Abstract
The brain at rest consists of spatially and temporal distributed but functionally connected regions that called intrinsic connectivity networks (ICNs). Resting state electroencephalography (rs-EEG) is a way to characterize brain networks without confounds associated with task EEG such as task difficulty and performance. A novel framework of how to study dynamic functional connectivity under the notion of functional connectivity microstates (FCμstates) and symbolic dynamics is further discussed. Furthermore, we introduced a way to construct a single integrated dynamic functional connectivity graph (IDFCG) that preserves both the strength of the connections between every pair of sensors but also the type of dominant intrinsic coupling modes (DICM). The whole methodology is demonstrated in a significant and unexplored task for EEG which is the definition of an objective Chronnectomic Brain Aged index (CBAI) extracted from resting-state data (N = 94 subjects) with both eyes-open and eyes-closed conditions. Novel features have been defined based on symbolic dynamics and the notion of DICM and FCμstates. The transition rate of FCμstates, the symbolic dynamics based on the evolution of FCμstates (the Markovian Entropy, the complexity index), the probability distribution of DICM, the novel Flexibility Index that captures the dynamic reconfiguration of DICM per pair of EEG sensors and the relative signal power constitute a valuable pool of features that can build the proposed CBAI. Here we applied a feature selection technique and Extreme Learning Machine (ELM) classifier to discriminate young adults from middle-aged and a Support Vector Regressor to build a linear model of the actual age based on EEG-based spatio-temporal features. The most significant type of features for both prediction of age and discrimination of young vs. adults age groups was the dynamic reconfiguration of dominant coupling modes derived from a subset of EEG sensor pairs. Specifically, our results revealed a very high prediction of age for eyes-open (R2 = 0.60; y = 0.79x + 8.03) and lower for eyes-closed (R2 = 0.48; y = 0.71x + 10.91) while we succeeded to correctly classify young vs. middle-age group with 97.8% accuracy in eyes-open and 87.2% for eyes-closed. Our results were reproduced also in a second dataset for further external validation of the whole analysis. The proposed methodology proved valuable for the characterization of the intrinsic properties of dynamic functional connectivity through the age untangling developmental differences using EEG resting-state recordings.
Highlights
Functional networks can be defined as spatio-temporal correlation of brain areas which normally involved in a task and are observed when subjects are not performing a specific task called resting-state (Biswal et al, 1995).Many neuroimaging efforts in functional magnetic resonance imaging have focused from studying cognitive subsystems directly linked to the experimental paradigms focusing on one or more subsystems (Turk-Browne, 2013)
We demonstrated a better modeling of dynamic functional connectivity graphs (DFCG) based on vector quantization approach (Dimitriadis et al, 2013a), if a preprocessing is added
We followed a leave-one out cross-validation scheme (LOOCV) in order to predict the age of a subject
Summary
Functional networks can be defined as spatio-temporal correlation of brain areas which normally involved in a task and are observed when subjects are not performing a specific task called resting-state (Biswal et al, 1995).Many neuroimaging efforts in functional magnetic resonance imaging (fMRI) have focused from studying cognitive subsystems directly linked to the experimental paradigms focusing on one or more subsystems (Turk-Browne, 2013). An increasing number of studies using rs-fMRI data are showing reproducibility and reliability of features and connectivity patterns extracted from the whole brain (Damoiseaux et al, 2006; Thomason et al, 2008; Shehzad et al, 2009; Van Dijk et al, 2010; Zuo et al, 2010; Song et al, 2012). It is high popular in fMRI studies to use machine learning techniques in corporation with fMRI BOLD activity and with brain networks. A variety of neuroimaging studies have clearly shown that machine learning algorithms applying to human recordings can extract novel insights human brain activity (Haynes and Rees, 2005; Cohen et al, 2011)
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