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Reproducibility of importance extraction methods in neural network based fMRI classification

View ORCID ProfileAthanasios Gotsopoulos, View ORCID ProfileHeini Saarimäki, View ORCID ProfileEnrico Glerean, View ORCID ProfileIiro P. Jääskeläinen, View ORCID ProfileMikko Sams, Lauri Nummenmaa, Jouko Lampinen
doi: https://doi.org/10.1101/197277
Athanasios Gotsopoulos
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
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Heini Saarimäki
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
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Enrico Glerean
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
c Turku PET Centre and Department of Psychology, University of Turku; Turku; Finland
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Iiro P. Jääskeläinen
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
b Advanced Magnetic Imaging (AMI) Centre, Aalto NeuroImaging; School of Science, Aalto University; Espoo; Finland
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Mikko Sams
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
d Department of Computer Science; School of Science, Aalto University; Espoo; Finland
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Lauri Nummenmaa
c Turku PET Centre and Department of Psychology, University of Turku; Turku; Finland
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Jouko Lampinen
a Brain and Mind Laboratory, Department of Neuroscience and Biomedical Engineering; School of Science, Aalto University; Espoo; Finland
d Department of Computer Science; School of Science, Aalto University; Espoo; Finland
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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. We conclude that importance maps are superior to univariate approaches for both detection of overlapping patterns and patterns with weak univariate information.

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Posted October 02, 2017.
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Reproducibility of importance extraction methods in neural network based fMRI classification
Athanasios Gotsopoulos, Heini Saarimäki, Enrico Glerean, Iiro P. Jääskeläinen, Mikko Sams, Lauri Nummenmaa, Jouko Lampinen
bioRxiv 197277; doi: https://doi.org/10.1101/197277
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Reproducibility of importance extraction methods in neural network based fMRI classification
Athanasios Gotsopoulos, Heini Saarimäki, Enrico Glerean, Iiro P. Jääskeläinen, Mikko Sams, Lauri Nummenmaa, Jouko Lampinen
bioRxiv 197277; doi: https://doi.org/10.1101/197277

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