Document Type

Article

Publication Date

1-1-2020

Journal

NeuroImage

Volume

204

URL with Digital Object Identifier

10.1016/j.neuroimage.2019.116241

Abstract

© 2019 Elsevier Inc. Resting-state functional MRI (RS-fMRI) is widely used to assess how strongly different brain areas are connected. However, this connection obtained by RS-fMRI, which is called functional connectivity (FC), simply refers to the correlation of blood oxygen level-dependent (BOLD) signals across time it has yet to be quantified how accurately FC reflects cellular connectivity (CC). In this study, we elucidated this relationship using RS-fMRI and quantitative tracer data in marmosets. In addition, we also elucidated the effects of distance between two brain regions on the relationship between FC and CC across seed region. To calculate FC, we used full correlation approach that is considered to reflect not only direct (monosynaptic connections) but also indirect pathways (polysynaptic connections). Our main findings are that: (1) overall FC obtained by RS-fMRI was highly correlated with tracer-based CC, but correlation coefficients varied remarkably across seed regions; (2) the strength of FC decreased with increase in the distance between two regions; (3) correlation coefficients between FC and CC after regressing out the effects of the distance between two regions still varied across seed regions, but some regions have strong correlations. These findings suggest that although FC reflects the strength of monosynaptic pathways, it is strongly affected by the distance between regions.

Notes

©2019 Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

This article was originally published as:

Hori, Y., Schaeffer, D. J., Gilbert, K. M., Hayrynen, L. K., Cléry, J. C., Gati, J. S., Menon, R. S., & Everling, S. (2020). Comparison of resting-state functional connectivity in marmosets with tracer-based cellular connectivity. NeuroImage, 204, 116241. https://doi.org/10.1016/j.neuroimage.2019.116241

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

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