Whole-brain fMRI data predicted by Amygdala-Electrical Fingerprint in Borderline Personality Disorder

Description: Measuring subcortical brain activity with the electroencephalogram (EEG) is an intricate problem, in particular when it comes to deep-brain structures such as the amygdala. To overcome the limited anatomical resolution and specificity of EEG, the Amyg-EFP has been developed based on simultaneously acquired fMRI and EEG. The algorithm underlying the computation of the Amyg-EFP uses time and frequency information from band-widths recorded with one scalp EEG-electrode, plus ground and reference (Meir-Hasson et al., 2016). The resulting signal is an EEG surrogate of Blood Oxygenation Level Dependent (BOLD) activation of a neural network encompassing the amygdala. Feedback from this network is supposed to facilitate neurofeedback training to downregulate amygdala activation. In order to transfer this technology to different labs and clinical centers, and to introduce Amyg-EFP to the treatment of a different clinical population, we aimed to investigate whether previous results are replicable and whether they generalize to BPD. This collection presents the second-level, whole-brain contrast map, received from statistical parametric mapping analysis with Amyg-EFP as predictor of whole-brain fMRI brain activation.

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