Understanding how the brain processes stimuli in a rich natural environment is a fundamental goal of neuroscience. Here, we showed a feature film to 10 healthy volunteers during functional magnetic resonance imaging (fMRI) of hemodynamic brain activity. We then annotated auditory and visual features of the motion picture to inform analysis of the hemodynamic data. The annotations were fitted to both voxel-wise data and brain network time courses extracted by independent component analysis (ICA). Auditory annotations correlated with two independent components (IC) disclosing two functional networks, one responding to variety of auditory stimulation and another responding preferentially to speech but parts of the network also responding to non-verbal communication. Visual feature annotations correlated with four ICs delineating visual areas according to their sensitivity to different visual stimulus features. In comparison, a separate voxel-wise general linear model based analysis disclosed brain areas preferentially responding to sound energy, speech, music, visual contrast edges, body motion and hand motion which largely overlapped the results revealed by ICA. Differences between the results of IC- and voxel-based analyses demonstrate that thorough analysis of voxel time courses is important for understanding the activity of specific sub-areas of the functional networks, while ICA is a valuable tool for revealing novel information about functional connectivity which need not be explained by the predefined model. Our results encourage the use of naturalistic stimuli and tasks in cognitive neuroimaging to study how the brain processes stimuli in rich natural environments.

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.

This paper proposes the concept of enactive system as an alternative to the standard human-computer system and elaborates the idea of content mediation as enactive media. The authors' system consists of the two elements coupled in a holistic manner by means of bodily and spatial involvement, or enactment. Such a system is recursive by nature, involving the impact of the technology on the human agent as well as the effect of the human experience on the technology. Instead of the standard explicit interface, there is an implicit connecting surface, based on unconscious psycho-physiological reactions. The aim is not only to point out an analytic approach to existing media systems but also to develop radically novel media concepts implied by the enactive systems.

How does the human brain recall and connect relevant memories with unfolding events? To study this, we presented 25 healthy subjects, during functional magnetic resonance imaging, the movie 'Memento' (director C. Nolan). In this movie, scenes are presented in chronologically reverse order with certain scenes briefly overlapping previously presented scenes. Such overlapping "key-frames" serve as effective memory cues for the viewers, prompting recall of relevant memories of the previously seen scene and connecting them with the concurrent scene. We hypothesized that these repeating key-frames serve as immediate recall cues and would facilitate reconstruction of the story piece-by-piece. The chronological version of Memento, shown in a separate experiment for another group of subjects, served as a control condition. Using multivariate event-related pattern analysis method and representational similarity analysis, focal fingerprint patterns of hemodynamic activity were found to emerge during presentation of key-frame scenes. This effect was present in higher-order cortical network with regions including precuneus, angular gyrus, cingulate gyrus, as well as lateral, superior, and middle frontal gyri within frontal poles. This network was right hemispheric dominant. These distributed patterns of brain activity appear to underlie ability to recall relevant memories and connect them with ongoing events, i.e., "what goes with what" in a complex story. Given the real-life likeness of cinematic experience, these results provide new insight into how the human brain recalls, given proper cues, relevant memories to facilitate understanding and prediction of everyday life events.

One of the challenges of naturalistic neurosciences using movie-viewing experiments is how to interpret observed brain activations in relation to the multiplicity of time-locked stimulus features. As previous studies have shown less inter-subject synchronization across viewers of random video footage than story-driven films, new methods need to be developed for analysis of less story-driven contents. To optimize the linkage between our fMRI data collected during viewing of a deliberately non-narrative silent film 'At Land' by Maya Deren (1944) and its annotated content, we combined the method of elastic-net regularization with the model-driven linear regression and the well-established data-driven independent component analysis (ICA) and inter-subject correlation (ISC) methods. In the linear regression analysis, both IC and region-of-interest (ROI) time-series were fitted with time-series of a total of 36 binary-valued and one real-valued tactile annotation of film features. The elastic-net regularization and cross-validation were applied in the ordinary least-squares linear regression in order to avoid over-fitting due to the multicollinearity of regressors, the results were compared against both the partial least-squares (PLS) regression and the un-regularized full-model regression. Non-parametric permutation testing scheme was applied to evaluate the statistical significance of regression. We found statistically significant correlation between the annotation model and 9 ICs out of 40 ICs. Regression analysis was also repeated for a large set of cubic ROIs covering the grey matter. Both IC- and ROI-based regression analyses revealed activations in parietal and occipital regions, with additional smaller clusters in the frontal lobe. Furthermore, we found elastic-net based regression more sensitive than PLS and un-regularized regression since it detected a larger number of significant ICs and ROIs. Along with the ISC ranking methods, our regression analysis proved a feasible method for ordering the ICs based on their functional relevance to the annotated cinematic features. The novelty of our method is - in comparison to the hypothesis-driven manual pre-selection and observation of some individual regressors biased by choice - in applying data-driven approach to all content features simultaneously. We found especially the combination of regularized regression and ICA useful when analyzing fMRI data obtained using non-narrative movie stimulus with a large set of complex and correlated features.