Brain dynamics during natural viewing conditions—A new guide for mapping connectivity in vivo

Abstract

We describe here a new way of obtaining maps of connectivity in the human brain based on interregional correlations of blood oxygen leveldependent (BOLD) signal during natural viewing conditions. We propose that anatomical connections are reflected in BOLD signal correlations during natural brain dynamics. This may provide a powerful approach to chart connectivity, more so than that based on the dresting stateT of the human brain, and it may complement diffusion tensor imaging. Our approach relies on natural brain dynamics and is therefore experimentally unbiased and independent of hypothesisdriven, specialized stimuli. It has the advantage that natural viewing leads to considerably stronger cortical activity than rest, thus facilitating detection of weaker connections. To validate our technique, we used functional magnetic resonance imaging (fMRI) to record BOLD signal while volunteers freely viewed a movie that was interrupted by resting periods. We used independent component analysis (ICA) to segregate cortical areas before characterizing the dynamics of their BOLD signal during free viewing and rest. Natural viewing and rest each revealed highly specific correlation maps, which reflected known anatomical connections. Examples are homologous regions in visual and auditory cortices in the two hemispheres and the language network consisting of Wernicke's area, Broca's area, and a premotor region. Correlations between regions known to be directly connected were always substantially higher than between nonconnected regions. Furthermore, compared to rest, natural viewing specifically increased correlations between anatomically connected regions while it decreased correlations between nonconnected regions. Our findings therefore demonstrate that natural viewing conditions lead to particularly specific interregional correlations and thus provide a powerful environment to reveal anatomical connectivity in vivo. D

Alterations of fronto-temporal connectivity during word encoding in schizophrenia

“…As expected (Biswal et al, 1995;Lowe et al, 1998;Bartels and Zeki, 2005) the strongest correlations were localized to the seed region and its contralateral homolog.…”

“…Furthermore, although no functional connectivity method can be assumed to relate directly to anatomical connectivity, within-subject correlations are more likely to relate to individual anatomy, enhancing the prospect of relating functional connectivity to measures of structural connectivity such as DTI. This prediction is borne out by studies that have shown correspondence between known anatomical connectivity patterns and functional connectivity measured by fMRI timeseries correlations (Koch et al, 2002;Quigley et al, 2003;JohansenBerg et al, 2004;Bartels and Zeki, 2005).…”

“…Within each subject, the average timeseries of each temporal seed ROI was correlated with the timeseries of each voxel in the brain (Bartels and Zeki, 2005;Fox et al, 2005). To correct for nonspecific global differences in correlation values, each subject's distribution was mean-centered at zero (Lowe et al, 1998).…”

“…Several specific approaches have been implemented (Horwitz, 2003), and their relative merits have not been established. The timeseries correlation method (Biswal et al, 1995;Lowe et al, 1998) examines interregional correlations within individual subjects over the timecourse of an experiment, and has been effectively applied to measure functional connectivity across a wide range of cognitive and physiological states (Li et al, 2000;Lowe et al, 2002;Greicius et al, 2003;Honey et al, 2003;Anand et al, 2005;Bartels and Zeki, 2005;Fox et al, 2005;Koshino et al, 2005;Menon and Levitin, 2005;Peltier et al, 2005).…”

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

“…Unlike traditional paradigms, in these cases there is little a priori knowledge about the timing/occurrence of the relevant events and, hence, the construction of “predictors” for data‐fitting is challenging [but see Bartels et al, 2008; Bordier et al, 2013; Lahnakoski et al, 2012a; Ogawa et al, 2013; Raz et al, 2012]. Indeed, most studies using naturalistic stimuli sought to identify patterns of activity based only on the data structure [i.e., data‐driven approaches, e.g., independent component analyses, Bartels and Zeki, 2005; Lahnakoski et al, 2012b; for review see Calhoun and Pearlson, 2012; intersubject correlation analyses, Hasson et al, 2004; cluster analyses, Heller et al, 2006]. Nonetheless, the available data‐driven methods entail several limitations that, here, we seek to overcome with a new approach that combines data‐driven and multivariate clustering techniques.…”