Accounting for Changing Structure in Functional Network Analysis of TBI Patients

• Published in: Frontiers in systems neuroscience 2020-08, Vol.14, p.42-42
• Main Authors: Dell'Italia, John, Johnson, Micah A, Vespa, Paul M, Monti, Martin M
• Format: Article
• Language: English
• Subjects: coma; disorders of consciousness; exponential random graph model; functional magnetic resonance imaging; network analysis; Neuroscience
• ISSN: 1662-5137; 1662-5137
• DOI: 10.3389/fnsys.2020.00042

Over the last 15 years, network analysis approaches based on MR data have allowed a renewed understanding of the relationship between brain function architecture and consciousness. Application of this approach to Disorders of Consciousness (DOC) highlights the relationship between specific aspects of network topology and levels of consciousness. Nonetheless, such applications do not acknowledge that DOC patients present with a dramatic level of heterogeneity in structural connectivity (SC) across groups (e.g., etiology, diagnostic categories) and within individual patients (e.g., over time), which possibly affects the level and quality of functional connectivity (FC) patterns that can be expressed. In addition, it is rarely acknowledged that the most frequently employed outcome metrics in the study of brain connectivity (e.g., degree distribution, inter- or intra-resting state network connectivity, and clustering coefficient) are interrelated and cannot be assumed to be independent of each other. We present empirical data showing that, when the two points above are not taken into consideration with an appropriate analytic model, it can lead to a misinterpretation of the role of each outcome metric in the graph's structure and thus misinterpretation of FC results. We show that failing to account for either SC or the inter-relation between outcome measures can lead to inflated false positives (FP) and/or false negatives (FN) in inter- or intra-resting state network connectivity results (defined, respectively, as a positive or negative result in network connectivity that is present when not accounting for SC and/or outcome measure inter-relation, but becomes not significant when accounting for all variables). Overall, we find that unconscious patients have lower rates of FP and FN for within cortical connectivity, lower rates of FN for cortico-subcortical connectivity, and lower rates of FP for within subcortical connectivity. These lower rates in unconscious patients may reflect differences in their triadic closure and SC metrics, which bias the interpretations of the inter- or intra-resting state network connectivity if the SC metrics and triadic closure are not modeled. We suggest that future studies of functional connectivity in DOC patients (i) incorporate where possible SC metrics and (ii) properly account for the intercorrelated nature of outcome variables.