Intracranial stimulation and EEG feature analysis reveal affective salience network specialization
Brian Metzger, Prathik Kalva, Madaline Mocchi, Brian Cui, Joshua A. Adkinson, Zhengjia Wang, Raissa Mathura, Kourtney Kanja, Jay R. Gavvala, Vaishnav Krishnan, Lu Lin, Atul Maheshwari, Ben Shofty, John F. Magnotti, Jon T. Willie, Sameer A. Sheth, Kelly R. Bijanki
Abstract
Emotion is represented in limbic and prefrontal brain areas, herein termed the affective salience network (ASN). Within the ASN, there are substantial unknowns about how valence and emotional intensity are processed-specifically, which nodes are associated with affective bias (a phenomenon in which participants interpret emotions in a manner consistent with their own mood). A recently developed feature detection approach ('specparam') was used to select dominant spectral features from human intracranial electrophysiological data, revealing affective specialization within specific nodes of the ASN. Spectral analysis of dominant features at the channel level suggests that dorsal anterior cingulate (dACC), anterior insula and ventral-medial prefrontal cortex (vmPFC) are sensitive to valence and intensity, while the amygdala is primarily sensitive to intensity. Akaike information criterion model comparisons corroborated the spectral analysis findings, suggesting all four nodes are more sensitive to intensity compared to valence. The data also revealed that activity in dACC and vmPFC were predictive of the extent of affective bias in the ratings of facial expressions-a proxy measure of instantaneous mood. To examine causality of the dACC in affective experience, 130 Hz continuous stimulation was applied to dACC while patients viewed and rated emotional faces. Faces were rated significantly happier during stimulation, even after accounting for differences in baseline ratings. Together the data suggest a causal role for dACC during the processing of external affective stimuli.