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Brain MRI Graphical Networks and Subgraphs: Developing a novel methodology for identifying small-scale underlying patterns responsible for large-scale functional differences between brains

Title:

Brain MRI Graphical Networks and Subgraphs: Developing a novel methodology for identifying small-scale underlying patterns responsible for large-scale functional differences between brains

Wright, Lindsay (2024) Brain MRI Graphical Networks and Subgraphs: Developing a novel methodology for identifying small-scale underlying patterns responsible for large-scale functional differences between brains. Masters thesis, Concordia University.

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Abstract

Magnetic resonance imaging (MRI) provides an unprecedented ability to investigate brain health and function. However, the high cost and high variability in the population limits its use in understanding complex diseases, which requires new methodologies. As such, the focus of this project was to define and identify a novel reorganisation of long Covid brain MRI data into network graphs and subgraphs based on functional, rather than spatial, connections between voxels. We define a physiological connectome: a graph in which the nodes are voxels, MRI metrics are node attributes, and edges are formed according to physiological similarity. From these graphs we define local neighborhood subgraphs containing each voxel’s set of nearest neighbours, which we examine for their properties. MRI features most strongly associated with high connectivity included low values of axial diffusivity (AD), mean diffusivity (MD), radial diffusivity (RD), and isotropic volume fraction (ISOVF); and mean values of intracellular volume fraction (ICVF). Orientation dispersion index (ODI) and fractional anisotropy (FA) values were highly variable. The general data trends outside of highly connected voxels include an inverse relationship between FA and ODI, RD and FA, AD and ODI, ICVF and RD, and ICVF and MD; a positive correlation between AD and MD, ODI and RD, and RD and MD. RD and AD, and ISOVF and ICVF do not demonstrate a clear trend. In future, these subgraphs will form the basis for a generalisable data augmentation and analysis method, to identify underlying patterns responsible for large-scale functional differences between brains.

Divisions:Concordia University > Faculty of Arts and Science > Physics
Item Type:Thesis (Masters)
Authors:Wright, Lindsay
Institution:Concordia University
Degree Name:M. Sc.
Program:Physics
Date:2 July 2024
Thesis Supervisor(s):Gauthier, Claudine and Mansbach, Re
Keywords:MRI, neuroimaging, brain, covid, long covid, cluster, network graph, subgraph, graphical neural network, deep learning, data augmentation, topology, persistent homology
ID Code:994198
Deposited By: Lindsay Wright
Deposited On:24 Oct 2024 18:56
Last Modified:24 Oct 2024 18:56

References:

Ajčević, M., Iscra, K., Furlanis, G., Michelutti, M., Miladinović, A., Buoite Stella, A., Ukmar, M., Assunta Cova, M., Accardo, A., & Manganotti, P. (2023). Cerebral hypoperfusion in post-COVID-19 cognitively impaired subjects revealed by arterial spin labeling MRI. Scientific Reports, 13(1), 5808. https://doi.org/10.1038/s41598-023-32275-3
Albert Einstein College of Medicine. (2014, Sep 23). Introducing MRI: Arterial spin labeling (54 of 56) [Video]. YouTube. https://www.youtube.com/watch?v=o6gqqeDfKGM
Alexander, A. L., Hasan, K., Kindlmann, G., Parker, D. L., & Tsuruda, J. S. (2000). A geometric analysis of diffusion tensor measurements of the human brain. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 44(2), 283-291. https://doi.org/10.1002/1522-2594(200008)44:2%3C283::aid-mrm16%3E3.0.co;2-v
Alexander, A. L., Hurley, S. A., Samsonov, A. A., Adluru, N., Hosseinbor, A. P., Mossahebi, P., Tromp, D. P. M., Zakszewski, E., & Field, A. S. (2011). Characterization of cerebral white matter properties using quantitative magnetic resonance imaging stains. Brain Connectivity, 1(6), 423-446. https://doi.org/10.1089/brain.2011.0071
Alexander, A. L., Tsuruda, J. S., & Parker, D. L. (1997). Elimination of eddy current artifacts in diffusion‐weighted echo‐planar images: The use of bipolar gradients. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 38(6), 1016-1021. https://doi.org/10.1002/mrm.1910380623
Asllani, I., Borogovac, A., & Brown, T. R. (2008). Regression algorithm correcting for partial volume effects in arterial spin labeling MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 60(6), 1362-1371. https://doi.org/10.1002/mrm.21670
Bastian, M., Heymann, S., & Jacomy, M. (2009, March). Gephi: An open source software for exploring and manipulating networks. In Proceedings of the international AAAI conference on web and social media (Vol. 3, No. 1, pp. 361-362). https://doi.org/10.1609/icwsm.v3i1.13937
Bazin, P. L., Plessis, V., Fan, A. P., Villringer, A., & Gauthier, C. J. (2016, April). Vessel segmentation from quantitative susceptibility maps for local oxygenation venography. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) (pp. 1135-1138). IEEE. https://doi.org/10.1109/ISBI.2016.7493466
Bentley, J. L. (1975). A survey of techniques for fixed radius near neighbor searching. Stanford University. Retrieved June 10, 2024, from https://exhibits.stanford.edu/stanford-pubs/catalog/bh705ys8528
Betzel, R. F., & Bassett, D. S. (2017). Generative models for network neuroscience: Prospects and promise. Journal of The Royal Society Interface, 14(136), 20170623. https://doi.org/10.1098/rsif.2017.0623
Biondetti, E., Cho, J., & Lee, H. (2023). Cerebral oxygen metabolism from MRI susceptibility. Neuroimage, 276, 120189. https://doi.org/10.1016/j.neuroimage.2023.120189
Bren, K. L., Eisenberg, R., & Gray, H. B. (2015). Discovery of the magnetic behavior of hemoglobin: A beginning of bioinorganic chemistry. Proceedings of the National Academy of Sciences, 112(43), 13123-13127. https://doi.org/10.1073/pnas.1515704112
Brett, M., Markiewicz, C. J., Hanke, M., Côté, M.-A., Cipollini, B., McCarthy, P., Jarecka, D., Cheng, C. P., Larson, E., Halchenko, Y. O., Cottaar, M., Ghosh, S., Wassermann, D., Gerhard, S., Lee, G. R., Baratz, Z., Wang, H.-T., Papadopoulos Orfanos, D., Kastman, E., … freec84. (2024). nipy/nibabel: 5.2.1 (5.2.1). Zenodo. https://doi.org/10.5281/zenodo.10714563
Brown, R. (1828). XXVII. A brief account of microscopical observations made in the months of June, July and August 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies. The Philosophical Magazine, 4(21), 161-173.
Bullmore, E., & Sporns, O. (2009). Complex brain networks: Graph theoretical analysis of structural and functional systems. Nature Reviews Neuroscience, 10(3), 186-198. https://doi.org/10.1038/nrn2575
Buxton, R. B., Frank, L. R., Wong, E. C., Siewert, B., Warach, S., & Edelman, R. R. (1998). A general kinetic model for quantitative perfusion imaging with arterial spin labeling. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 40(3), 383-396. https://doi.org/10.1002/mrm.1910400308
Carfì, A., Bernabei, R., & Landi, F. (2020). Persistent symptoms in patients after acute COVID-19. Jama, 324(6), 603-605.
Chlap, P., Min, H., Vandenberg, N., Dowling, J., Holloway, L., & Haworth, A. (2021). A review of medical image data augmentation techniques for deep learning applications. Journal of Medical Imaging and Radiation Oncology, 65(5), 545-563. https://doi.org/10.1111/1754-9485.13261
CMB-group. (2020). pyemma.plots.plot_free_energy. PyEMMA 2.5.7 documentation. Retrieved June 19, 2024, from http://www.emma-project.org/latest/api/generated/pyemma.plots.plot_free_energy.html
Cover, T., & Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
Crooks, G. E. (2007). Measuring thermodynamic length. Physical Review Letters, 99(10), 100602. https://doi.org/10.1103/PhysRevLett.99.100602
De Boer, R. W. (1995). Magnetization transfer contrast. Part, 1, 64-73.
Deng, W., Zhang, B., Zou, W., Zhang, X., Cheng, X., Guan, L., Lin, Y., Lao, G., Ye, B., Li, X., Yang, C., Ning, Y., & Cao, L. (2019). Abnormal degree centrality associated with cognitive dysfunctions in early bipolar disorder. Frontiers in Psychiatry, 10, 140. https://doi.org/10.3389/fpsyt.2019.00140
Descoteaux, M. (1999). High angular resolution diffusion imaging (HARDI). Wiley Encyclopedia of Electrical and Electronics Engineering, 1-25. https://doi.org/10.1002/047134608X.W8258
Detre, J. A., Rao, H., Wang, D. J., Chen, Y. F., & Wang, Z. (2012). Applications of arterial spin labeled MRI in the brain. Journal of Magnetic Resonance Imaging, 35(5), 1026-1037. https://doi.org/10.1002/jmri.23581
Douaud, G., Lee, S., Alfaro-Almagro, F., Arthofer, C., Wang, C., McCarthy, P., Lange, F., Andersson, J. L. R., Griffanti, L., Duff, E., Jbabdi, S., Taschler, B., Keating, P., Winkler, A. M., Collins, R., Matthews, P. M., Allen, N., Miller, K. L., Nichols, T. E., & Smith, S. M. (2022). SARS-CoV-2 is associated with changes in brain structure in UK Biobank. Nature, 604(7907), 697-707.
Duyn, J. (2013). MR susceptibility imaging. Journal of Magnetic Resonance, 229, 198-207. https://doi.org/10.1016/j.jmr.2012.11.01
Duyn, J. H., & Schenck, J. (2017). Contributions to magnetic susceptibility of brain tissue. NMR in Biomedicine, 30(4), e3546. https://doi.org/10.1002/nbm.3546
Dymerska, B., Eckstein, K., Bachrata, B., Siow, B., Trattnig, S., Shmueli, K., & Robinson, S. D. (2021). Phase unwrapping with a rapid opensource minimum spanning tree algorithm (ROMEO). Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 85(4), 2294-2308. https://doi.org/10.1002/mrm.28563
Educative. (n.d.). Sparse matrices in Python. Retrieved June 17, 2024, from https://www.educative.io/answers/sparse-matrices-in-python
Edzes, H. T., & Samulski, E. T. (1977). Cross relaxation and spin diffusion in the proton NMR of hydrated collagen. Nature, 265(5594), 521-523. https://doi.org/10.1038/265521a0
Einstein, A. (1905). über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Annalen der Physik, 322(8), 549-560.
Elster, A. (n.d. a). T1 Relaxation: Definition. Questions and Answers in MRI. Retrieved May 29, 2024, from https://mriquestions.com/what-is-t1.html
Elster, A. (n.d. b). T2 Relaxation: Definition. Questions and Answers in MRI. Retrieved May 29, 2024, from https://mriquestions.com/what-is-t2.html
Elster, A. (n.d. c). T2 vs T2*. Questions and Answers in MRI. Retrieved May 29, 2024, from https://mriquestions.com/t2-vs-t2.html
Elster, A. (n.d. d). Free Induction Decay. Questions and Answers in MRI. Retrieved May 29, 2024, from https://mriquestions.com/free-induction-decay.html
Elster, A. (n.d. e). Gradient Echo (GRE). Questions and Answers in MRI. Retrieved May 20, 2024, from https://mriquestions.com/gradient-echo.html
Fan, A. P., Bilgic, B., Gagnon, L., Witzel, T., Bhat, H., Rosen, B. R., & Adalsteinsson, E. (2014). Quantitative oxygenation venography from MRI phase. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 72(1), 149-159. https://doi.org/10.1002/mrm.24918
Fan, A. P., Khalil, A. A., Fiebach, J. B., Zaharchuk, G., Villringer, A., Villringer, K., & Gauthier, C. J. (2020). Elevated brain oxygen extraction fraction measured by MRI susceptibility relates to perfusion status in acute ischemic stroke. Journal of Cerebral Blood Flow & Metabolism, 40(3), 539-551. https://doi.org/10.1177/0271678X198279
Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). “Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12(3), 189-198. https://doi.org/10.1016/0022-3956(75)90026-6
Fornito, A., Zalesky, A., & Breakspear, M. (2013). Graph analysis of the human connectome: Promise, progress, and pitfalls. Neuroimage, 80, 426-444. https://doi.org/10.1016/j.neuroimage.2013.04.087
Fox, R. J., Beall, E., Bhattacharyya, P., Chen, J. T., & Sakaie, K. (2011). Advanced MRI in multiple sclerosis: Current status and future challenges. Neurologic Clinics, 29(2), 357-380. https://doi.org/10.1016/j.ncl.2010.12.011
Giunta, S., Giordani, C., De Luca, M., & Olivieri, F. (2024). Long-COVID-19 autonomic dysfunction: an integrated view in the framework of inflammaging. Mechanisms of Ageing and Development, 111915.
Gonçalves, N., Nikkilä, J., & Vigário, R. (2009). Partial clustering for tissue segmentation in MRI. In Advances in Neuro-Information Processing: 15th International Conference, ICONIP 2008, Auckland, New Zealand, November 25-28, 2008, Revised Selected Papers, Part II 15 (pp. 559-566). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-03040-6_68
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Grade, M., Hernandez Tamames, J. A., Pizzini, F. B., Achten, E., Golay, X., & Smits, M. (2015). A neuroradiologist’s guide to arterial spin labeling MRI in clinical practice. Neuroradiology, 57, 1181-1202. https://doi.org/10.1007/s00234-015-1571-z
Guillén, N., Pérez-Millan, A., Falgàs, N., Lledó-Ibáñez, G. M., Rami, L., Sarto, J., Botí, M., Arnaldos-Pérez, C., Ruiz-García, R., Naranjo, L., Segura, B., Balasa, M., Sala-Llonch, R., Lladó, A., Gray, S. M., Johannesen, J. K., Pantoni, M. M., Rutledge, G. A., Sawant, R., ... Sanchez-Valle, R. (2024). Cognitive profile, neuroimaging and fluid biomarkers in post-acute COVID-19 syndrome. Scientific Reports, 14(1), 12927.
Haacke, E. M., Liu, S., Buch, S., Zheng, W., Wu, D., & Ye, Y. (2015). Quantitative susceptibility mapping: Current status and future directions. Magnetic Resonance Imaging, 33(1), 1-25. https://doi.org/10.1016/j.mri.2014.09.004
Haller, S., Zaharchuk, G., Thomas, D. L., Lovblad, K. O., Barkhof, F., & Golay, X. (2016). Arterial spin labeling perfusion of the brain: emerging clinical applications. Radiology, 281(2), 337-356.
Hasan, K. M., Alexander, A. L., & Narayana, P. A. (2004). Does fractional anisotropy have better noise immunity characteristics than relative anisotropy in diffusion tensor MRI? An analytical approach. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 51(2), 413-417. https://doi.org/10.1002/mrm.10682
Hastie, T., Tibshirani, R., Friedman, J. H., & Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction (Vol. 2, pp. 1-758). New York: springer.
Havran, Vlastimil. (2016). [Lecture slides]. Data Structures for Computer Graphics: point based representations and data structures. Retrieved June 10, 2024 from https://slideplayer.com/slide/6318865/
Helms, G., Dathe, H., Kallenberg, K., & Dechent, P. (2008). High‐resolution maps of magnetization transfer with inherent correction for RF inhomogeneity and T1 relaxation obtained from 3D FLASH MRI. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 60(6), 1396-1407. https://doi.org/10.1002/mrm.21732
Henkelman, R. M., Stanisz, G. J., & Graham, S. J. (2001). Magnetization transfer in MRI: a review. NMR in Biomedicine: An International Journal Devoted to the Development and Application of Magnetic Resonance In Vivo, 14(2), 57-64. https://doi.org/10.1002/nbm.683
Hidalgo‐Tobon, S. S. (2010). Theory of gradient coil design methods for magnetic resonance imaging. Concepts in Magnetic Resonance Part A, 36(4), 223-242. https://doi.org/10.1002/cmr.a.20163
Horowitz, T., Dudouet, P., Campion, J. Y., Kaphan, E., Radulesco, T., Gonzalez, S., Cammilleri, S., Ménard, A., & Guedj, E. (2024). Persistent brain metabolic impairment in long COVID patients with persistent clinical symptoms: a nine-month follow-up [18F] FDG-PET study. European Journal of Nuclear Medicine and Molecular Imaging, 1-8. https://doi.org/10.1007/s00259-024-06775-x
Hosp, J. A., Reisert, M., Dressing, A., Götz, V., Kellner, E., Mast, H., Arndt, S., Waller, C. F., Wagner, D., Rieg, S., Urbach, H., Weiller, C., Schröter, N., & Rau, A. (2024). Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction and fatigue. Nature Communications, 15(1), 4256. https://doi.org/10.1038/s41467-024-48651-0
Hou, X., Guo, P., Wang, P., Liu, P., Lin, D. D. M., Fan, H., Li, Y., Wei, Z., Lin, Z., Jiang, D., Jin, J., Kelly, C., Pillai, J. J., Huang, J., Pinho, M. C., Thomas, B. P., Welch, B. G., Park, D. C., Patel, V. M., ... Lu, H. (2023). Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI. npj Digital Medicine, 6(1), 116. https://doi.org/10.1038/s41746-023-00859-y
Huck, J., Wanner, Y., Fan, A. P., Jäger, A. T., Grahl, S., Schneider, U., Villringer, A., Steele, C. J., Tardif, C. L., Bazin, P. L., & Gauthier, C. J. (2019). High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps. Brain Structure and Function, 224, 2467-2485. https://doi.org/10.1016/j.cag.2011.02.001
Huisman, T. A. G. M. (2010). Diffusion-weighted and diffusion tensor imaging of the brain, made easy. Cancer Imaging, 10(1A), S163. https://doi.org/10.1102/1470-7330.2010.9023
Huo, J., Wu, J., Cao, J., & Wang, G. (2018). Supervoxel based method for multi-atlas segmentation of brain MR images. NeuroImage, 175, 201-214. https://doi.org/10.1016/j.neuroimage.2018.04.001
Intzandt, B., Sabra, D., Foster, C., Desjardins-Crépeau, L., Hoge, R. D., Steele, C. J., Bherer, L., & Gauthier, C. J. (2020). Higher cardiovascular fitness level is associated with lower cerebrovascular reactivity and perfusion in healthy older adults. Journal of Cerebral Blood Flow & Metabolism, 40(7), 1468-1481. https://doi.org/10.1177/0271678X198628
Jenkinson, M., Pechaud, M., & Smith, S. (2005). BET2: MR-based estimation of brain, skull and scalp surfaces. In Eleventh Annual Meeting of the Organization for Human Brain Mapping, 2005.
Jespersen, S. N., Leigland, L. A., Cornea, A., & Kroenke, C. D. (2011). Determination of axonal and dendritic orientation distributions within the developing cerebral cortex by diffusion tensor imaging. IEEE Transactions on Medical Imaging, 31(1), 16-32. https://doi.org/10.1109/TMI.2011.2162099
Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. http://doi.org/10.1098/rsta.2015.0202
Jones, J., Murphy, A., Bell, D., Gaillard, F., & Di Muzio, B. (2021, Sep 19). Net magnetisation vector. Radiopedia.org. https://doi.org/10.53347/rID-6316
Kamli, A., Saouli, R., Batatia, H., Naceur, M. B., & Youkana, I. (2020). Synthetic medical image generator for data augmentation and anonymisation based on generative adversarial network for glioblastoma tumors growth prediction. IET Image Processing, 14(16), 4248-4257. https://doi.org/10.1049/iet-ipr.2020.1141
Karakuzu, A., Boudreau, M., Duval, T., Boshkovski, T., Leppert, I., Cabana, J. F., Gagnon, I., Beliveau, P., Pike, G. B., Cohen-Adad, J., & Stikov, N. (2020). qMRLab: Quantitative MRI analysis, under one umbrella. Journal of Open Source Software, 5(53). https://doi.org/10.21105/joss.02343
Kazemifar, S., McGuire, S., Timmerman, R., Wardak, Z., Nguyen, D., Park, Y., Jiang, S., & Owrangi, A. (2019). MRI-only brain radiotherapy: Assessing the dosimetric accuracy of synthetic CT images generated using a deep learning approach. Radiotherapy and Oncology, 136, 56-63. https://doi.org/10.1016/j.radonc.2019.03.026
Kusztos, R., Dimitri, G. M., & Lió, P. (2020). Neural Models for Brain Networks Connectivity Analysis. In Computational Intelligence Methods for Bioinformatics and Biostatistics: 15th International Meeting, CIBB 2018, Caparica, Portugal, September 6–8, 2018, Revised Selected Papers 15 (pp. 212-226). Springer International Publishing. https://doi.org/10.1007/978-3-030-34585-3_19
Langkammer, C., Bredies, K., Poser, B. A., Barth, M., Reishofer, G., Fan, A. P., Bilgic, B., Fazekas, F., Mainero, C., & Ropele, S. (2015). Fast quantitative susceptibility mapping using 3D EPI and total generalized variation. Neuroimage, 111, 622-630. https://doi.org/10.1016/j.neuroimage.2015.02.041
Le Bihan, D., Mangin, J. F., Poupon, C., Clark, C. A., Pappata, S., Molko, N., & Chabriat, H. (2001). Diffusion tensor imaging: concepts and applications. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 13(4), 534-546. https://doi.org/10.1002/jmri.1076
Levesque, I., Sled, J. G., Narayanan, S., Santos, A. C., Brass, S. D., Francis, S. J., Arnold, D. L., & Pike, G. B. (2005). The role of edema and demyelination in chronic T1 black holes: a quantitative magnetization transfer study. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 21(2), 103-110. https://doi.org/10.1002/jmri.20231
Li S., Murphy A, Knipe H., & Gaillard, F. (2020, Apr 2). Energy difference between spin up and spin down states. Radiopaedia.org. https://doi.org/10.53347/rID-54148
Liang, Z. P., & Lauterbur, P. C. (2000). Principles of magnetic resonance imaging (pp. 1-7). Bellingham: SPIE Optical Engineering Press.
Lin, E. C. (2010, December). Radiation risk from medical imaging. Mayo Clinic Proceedings (Vol. 85, No. 12, pp. 1142-1146). Elsevier. https://doi.org/10.4065/mcp.2010.0260
Lin, Q., Zhu, F. Y., Shu, Y. Q., Zhu, P. W., Ye, L., Shi, W. Q., Min, Y. L., Li, B., Yuan, Q., & Shao, Y. (2021). Altered brain network centrality in middle‐aged patients with retinitis pigmentosa: A resting‐state functional magnetic resonance imaging study. Brain and Behavior, 11(2), e01983. https://doi.org/10.1002/brb3.1983
Liu, P., De Vis, J., & Lu, H. (2019). Cerebrovascular reactivity (CVR) MRI with CO2 challenge: A technical review. NeuroImage, 187, 104. https://doi.org/10.1016/j.neuroimage.2018.03.047
Liu, Y., Dong, J., Song, Q., Zhang, N., Wang, W., Gao, B., Tian, S., Dong, C., Liang, Z., Xie, L., & Miao, Y. (2021). Correlation between cerebral venous oxygen level and cognitive status in patients with Alzheimer’s disease using quantitative susceptibility mapping. Frontiers in Neuroscience, 14, 570848. https://doi.org/10.3389/fnins.2020.570848
Longoni, G., Martinez Chavez, E., Young, K., Brown, R. A., Bells, S., Fetco, D., Kim, L., Grover, S. A., Costello, F., Reginald, A., Bar-Or, A., Marrie, R. A., Arnold, D. L., Narayanan, S., Branson, H. M., Banwell, B. L., Sled, J. G., Mabbott, D. J., & Yeh, E. A. (2023). Magnetization transfer saturation reveals subclinical optic nerve injury in pediatric-onset multiple sclerosis. Multiple Sclerosis Journal, 29(2), 212-220. https://doi.org/10.1177/13524585221137500
Magritek. (2016, July 18). Gradients in NMR spectroscopy – Part 5: the pulsed gradient spin echo (PGSE) experiment. Retrieved May 20, 2024, from https://magritek.com/2016/07/18/gradients-in-nmr-spectroscopy-part-5-the-pulsed-gradient-spin-echo-pgse-experiment/
Mansbach, R. A., & Ferguson, A. L. (2015). Machine learning of single molecule free energy surfaces and the impact of chemistry and environment upon structure and dynamics. The Journal of Chemical Physics, 142(10). https://doi.org/10.1063/1.4914144
Mercadante, A. A., & Tadi, P. (2023). Neuroanatomy, gray matter. StatPearls [Internet]. StatPearls Publishing. Retrieved May 29, 2024, from https://www.ncbi.nlm.nih.gov/books/NBK553239/
Mitchell, T. M., & Mitchell, T. M. (1997). Machine learning (Vol. 1, No. 9). New York: McGraw-hill.
Maneewongvatana, S., & Mount, D. M. (1999). Analysis of approximate nearest neighbor searching with clustered point sets. arXiv preprint cs/9901013.
Morell, P., & Quarles, R. H. (1999). The myelin sheath. Basic Neurochemistry: Molecular, Cellular and Medical Aspects, 6. Retrieved May 29, 2024, from https://www.ncbi.nlm.nih.gov/books/NBK20402/
Naitzat, G., Zhitnikov, A., & Lim, L. H. (2020). Topology of deep neural networks. Journal of Machine Learning Research, 21(184), 1-40. https://doi.org/10.1089/brain.2011.0071
Narasimhulu, Y., Suthar, A., Pasunuri, R., & Vadlamudi, C. V. (2021). CKD-Tree: An Improved KD-Tree Construction Algorithm. ISIC, (pp. 211-218).
National Institute of Biomedical Imaging and Bioengineering. (n.d.). Magnetic resonance imaging (MRI). Retrieved December 4, 2022, from https://www.nibib.nih.gov/science-education/science-topics/magnetic-resonance-imaging-mri
National Institute of Standards and Technology. (2017). Minkowski distance. Retrieved Jun 19, 2024, from https://www.itl.nist.gov/div898/software/dataplot/refman2/auxillar/minkdist.htm
Nguyen, K. P., Fatt, C. C., Treacher, A., Mellema, C., Trivedi, M. H., & Montillo, A. (2020, March). Anatomically informed data augmentation for functional MRI with applications to deep learning. Medical Imaging 2020: Image Processing (Vol. 11313, pp. 172-177). SPIE. https://doi.org/10.48550/arXiv.1910.08112
Numpy Developers. (n.d.). Statistics. NumPy v2.0 Manual. Retrieved June 21, 2024, from https://numpy.org/doc/stable/reference/routines.statistics.html
Olsson, H., Andersen, M., Wirestam, R., & Helms, G. (2021). Mapping magnetization transfer saturation (MTsat) in human brain at 7T: Protocol optimization under specific absorption rate constraints. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 86(5), 2562-2576. https://doi.org/10.1002/mrm.28899
Ottet, M. C., Schaer, M., Debbané, M., Cammoun, L., Thiran, J. P., & Eliez, S. (2013). Graph theory reveals dysconnected hubs in 22q11DS and altered nodal efficiency in patients with hallucinations. Frontiers in Human Neuroscience, 7, 402. https://doi.org/10.3389/fnhum.2013.00402
Paterson, R. W., Brown, R. L., Benjamin, L., Nortley, R., Wiethoff, S., Bharucha, T., Jayaseelan, D. L., Kumar, G., Raftopoulos, R. E., Zambreanu, L., Vivekanandam, V., Khoo, A., Geraldes, R., Chinthapalli, K., Boyd, E., Tuzlali, H., Price, G., Christofi, G., Morrow, J., McNamara, P., McLoughlin, B., ... Zandi, M. S. (2020). The emerging spectrum of COVID-19 neurology: clinical, radiological and laboratory findings. Brain, 143(10), 3104-3120. https://doi.org/10.1093/brain/awaa240
Papadakis, N. G., Xing, D., Houston, G. C., Smith, J. M., Smith, M. I., James, M. F., Parsons, A. A., Huang, C. L., Hall, L. D., & Carpenter, T. A. (1999). A study of rotationally invariant and symmetric indices of diffusion anisotropy. Magnetic Resonance Imaging, 17(6), 881-892. https://doi.org/10.1016/S0730-725X(99)00029-6
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Köpf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., ... Chintala, S. (2019). Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems, 32. https://doi.org/10.48550/arXiv.1912.01703
Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011). A Bayesian model of shape and appearance for subcortical brain segmentation. Neuroimage, 56(3), 907-922. https://doi.org/10.1016/j.neuroimage.2011.02.046s
Pinaya, W. H., Tudosiu, P. D., Dafflon, J., Da Costa, P. F., Fernandez, V., Nachev, P., Ourselin, S., & Cardoso, M. J. (2022, September). Brain imaging generation with latent diffusion models. In MICCAI Workshop on Deep Generative Models (pp. 117-126). Cham: Springer Nature Switzerland. https://doi.org/10.48550/arXiv.2209.07162
Raj, A., Kuceyeski, A., & Weiner, M. (2012). A network diffusion model of disease progression in dementia. Neuron, 73(6), 1204-1215. https://doi.org/10.1016/j.neuron.2011.12.040
Raveendran, A. V., Jayadevan, R., & Sashidharan, S. (2021). Long COVID: an overview. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 15(3), 869-875.
Reese, T. G., Heid, O., Weisskoff, R. M., & Wedeen, V. J. (2003). Reduction of eddy‐current‐induced distortion in diffusion MRI using a twice‐refocused spin echo. Magnetic Resonance in Medicine: An Official Journal of the International Society for Magnetic Resonance in Medicine, 49(1), 177-182.
Rocks, J. W., Liu, A. J., & Katifori, E. (2020). Revealing structure-function relationships in functional flow networks via persistent homology. Physical Review Research, 2(3), 033234. https://doi.org/10.1103/PhysRevResearch.2.033234
Rokem, A. (2015). GitHub - uwescience/shablona: A template for small scientific python projects. Retrieved Jun 29, 2023, from https://github.com/uwescience/shablona
Rosenbloom, M., Sullivan, E. V., & Pfefferbaum, A. (2003). Using magnetic resonance imaging and diffusion tensor imaging to assess brain damage in alcoholics. Alcohol Research & Health, 27(2), 146.
Sanchez-Lengeling, B., Reif, E., Pearce, A., & Wiltschko, A. B. (2021). A gentle introduction to graph neural networks. Distill, 6(9), e33. https://doi.org/10.23915/distill.00033
Schenck, J. F. (1996). The role of magnetic susceptibility in magnetic resonance imaging: MRI magnetic compatibility of the first and second kinds. Medical Physics, 23(6), 815-850. https://doi.org/10.1118/1.597854
Schilling, K. G., Chad, J. A., Chamberland, M., Nozais, V., Rheault, F., Archer, D., Li, M., Gao, Y., Cai, L., Del’Acqua, F., Newton, A., Moyer, D., Gore, J., Lebel, C., & Landman, B. A. (2023). White matter tract microstructure, macrostructure, and associated cortical gray matter morphology across the lifespan. Imaging Neuroscience, 1, 1-24. https://doi.org/10.1162/imag_a_00050
Schweser, F., Deistung, A., & Reichenbach, J. R. (2016). Foundations of MRI phase imaging and processing for Quantitative Susceptibility Mapping (QSM). Zeitschrift für medizinische Physik, 26(1), 6-34. https://doi.org/10.1016/j.zemedi.2015.10.002
Scikit-learn developers. (n.d. a). Comparing different clustering algorithms on toy datasets. scikit-learn 1.5.0 documentation. Retrieved June 11, 2024, from https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
Scikit-learn developers. (n.d. b). StandardScaler. scikit-learn 1.5.0 documentation. Retrieved June 19, 2024, from https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html
SciPy community. (n.d. a). scipy.spatial.cKDTree. SciPy v1.13.1 Manual. Retrieved June 19, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.html
SciPy community. (n.d. b). scipy.spatial.cKDTree.query_ball_point. SciPy v1.13.1 Manual. Retrieved June 19, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.query_ball_point.html
SciPy community. (n.d. c). scipy.spatial.cKDTree.sparse_distance_matrix. SciPy v1.13.1 Manual. Retrieved June 19, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.cKDTree.sparse_distance_matrix.html
SciPy community. (n.d. d). scipy.spatial.distance. SciPy v1.13.1 Manual. Retrieved June 19, 2024, from https://docs.scipy.org/doc/scipy/reference/spatial.distance.html
SciPy community. (n.d. e). scipy.sparse.csr_matrix. SciPy v1.13.1 Manual. Retrieved June 21, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.csr_matrix.html
SciPy community. (n.d. f). scipy.sparse.dok_matrix. SciPy v1.13.1 Manual. Retrieved June 21, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.sparse.dok_matrix.html
SciPy community. (n.d. g). Sparse matrices (scipy.sparse). SciPy v1.13.1 Manual. Retrieved June 17, 2024, from https://docs.scipy.org/doc/scipy/reference/sparse.html
SciPy community. (n.d. h). Spatial kdtree class. SciPy v1.14.0 Manual. Retrieved June 5, 2024, from https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.KDTree.html
Serai, S. D. (2022). Basics of magnetic resonance imaging and quantitative parameters T1, T2, T2*, T1rho and diffusion-weighted imaging. Pediatric Radiology, 52(2), 217-227.
Sled, J. G. (2018). Modelling and interpretation of magnetization transfer imaging in the brain. Neuroimage, 182, 128-135. https://doi.org/10.1016/j.neuroimage.2017.11.065
Slessarev, M., Han, J., Mardimae, A., Prisman, E., Preiss, D., Volgyesi, G., Ansel, C., Duffin, J. and Fisher, J.A. (2007), Prospective targeting and control of end-tidal CO2 and O2 concentrations. The Journal of Physiology, 581: 1207-1219. https://doi.org/10.1113/jphysiol.2007.129395
Smith, R. E., Tournier, J. D., Calamante, F., & Connelly, A. (2012). Anatomically-constrained tractography: improved diffusion MRI streamlines tractography through effective use of anatomical information. Neuroimage, 62(3), 1924-1938. https://doi.org/10.1016/j.neuroimage.2012.06.005
Smith, S. M. (2002). Fast robust automated brain extraction. Human Brain Mapping, 17(3), 143-155. https://doi.org/10.1002/hbm.10062
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M., & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23 Suppl 1, S208–S219. https://doi.org/10.1016/j.neuroimage.2004.07.051
Sorensen, A. G., Wu, O., Copen, W. A., Davis, T. L., Gonzalez, R. G., Koroshetz, W. J., Reese, T. G., Rosen, B. R., Wedeen, V. J., & Weisskoff, R. M. (1999). Human acute cerebral ischemia: Detection of changes in water diffusion anisotropy by using MR imaging. Radiology, 212(3), 785-792. https://doi.org/10.1148/radiology.212.3.r99se24785
Sporns, O. (2018). Graph theory methods: applications in brain networks. Dialogues in Clinical Neuroscience, 20(2), 111-121. https://doi.org/10.31887/DCNS.2018.20.2/osporns
Stejskal, E. O., & Tanner, J. E. (1965). Spin diffusion measurements: spin echoes in the presence of a time‐dependent field gradient. The Journal of Chemical Physics, 42(1), 288-292. https://doi.org/10.1063/1.1695690
Suri, R. E. (2003, September). Application of independent component analysis to microarray data. In IEMC'03 Proceedings. Managing Technologically Driven Organizations: The Human Side of Innovation and Change (IEEE Cat. No. 03CH37502) (pp. 375-378). IEEE. https://doi.org/10.1109/KIMAS.2003.1245073.
Tharwat, A. (2021). Independent component analysis: An introduction. Applied Computing and Informatics, 17(2), 222-249. https://doi.org/10.1016/j.aci.2018.08.006
Thornhill Medical. (2017). RespirAct RA-MR Operator’s Manual. Thornhill Research Inc. Retrieved on Jan 29, 2024 from https://thornhillmedical.ca/resources/respiract-ra-mr/manual
Thiessen, J. D., Zhang, Y., Zhang, H., Wang, L., Buist, R., Del Bigio, M. R., Kong, J., Li, X. M., & Martin, M. (2013). Quantitative MRI and ultrastructural examination of the cuprizone mouse model of demyelination. NMR in Biomedicine, 26(11), 1562-1581. https://doi.org/10.1002/nbm.2992
Tromp, D. (2015, October 8). The diffusion tensor, and its relation to FA, MD, AD and RD. Diffusion Imaging. https://www.diffusion-imaging.com/2015/10/what-is-diffusion-tensor.html
Tromp, D. (2023). DTI Scalars (FA, MD, AD, RD)-How do they relate to brain structure?. Authorea Preprints. https://doi.org/10.15200/winn.146119.94778
Waikhom, L., & Patgiri, R. (2023). A survey of graph neural networks in various learning paradigms: methods, applications, and challenges. Artificial Intelligence Review, 56(7), 6295-6364. https://doi.org/10.1007/s10462-022-10321-2
Wang, R., Bashyam, V., Yang, Z., Yu, F., Tassopoulou, V., Chintapalli, S. S., Skampardoni. I., Sreepada, L. P., Sahoo, D., Nikita, K., Abdulkadir, Wen, J., & Davatzikos, C. (2023). Applications of generative adversarial networks in neuroimaging and clinical neuroscience. Neuroimage, 269, 119898. https://doi.org/10.1016/j.neuroimage.2023.119898
Wang, T., Lei, Y., Fu, Y., Wynne, J. F., Curran, W. J., Liu, T., & Yang, X. (2021). A review on medical imaging synthesis using deep learning and its clinical applications. Journal of Applied Clinical Medical Physics, 22(1), 11-36. https://doi.org/10.1002/acm2.13121
Wei, W., Poirion, E., Bodini, B., Tonietto, M., Durrleman, S., Colliot, O., Stankoff, B., & ... Ayache, N. (2020). Predicting PET-derived myelin content from multisequence MRI for individual longitudinal analysis in multiple sclerosis. NeuroImage, 223, 117308. https://doi.org/10.1016/j.neuroimage.2020.117308
Wilson, R.J. (1996). Introduction to graph theory (4th ed.). Pearson.
Wu, W. C., Jiang, S. F., Yang, S. C., & Lien, S. H. (2011). Pseudocontinuous arterial spin labeling perfusion magnetic resonance imaging—A normative study of reproducibility in the human brain. Neuroimage, 56(3), 1244-1250. https://doi.org/10.1016/j.neuroimage.2011.02.080
Xu, D., & Tian, Y. (2015). A comprehensive survey of clustering algorithms. Annals of Data Science, 2, 165-193. https://doi.org/10.1007/s40745-015-0040-1
Xu, L., Li, Q., Myers, M., Chen, Q., & Li, X. (2019). Application of nuclear magnetic resonance technology to carbon capture, utilization and storage: A review. Journal of Rock Mechanics and Geotechnical Engineering, 11(4), 892-908.
Xu, Q. H., Li, Q. Y., Yu, K., Ge, Q. M., Shi, W. Q., Li, B., Liang, R. B., Lin, Q., Zhang, Y. Q., & Shao, Y. (2020). Altered brain network centrality in patients with diabetic optic neuropathy: a resting-state FMRI study. Endocrine Practice, 26(12), 1399-1405. https://doi.org/10.4158/EP-2020-0045
Xu, R., & Wunsch, D. (2005). Survey of clustering algorithms. IEEE Transactions on Neural Networks, 16(3), 645-678. https://doi.org/10.1109/TNN.2005.845141
Zhang, H., Hubbard, P. L., Parker, G. J., & Alexander, D. C. (2011). Axon diameter mapping in the presence of orientation dispersion with diffusion MRI. Neuroimage, 56(3), 1301-1315. https://doi.org/10.1016/j.neuroimage.2011.01.084
Zhang, H., Schneider, T., Wheeler-Kingshott, C. A., & Alexander, D. C. (2012). NODDI: practical in vivo neurite orientation dispersion and density imaging of the human brain. Neuroimage, 61(4), 1000-1016. https://doi.org/10.1016/j.neuroimage.2012.03.072
Zhang, Y., Brady, M., & Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions on Medical Imaging, 20(1), 45-57. https://doi.org/10.1109/42.906424
Zhao Y, Liang Q, Jiang Z, Mei H, Zeng N, Su S, Wu S, Ge Y, Li P, Lin X, & Yuan K. (2024). Brain abnormalities in survivors of COVID-19 after 2-year recovery: a functional MRI study. The Lancet Regional Health–Western Pacific, 47.
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C. & Sun, M. (2020). Graph neural networks: A review of methods and applications. AI open, 1, 57-81. https://doi.org/10.1016/j.aiopen.2021.01.001
Zhou, Q., Womer, F. Y., Kong, L., Wu, F., Jiang, X., Zhou, Y., Wang, D., Bai, C., Chang, M., Fan, G., Xu, K., He, Y., Tang, Y., & Wang, F. (2017). Trait-related cortical-subcortical dissociation in bipolar disorder: analysis of network degree centrality. The Journal of Clinical Psychiatry, 78(5), 3831. https://doi.org/10.4088/JCP.15m10091
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