Nicole Vike | Searcharxiv

arXiv2019

Brain perfusion mediates the relationship between miRNA levels and postural control

Transcriptomics, regional cerebral blood flow (rCBF), and a spatial motor virtual reality task were integrated using mediation analysis in a novel demonstration of "imaging omics". Data collected in NCAA Division I football athletes cleared for play before in-season training showed significant relationships in a) elevated levels of miR-30d and miR-92a to elevated putamen rCBF, (b) elevated putamen rCBF to compromised balance scores, and (c) compromised balance scores to elevated miRNA levels. rCBF acted as a mediator variable (minimum 70% mediation, significant Sobel's test) between abnormal miRNA levels and compromised balance scores. Given the involvement of these miRNAs in inflammation and immune function, and that vascular perfusion is a component of the inflammatory response, these findings support a chronic inflammatory model of repetitive head acceleration events (HAEs). rCBF, a systems biology measure, was necessary for miRNA to affect behavior. These results suggest miRNA as a potential diagnostic biomarker for repetitive HAEs.

q-bio.NC↗

arXiv2019

Uncovering Multi-Site Identifiability Based on Resting-State Functional Connectomes

Multi-site studies are becoming important to increase statistical power, enhance generalizability, and to improve the likelihood of pooling relevant subgroups together activities. Even with harmonized imaging sequences, site-dependent variability can mask the advantages of these multi-site studies. The aim of this study was to assess multi-site reproducibility in resting-state functional connectivity fingerprints, and to improve identifiability of functional connectomes. The individual fingerprinting of functional connectivity profiles is promising due to its potential as a robust neuroimaging biomarker. We evaluated, on two independent multi-site datasets, individual fingerprints in test-retest visit pairs within and across two sites and present a generalized framework based on principal component analysis to improve identifiability. Those components that maximized differential identifiability of a training dataset were used as an orthogonal connectivity basis to reconstruct the functional connectomes of training and validation sets. The optimally reconstructed functional connectomes showed a substantial improvement in individual fingerprinting within and across the two sites relative to the original data. A notable increase in ICC values for functional edges and resting-state networks was also observed. Improvements in identifiability were not found to be affected by global signal regression. Post-hoc analyses assessed the effect of the number of fMRI volumes on identifiability and showed that multi-site differential identifiability was for all cases maximized after optimal reconstruction. The generalizability of the optimal set of orthogonal basis of each dataset was evaluated through a leave-one-out procedure. Overall, results demonstrate that the framework presented in this study systematically improves identifiability in resting-state functional connectomes in multi-site studies.

q-bio.NC↗