Limbic Activity Modulation Guided by Functional Magnetic Resonance Imaging–Inspired Electroencephalography Improves Implicit Emotion Regulation

Jackob N. Keynan, Yehudit Meir-Hasson, Gadi Gilam, Avihay Cohen, Gilan Jackont, Sivan Kinreich, Limor Ikar, Ayelet Or-Borichev, Amit Etkin, Anett Gyurak, Ilana Klovatch, Nathan Intrator, Talma Hendler*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

The amygdala has a pivotal role in processing traumatic stress; hence, gaining control over its activity could facilitate adaptive mechanism and recovery. To date, amygdala volitional regulation could be obtained only via real-time functional magnetic resonance imaging (fMRI), a highly inaccessible procedure. The current article presents high-impact neurobehavioral implications of a novel imaging approach that enables bedside monitoring of amygdala activity using fMRI-inspired electroencephalography (EEG), hereafter termed amygdala-electrical fingerprint (amyg-EFP). Simultaneous EEG/fMRI indicated that the amyg-EFP reliably predicts amygdala-blood oxygen level–dependent activity. Implementing the amyg-EFP in neurofeedback demonstrated that learned downregulation of the amyg-EFP facilitated volitional downregulation of amygdala-blood oxygen level–dependent activity via real-time fMRI and manifested as reduced amygdala reactivity to visual stimuli. Behavioral evidence further emphasized the therapeutic potential of this approach by showing improved implicit emotion regulation following amyg-EFP neurofeedback. Additional EFP models denoting different brain regions could provide a library of localized activity for low-cost and highly accessible brain-based diagnosis and treatment.

Original languageEnglish
Pages (from-to)490-496
Number of pages7
JournalBiological Psychiatry
Volume80
Issue number6
DOIs
StatePublished - 15 Sep 2016
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2016 Society of Biological Psychiatry

Keywords

  • Amygdala
  • Brain-computer interface
  • EEG neurofeedback
  • Machine learning
  • Real-time fMRI
  • Stress

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