Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams
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vision-03-00008.pdf
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5.57 MB
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Checksum (MD5)
5e8d76d0b5d0290447b2fbdad75f54ad
Author(s)
Lahner, Benjamin
Cichy, Radoslaw Martin
Oliva, Aude
Cichy, Radoslaw
Mohsenzadeh, Yalda
Mullin, Caitlin
Date Issued
February 2019
Journal
Vision
Publisher
Multidisciplinary Digital Publishing Institute (MDPI)
Citation
Mohsenzadeh, Yalda et al. "Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams." Vision 3, 1 (February 2019): 8 © 2019 The Authors
Version
Final published version
Abstract
To build a representation of what we see, the human brain recruits regions throughout the visual cortex in cascading sequence. Recently, an approach was proposed to evaluate the dynamics of visual perception in high spatiotemporal resolution at the scale of the whole brain. This method combined functional magnetic resonance imaging (fMRI) data with magnetoencephalography (MEG) data using representational similarity analysis and revealed a hierarchical progression from primary visual cortex through the dorsal and ventral streams. To assess the replicability of this method, we here present the results of a visual recognition neuro-imaging fusion experiment and compare them within and across experimental settings. We evaluated the reliability of this method by assessing the consistency of the results under similar test conditions, showing high agreement within participants. We then generalized these results to a separate group of individuals and visual input by comparing them to the fMRI-MEG fusion data of Cichy et al (2016), revealing a highly similar temporal progression recruiting both the dorsal and ventral streams. Together these results are a testament to the reproducibility of the fMRI-MEG fusion approach and allows for the interpretation of these spatiotemporal dynamic in a broader context. Keywords: spatiotemporal neural dynamics; vision; dorsal and ventral streams; multivariate pattern analysis; representational similarity analysis; fMRI; MEG
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.3390/vision3010008
