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Cited by 332

The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.

High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain

Open access
Nasiriavanaki, Mohammadreza, Xia, Jun, Wan, Hanlin, Bauer, Adam Quentin, Culver, Joseph P., Wang, Lihong V.·2014·Proceedings of the National Academy of Sciences

Abstract

Significance: Clinical studies have demonstrated that resting-state functional connectivity (RSFC) is altered in many brain disorders. However, current RSFC imaging techniques cannot be easily applied to mice, the most widely used model species for human brain disease studies. Utilizing optical excitation and acoustic detection, we have developed a functional connectivity photoacoustic tomography (fcPAT) system, which allows noninvasive imaging of RSFC in the mouse brain, with a large field of view and high spatial resolution. In this article, we describe the unique strengths of fcPAT, as demonstrated by our experimental results. Considering the tremendous amount of brain research in mouse models, this work will elicit broad interest in many related fields.

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Metadata

Authors
Nasiriavanaki, M., Xia, J., Wan, H., Bauer, A. Q., Culver, J. P., & Wang, L. V.
Venue
Proceedings of the National Academy of Sciences
Published
2013-12-23
Volume
111
Issue
1
Pages
21-26
ISSN
  • 0027-8424 (ISSN-L)
  • 1091-6490
OpenAlex
W1990608819
Cited by
332
References
52
FWCI
11.51
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain. https://app.cochat.ai/papers/10.1073/pnas.1311868111
The paper
Nasiriavanaki, M., Xia, J., Wan, H., Bauer, A. Q., Culver, J. P., & Wang, L. V. (2014). High-resolution photoacoustic tomography of resting-state functional connectivity in the mouse brain. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1311868111

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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.