Learning Centre

Optimizing methods for linking cinematic features to fMRI data

 |  Login

Show simple item record

dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Kauttonen, Janne
dc.contributor.author Hlushchuk, Yevhen
dc.contributor.author Tikka, Pia
dc.date.accessioned 2017-05-11T09:05:08Z
dc.date.available 2017-05-11T09:05:08Z
dc.date.issued 2015
dc.identifier.citation 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 en
dc.identifier.issn 1053-8119
dc.identifier.issn 1095-9572
dc.identifier.other PURE UUID: b6b4cdba-5f9d-4e43-915b-f172bd320c90
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/optimizing-methods-for-linking-cinematic-features-to-fmri-data(b6b4cdba-5f9d-4e43-915b-f172bd320c90).html
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/11638562/1_s2.0_S1053811915000907_main.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/25812
dc.description.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. en
dc.format.extent 136-148
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries NEUROIMAGE en
dc.relation.ispartofseries Volume 110, issue 15 en
dc.rights openAccess en
dc.subject.other 113 Computer and information sciences en
dc.subject.other 6132 Visual arts and design en
dc.subject.other 6131 Theatre, dance, music, other performing arts en
dc.title Optimizing methods for linking cinematic features to fMRI data en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Department of Neuroscience and Biomedical Engineering en
dc.contributor.department Department of Film, Television and Scenography en
dc.subject.keyword fMRI
dc.subject.keyword Neurocinematics
dc.subject.keyword Elastic-net regularization
dc.subject.keyword Linear regression
dc.subject.keyword Independent component analysis
dc.subject.keyword Naturalistic stimuli
dc.subject.keyword Annotation
dc.subject.keyword 113 Computer and information sciences
dc.subject.keyword 6132 Visual arts and design
dc.subject.keyword 6131 Theatre, dance, music, other performing arts
dc.identifier.urn URN:NBN:fi:aalto-201705114187
dc.identifier.doi 10.1016/j.neuroimage.2015.01.063
dc.type.version publishedVersion

Files in this item

Files Size Format View

There are no open access files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search archive

Advanced Search

article-iconSubmit a publication