Reproducibility of importance extraction methods in neural network based fMRI classification

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Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
Date
2018-11-01
Major/Subject
Mcode
Degree programme
Language
en
Pages
11
44-54
Series
NeuroImage, Volume 181
Abstract
Recent advances in machine learning allow faster training, improved performance and increased interpretability of classification techniques. Consequently, their application in neuroscience is rapidly increasing. While classification approaches have proved useful in functional magnetic resonance imaging (fMRI) studies, there are concerns regarding extraction, reproducibility and visualization of brain regions that contribute most significantly to the classification. We addressed these issues using an fMRI classification scheme based on neural networks and compared a set of methods for extraction of category-related voxel importances in three simulated and two empirical datasets. The simulation data revealed that the proposed scheme successfully detects spatially distributed and overlapping activation patterns upon successful classification. Application of the proposed classification scheme to two previously published empirical fMRI datasets revealed robust importance maps that extensively overlap with univariate maps but also provide complementary information. Our results demonstrate increased statistical power of importance maps compared to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.
Description
Keywords
Classification, fMRI, Importance maps, MVPA, Neural networks, Pattern reproducibility
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Citation
Gotsopoulos, A, Saarimäki, H, Glerean, E, Jääskeläinen, I P, Sams, M, Nummenmaa, L & Lampinen, J 2018, ' Reproducibility of importance extraction methods in neural network based fMRI classification ', NeuroImage, vol. 181, pp. 44-54 . https://doi.org/10.1016/j.neuroimage.2018.06.076