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Open AccessArticle

Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams

1
Computer Science and AI Lab., Massachusetts Institute of Technology, Cambridge, MA 02139, USA
2
Department of Biomedical Engineering, Boston University, Boston, MA 02215, USA
3
Department of Education and Psychology, Freie Universität Berlin, Berlin 14195, Germany
*
Author to whom correspondence should be addressed.
These authors contributed equally.
Received: 22 October 2018 / Revised: 7 January 2019 / Accepted: 13 January 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Visual Perception and Its Neural Mechanisms)
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. View Full-Text
Keywords: spatiotemporal neural dynamics; vision; dorsal and ventral streams; multivariate pattern analysis; representational similarity analysis; fMRI; MEG spatiotemporal neural dynamics; vision; dorsal and ventral streams; multivariate pattern analysis; representational similarity analysis; fMRI; MEG
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Mohsenzadeh, Y.; Mullin, C.; Lahner, B.; Cichy, R.M.; Oliva, A. Reliability and Generalizability of Similarity-Based Fusion of MEG and fMRI Data in Human Ventral and Dorsal Visual Streams. Vision 2019, 3, 8.

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