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.

Optimizing differential identifiability improves connectome predictive modeling of cognitive deficits from functional connectivity in Alzheimer's disease

“…While FC shows differential group level associations across cognitive outcomes (Amico, Arenas, & Goni, 2019;Amico & Goñi, 2018) and across disease conditions (Badhwar et al, 2017;Brier, Thomas, & Ances, 2014;Contreras et al, 2017;Fornito & Bullmore, 2015;Svaldi et al, 2018), it falls short of predicting clinically meaningful outcomes at the individual level. The reason for this, is insufficient "fingerprint" or within-subject reliability and betweensubject differentiability to capture individual differences that may be related to cognition or behavior (Amico & Goñi, 2018;Finn et al, 2015;Mars, Passingham, & Jbabdi, 2018;Pallares et al, 2018;Satterthwaite, Xia, & Bassett, 2018;Seitzman et al, 2019).…”

“…Furthermore, it has been shown that individual level fingerprinting improves with longer scan length (Amico & Goñi, 2018;Noble et al, 2017) and when subjects are performing specific tasks (Finn et al, 2017). Finally, there is evidence that FC fingerprint is reduced in individuals with neurologic or psychiatric conditions (Kaufmann et al, 2017(Kaufmann et al, , 2018Svaldi et al, 2018), making association of FC with disease related phenotypes more difficult.…”

Uncovering differential identifiability in network properties of human brain functional connectomes

“…The “identifiability framework” (Amico and Goñi, 2018b ), based on the group-level principal component analysis of functional connectomes that maximizes differential identifiability , has been shown to improve functional connectome fingerprints within and across sites, for a variety of fMRI tasks, over a wide range of scanning length, and with and without global signal regression (Amico and Goñi, 2018b ; Bari, Amico, Vike, Talavage, & Goñi, 2019 ). Additionally, it has been shown that maximizing differential identifiability on the functional connectomes provides more robust and reliable associations with cognition (Svaldi, Goñi, Abbas, et al, 2019 ) as well as with disease progression (Svaldi, Goñi, Sanjay, et al, 2019 ). The natural next step is to assess the impact of such a procedure on subsequent network measurements that characterize topological and communication properties of functional brain networks.…”

“…An open question of great relevance for the brain connectomics community is how to measure and uncover subject fingerprints in network measurements of functional connectivity. Uncovering reliable connectivity fingerprints is crucial when assessing clinical populations and when ultimately mapping cognitive characteristics into connectivity (Scheinost et al, 2019 ; Shen et al, 2017 ; Svaldi, Goñi, Sanjay, et al, 2019 ). Our hypothesis is that improvement in FC fingerprints should also “propagate” to network derived measurements.…”

Identifiability of structural networks of nonlinear electronic oscillators

“…Such framework has been shown to uncover functional connectome fingerprints within and across sites, for a variety of fMRI tasks, over a wide range of scanning length, and with and without global signal regression 12 , 13 . Additionally, it has been shown that it provides more robust and reliable associations between connectivity and cognition 14 as well as with disease progression in neurodegeneration 15 . Finally, it has been recently assessed the positive effect of such framework on uncovering fingerprints on network measurements derived from functional connectomes 16 .…”