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...
Face recognition is of major social importance and involves highly selective brain regions thought to be organized in a distributed functional network. However, the exact architecture of interconnections between these regions remains unknown. We used functional magnetic resonance imaging to identify face-responsive regions in 22 participants and then employed diffusion tensor imaging with probabilistic tractography to establish the white-matter pathways between these functionally defined regions. We identified strong white-matter connections between the occipital face area (OFA) and fusiform face area (FFA), with a significant right-hemisphere predominance. We found no evidence for direct anatomical connections between FFA and superior temporal sulcus (STS) or between OFA and STS, contrary to predictions based on current cognitive models. Instead, our findings point to segregated processing along a ventral extrastriate visual pathway to OFA-FFA and another more dorsal system connected to STS and frontoparietal areas. In addition, early occipital areas were found to have direct connections to the amygdala, which might underlie a rapid recruitment of limbic brain areas by visual inputs bypassing more elaborate extrastriate cortical processing. These results unveil the structural neural architecture of the human face recognition system and provide new insights on how distributed face-responsive areas may work together.
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