Commonality and variance of resting-state networks in common marmoset brains

TL;DR

This study identifies resting-state brain networks in common marmosets using extensive imaging data, revealing new networks that could help understand neurodegenerative diseases.

Contribution

The study identifies three novel resting-state networks in marmosets using a large dataset and evaluates their potential for neurodegenerative disease research.

Findings

16 resting-state networks were detected in marmosets, including three previously unreported networks.

The study provides insights into the commonality and variability of these networks across individuals.

The findings may aid in understanding the functional effects of neurodegenerative diseases.

Abstract

Animal models of brain function are critical for the study of human diseases and development of effective interventions. Resting-state network (RSN) analysis is a powerful tool for evaluating brain function and performing comparisons across animal species. Several studies have reported RSNs in the common marmoset (Callithrix
jacchus; marmoset), a non-human primate. However, it is necessary to identify RSNs and evaluate commonality and inter-individual variance through analyses using a larger amount of data. In this study, we present marmoset RSNs detected using > 100,000 time-course image volumes of resting-state functional magnetic resonance imaging data with careful preprocessing. In addition, we extracted brain regions involved in the composition of these RSNs to understand the differences between humans and marmosets. We detected 16 RSNs in major marmosets, three of which were novel networks that have not been previously reported in marmosets. Since these RSNs possess the potential for use in the functional evaluation of neurodegenerative diseases, the data in this study will significantly contribute to the understanding of the functional effects of neurodegenerative diseases.

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