title: Optimizing methods for linking cinematic features to fMRI data.
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.
authors: Department of Film, Television and Scenography, Aalto University School of Arts, Design and Architecture, FI-00076 AALTO, Finland; Aalto NeuroImaging, Aalto University, FI-00076 AALTO, Finland. Electronic address: janne.kauttonen@gmail.com.
authors: Janne
authors: J
authors: Kauttonen
authors: Department of Film, Television and Scenography, Aalto University School of Arts, Design and Architecture, FI-00076 AALTO, Finland; Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University, FI-00076 AALTO, Finland; Aalto NeuroImaging, Aalto University, FI-00076 AALTO, Finland; Department of Radiology, Hospital District of Helsinki and Uusimaa (HUS), HUS Medical Imaging Center, Helsinki University Central Hospital (HUCH), University of Helsinki, Helsinki, Finland.
authors: Yevhen
authors: Y
authors: Hlushchuk
authors: Department of Film, Television and Scenography, Aalto University School of Arts, Design and Architecture, FI-00076 AALTO, Finland; Aalto NeuroImaging, Aalto University, FI-00076 AALTO, Finland.
authors: Pia
authors: P
authors: Tikka
mesh_terms: D000328:Adult; D001931:Brain Mapping; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009040:Motion Pictures; D010775:Photic Stimulation; D025341:Principal Component Analysis; D055815:Young Adult
keywords: Annotation; Elastic-net regularization; Independent component analysis; Linear regression; Naturalistic stimuli; Neurocinematics; fMRI