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Neuroimage. 2017 Mar 1;148:219-229. doi: 10.1016/j.neuroimage.2016.12.023. Epub 2017 Jan 9.

Multimodal evaluation of the amygdala's functional connectivity.

Author information

1
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA. Electronic address: kerestesrv@upmc.edu.
2
Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA.
3
Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Heinrich-Heine University Düsseldorf, Germany; Institute of Systems Neuroscience, School of Medicine, Heinrich-Heine University Düsseldorf, Germany.

Abstract

The amygdala is one of the most extensively studied human brain regions and undisputedly plays a central role in many psychiatric disorders. However, an outstanding question is whether connectivity of amygdala subregions, specifically the centromedial (CM), laterobasal (LB) and superficial (SF) nuclei, are modulated by brain state (i.e., task vs. rest). Here, using a multimodal approach, we directly compared meta-analytic connectivity modeling (MACM) and specific co-activation likelihood estimation (SCALE)-derived estimates of CM, LB and SF task-based co-activation to the functional connectivity of these nuclei as assessed by resting state fmri (rs-fmri). Finally, using a preexisting resting state functional connectivity-derived cortical parcellation, we examined both MACM and rs-fmri amygdala subregion connectivity with 17 large-scale networks, to explicitly address how the amygdala interacts with other large-scale neural networks. Analyses revealed strong differentiation of CM, LB and SF connectivity patterns with other brain regions, both in task-dependent and task-independent contexts. All three regions, however, showed convergent connectivity with the right ventrolateral prefrontal cortex (VLPFC) that was not driven by high base rate levels of activation. Similar patterns of connectivity across rs-fmri and MACM were observed for each subregion, suggesting a similar network architecture of amygdala connectivity with the rest of the brain across tasks and resting state for each subregion, that may be modified in the context of specific task demands. These findings support animal models that posit a parallel model of amygdala functioning, but importantly, also modify this position to suggest integrative processing in the amygdala.

KEYWORDS:

Amygdala; Meta-analytic connectivity modeling (MACM); Resting state; Specific co-activation likelihood estimation (SCALE); Ventrolateral prefrontal cortex (VLPFC)

PMID:
28089676
PMCID:
PMC5416470
DOI:
10.1016/j.neuroimage.2016.12.023
[Indexed for MEDLINE]
Free PMC Article

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