Towards understanding the nature of direct functional connectivity in visual brain network

• URL: http://arxiv.org/abs/2403.11480v1
• Date: Mon, 18 Mar 2024 05:03:07 GMT
• Title: Towards understanding the nature of direct functional connectivity in visual brain network
• Authors: Debanjali Bhattacharya, Neelam Sinha,
• Abstract summary: A comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN) In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation.
• Score: 3.038642416291856
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Recent advances in neuroimaging have enabled studies in functional connectivity (FC) of human brain, alongside investigation of the neuronal basis of cognition. One important FC study is the representation of vision in human brain. The release of publicly available dataset BOLD5000 has made it possible to study the brain dynamics during visual tasks in greater detail. In this paper, a comprehensive analysis of fMRI time series (TS) has been performed to explore different types of visual brain networks (VBN). The novelty of this work lies in (1) constructing VBN with consistently significant direct connectivity using both marginal and partial correlation, which is further analyzed using graph theoretic measures, (2) classification of VBNs as formed by image complexity-specific TS, using graphical features. In image complexity-specific VBN classification, XGBoost yields average accuracy in the range of 86.5% to 91.5% for positively correlated VBN, which is 2% greater than that using negative correlation. This result not only reflects the distinguishing graphical characteristics of each image complexity-specific VBN, but also highlights the importance of studying both positively correlated and negatively correlated VBN to understand the how differently brain functions while viewing different complexities of real-world images.

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