Cingulate and prefrontal cortex effective connectivity during emotion inhibition in bipolar disorder
Poster No:
1525
Submission Type:
Abstract Submission
Authors:
Jacqueline Quirke1, Leila Nabulsi2, Genevieve McPhilemy1, Fiona Martyn1, Brian Hallahan1, Colm McDonald1, Maria Dauvermann3, Dara Cannon1
Institutions:
1University of Galway, Galway, Ireland, 2University of Southern California, Marina Del Rey, United States, 3University of Birmingham, Birmingham, United Kingdom
First Author:
Co-Author(s):
Introduction:
Emotion inhibition deficits are a core feature in bipolar disorder (BD), greatly affecting quality of life. Prefrontal regions, including the dorsolateral and ventrolateral prefrontal cortex (dlPFC and vlPFC respectively) and cingulate cortices, are involved in emotion processing and inhibition (Wessa & Linke, 2009). They are differentially connected in BD (Wessa & Linke, 2009) and may underlie emotional dysregulation. Dysconnectivity in prefrontal regions may correspond to deficits in attention, salience attribution, and inhibition of emotional information, especially negatively valenced stimuli (Nabulsi et al., 2022; Hummer et al., 2013). Few effective connectivity studies exist with regards to BD and emotion inhibition, yet they may increase our understanding of altered networks underlying emotion inhibition deficits in BD. Here, we used Dynamic Causal Modeling (DCM) for functional Magnetic Resonance Imaging (fMRI) (Friston et al., 2003) and assessed modulatory connection strengths during an emotion inhibition task (Lisiecka et al., 2012) using negatively valenced stimuli in BD and healthy controls (HC).
Methods:
Participants with BD met DSM-V criteria for type-I or -II BD. Subjects underwent 3T fMRI scanning during an emotion inhibition task (Lisiecka et al., 2012). Blood oxygen level-dependent (BOLD) responses during the emotion inhibition task have been reported previously (Nabulsi et al., 2022). Bilinear DCM analysis was conducted (SPM12 v7771, DCM 12.5, Matlab R2022b) across eight models including the right anterior cingulate cortex (ACC), posterior cingulate cortex (PCC), dlPFC, and vlPFC (Figure 1). Bayesian Model Selection (BMS) at the group level was performed to compare models between BD and HC. Individual effective connectivity measures for the modulatory connection of the optimal model were extracted and compared between groups. A post-hoc Pearson correlation assessed the relationship between the modulatory effective connectivity strengths and the behavioral accuracy scores on the emotion inhibition task (SPSS v27, IBM).
Results:
The BD (n=33, age mean years±standard deviation=40.58±11.47) and HC (n=49, 39.71±13.42) groups did not differ in age (U=790.50, p=0.87) or gender (χ2=0.35, 0.56). Subjects with BD performed more poorly on the emotion inhibition task (U=507, p=0.02). Across groups, during inhibition of negatively valenced stimuli, Model 1 was the optimal model with a modulation on the connection from the PCC to the dlPFC (Exceedance probability (EP)=0.56; Figure 1). This model explained the given fMRI data with the highest probability in both BD (EP=0.54) and HC (EP=0.55) groups. We did not observe a significant difference in modulatory connection strength between groups (U=758, p=0.63). Post-hoc analyses found no significant relationship between connection strength and emotion inhibition task accuracy across subjects (r(77)=0.01, p=0.97) or between groups (BD: r(29)=0.19, p=0.31; HC: r(46)=-0.14, p=0.36).
Conclusions:
Overall, the model with modulatory effective connectivity between the PCC and dlPFC was the best model to explain the fMRI data across all individuals when inhibiting negatively valenced stimuli. Some effective connectivity studies have found reduced effective connectivity from the dlPFC to the amygdala during emotion processing in depressed BD (Radaelli et al., 2015; Perry et al., 2018). However, consistency of optimal models across groups may point to similar effective connectivity of emotion inhibition regions during BD euthymia compared with controls. This work adds directional, mechanistic insight implicating prefrontal and cingulate effective connectivity during emotion inhibition in BD, corroborating PCC and dlPFC involvement in emotion processing (Maddock et al., 2003; Wessa & Linke, 2009), which appears to persist during periods of remission.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Emotion, Motivation and Social Neuroscience:
Emotion and Motivation Other
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems 2
Keywords:
Affective Disorders
Emotions
FUNCTIONAL MRI
Other - Dynamic causal modeling
1|2Indicates the priority used for review
Provide references using author date format
Hummer, T.A. (2013), ‘Emotional response inhibition in bipolar disorder: a functional magnetic resonance imaging study of trait- and state-related abnormalities’, Biological Psychiatry, vol. 73, no. 2, pp. 136-43.
Lisiecka, D.M. (2012), ‘Altered inhibition of negative emotions in subjects at family risk of major depressive disorder’, Journal of Psychiatric Research, vol. 46, pp. 181–8.
Maddock, R.J. (2003), ‘Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task’, Human Brain Mapping, vol. 18, pp. 30-41.
Nabulsi, L. (2022), ‘Normalization of impaired emotion inhibition in bipolar disorder mediated by cholinergic neurotransmission in the cingulate cortex’, Neuropsychopharmacology, vol. 47, pp. 1643–1651.
Perry, A. (2018), ‘Connectomics of Bipolar Disorder: A Critical Review, and Evidence for Dynamic Instabilities within Interoceptive Networks’, Molecular Psychiatry, vol. 24, no. 9, pp. 1296–1318.
Radaelli, D. (2015), ‘Fronto-limbic disconnection in bipolar disorder’, European Psychiatry, vol. 30, no. 1, pp. 82-88.
Wessa, M. (2009), ‘Emotional processing in bipolar disorder: Behavioural and neuroimaging findings’, International Review of Psychiatry, vol. 21, no. 4, pp. 357-367.