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. Vision2019, 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.
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. Vision2019, 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.
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.
Keywords
spatiotemporal neural dynamics; vision; dorsal and ventral streams; multivariate pattern analysis; representational similarity analysis; fMRI; MEG
Subject
Biology and Life Sciences, Neuroscience and Neurology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
