| Literature DB >> 26996601 |
Jackob N Keynan1, Yehudit Meir-Hasson2, Gadi Gilam1, Avihay Cohen3, Gilan Jackont1, Sivan Kinreich1, Limor Ikar3, Ayelet Or-Borichev1, Amit Etkin4, Anett Gyurak4, Ilana Klovatch3, Nathan Intrator5, Talma Hendler6.
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.Entities:
Keywords: Amygdala; Brain-computer interface; EEG neurofeedback; Machine learning; Real-time fMRI; Stress
Mesh:
Year: 2016 PMID: 26996601 DOI: 10.1016/j.biopsych.2015.12.024
Source DB: PubMed Journal: Biol Psychiatry ISSN: 0006-3223 Impact factor: 13.382