Intersubject Synchronization of Cortical Activity During Natural Vision

Abstract

To what extent do all brains work alike during natural conditions? We explored this question by letting five subjects freely view half an hour of a popular movie while undergoing functional brain imaging. Applying an unbiased analysis in which spatiotemporal activity patterns in one brain were used to "model" activity in another brain, we found a striking level of voxel-by-voxel synchronization between individuals, not only in primary and secondary visual and auditory areas but also in association cortices. The results reveal a surprising tendency of individual brains to "tick collectively" during natural vision. The intersubject synchronization consisted of a widespread cortical activation pattern correlated with emotionally arousing scenes and regionally selective components. The characteristics of these activations were revealed with the use of an open-ended "reverse-correlation" approach, which inverts the conventional analysis by letting the brain signals themselves "pick up" the optimal stimuli for each specialized cortical area.

Divergent neural responses to narrative speech in disorders of consciousness

“…In healthy populations, attention to speech results in entrainment of the subject's evoked activity not only to low‐level features of the stimulus itself, but also the cortical activity of other subjects experiencing the same stimulus 18, 19, 20, 21. This effect has been observed in visual as well as auditory contexts for EEG,19, 20, 22 functional magnetic resonance imaging,23, 24 and magnetoencephalography 25, 26. Moreover, intersubject correlation (ISC) of EEG evoked responses has been shown to discriminate attention better than conventional EEG measures19 and is able to predict selective auditory attention during auditory stream segregation 27, 28.…”

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

“…Finally, the main purpose/application of the method is investigate network dynamics involved in the processing of complex and naturalistic stimuli; that is, when the construction of predictors for GLM analyses is particularly challenging. Because each bMode includes a set of relevant time‐windows (when), future work will focus on re‐examining the stimuli at the relevant time‐points with the aim of associating each bMode with specific aspects of the stimuli [cf., Hasson et al, 2004; see also Crossley et al, 2013, who combined data‐driven network identification with database‐derived “functional labels”]. This will add an additional dimension to the output of the algorithm, which would then include a specific “functionality” for each bMode.…”

“…To do this, we perform a variant of the intersubject correlation analysis proposed by Hasson et al [2004] but using short time windows [see also Glerean et al, 2012]. …”

“…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.…”

“…The method is fully data‐driven and does not require any a priori knowledge about the stimuli [see also Pajula et al, 2012]. The ISS has been used to investigate brain activity associated with several different processes, including memory encoding [Hasson et al, 2008], auditory oddball task [Hejnar et al, 2007], or narrative speech comprehension [Wilson et al, 2008]; and it is often used to examine brain associated with complex, naturalistic stimuli like movies [Hasson et al, 2004; Golland et al, 2007; Jaaskelainen et al, 2008; see also Hasson et al, 2010, for review]. However, unlike tensor‐ICA, the ISS is a mass uni‐variate approach that considers every single voxel/region in isolation and does not provide us with any information about possible inter‐regional effects (functional networks).…”