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Reproducibility of importance extraction methods in neural network based fMRI classification.

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.

journal_name

Neuroimage

journal_title

NeuroImage

authors

Gotsopoulos A,Saarimäki H,Glerean E,Jääskeläinen IP,Sams M,Nummenmaa L,Lampinen J

doi

10.1016/j.neuroimage.2018.06.076

subject

Has Abstract

pub_date

2018-11-01 00:00:00

pages

44-54

eissn

1053-8119

issn

1095-9572

pii

S1053-8119(18)30583-4

journal_volume

181

pub_type

杂志文章

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