Influence of reward sensitivity on resting-state networks in OCD patients and healthy controls
Poster No:
493
Submission Type:
Abstract Submission
Authors:
Daniela Costa1, Celina Gomes2, Pedro Morgado1, Maria Picó-Pérez3
Institutions:
1Life and Health Sciences Research Institute (ICVS), Braga, Portugal, 2Clinical Academic Center – Braga, Braga, Portugal, 3Jaume I University, Castelló de la Plana, Spain
First Author:
Co-Author(s):
Introduction:
Motivational tendencies are fundamental to improving performance and goal achievement, even more so in the context of psychiatric disorders. Despite this, personality from the perspective of inhibition and approach behavior has rarely been studied in the context of obsessive-compulsive disorder (OCD), and never in association with brain imaging. Thus, in this study we aim to explore the association between reward sensitivity as measured by the Behavioral Inhibition/Behavioral Activation Scales (BIS/BAS; Moreira et al., 2015) and resting-state network connectivity in OCD patients compared to a matched control sample.
Methods:
Twenty-nine OCD patients and 22 controls participated in the study. Sociodemographic and clinical data was collected, as well as the BIS/BAS and the Yale-Brown Obsessive-Compulsive Scale (Y-BOCS). Groups were compared on these variables using independent-sample t-tests in JASP, and Pearson's r correlations between the BIS/BAS and the Y-BOCS were performed for the patient group.
Neuroimaging data was acquired in a Siemens Verio 3T, and preprocessed using fMRIPrep 20.2.5 (Esteban et al., 2019), which is based on Nipype 1.6.1 (Gorgolewski et al., 2011). Resting-state network (RSN) maps were analyzed voxel-wise through a probabilistic Independent Component Analysis (ICA) as implemented in Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC), distributed with FSL (Beckmann & Smith, 2004). Only components visually identified to represent typical RSNs (Horowitz-Kraus et al., 2015) were used in further statistical analyses, which included: the primary visual network, higher visual network, dorsal attention network (DAN), basal ganglia network, default mode network (DMN), limbic network, auditory network, and cerebellar network.
Finally, the RSNs functional connectivity (FC) was first compared between groups, using independent samples t-tests within a non-parametric permutation procedure implemented in the randomise tool of FSL (Winkler et al., 2014), and a threshold-free cluster enhancement (TFCE) correction at an α=.05. For each contrast, 5000 permutations were performed. Then, multiple regression analyses were also performed including either BIS or BAS subscales in the model in interaction with group.
Neuroimaging data was acquired in a Siemens Verio 3T, and preprocessed using fMRIPrep 20.2.5 (Esteban et al., 2019), which is based on Nipype 1.6.1 (Gorgolewski et al., 2011). Resting-state network (RSN) maps were analyzed voxel-wise through a probabilistic Independent Component Analysis (ICA) as implemented in Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC), distributed with FSL (Beckmann & Smith, 2004). Only components visually identified to represent typical RSNs (Horowitz-Kraus et al., 2015) were used in further statistical analyses, which included: the primary visual network, higher visual network, dorsal attention network (DAN), basal ganglia network, default mode network (DMN), limbic network, auditory network, and cerebellar network.
Finally, the RSNs functional connectivity (FC) was first compared between groups, using independent samples t-tests within a non-parametric permutation procedure implemented in the randomise tool of FSL (Winkler et al., 2014), and a threshold-free cluster enhancement (TFCE) correction at an α=.05. For each contrast, 5000 permutations were performed. Then, multiple regression analyses were also performed including either BIS or BAS subscales in the model in interaction with group.
Results:
Groups were matched on age, education level, and sex/gender distribution. Patients with OCD presented higher behavioral inhibition but no differences in the BAS subscales, and a significant positive correlation between the BIS and Y-BOCS Obsessions.
Regarding the RSNs group comparison, OCD patients presented increased FC in the primary visual, the higher visual and the basal ganglia networks (see Figure 1). Regarding BIS/BAS associations, BIS scores were positively associated with DMN FC in patients with OCD. Also in patients, the primary visual network FC was negatively associated with BAS Reward Responsiveness and BAS Drive, while BAS Drive was also negatively associated with the FC of the DMN, the higher visual, the auditory and the basal ganglia networks, and BAS Fun Seeking was negatively associated with limbic network FC. On the other hand, BAS Reward Responsiveness was positively associated with the FC of the basal ganglia network in patients (see Figure 2).
Regarding the RSNs group comparison, OCD patients presented increased FC in the primary visual, the higher visual and the basal ganglia networks (see Figure 1). Regarding BIS/BAS associations, BIS scores were positively associated with DMN FC in patients with OCD. Also in patients, the primary visual network FC was negatively associated with BAS Reward Responsiveness and BAS Drive, while BAS Drive was also negatively associated with the FC of the DMN, the higher visual, the auditory and the basal ganglia networks, and BAS Fun Seeking was negatively associated with limbic network FC. On the other hand, BAS Reward Responsiveness was positively associated with the FC of the basal ganglia network in patients (see Figure 2).
·Figure 1
·Figure 2
Conclusions:
We found significant differences between patients and controls at the behavioral level on the BIS scale but not on the BAS subscales, while at the brain-level, there were widespread associations between both BIS and BAS subscales and RSNs FC. The RSNs found to be associated with BIS/BAS have been previously reported to be critically involved in motivated behavior and in the pathophysiology of OCD, such as the basal ganglia and the limbic network, but also the DMN and the visual networks. This approach could help identify different neuropsychological and neural profiles within OCD, which could eventually guide individualized treatment selection depending on each patient's characteristics.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Emotion, Motivation and Social Neuroscience:
Reward and Punishment
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Task-Independent and Resting-State Analysis 2
Keywords:
Basal Ganglia
FUNCTIONAL MRI
Obessive Compulsive Disorder
Psychiatric Disorders
Other - Resting-state; Personality; Motivation
1|2Indicates the priority used for review
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