Decoding Temporal Dynamics of Emotion Regulation: Reinterpretation, Distraction, and Mindfulness
Poster No:
395
Submission Type:
Abstract Submission
Authors:
Masaya Misaki1,2,3, Aki Tsuchiyagaito1,2, Salvador Guinjoan1,3, Martin Paulus1
Institutions:
1Laureate Institute for Brain Research, Tulsa, OK, 2Oxley College of Health and Natural Sciences, The University of Tulsa, Tulsa, OK, 3Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa, Tulsa, OK
First Author:
Masaya Misaki
Laureate Institute for Brain Research|Oxley College of Health and Natural Sciences, The University of Tulsa|Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa
Tulsa, OK|Tulsa, OK|Tulsa, OK
Laureate Institute for Brain Research|Oxley College of Health and Natural Sciences, The University of Tulsa|Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa
Tulsa, OK|Tulsa, OK|Tulsa, OK
Co-Author(s):
Aki Tsuchiyagaito
Laureate Institute for Brain Research|Oxley College of Health and Natural Sciences, The University of Tulsa
Tulsa, OK|Tulsa, OK
Laureate Institute for Brain Research|Oxley College of Health and Natural Sciences, The University of Tulsa
Tulsa, OK|Tulsa, OK
Salvador Guinjoan
Laureate Institute for Brain Research|Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa
Tulsa, OK|Tulsa, OK
Laureate Institute for Brain Research|Department of Psychiatry, Oklahoma University Health Sciences Center at Tulsa
Tulsa, OK|Tulsa, OK
Introduction:
The regulation of negative emotions is essential to overall well-being [1]. Deficits in this capacity are frequently implicated as a core factor driving repetitive negative thinking, a transdiagnostic symptom prevalent in mood and anxiety disorders [2]. Although various strategies are employed to regulate negative emotions, the neural mechanisms underlying these approaches and their temporal dynamics remain poorly understood [3]. Traditional reductionistic frameworks, which often focus on isolated emotion regulation mechanisms, may fail to capture the dynamic nature of emotional processing. This study adopts a systems-based approach, emphasizing the importance of understanding emotion regulation strategies within the context of complex neural systems. It aims to evaluate the distinct neural mechanisms and temporal dynamics underlying three regulation strategies-reinterpretation, distraction, and mindfulness-using a machine learning decoder to track emotional state changes over time.
Methods:
Thirty-five healthy individuals (24 females, aged 18-64) underwent fMRI while completing a thought induction task involving rumination, worry, and positive thinking related to autobiographical events [4], followed by negative emotion regulation tasks using reinterpretation, distraction, and mindfulness (focusing on breathing). A machine learning classifier was trained on whole brain fMRI signals obtained during the thought induction task to differentiate mental states. This trained decoder was subsequently applied to brain activation data recorded during the regulation tasks to track the temporal dynamics of emotional state changes. The rates of negative emotion reduction for each strategy were quantified by fitting an exponential decay function, P=a⋅exp(-τ(t-b))+c, where P represents the probability of the decoder's emotional state, t is the time from the onset of regulation, τ quantifies the rate of emotional reduction, and a, b, and c are the scaling, onset shift, and intercept parameters. All parameters were constrained to positive values to ensure interpretability.
Results:
Figure 1 illustrates the timecourse of decoded emotional states during the thought induction phase and subsequent regulation. Analysis of the rate of emotional reduction (τ) indicated that mindfulness resulted in the fastest reduction in negative emotional states, as reflected by the largest τ, followed by distraction. Reinterpretation was significantly slower, exhibiting a significantly smaller decay parameter compared to mindfulness (corrected p=.040). There was no significant difference in the rate of emotional reduction between types of negative emotions (p=.757) or in the interaction between emotion types and regulation strategies (p=.172).
Figure 2 shows brain activation maps during regulation blocks (p<.001, cluster level p<.05). Distraction and reinterpretation were associated with the activation of specific brain regions, including the supplementary motor area and lateral prefrontal cortex, which are involved in cognitive control. In contrast, mindfulness reduced brain activity across a broader range of regions.
Figure 2 shows brain activation maps during regulation blocks (p<.001, cluster level p<.05). Distraction and reinterpretation were associated with the activation of specific brain regions, including the supplementary motor area and lateral prefrontal cortex, which are involved in cognitive control. In contrast, mindfulness reduced brain activity across a broader range of regions.
·Figure 1. Decoded time course of emotional state probabilities from machine learning classifier outputs. Shaded bands indicate the standard error of the mean.
·Figure 2. Brain activations associated with each emotion regulation process. The maps are displayed on an inflated cortical surface, with subcortical and cerebellar regions shown on axial slice maps.
Conclusions:
Framing emotion regulation as a system-level process highlights the emergent dynamics of neural activity during regulation. Mindfulness facilitates rapid state transitions by broadly suppressing neural activity, aligning with self-organized criticality in systems psychiatry. In contrast, distraction and reinterpretation depend on cognitive control mechanisms, reflecting slower but more stable processes. These findings suggest that different strategies engage distinct neural attractor states, with mindfulness enabling faster transitions out of maladaptive states.
Future research should explore how these neural mechanisms relate to repetitive negative thinking in mental disorders. Incorporating probabilistic models to account for individual variability and investigating these strategies in clinical populations could advance personalized approaches to emotion regulation.
Future research should explore how these neural mechanisms relate to repetitive negative thinking in mental disorders. Incorporating probabilistic models to account for individual variability and investigating these strategies in clinical populations could advance personalized approaches to emotion regulation.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 2
Keywords:
Emotions
Machine Learning
Other - Emotion regulation;Mindfulness;Temporal dynamics
1|2Indicates the priority used for review
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2. Klemanski DH, Curtiss J, McLaughlin KA, et al. (2017), ’Emotion Regulation and the Transdiagnostic Role of Repetitive Negative Thinking in Adolescents with Social Anxiety and Depression’, Cognitive Therapy and Research, vol. 41, no. 2, pp. 206-219.
3. Moodie CA, Suri G, Goerlitz DS, et al. (2020), ’The neural bases of cognitive emotion regulation: The roles of strategy and intensity’, Cognitive, Affective, & Behavioral Neuroscience, vol. 20, no. 2, pp. 387-407.
4. Misaki M, Tsuchiyagaito A, Guinjoan S, et al. (2024), ’Aging increases the distinctiveness of emotional brain states across rumination, worry, and positive thinking’, bioRxiv, p. 2024.2010.2029.620853.