Acute Stress Attenuates Volatility-Related Neural Activity in the Dorsolateral Prefrontal Cortex
Poster No:
398
Submission Type:
Abstract Submission
Authors:
Wenshan Dong1, Shaozheng Qin2, Qi Chen3
Institutions:
1South China Normal University, Guangzhou, Guangdong, 2State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijng, Beijing, 3School of Psychology, Shenzhen University, Shenzhen, Guangdong
First Author:
Co-Author(s):
Shaozheng Qin
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijng, Beijing
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijng, Beijing
Introduction:
Stress is fundamentally linked to uncertainty, and volatility intensifies it, resulting in heightened stress. Previous studies suggest that both humans and animals adjust their learning strategies based on environmental volatility. When outcome probabilities are stable, no strategy update is needed, even with stochastic outcomes. However, in volatile conditions, each result influences subsequent strategy adjustments, increasing the learning rate. For example, if the stove's flame flickers unpredictably, it may initially seem minor, but persistent flickering prompts changes in approach, such as checking the stove or altering cooking methods. This adaptation reflects how we balance prior expectations with new information. Stress, however, disrupts this balance, hindering the ability to maintain stability in predictable conditions or to adapt flexibly when uncertainty increases.
Methods:
Participants
A total of 67 participants (38 females) with normal or corrected-to-normal vision and no known history of neurological or psychiatric disorders participated in our study. All participants were university students with a mean age of 20.568. There were 61 valid subjects, of which 30 were in the control group and 31 in the stress group. The study was approved by the Ethics Committee IRB of Peking University.
Procedure
Experiments were conducted between 1:30 p.m. and 5:00 p.m. Upon arrival, participants rested for 30 minutes, after which baseline heart rate and saliva samples (t0) were collected. The protocol included two components: the Trier Social Stress Test (TSST) and a probabilistic reversal learning task, the latter performed concurrently with MRI scanning. Saliva samples were collected at specific intervals after the TSST: immediately (t1), at 10 minutes (t2), 20 minutes (t3), 35 minutes (t4), 50 minutes (t5), and 120 minutes (t6). Participants completed two probabilistic reversal learning tasks, with saliva samples collected between t2-t3 and t3-t4.
Model space
Four perceptual models were applied: (i) the HGF_startpoint model, a three-level hierarchical framework incorporating individual differences in initial beliefs about environmental volatility (û30) ; (ii) the standard HGF model, which assumes fixed starting values for these beliefs across individuals; and two simpler reinforcement learning models: (iii) the Rescorla-Wagner (RW) model with a fixed learning rate, and (iv) the Sutton model, which updates values based on prediction error with a variable learning rate.
A total of 67 participants (38 females) with normal or corrected-to-normal vision and no known history of neurological or psychiatric disorders participated in our study. All participants were university students with a mean age of 20.568. There were 61 valid subjects, of which 30 were in the control group and 31 in the stress group. The study was approved by the Ethics Committee IRB of Peking University.
Procedure
Experiments were conducted between 1:30 p.m. and 5:00 p.m. Upon arrival, participants rested for 30 minutes, after which baseline heart rate and saliva samples (t0) were collected. The protocol included two components: the Trier Social Stress Test (TSST) and a probabilistic reversal learning task, the latter performed concurrently with MRI scanning. Saliva samples were collected at specific intervals after the TSST: immediately (t1), at 10 minutes (t2), 20 minutes (t3), 35 minutes (t4), 50 minutes (t5), and 120 minutes (t6). Participants completed two probabilistic reversal learning tasks, with saliva samples collected between t2-t3 and t3-t4.
Model space
Four perceptual models were applied: (i) the HGF_startpoint model, a three-level hierarchical framework incorporating individual differences in initial beliefs about environmental volatility (û30) ; (ii) the standard HGF model, which assumes fixed starting values for these beliefs across individuals; and two simpler reinforcement learning models: (iii) the Rescorla-Wagner (RW) model with a fixed learning rate, and (iv) the Sutton model, which updates values based on prediction error with a variable learning rate.
·The experimental procedure and timeline for saliva sampling
Results:
Salivary cortisol concentrations were significantly higher in the stress group compared to the control group from t1 to t5, confirming successful stress induction.
Behaviorally, a 2 (stress vs. control) × 2 (stable vs. volatile) × 2 (Run1 vs. Run2) ANOVA revealed significant three-way interactions for task performance and rule-switching probability (PSwitch). In Run1, the control group showed condition differences, while the stress group did not. By Run2, neither group showed differences.
Modeling results indicated the HGF_startpoint model was superior, with a significant interaction between experimental manipulation and scan order was observed for both the initial (û30) and updated (û3) estimates of environmental volatility. In the control group, estimates increased over time, while in the stress group, estimates remained consistently high.
fMRI results revealed a significant group × feedback interactions in the right dorsolateral prefrontal cortex (dlPFC_R) and the cuneus, with dlPFC_R activation varying systematically with û3.
Behaviorally, a 2 (stress vs. control) × 2 (stable vs. volatile) × 2 (Run1 vs. Run2) ANOVA revealed significant three-way interactions for task performance and rule-switching probability (PSwitch). In Run1, the control group showed condition differences, while the stress group did not. By Run2, neither group showed differences.
Modeling results indicated the HGF_startpoint model was superior, with a significant interaction between experimental manipulation and scan order was observed for both the initial (û30) and updated (û3) estimates of environmental volatility. In the control group, estimates increased over time, while in the stress group, estimates remained consistently high.
fMRI results revealed a significant group × feedback interactions in the right dorsolateral prefrontal cortex (dlPFC_R) and the cuneus, with dlPFC_R activation varying systematically with û3.
·Salivary cortisol, behavior, and modelling results
Conclusions:
Acute stress significantly attenuated DLPFC activation during volatility processing compared to a non-stressed control group. These findings elucidate the neural mechanisms underlying stress-induced disruptions in adaptive learning and provide novel insights into the cognitive and neural consequences of stress in dynamic environments.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Activation (eg. BOLD task-fMRI)
Bayesian Modeling 2
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
FUNCTIONAL MRI
Learning
Modeling
Perception
1|2Indicates the priority used for review
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Provide references using APA citation style.
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