Coocurrence and non-linear characterization of multiple RSN in Disorder of Consciousness
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People
- Francisco Gómez
- Andrea Soddu (London Ontario)
Summary
In the recent years, fMRI resting state activity has provided a set of powerful biomarkers for many pathological altered brain conditions such as, Alzheimer, Parkinson and disorders of consciousness, among others. The main idea is to register BOLD fluctuations under no stimuli conditions. This protocol provides a simple experimental alternative to study brain activity. In contrasts, data analysis of these brain measurements represents an important methodological challenge. The most popular alternative to study these autonomous signals is the use functional connectivity tools. These methods allow characterizing sets of brain regions with similar dynamic behaviors. Large amount functional connectivity evidence in resting state suggests that the brain activity in this state is organized in resting state networks (RSN) of behavioral/cognitive relevance. In the last years, at least ten RSNs have been consistently identified in Controls. The study of the activation patterns of these networks, in control subjects and pathological subjects is an active problem in neurosciences. Recently, we proposed a novel method to characterize multiple of these RSNs at individual level. The method was successfully used in the discrimination of disorder of consciousness (DOC) patients and healthy controls. Interestingly, this study suggested than networks maybe differently affected during these brain altered conditions. In this work, we hypothesized than these differences in RNSs alteration will be reflected in changes of the RSN relationships. Specifically, we expect changes in the coocurrence patterns and the connectivity patterns between networks. By exploiting these relationships we aim to improve the characterization capacities in DOC patients.
Method overview
Data sources
Results (Expected)
- Method conference. Model proposal.
- Journal article. Complete model and application to clinical data.
