Optimizing methods for linking cinematic features to fMRI data

Janne Kauttonen,Yevhen Hlushchuk,Pia Tikka

doi:10.1016/j.neuroimage.2015.01.063

Janne Kauttonen, Yevhen Hlushchuk + Show 1 more

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https://doi.org/10.1016/j.neuroimage.2015.01.063

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Abstract

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.

Highlights

The neuroimaging experiment settings using free-viewing of movies as stimuli, referred to as naturalistic neuroscience, have drawn increasing interest among cognitive neuroscientists
We calculated variance inflation factor (VIF), which shows to what extent each individual annotation is affected by the multicollienarity (Fig. 1)
It clearly distinguishes annotations that relate to cinematographic methods with higher values above the red line (VIF = 5) and annotations that relate to character's bodily actions or aspects below the red line

Summary

Introduction

The neuroimaging experiment settings using free-viewing of movies as stimuli, referred to as naturalistic neuroscience, have drawn increasing interest among cognitive neuroscientists. Conventional neuroimaging experiments that use artificial stimuli such as still images, beep sounds, or check boards in relatively isolated conditions, have greatly accumulated our understanding on specific brain phenomena. The first study that used narrative film in functional magnetic resonance imaging (fMRI), helped to understand how brain responds to event-boundaries in a continuous film stimulus (Zacks et al, 2001). A range of studies have paved the way for relating complex naturalistic stimulus events to complex neural events detected in the brain using fMRI (Bartels and Zeki, 2004a,b; Bartels et al, 2008; Hasson et al, 2004; Jääskeläinen et al, 2008; Lahnakoski et al, 2012a, b; Zacks et al, 2010), and, more recently, magnetoencephalography (MEG; Lankinen et al, 2014)

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