Spatiotemporal Dynamics of the Human Emotion Network

The overall goal of this study is to elucidate how emotion network dynamics relate to the behavioral, autonomic, and experiential changes that accompany emotions and to investigate how emotion network dysfunction relates to affective symptoms. Affective symptoms are a common feature of neuropsychiatric disorders that reflect dysfunction in a distributed brain network that supports emotion. How aberrant functioning in a single emotion network underlies a wide range of affective symptoms, such as depression and anxiety, is not well understood. Anchored by the anterior cingulate cortex and ventral anterior insula, the emotion network responds to numerous affective stimuli. The recording of neural activity directly from the cortical surface from individuals is a promising approach since intracranial electroencephalography (iEEG) can provide direct estimates of neuronal populations to map the spatiotemporal dynamics of the emotion network at a millisecond level resolution. This study will exam how activity within emotion network hubs changes during emotions and how emotion network properties make some individuals more vulnerable to affective symptoms than others. A multidisciplinary approach is critical for understanding the dynamic brain network to advance neuroanatomical models of emotions and for guiding the development of novel treatments for affective symptoms.

Intracranial EEG (iEEG) recordings make it possible to obtain direct measures of emotion network dynamics not possible with other methods. This study proposes to conduct a multimodal assessment of emotions in patients with epilepsy who are undergoing surgery for seizure localization. Electrodes will be placed in sites based on the clinical needs of each patient and will include sites within and outside of the emotion network. The research study team will examine measures of behavior, autonomic nervous system (ANS) activity, and subjective experience to continuous iEEG recordings to determine how emotion network dynamics relate to emotions and affective symptoms. Affective symptoms are common in epilepsy, with nearly one third of patients meeting diagnostic criteria for a severe anxiety or depressive disorder during their lifetimes. This clinical population offers a unique opportunity to obtain direct recordings of emotion network activity with concurrent measures of emotion physiology and behavior. The aims of this study seeks to determine how emotion network activity relates to naturalistic affective behaviors (Aim 1), whether we can uncover the unique neural signatures of discrete emotions and examine their relation to task-based measures of emotional reactivity (Aim 2), and whether electrical stimulation of emotion network changes network activity and alters emotions, mood, and anxiety (Aim 3). Together, these aims will help to uncover the neural mechanisms that produce, sense, and regulate emotions. These results should heavily impact the current understanding of the neurobiological systems that underlie affective symptoms and, therefore, will have significant implications for identifying biomarkers of system dysfunction to guide development of new treatments.

Participants viewed visual stimuli while undergoing stimulation of brain regions involved in emotion during their inpatient hospitalization at the University of California, San Francisco (UCSF).

View emotionally evocative videos and undergo stimulation of brain regions involved in emotion. In a 20-minute block (5 total blocks), eighteen 30-second movie clips will be used to elicit different categories of emotions (sadness, fear, disgust, awe, affection, and amusement). Additionally, each hub of the emotion network will be stimulated to identify areas that generate similar emotional states as those elicited by the videos. Each stimulation trial consists of 3 minutes of 1-3 mA, 50 Hz, 100 us pulse-width stimulation.

The mean change from baseline in heart rate is calculated as the average change in heart rate across trials of each emotion video type and stimulated network hub (units: beats/minute). The change from baseline in heart rate on each trial is calculated as: change = (mean heart rate during a trial) - (mean heart rate prior to the trial). Note: this is one of three components of the autonomic nervous system activity outcome measure.

The mean change from baseline in respiratory rate is calculated as the average change in respiratory rate across trials of each emotion video type and stimulated network hub (units: breaths/minute). The change from baseline in respiratory rate on each trial is calculated as: change = (mean respiratory rate during a trial) - (mean respiratory rate prior to the trial). Note: this is one of three components of the autonomic nervous system activity outcome measure.

The mean change from baseline in Electrodermal Activity (EDA) is calculated as the average change in EDA across trials of each emotion video type and stimulated network hub (units: microsiemens).The change from baseline in electrodermal activity on each trial is calculated as: change = (mean electrodermal activity during a trial) - (mean electrodermal activity prior to the trial). Note: this is one of three components of the autonomic nervous system activity outcome measure.

Mean facial expression activity (units: activation intensity) is quantified for each video emotion type and stimulated network hub. This is measured using the validated Emotional Facial Action Coding System (FACS) to provide a continuous rating of activity in 28 facial muscles using an intensity scale from 1 (trace) to 5 (maximum). The activity scores of all muscles and across the duration of a trial will be summed to quantify the total facial expression activity of that trial. Trials will be averaged for each video emotion type and stimulated network hub to obtain mean facial expression activity.

The mean change from baseline in neural activity is calculated as the average change in neural activity across trials of each emotion video type and stimulated network hub (units: z-scored neural activity relative to the pretrial baseline). The change from baseline in neural activity on each trial is calculated as: change = (z-scored mean neural activity across the network during a trial) - (z-scored mean neural activity across the network prior to the trial).

The mean subjective experience is calculated as the average subjective experience rating across trials of each emotion video type and stimulated network hub (units: arbitrary units [a.u.] on a scale from 0-10; 0 = no emotion felt, 10 = maximum emotion felt ). Trials will be averaged for each video emotion type and stimulated network hub to obtain mean subjective experience.

Inclusion Criteria: Participants with epilepsy at UCSF undergoing surgical electrode implantation for seizure localization and Participants with electrodes implanted in at least two regions of interest who are willing and able to cooperate with study tasks. Exclusion Criteria: Participants who lack capacity or decline to provide informed consent, Participants who have significant cerebral lesions or Participants with cognitive deficits that preclude reliable completion of study tasks.