reports, 9
David Berthelot, Tom Schumm, Luke Metz (2017)
BEGAN: Boundary Equilibrium Generative Adversarial NetworksArXiv, abs/1703.10717
H. Buchholz, F. Wenzel, M. Gartenschläger, F. Thiele, S. Young, S. Reuss, M. Schreckenberger (2015)
Construction and comparative evaluation of different activity detection methods in brain FDG-PETBioMedical Engineering OnLine, 14
David Silver, Julian Schrittwieser, K. Simonyan, Ioannis Antonoglou, Aja Huang, A. Guez, T. Hubert, Lucas baker, Matthew Lai, A. Bolton, Yutian Chen, T. Lillicrap, Fan Hui, L. Sifre, George Driessche, T. Graepel, D. Hassabis (2017)
Mastering the game of Go without human knowledgeNature, 550
Adam Santoro, David Raposo, D. Barrett, Mateusz Malinowski, Razvan Pascanu, P. Battaglia, T. Lillicrap (2017)
A simple neural network module for relational reasoning
Melanie Weber, Emil Saucan, J. Jost (2016)
Characterizing complex networks with Forman-Ricci curvature and associated geometric flowsArXiv, abs/1607.08654
D. Krioukov, Fragkiskos Papadopoulos, M. Kitsak, Amin Vahdat, M. Boguñá (2010)
Hyperbolic Geometry of Complex NetworksPhysical review. E, Statistical, nonlinear, and soft matter physics, 82 3 Pt 2
Hongyoon Choi, Hyejin Kang, D. Lee (2017)
Predicting Aging of Brain Metabolic Topography Using Variational AutoencoderFrontiers in Aging Neuroscience, 10
Karl Friston (2011)
Functional and Effective Connectivity: A ReviewBrain connectivity, 1 1
Meichen Yu, A. Hillebrand, A. Gouw, C. Stam (2017)
Horizontal visibility graph transfer entropy (HVG-TE): A novel metric to characterize directed connectivity in large-scale brain networksNeuroImage, 156
Youngjo Lee, J. Bjørnstad (2013)
Extended likelihood approach to large‐scale multiple testingJournal of the Royal Statistical Society: Series B (Statistical Methodology), 75
M. Chung, Hyekyoung Lee, Andrey Gritsenko, Alex DiChristofano, Dustin Pluta, H. Ombao, V. Solo (2018)
Topological Brain Network DistancesarXiv: Applications
Donghwi Hwang, K. Kim, S. Kang, Seongho Seo, J. Paeng, D. Lee, J. Lee (2018)
Improving the Accuracy of Simultaneously Reconstructed Activity and Attenuation Maps Using Deep LearningThe Journal of Nuclear Medicine, 59
Huai-Ping Lee, M. Foskey, M. Niethammer, Pavel Krajcevski, M. Lin (2012)
Simulation-Based Joint Estimation of Body Deformation and Elasticity Parameters for Medical Image AnalysisIEEE Transactions on Medical Imaging, 31
Hyekyoung Lee, M. Chung, Hyejin Kang, Boong-Nyun Kim, Dong Lee (2011)
Computing the Shape of Brain Networks Using Graph Filtration and Gromov-Hausdorff MetricMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention, 14 Pt 2
Raul Vicente, M. Wibral, Michael Lindner, G. Pipa (2010)
Transfer entropy—a model-free measure of effective connectivity for the neurosciencesJournal of Computational Neuroscience, 30
F. Caron, E. Fox (2014)
Sparse graphs using exchangeable random measuresJournal of the Royal Statistical Society. Series B, Statistical Methodology, 79
Stephen Smith, D. Vidaurre, C. Beckmann, M. Glasser, M. Jenkinson, K. Miller, Thomas Nichols, E. Robinson, G. Salimi-Khorshidi, M. Woolrich, D. Barch, K. Uğurbil, D. Essen (2013)
Functional connectomics from resting-state fMRITrends in Cognitive Sciences, 17
Rosalie Putten, I. Mengarelli, K. Guan, J. Zegers, A. Ginneken, A. Verkerk, R. Wilders (2015)
Ion channelopathies in human induced pluripotent stem cell derived cardiomyocytes: a dynamic clamp study with virtual IK1Frontiers in Physiology, 6
Jarang Hahm, Hyekyoung Lee, Hyojin Park, E. Kang, Yu Kim, C. Chung, Hyejin Kang, Dong Lee (2017)
Gating of memory encoding of time-delayed cross-frequency MEG networks revealed by graph filtration based on persistent homologyScientific Reports, 7
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.
Nuclear Medicine and Molecular Imaging – Springer Journals
Published: Jan 15, 2019
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