The Rhesus Monkey Connectome Predicts Disrupted Functional Networks Resulting from Pharmacogenetic Inactivation of the Amygdala

David S. Grayson, Eliza Bliss-Moreau, Christopher J. Machado, Jeffrey Bennett, Kelly Shen, Kathleen A. Grant, Damien A. Fair, David G. Amaral

Research output: Contribution to journalArticlepeer-review

95 Scopus citations


Contemporary research suggests that the mammalian brain is a complex system, implying that damage to even a single functional area could have widespread consequences across the system. To test this hypothesis, we pharmacogenetically inactivated the rhesus monkey amygdala, a subcortical region with distributed and well-defined cortical connectivity. We then examined the impact of that perturbation on global network organization using resting-state functional connectivity MRI. Amygdala inactivation disrupted amygdalocortical communication and distributed corticocortical coupling across multiple functional brain systems. Altered coupling was explained using a graph-based analysis of experimentally established structural connectivity to simulate disconnection of the amygdala. Communication capacity via monosynaptic and polysynaptic pathways, in aggregate, largely accounted for the correlational structure of endogenous brain activity and many of the non-local changes that resulted from amygdala inactivation. These results highlight the structural basis of distributed neural activity and suggest a strategy for linking focal neuropathology to remote neurophysiological changes.

Original languageEnglish (US)
Pages (from-to)453-466
Number of pages14
Issue number2
StatePublished - Jul 20 2016
Externally publishedYes

Bibliographical note

Funding Information:
This research was supported by NIH grants R21 MH098585 (D.G.A.), R01 MH096773 (D.A.F.), UL1TR000128 (D.A.F.), AA109431 (K.A.G.), AA13510 (K.A.G.), and K99 MH10138 (E.B.-M.) and was conducted in part at the California National Primate Research Center (OD011107). We thank Joe Mandeville and Bruce Jenkins for imaging consultation; Bryan Roth for advice on DREADD experiments; and Gustavo Deco, Gleb Bezgin, and Anthony McIntosh for contributing analytic resources.

Publisher Copyright:
© 2016 Elsevier Inc.


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