Intracranial stimulation and EEG feature analysis reveal affective salience network specialization.

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.

Psychometric Properties of a Novel Affective Bias Task and its Application in Clinical and Non-Clinical Populations.

To mitigate limitations in self-reported mood assessments, we introduce a novel affective bias task (ABT). The task quantifies instantaneous emotional state by leveraging the phenomenon of affective bias, in which people interpret external emotional stimuli in a manner consistent with their current emotional state. This study establishes task stability in measuring and tracking depressive symptoms in clinical and non-clinical populations. Initial assessment in a large non-clinical sample established normative ratings. Depressive symptoms were tracked relative to task performance in a non-clinical sample, as well as in a clinical cohort undergoing surgical evaluation for severe epilepsy. In both cohorts, a stronger negative affective bias was associated with higher Beck Depression Inventory (BDI-II) scores. The ABT exhibits high stability and interrater reliability, as well as construct validity in predicting depression levels in both cohorts, suggesting the task as a reliable proxy for mood and a diagnostic tool for detecting depressive symptoms.