Applying Joint Graph Embedding to Study Alzheimer’s Neurodegeneration Patterns in Volumetric Data

Abraham, A., Dohmatob, E., Thirion, B., Samaras, D., & Varoquaux, G. (2013). Extracting brain regions from rest fmri with total-variation constrained dictionary learning. In International Conference on Medical Image Computing and Computer-assisted Intervention, pages 607–615. Springer.

Albert, M. S., DeKosky, S. T., Dickson, D., Dubois, B., Feldman, H. H., Fox, N. C., Gamst, A., Holtzman, D. M., Jagust, W. J., Petersen, R. C., et al. (2011). The diagnosis of mild cognitive impairment due to alzheimer’s disease: recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 270–279.

Article  Google Scholar 

Association, A. (2019). 2019 alzheimer’s disease facts and figures. Alzheimer’s & Dementia, 15(3), 321–387.

Article  Google Scholar 

Belkin, M., & Niyogi, P. (2003). Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation, 15(6), 1373–1396.

Article  Google Scholar 

Bernal-Rusiel, J. L., Reuter, M., Greve, D. N., Fischl, B., Sabuncu, M. R., Initiative, A. D. N., et al. (2013). Spatiotemporal linear mixed effects modeling for the mass-univariate analysis of longitudinal neuroimage data. Neuroimage, 81, 358–370.

Article  PubMed  Google Scholar 

Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar mri. Magnetic Resonance in Medicine, 34(4), 537–541.

Article  CAS  PubMed  Google Scholar 

Braak, H., & Braak, E. (1991). Neuropathological stageing of alzheimer-related changes. Acta Neuropathologica, 82(4), 239–259.

Article  CAS  PubMed  Google Scholar 

Brandl, G. (2021). Sphinx documentation. http://sphinx-doc.org/sphinx.pdf

Bullmore, E. T., & Bassett, D. S. (2011). Brain graphs: Graphical models of the human brain connectome. Annual Review of Clinical Psychology, 7, 113–140.

Article  PubMed  Google Scholar 

Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001). A method for making group inferences from functional mri data using independent component analysis. Human Brain Mapping, 14(3), 140–151.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Chung, J., Bridgeford, E., Arroyo, J., Pedigo, B. D., Saad-Eldin, A., Gopalakrishnan, V., Xiang, L., Priebe, C. E., & Vogelstein, J. T. (2021). Statistical connectomics. Annual Review of Statistics and its Application, 8, 463–492.

Article  Google Scholar 

Cohen, J. D., Daw, N., Engelhardt, B., Hasson, U., Li, K., Niv, Y., Norman, K. A., Pillow, J., Ramadge, P. J., Turk-Browne, N. B., et al. (2017). Computational approaches to fmri analysis. Nature Neuroscience, 20(3), 304–313.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Cole, D. M., Smith, S. M., & Beckmann, C. F. (2010). Advances and pitfalls in the analysis and interpretation of resting-state fmri data. Frontiers in Systems Neuroscience, page 8.

D’Souza, N. S., Nebel, M. B., Wymbs, N., Mostofsky, S., & Venkataraman, A. (2019). Integrating neural networks and dictionary learning for multidimensional clinical characterizations from functional connectomics data. In International Conference on Medical Image Computing and Computer-Assisted Intervention, pages 709–717. Springer.

Elam, J. S., Glasser, M. F., Harms, M. P., Sotiropoulos, S. N., Andersson, J. L., Burgess, G. C., Curtiss, S. W., Oostenveld, R., Larson-Prior, L. J., Schoffelen, J.-M., et al. (2021). The human connectome project: a retrospective. NeuroImage, 244, 118543.

Article  CAS  PubMed  Google Scholar 

Fornito, A., Zalesky, A., & Bullmore, E. (2016). Fundamentals of Brain Network Analysis. Academic Press.

Google Scholar 

Hayasaka, S., Phan, K. L., Liberzon, I., Worsley, K. J., & Nichols, T. E. (2004). Nonstationary cluster-size inference with random field and permutation methods. Neuroimage, 22(2), 676–687.

Article  PubMed  Google Scholar 

Jack, C. R., Bennett, D. A., Blennow, K., Carrillo, M. C., Feldman, H. H., Frisoni, G. B., Hampel, H., Jagust, W. J., Johnson, K. A., Knopman, D. S., et al. (2016). A/t/n: an unbiased descriptive classification scheme for alzheimer disease biomarkers. Neurology, 87(5), 539–547.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Jack, C. R., Jr., Bernstein, M. A., Fox, N. C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P. J., Whitwell, L. J., Ward, C., et al. (2008). The alzheimer’s disease neuroimaging initiative (adni): Mri methods. Journal of Magnetic Resonance Imaging: An Official Journal of the International Society for Magnetic Resonance in Medicine, 27(4), 685–691.

Article  Google Scholar 

Jack, C. R., Jr., & Holtzman, D. M. (2013). Biomarker modeling of alzheimer’s disease. Neuron, 80(6), 1347–1358.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Khosla, M., Jamison, K., Ngo, G. H., Kuceyeski, A., & Sabuncu, M. R. (2019). Machine learning in resting-state fmri analysis. Magnetic Resonance Imaging, 64, 101–121.

Article  PubMed  PubMed Central  Google Scholar 

Kiar, G., Bridgeford, E. W., Roncai, W. R. G., for Reliability, C., (CoRR), R., Chandrashekhar, V., Mhembere, D., Ryman, S., Zuo, X.-N., Margulies, D. S., Craddock, R. C., et al. (2017). A high-throughput pipeline identifies robust connectomes but troublesome variability. bioRxiv, page 188706.

Kordower, J. H. (2014). The prion hypothesis of parkinson’s disease: this hot topic just got hotter.

Lee, M. H., Hacker, C. D., Snyder, A. Z., Corbetta, M., Zhang, D., Leuthardt, E. C., & Shimony, J. S. (2012). Clustering of resting state networks. PloS one, 7(7), e40370.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Lee, M. H., Smyser, C. D., & Shimony, J. S. (2013). Resting-state fmri: a review of methods and clinical applications. American Journal of Neuroradiology, 34(10), 1866–1872.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Levin, K., Athreya, A., Tang, M., Lyzinski, V., Park, Y., & Priebe, C. E. (2017). A central limit theorem for an omnibus embedding of multiple random graphs and implications for multiscale network inference. arXiv preprint arXiv:1705.09355

Lewis-Peacock, J. A., & Norman, K. A. (2014). Multi-voxel pattern analysis of fmri data. The Cognitive Neurosciences, 512, 911–920.

Google Scholar 

Lyu, X., Duong, M. T., Xie, L., de Flores, R., Richardson, H., Hwang, G., Wisse, L. E., DiCalogero, M., McMillan, C. T., Robinson, J. L., et al. (2023). Tau-neurodegeneration mismatch reveals vulnerability and resilience to comorbidities in alzheimer’s continuum. medRxiv, pages 2023–02.

McKhann, G. M., Knopman, D. S., Chertkow, H., Hyman, B. T., Jack, C. R., Jr., Kawas, C. H., Klunk, W. E., Koroshetz, W. J., Manly, J. J., Mayeux, R., et al. (2011). The diagnosis of dementia due to alzheimer’s disease: Recommendations from the national institute on aging-alzheimer’s association workgroups on diagnostic guidelines for alzheimer’s disease. Alzheimer’s & Dementia, 7(3), 263–269.

Article  Google Scholar 

Miller, M. I., Ratnanather, J. T., Tward, D. J., Brown, T., Lee, D. S., Ketcha, M., Mori, K., Wang, M.-C., Mori, S., Albert, M. S., et al. (2015). Network neurodegeneration in alzheimer’s disease via mri based shape diffeomorphometry and high-field atlasing. Frontiers in Bioengineering and Biotechnology, 3, 54.

Article  PubMed  PubMed Central  Google Scholar 

Miller, M. I., Younes, L., Ratnanather, J. T., Brown, T., Trinh, H., Lee, D. S., Tward, D., Mahon, P. B., Mori, S., Albert, M., et al. (2015). Amygdalar atrophy in symptomatic alzheimer’s disease based on diffeomorphometry: the biocard cohort. Neurobiology of Aging, 36, S3–S10.

Article  PubMed  Google Scholar 

Miller, M. I., Younes, L., Ratnanather, J. T., Brown, T., Trinh, H., Postell, E., Lee, D. S., Wang, M.-C., Mori, S., O’Brien, R., et al. (2013). The diffeomorphometry of temporal lobe structures in preclinical alzheimer’s disease. NeuroImage: Clinical, 3:352–360.

Morris, J. C. (1991). The clinical dementia rating (cdr): Current version and. Young, 41, 1588–1592.

Google Scholar 

Neuberg, L. G. (2003). Causality: models, reasoning, and inference, by judea pearl, cambridge university press, 2000. Econometric Theory, 19(4):675–685.

Nichols, T., & Hayasaka, S. (2003). Controlling the familywise error rate in functional neuroimaging: a comparative review. Statistical Methods in Medical Research, 12(5), 419–446.

Article  PubMed  Google Scholar 

Pengas, G., Williams, G., Acosta-Cabronero, J., Ash, T., Hong, Y., Izquierdo-Garcia, D., Fryer, T., Hodges, J., & Nestor, P. (2012). The relationship of topographical memory performance to regional neurodegeneration in alzheimer’s disease. Frontiers in Aging Neuroscience, 4, 17.

Article  PubMed  PubMed Central  Google Scholar 

Ross, C. A., Aylward, E. H., Wild, E. J., Langbehn, D. R., Long, J. D., Warner, J. H., Scahill, R. I., Leavitt, B. R., Stout, J. C., Paulsen, J. S., et al. (2014). Huntington disease: natural history, biomarkers and prospects for therapeutics. Nature Reviews Neurology, 10(4), 204–216.

Article  CAS  PubMed  Google Scholar 

Sadaghiani, S., Trotman, W., Lim, S. A., Chung, E., Ittyerah, R., Ravikumar, S., Khandelwal, P., Prabhakaran, K., Lavery, M. L., Ohm, D. T., et al. (2022). Associations of phosphorylated tau pathology with whole-hemisphere ex vivo morphometry in 7 tesla mri. Alzheimer’s & Dementia.

Small, S. A., Schobel, S. A., Buxton, R. B., Witter, M. P., & Barnes, C. A. (2011). A pathophysiological framework of hippocampal dysfunction in ageing and disease. Nature Reviews Neuroscience, 12(10), 585–601.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Stouffer, K. M., Chen, C., Kulason, S., Xu, E., Witter, M. P., Ceritoglu, C., Albert, M. S., Mori, S., Troncoso, J., Tward, D. J., et al. (2023). Early amygdala and erc atrophy linked to 3d reconstruction of rostral neurofibrillary tau tangle pathology in alzheimer’s disease. NeuroImage: Clinical, 38:103374.

Stouffer, K. M., Witter, M. P., Tward, D. J., & Miller, M. I. (2022). Projective diffeomorphic mapping of molecular digital pathology with tissue mri. Communications Engineering, 1(1), 44.

Article  PubMed  PubMed Central  Google Scholar 

Sussman, D. L., Tang, M., Fishkind, D. E., & Priebe, C. E. (2012). A consistent adjacency spectral embedding for stochastic blockmodel graphs. Journal of the American Statistical Association, 107(499), 1119–1128.

Article  CAS  Google Scholar 

Tward, D., Brown, T., Kageyama, Y., Patel, J., Hou, Z., Mori, S., Albert, M., Troncoso, J., & Miller, M. (2020). Diffeomorphic registration with intensity transformation and missing data: Application to 3d digital pathology of alzheimer’s disease. Frontiers in Neuroscience, 14, 52.

Article  PubMed  PubMed Central  Google Scholar 

Van Den Heuvel, M. P., & Pol, H. E. H. (2010). Exploring the brain network: a review on resting-state fmri functional connectivity. European Neuropsychopharmacology, 20(8), 519–534.

Article  PubMed  Google Scholar 

Visanji, N. P., Brooks, P. L., Hazrati, L.-N., & Lang, A. E. (2013). The prion hypothesis in parkinson’s disease: Braak to the future. Acta Neuropathologica Communications, 1(1), 1–12.

Article  Google Scholar 

Wang, S., Arroyo, J., Vogelstein, J. T., & Priebe, C. E. (2019). Joint embedding of graphs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 43(4), 1324–1336.

Article  Google Scholar 

Wyman, B. T., Harvey, D. J., Crawford, K., Bernstein, M. A., Carmichael, O., Cole, P. E., Crane, P. K., DeCarli, C., Fox, N. C., Gunter, J. L., et al. (2013). Standardization of analysis sets for reporting results from adni mri data. Alzheimer’s & Dementia, 9(3), 332–337.

Article  Google Scholar 

Xie, L., Wisse, L. E., Das, S. R., Lyu, X., de Flores, R., Yushkevich, P. A., & Wolk, D. A. (2022). Tau burden is associated with cross-sectional and longitudinal neurodegeneration in the medial temporal lobe in cognitively normal individuals. Alzheimer’s & Dementia, 18, e067095.

Article  Google Scho

留言 (0)

沒有登入
gif