Fedele, Tommaso; Tzovara, Athina; Steiger, Bettina; Hilfiker, Peter; Grunwald, Thomas; Stieglitz, Lennart; Jokeit, Hennric; Sarthein, Johannes (2020). The relation between neuronal firing, local field potentials and hemodynamic activity in the human amygdala in response to aversive dynamic visual stimuli. NeuroImage, 213, p. 116705. Elsevier 10.1016/j.neuroimage.2020.116705
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The amygdala is a central part of networks of brain regions underlying perception and cognition, in particular related to processing of emotionally salient stimuli. Invasive electrophysiological and hemodynamic measurements are commonly used to evaluate functions of the human amygdala, but a comprehensive understanding of their relation is still lacking. Here, we aimed at investigating the link between fast and slow frequency amygdalar oscillations, neuronal firing and hemodynamic responses. To this aim, we recorded intracranial electroencephalography (iEEG), hemodynamic responses and single neuron activity from the amygdala of patients with epilepsy. Patients were presented with dynamic visual sequences of fearful faces (aversive condition), interleaved with sequences of neutral landscapes (neutral condition). Comparing responses to aversive versus neutral stimuli across participants, we observed enhanced high gamma power (HGP, >60 Hz) during the first 2 s of aversive sequence viewing, and reduced delta power (1–4 Hz) lasting up to 18 s. In 5 participants with implanted microwires, neuronal firing rates were enhanced following aversive stimuli, and exhibited positive correlation with HGP and hemodynamic responses. Our results show that high gamma power, neuronal firing and BOLD responses from the human amygdala are co-modulated. Our findings provide, for the first time, a comprehensive investigation of amygdalar responses to aversive stimuli, ranging from single-neuron spikes to local field potentials and hemodynamic responses.
Item Type: |
Journal Article (Original Article) |
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Division/Institute: |
08 Faculty of Science > Institute of Computer Science (INF) 08 Faculty of Science > Institute of Computer Science (INF) > Cognitive Computational Neuroscience (CCN) |
UniBE Contributor: |
Tzovara, Athina |
Subjects: |
000 Computer science, knowledge & systems 500 Science > 510 Mathematics |
ISSN: |
1053-8119 |
Publisher: |
Elsevier |
Language: |
English |
Submitter: |
Athina Tzovara |
Date Deposited: |
01 Apr 2021 15:42 |
Last Modified: |
05 Dec 2022 15:50 |
Publisher DOI: |
10.1016/j.neuroimage.2020.116705 |
PubMed ID: |
32165266 |
BORIS DOI: |
10.48350/155151 |
URI: |
https://boris.unibe.ch/id/eprint/155151 |
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