Basolateral Amygdala Neurons Maintain Aversive Emotional Salience

Abstract

BLA neurons serve a well-accepted role in fear conditioning and fear extinction. However, the specific learning processes related to their activity at different times during learning remain poorly understood. We addressed this using behavioral tasks isolating distinct aspects of fear learning in male rats. We show that brief optogenetic inhibition of BLA neurons around moments of aversive reinforcement or nonreinforcement causes reductions in the salience of conditioned stimuli, rendering these stimuli less able to be learned about and less able to control fear or safety behaviors. This salience reduction was stimulus-specific, long-lasting, and specific to learning about, or responding to, the same aversive outcome, precisely the goals of therapeutic interventions in human anxiety disorders. Our findings identify a core learning process disrupted by brief BLA optogenetic inhibition. They show that a primary function of the unconditioned stimulus-evoked activity of BLA neurons is to maintain the salience of conditioned stimuli that precede it. This maintenance of salience is a necessary precursor for these stimuli to gain and maintain control over fear and safety behavior. The amygdala is essential for learning to fear and learning to reduce fear. However, the specific roles served by activity of different amygdala neurons at different times during learning is poorly understood. We used behavioral tasks isolating distinct aspects of learning in rats to show that brief optogenetic inhibition of BLA neurons around moments of reinforcement or nonreinforcement disrupts maintenance of conditioned stimulus salience. This causes a stimulus-specific and long-lasting deficit in the ability of the conditioned stimulus to be learned about or control fear responses. These consequences are the precisely goals of therapeutic interventions in human anxiety disorders. Our findings identify a core learning process disrupted by brief BLA optogenetic inhibition.

Analyzing Event-Related Transients: Confidence Intervals, Permutation Tests, and Consecutive Thresholds

“…emulate subject-based analysis, a subject population for each condition (n = 1,000; Figures 2A,B) was generated by randomly sampling and averaging 1-31 lines from their respective activity populations. These parameters correspond approximately to data generated by fiber photometry recordings (Sengupta et al, 2018;Choi et al, 2019).…”

“…To illustrate the effectiveness of these methods on real data, we applied them to the exemplar data depicted in Figure 1 (from Sengupta et al, 2018). Both tCI and bCI readily identified Family-wise Type I error rate (FWER) was determined as any false rejection of the null across the time window (i.e., detection of a "significant transient" from signals drawn from the null population).…”

“…Lastly, we use tCI, bCI and permutation tests to analyze the exemplar ERT and null data in Figure 1 (from Sengupta et al, 2018). The exemplar ERT data is of CS+ offset (coincident with shock delivery) trials on day 1 of fear conditioning (n = 23), whereas exemplar null data is of CS− offset (no shock comparison) trials on day 3 of fear conditioning (n = 24) 1 .…”

“…However, trends may still exist in the data (e.g., a general decrease in a signal across the session) that impact peri-event signals and analysis. A common means to combat these trends are to detrend dF/F and/or zero peri-event signals to a pre-event baseline (Lerner et al, 2015;Pascoli et al, 2018;Sengupta et al, 2018;Choi et al, 2019). Although putatively effective, it remains important to consider the potential unintended effects of normalization and interpret accordingly (e.g., effects are relative to a particular baseline).…”

“…The most common method involves obtaining a single number statistic quantifying a specific feature of the peri-event dF, such as the Area Under the Curve (AUC) or peak dF. This statistic is then used as input for null hypothesis tests, the results of which form the basis of interpretation (Gunaydin et al, 2014;Lerner et al, 2015;Sengupta et al, 2018).…”