Unravelling sex-specific neural patterns associated with negative emotions
Poster No:
716
Submission Type:
Abstract Submission
Authors:
Tajwar Sultana1, Dua Ijaz1, Fareha Asif Khan1, Maryam Misaal1, Elvisha Dhamala2, Adeel Razi3
Institutions:
1NED University of Engineering and Technology, Karachi, Sindh, 2Feinstein Institutes for Medical Research, Glen Oaks, NY, 3Monash University, Melbourne, Australia
First Author:
Co-Author(s):
Introduction:
Do the male and female brains have different connectivity patterns for emotions? This question is of importance because sex is a crucial aspect of human identity and has been the subject of numerous studies and research over the past years [1]. Negative emotions are feelings that are generally unpleasant and associated with unpleasant experiences, such as sadness, anger, fear, disgust, and frustration. Various studies have explored sex differences in response to negative emotion regulation, reactivity, experiences, and perception [2]–[5]. Ignoring these distinctive outcomes between males and females may result in unsuccessful interventions and therapies for internalizing disorders such as anxiety and depression. Although there has been extensive research on sex differences in emotions [1]–[3], there is no previous work on investigating the sex differences in resting-state brain effective connectivity, specifically related to basic negative emotion. In this study, we expand upon the previous studies and investigate the underlying neural mechanisms of emotional trait-like personality in male and female using resting-state fMRI.
Methods:
Our dataset consists of 1079 preprocessed resting-state fMRI scans from Human Connectome Project (HCP) that was acquired using 3T MRI scanner and their emotional assessment using NIH toolbox emotion battery. Some of the subjects, as has also been previously reported [6], showed the minimum scores within each emotion group ratings resulting in a bimodal distribution. The cause of this anomaly remains unknown. We removed those 176 subjects and ended up with 410 male and 493 female subjects. These subjects were divided into two groups (of males and females) which were subdivided according to their score levels (high, moderate, and low) for each emotion category (anger-affect, fear-affect, and sadness). The ROIs were taken from three well-known resting-state networks that have a high significance in emotional processing namely default mode, executive, and salience networks. The selected regions in each brain network are 1) default mode network: posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC) and bilateral hippocampus (lHP and rHP); 2) salience network: dorsal anterior cingulate cortex (dACC), bilateral anterior insula (lAI and rAI) and bilateral amygdala (lAMG and rAMG) and; 3) executive network: bilateral dorsal lateral prefrontal cortex (lDLPFC and rDLPFC).
A fully connected spectral dynamic causal model over 11 ROIs was estimated for each subject [7], [8]. Then in the group-level analysis, using parametric empirical Bayes (PEB) [9], [10], associations were found between estimated model parameters and self-reported scores in which estimated effective connectivity for each subject was taken to group-level to estimate group effects.
A fully connected spectral dynamic causal model over 11 ROIs was estimated for each subject [7], [8]. Then in the group-level analysis, using parametric empirical Bayes (PEB) [9], [10], associations were found between estimated model parameters and self-reported scores in which estimated effective connectivity for each subject was taken to group-level to estimate group effects.
·Methodology flow chart
Results:
We only report our group level PEB results with strong evidence that is at a posterior probability > 0.95. Our results have shown strongest negative association of self-connection of hippocampus with each of the heightened negative emotions in females. In males, our study revealed the associations of the self-connection of dACC, inhibitory connection from right amygdala to dACC and inhibitory connection from dACC to left hippocampus with high fear-affect, anger-affect and sadness respectively.
·We only show the top most associations of effective connectivity with self-reported basic negative emotions, in both male and female groups. Green and red lines show positive and negative associations
Conclusions:
Our research provide evidence that the attitude towards basic negative emotions has different underlying neural mechanism in males and females. The key results signify the prominent roles of dACC and left hippocampus in heightened negative emotions of anger, fear and sadness in male and female respectively. Our investigation suggests that targeting these specific brain areas for sex-specific therapies and interventions for psychopathology treatment may result in better health outcomes.
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis
Novel Imaging Acquisition Methods:
BOLD fMRI
Keywords:
Computational Neuroscience
Data analysis
Design and Analysis
Emotions
FUNCTIONAL MRI
Other - Effective connectivity, Dynamic causal modelling, spectral DCM
1|2Indicates the priority used for review
Provide references using author date format
Deng, Yaling, et al. (2016) “Gender Differences in Emotional Response: Inconsistency between Experience and Expressivity.” PloS One, vol. 11, no. 6, PLoS One
Domes, Gregor, et al. (2010) “The Neural Correlates of Sex Differences in Emotional Reactivity and Emotion Regulation.” Human Brain Mapping, vol. 31, no. 5, Hum Brain Mapp
Fischer, Agneta H., et al. (2018) “Gender Differences in Emotion Perception and Self-Reported Emotional Intelligence: A Test of the Emotion Sensitivity Hypothesis.” PloS One, vol. 13, no. 1, PLoS One
Friston, Karl J, et al. (2014) “A DCM for Resting State FMRI.” NeuroImage, vol. 94, pp. 396–407
Friston, Karl J., et al. (2016) “Bayesian Model Reduction and Empirical Bayes for Group (DCM) Studies.” NeuroImage, vol. 128, Academic Press Inc., pp. 413–31
Razi, Adeel, et al. (2015) “Construct Validation of a DCM for Resting State FMRI.” NeuroImage, vol. 106, Academic Press Inc., pp. 1–14,
Stoica, T., et al. (2021) “Gender Differences in Functional Connectivity during Emotion Regulation.” Neuropsychologia, vol. 156, Neuropsychologia
Whittle, Sarah, et al. (2011) “Sex Differences in the Neural Correlates of Emotion: Evidence from Neuroimaging.” Biological Psychology, vol. 87, no. 3, pp. 319–33
Zeidman, Peter, et al. (2019) “A Guide to Group Effective Connectivity Analysis, Part 2: Second Level Analysis with PEB.” NeuroImage, vol. 200, Academic Press Inc., pp. 12–25,