Abstract

Resting state functional connectivity refers to the temporal correlations between spontaneous hemodynamic signals obtained using functional magnetic resonance imaging. This technique has demonstrated that the structure and dynamics of identifiable networks are altered in psychiatric and neurological disease states. Thus, resting state network organizations can be used as a diagnostic, or prognostic recovery indicator. However, much about the physiological basis of this technique is unknown. Thus, providing a translational bridge to an optimal animal model, the macaque, in which invasive circuit manipulations are possible, is of utmost importance. Current approaches to resting state measurements in macaques face unique challenges associated with signal-to-noise, the need for contrast agents limiting translatability, and within-subject designs. These limitations can, in principle, be overcome through ultra-high magnetic fields. However, imaging at magnetic fields above 7T has yet to be adapted for fMRI in macaques. Here, we demonstrate that the combination of high channel count transmitter and receiver arrays, optimized pulse sequences, and careful anesthesia regimens, allows for detailed single-subject resting state analysis at high resolutions using a 10.5 Tesla scanner. In this study, we uncover thirty spatially detailed resting state components that are highly robust across individual macaques and closely resemble the quality and findings of connectomes from large human datasets. This detailed map of the rsfMRI ‘macaque connectome’ will be the basis for future neurobiological circuit manipulation work, providing valuable biological insights into human connectomics.

Original languageEnglish (US)
Article number117349
JournalNeuroImage
Volume223
DOIs
StatePublished - Dec 2020

Bibliographical note

Funding Information:
We thank Steve Jungst for continuing support with our coils and hardware setup. We thank Hannah Lee, Jen Holmberg, Adriana Cushnie, Tanya Casta, and Megan Monko for support with animal care and data acquisition. We thank Research Animal Resources at UMN, especially Whitney McGee and Anne Merley, for helping us implement new and improved anesthesia protocols.

Funding Information:
This work was supported by NIH grants P30 DA048742 (to AZ, SRH, BYH and JZ), RF1 MH116978 (to EY), R01 DA038615 (to BYH), U01 EB025144 (to KU), by a P41 EB027061 (to KU, EY, NH, GA, BYH and JZ), R01 MH118257 (to SRH), an NINDS R01 NS081118 and P50 NS098573 Udall center to NH by an award from MNFutures to BYH, from the Digital Technologies Initiative to JZ, and BYH, from the Templeton Foundation to BYH, a Young Investigator Award from the Brain & Behavior Research Foundation to SRH, a Medical Discovery Team on Addiction Pilot Grant to SRH and BYH, and a UMN AIRP award to JZ, BYH, SRH and AZ.

Publisher Copyright:
© 2020 The Authors

Keywords

  • Functional MRI (fMRI)
  • Functional connectivity
  • Resting-state
  • Rhesus macaque
  • Spontaneous activity

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

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