Reproducibility of importance extraction methods in neural network based fMRI classification

Research output: Contribution to journalArticleScientificpeer-review

Researchers

Research units

  • University of Turku

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.

Details

Original languageEnglish
Pages (from-to)44-54
Number of pages11
JournalNeuroImage
Volume181
Publication statusPublished - 1 Nov 2018
MoE publication typeA1 Journal article-refereed

    Research areas

  • Classification, fMRI, Importance maps, MVPA, Neural networks, Pattern reproducibility

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