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Ben Thomas edited this page Jun 4, 2019 · 18 revisions

Publications using PETPVC

  • Van Egroo, M., Narbutas, J., Chylinski, D., González, P. V., Ghaemmaghami, P., Muto, V., … Vandewalle, G. (2019). Preserved wake-dependent cortical excitability dynamics predict cognitive fitness beyond age-related brain alterations, BioRxiv, 644096. [DOI]

  • Guo, T. (2019). Spatial pattern of beta-amyloid accumulation in Alzheimer’s disease as measured with positron emission tomography, PhD Thesis. [Link]

  • Ziontz, J., Bilgel, M., Shafer, A. T., Moghekar, A., & Elkins, W. (2019). Tau pathology in cognitively normal older adults : Associations with brain atrophy and cognitive decline, BioRxiv, 611186. [DOI]

  • Stefanovski, L., Triebkorn, P., Spiegler, A., Diaz-Cortes, M.-A., Solodkin, A., Jirsa, V., … Ritter, P. (2019). Linking molecular pathways and large-scale computational modeling to assess candidate disease mechanisms and pharmacodynamics in Alzheimer’s disease, BioRxiv, 600205. [DOI]

  • Routier, A. (2018). Imagerie cérébrale multimodale pour l’étude des aphasies primaires progressives. Imagerie médicale, Sorbonne Université UPMC, Français. [Link]

  • Marcoux, A., Burgos, N., Bertrand, A., Routier, A., Wen, J., Samper-Gonzalez, J., … Colliot, O. (2018). A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI, Frontiers in Neuroinformatics, 12:94. [DOI]

  • Funck, T., Kevin, L., Toussaint, P.-J., Rick, H., Dagher, A., Evans, A. C., & Thiel, A. (2018). APPIAN : Automated Pipeline for PET Image analysis, Frontiers in Neuroinformatics, 12:64. [DOI]

  • Samper-González, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., … Colliot, O. (2018). Reproducible evaluation of classification methods in Alzheimer’s disease: Framework and application to MRI and PET data. NeuroImage, 183, 504–521. [DOI]

  • Guo, T., Dukart, J., Brendel, M., Rominger, A., & Grimmer, T. (2018). Rate of β-amyloid accumulation varies with baseline amyloid burden : Implications for anti-amyloid drug trials. Alzheimer’s & Dementia, (July), 1–10. [DOI]

  • Sabri, O., Meyer, P. M., Gräf, S., Hesse, S., Wilke, S., Becker, G.-A., … Brust, P. (2018). Cognitive correlates of α4β2 nicotinic acetylcholine receptors in mild Alzheimer’s dementia. Brain, awy099. [DOI]

  • Samper-González, J., Burgos, N., Bottani, S., Fontanella, S., Lu, P., Marcoux, A., … (2018). Reproducible evaluation of classification methods in Alzheimer’s disease: framework and application to MRI and PET data. bioRxiv, 274324. [DOI]

  • Marcoux, A., Burgos, N., Bertrand, A., Routier, A., Wen, J., Samper-Gonzalez, J., … Colliot, O. (2018). A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI. In Annual meeting of the Organization for Human Brain Mapping - OHBM 2018. Singapore, Singapore. [Link]

  • Xu, Z., Gao, M., Papadakis, G. Z., Luna, B., Jain, S., Mollura, D. J., & Bagci, U. (2018). Joint Solution for PET Image Segmentation, Denoising, and Partial Volume Correction. Medical Image Analysis, [DOI]

  • Phillips, J. S., Das, S. R., McMillan, C. T., Irwin, D. J., Roll, E. E., Da Re, F., … Grossman, M. (2017). Tau PET imaging predicts cognition in atypical variants of Alzheimer’s disease. Human Brain Mapping, 1–18. [DOI]

  • Shidahara, M., Thomas, B. A., Okamura, N., Ibaraki, M., Matsubara, K., Oyama, S., … Watabe, H. (2017). A comparison of five partial volume correction methods for Tau and Amyloid PET imaging with [18F]THK5351 and [11C]PIB. Annals of Nuclear Medicine, 31, 563–569. [DOI]

  • Savio, A. M., Schutte, M., Graña, M., & Yakushev, I. (2017). Pypes: Workflows for Processing Multimodal Neuroimaging Data. Frontiers in Neuroinformatics, 11, 1–6. [DOI]

  • Bernier, M., Croteau, E., Castellano, C.-A., Cunnane, S. C., & Whittingstall, K. (2017). Spatial distribution of resting-state BOLD regional homogeneity as a predictor of brain glucose uptake: A study in healthy aging. NeuroImage, 150, 14–22. [DOI]

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