Towards Subject and Diagnostic Identifiability in the Alzheimer’s Disease Spectrum Based on Functional Connectomes

Abstract

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.

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Svaldi, D. O., Goñi, J., Sanjay, A. B., Amico, E., Risacher, S. L., West, J. D., ... & Apostolova, L. (2018, January). Towards subject and diagnostic identifiability in the alzheimer’s disease spectrum based on functional connectomes. In 2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018 and 1st International Workshop on Integrating Medical Imaging and Non-Imaging Modalities, Beyond MIC 2018 Held in Conjunction with 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 (pp. 74-82). Springer Verlag. https://doi.org/10.1007/978-3-030-00689-1_8
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2nd International Workshop on Graphs in Biomedical Image Analysis, GRAIL 2018
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