Principal components of functional connectivity: A new approach to study dynamic brain connectivity during rest

Abstract

Functional connectivity (FC) as measured by correlation between fMRI BOLD time courses of distinct brain regions has revealed meaningful organization of spontaneous fluctuations in the resting brain. However, an increasing amount of evidence points to non-stationarity of FC; i.e., FC dynamically changes over time reflecting additional and rich information about brain organization, but representing new challenges for analysis and interpretation. Here, we propose a data-driven approach based on principal component analysis (PCA) to reveal hidden patterns of coherent FC dynamics across multiple subjects. We demonstrate the feasibility and relevance of this new approach by examining the differences in dynamic FC between 13 healthy control subjects and 15 minimally disabled relapse-remitting multiple sclerosis patients. We estimated whole-brain dynamic FC of regionally-averaged BOLD activity using sliding time windows. We then used PCA to identify FC patterns, termed "eigenconnectivities", that reflect meaningful patterns in FC fluctuations. We then assessed the contributions of these patterns to the dynamic FC at any given time point and identified a network of connections centered on the default-mode network with altered contribution in patients. Our results complement traditional stationary analyses, and reveal novel insights into brain connectivity dynamics and their modulation in a neurodegenerative disease.© 2013 Elsevier Inc. All rights reserved. IntroductionSpontaneous fluctuations of the functional MRI (fMRI) bloodoxygen-level-dependent (BOLD) signal are not random but temporally coherent between distinct brain regions. While these fluctuations were long considered as "noise", Biswal et al. (1995) showed that fluctuations of motor areas were correlated even in the absence of a motor task. Several other networks of coherent BOLD activity between remote brain regions have since been identified, including visual, auditory, language and attention networks, and a network called the "default mode network" (DMN) which reduces its activity during attentiondemanding tasks. These networks of regions with coherent activity during rest are consistent across subjects and closely resemble the brain's functional organization of evoked responses (Damoiseaux et al., 2006;Fox and Raichle, 2007;Laird et al., 2011;Smith et al., 2009). Coherent BOLD activity persists during sleep and in anesthetized monkeys, suggesting that it reflects a fundamental property of the brain's functional organization (Larson-Prior et al., 2009;Vincent et al., 2007).Coherent BOLD activity, known as "functional connectivity" (FC), is modulated by learning (Bassett et al., 2011), cognitive and affective states (Cribben et al., 2012;Ekman et al., 2012;Eryilmaz et al., 2011;Richiardi et al., 2011;Shirer et al., 2012) and also spontaneously Chang and Glover, 2010; Kitzbichler et al., 2009). Chang andGlover (2010) showed that FC between the posterior cingulate cortex, a key region of the default mode network, and various other brain regions was highly dy...

On spurious and real fluctuations of dynamic functional connectivity during rest

“…Because fluctuations in xy are low-pass filtered by the convolution with h with cut-off frequency 1/w = f min , the slow modulation term-which in this case is a true fluctuation of dynFC-is recovered as long as f 0 b f min and f − f 0 ≈ f N f min . The influence of the window length on its low-pass filtering effect has previously been noted by Handwerker et al (2012) and less variable dynFC with longer windows is a well documented empirical observation (e.g., Chang and Glover, 2010;Hutchison et al, 2013b;Leonardi et al, 2013). The spectral selectivity of the windowing operation can be improved by using tapering; e.g., Hamming filter (Handwerker et al, 2012), Gaussian filter (Allen et al, 2014), or other windows with smooth roll-off at the edges (Smith et al, 2012).…”

“…Functional connectivity (FC), which is estimated by correlation of BOLD activity, identifies coherent brain activity in distributed and reproducible networks. FC has revealed reorganization of brain networks during cognitive tasks (Ekman et al, 2012;Lewis et al, 2009;Richiardi et al, 2011Richiardi et al, , 2013Shirer et al, 2012), but also at rest (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013b;Kang et al, 2011;Leonardi et al, 2013;Majeed et al, 2011;Smith et al, 2012). To study changes in FC over time sliding-window correlation analysis, where the correlation is estimated for brain activity during multiple, possibly overlapping temporal segments (typically 30-60 s), has been widely deployed (Allen et al, 2014;Chang and Glover, 2010;Hutchison et al, 2013a;Sakoglu et al, 2010).…”

Total-activation regularized deconvolution of resting-state fMRI leads to reproducible networks with spatial overlap

“…In particular iCAPs, DMN (8) occurs with anterior sa auditory (1) but with opposite signs, and with visual (3) with opposite signs (see for the most frequent 20 iCAP combina overlapping iCAPs). With more than two and ACC (13) show increased overlap motor, and/or visual iCAPs. Attention (2) visual (3, 4), and precuneus (5) often occur DMN (8) for a large number of overlappi total on-time, DMN (8) is present B38% o or in specific combinations with other iCAP components such as motor (7; 28%) and almost all the combinations, we notice t to their absolute-valued z-score (|z|Z 1).…”

“…8 for the most frequent 20 iCAP combinations for each set of overlapping iCAPs). With more than two iCAPs, the DMN (8) and ACC (13) show increased overlap when combined with motor, and/or visual iCAPs. Attention (2) further combines with visual (3,4), and precuneus (5) often occurs in combination with DMN (8) for a large number of overlapping iCAPs.…”

“…Recent studies of functional connectivity have presented evidence towards the non-stationary brain dynamics [12], [13]. Existing rs-fMRI methods have been revisited; i.e., ICA and seed-based connectivity analysis have been adapted to allow for spatially overlapping patterns [14]- [18].…”