Title: | Optimizing methods for linking cinematic features to fMRI data |
Author(s): | Kauttonen, Janne ; Hlushchuk, Yevhen ; Tikka, Pia |
Date: | 2015 |
Language: | en |
Pages: | 136-148 |
Department: | Department of Neuroscience and Biomedical Engineering Department of Film, Television and Scenography |
Series: | NEUROIMAGE, Volume 110, issue 15 |
ISSN: | 1053-8119 1095-9572 |
DOI-number: | 10.1016/j.neuroimage.2015.01.063 |
Subject: | 113 Computer and information sciences, 6132 Visual arts and design, 6131 Theatre, dance, music, other performing arts |
Keywords: | fMRI, Neurocinematics, Elastic-net regularization, Linear regression, Independent component analysis, Naturalistic stimuli, Annotation, 113 Computer and information sciences, 6132 Visual arts and design, 6131 Theatre, dance, music, other performing arts |
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Kauttonen , J , Hlushchuk , Y & Tikka , P 2015 , ' Optimizing methods for linking cinematic features to fMRI data ' , NeuroImage , vol. 110 , no. 15 , pp. 136-148 . 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 andcross-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.
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