Svaldi, a post-doctoral researcher at IU School of Medicine, and external member of the CONNplexity Lab, gave a presentation on "Towards Subject and Diagnostic Identifiability in the Alzheimer's Disease Spectrum Based on Functional Connectomes" at the "Beyond Medical Imaging Workshop" at the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in Granada, Spain. The work seeks to improve the diagnostic capacity of functional connectivity biomarkers and involves a two-step, group-level principal-component-based framework to increase Alzheimer's disease signature in individual 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 (FC) 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. This research validates 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 levels. Finally, we evaluate a posteriori association between connectivity mode weights with longitudinal neurocognitive outcomes.

Publication available here: https://link.springer.com/book/10.1007/978-3-030-00689-1