Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 22 October 2018 / Approved: 23 October 2018 / Online: 23 October 2018 (06:41:16 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Vision 2019, 3, 8
DOI: 10.3390/vision3010008

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, here we present 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.

Subject Areas

spatiotemporal neural dynamics; vision; dorsal and ventral streams; multivariate pattern analysis; representational similarity analysis; fMRI; MEG

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