Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum based on Functional Connectomes
Diana O. Svaldi, Joaquín Goñi, Apoorva Bharthur Sanjay, Enrico Amico, Shannon L. Risacher, John D. West, Mario Dzemidzic, Andrew Saykin, Liana Apostolova
备注：
8 pages, 3 tables, 3 figures
发表信息：
In: Stoyanov D. et al. (eds) Graphs in Biomedical Image Analysis and Integrating Medical Imaging and Non-Imaging Modalities. GRAIL 2018, Beyond MIC 2018. Lecture Notes in Computer Science, vol 11044. Springer, Cham
Alzheimer's disease (AD) is the only major cause of mortality in the world without an effective disease modifying treatment. Evidence supporting the so called disconnection hypothesis suggests that functional connectivity biomarkers may have clinical potential for early detection of AD. However, known issues with low test-retest reliability and signal to noise in functional connectivity may prevent accuracy and subsequent predictive capacity. We validate the utility of a novel principal component based diagnostic identifiability framework to increase separation in functional connectivity across the Alzheimer's spectrum by identifying and reconstructing FC using only AD sensitive components or connectivity modes. We show that this framework (1) increases test-retest correspondence and (2) allows for better separation, in functional connectivity, of diagnostic groups both at the whole brain and individual resting state network level. Finally, we evaluate a posteriori the association between connectivity mode weights with longitudinal neurocognitive outcomes.
Simulating Alzheimer's amyloid-like pathology on a fly central nervous system connectome
Indiana University School of Medicine（印第安纳大学医学院）；Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine（印第安纳大学医学院阿尔茨海默病研究中心）
Characterizing Continuous and Discrete Hybrid Latent Spaces for Structural Connectomes
Vanderbilt University（范德堡大学）；National Institute on Aging, National Institutes of Health（美国国立卫生研究院国家老龄化研究所）；Vanderbilt University Medical Center（范德堡大学医学中心）
High-fidelity and Network-based Spatio-temporal Mathematical Models of Alzheimer's Disease Progression and their Validation Against PET-SUVR Imaging Data
ICAR, National Research Council (CNR), Palermo, Italy（国家研究委员会 ICAR 研究所）；Institute of Physics and Astronomy, University of Potsdam, Germany（波茨坦大学物理与天文学研究所）；DSMN, Ca’ Foscari University of Venice, Italy（威尼斯卡福斯卡里大学 DSMN）；VSB Technical University of Ostrava, Czechia（俄斯特拉发 VSB 技术大学）；Potsdam Institute for Climate Impact Research (PIK), Germany（波茨坦气候影响研究所）；Institute of Geosciences, University of Potsdam, Germany（波茨坦大学地球科学研究所）
