Elsevier

NeuroImage

Volume 45, Issue 2, 1 April 2009, Pages 614-626
NeuroImage

Functional connectivity of the human amygdala using resting state fMRI

https://doi.org/10.1016/j.neuroimage.2008.11.030Get rights and content

Abstract

The amygdala is composed of structurally and functionally distinct nuclei that contribute to the processing of emotion through interactions with other subcortical and cortical structures. While these circuits have been studied extensively in animals, human neuroimaging investigations of amygdala-based networks have typically considered the amygdala as a single structure, which likely masks contributions of individual amygdala subdivisions. The present study uses resting state functional magnetic resonance imaging (fMRI) to test whether distinct functional connectivity patterns, like those observed in animal studies, can be detected across three amygdala subdivisions: laterobasal, centromedial, and superficial. In a sample of 65 healthy adults, voxelwise regression analyses demonstrated positively-predicted ventral and negatively-predicted dorsal networks associated with the total amygdala, consistent with previous animal and human studies. Investigation of individual amygdala subdivisions revealed distinct differences in connectivity patterns within the amygdala and throughout the brain. Spontaneous activity in the laterobasal subdivision predicted activity in temporal and frontal regions, while activity in the centromedial nuclei predicted activity primarily in striatum. Activity in the superficial subdivision positively predicted activity throughout the limbic lobe. These findings suggest that resting state fMRI can be used to investigate human amygdala networks at a greater level of detail than previously appreciated, allowing for the further advancement of translational models.

Section snippets

Participants

Sixty-five right-handed (as assessed by the Edinburgh Handedness Inventory; Oldfield, 1971) native English-speaking participants with no history of psychiatric or neurological illness (confirmed by psychiatric clinical assessment) were enrolled (33 males; mean age: 29.3 ± 7.9 years). The study was approved by the NYU School of Medicine and New York University institutional review boards. Signed informed consent was obtained prior to participation.

Data acquisition and image preprocessing

Resting state data were acquired on a Siemens

Total amygdala

Spontaneous activity in the amygdala positively predicted spontaneous activity in medial prefrontal regions including medial frontal gyrus (BA 10) and rostral ACC (BA 32), as well as a small region of dorsal ACC (BA 24). Other regions positively predicted by amygdala activity included insula, thalamus, and striatum. Conversely, amygdala activity negatively predicted activity in dorsal and posterior regions such as superior frontal gyrus (BA 6/8), bilateral middle frontal gyrus, posterior

Discussion

By mapping temporally correlated patterns of low frequency spontaneous activity during rest, we detected distinct functional networks associated with three amygdala subdivisions. These results demonstrate the potential of resting state fMRI to make fine-tuned distinctions within amygdala circuits in vivo, and as such, contribute to the growing literature supporting translational models of amygdala function.

Analyses of the amygdala as a single region revealed patterns of functional connectivity

Acknowledgments

The authors wish to thank David Stark and Katie Hiler for their assistance with supplementary materials and are grateful to those individuals who volunteered their time to participate in this study. This work was supported in part by NIMH (K23 MH074821).

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