Nucleus Accumbens-Default Mode Network Functional Connectivity is Linked to Social Functioning
Poster No:
1674
Submission Type:
Abstract Submission
Authors:
Chloe Retika1, Gaon Kim1, Paul Thompson2, Katherine Lawrence1
Institutions:
1USC, Marina Del Rey, CA, 2University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Reward systems are impacted in neurodevelopmental and neuropsychiatric disorders that occur during development, such as autism and depression. However, it is not yet fully understood how individual differences during development relate to functional connectivity of the reward system. In this study, we examined 7,080 subjects in late childhood from the Adolescent Brain Cognitive Development (ABCD) database to begin characterizing these individual differences. We focused on functional connectivity of a core structure in the reward system – the nucleus accumbens (NAc) – and a measure of social functioning, the Social Responsiveness Scale (SRS).
Methods:
We analyzed resting-state brain functional magnetic resonance imaging (rsfMRI) data from 7080 children available through the ABCD study (9-10 years, 49.3% female), a population-based study of brain development spanning 21 sites. Participants were included in our analyses if they had complete rsfMRI scans acquired on 3T scanners, covariate demographic data (sex, scanner, parental education, household income), and SRS scores. Subjects who did not pass rsfMRI quality control were excluded. Additionally, due to statistical models failing to converge when including relatedness as a random effect, only one child was randomly retained from each sibling pair.
The traits measured by the SRS include social skills – such as an individual's ability to recognize social cues, interpret social behavior, reciprocate social communication, demonstrate social motivation – and the amount of displayed stereotypic behavior; SRS scores are elevated in autism, but normally distributed across the population.
Complete rsfMRI acquisition and processing protocols are detailed elsewhere (Hagler, 2019). Briefly, rsfMRI data was acquired using multiband echo-planar imaging with a slice acceleration factor of 6 (15-20 minutes, 2.4 mm isotropic, TR=800 ms) (Hagler, 2019). Standard rsfMRI pre-processing was completed, excluding motion-contaminated volumes based on a threshold of 0.3 mm displacement (Power, 2014). For each pair of 333 Gordon parcellation ROIs – which are derived from 13 distinct networks (Gordon, 2016) – correlation values were Fisher transformed into z-statistics and averaged to provide a summary of correlation strength for each network (Van Dijk, 2010). The 13 cortical resting-state networks from the Gordon parcellation included: auditory, cingulo-opercular, cingulo-parietal, default mode (DMN), dorsal attention, fronto-parietal, none, retrosplenial, salience, sensorimotor mouth, sensorimotor hand, ventral attention, and visual (Gordon, 2016).
Using linear mixed effects models, we evaluated the association between total SRS scores and NAc functional connectivity with each cortical brain network, averaging between the left and right NAc. The false discovery rate was used to correct for multiple comparisons. In the regression, fixed effects included sex, household income, highest parental education, age, and total SRS scores. Scanner was designated as a random effect to account for between scanner variation.
The traits measured by the SRS include social skills – such as an individual's ability to recognize social cues, interpret social behavior, reciprocate social communication, demonstrate social motivation – and the amount of displayed stereotypic behavior; SRS scores are elevated in autism, but normally distributed across the population.
Complete rsfMRI acquisition and processing protocols are detailed elsewhere (Hagler, 2019). Briefly, rsfMRI data was acquired using multiband echo-planar imaging with a slice acceleration factor of 6 (15-20 minutes, 2.4 mm isotropic, TR=800 ms) (Hagler, 2019). Standard rsfMRI pre-processing was completed, excluding motion-contaminated volumes based on a threshold of 0.3 mm displacement (Power, 2014). For each pair of 333 Gordon parcellation ROIs – which are derived from 13 distinct networks (Gordon, 2016) – correlation values were Fisher transformed into z-statistics and averaged to provide a summary of correlation strength for each network (Van Dijk, 2010). The 13 cortical resting-state networks from the Gordon parcellation included: auditory, cingulo-opercular, cingulo-parietal, default mode (DMN), dorsal attention, fronto-parietal, none, retrosplenial, salience, sensorimotor mouth, sensorimotor hand, ventral attention, and visual (Gordon, 2016).
Using linear mixed effects models, we evaluated the association between total SRS scores and NAc functional connectivity with each cortical brain network, averaging between the left and right NAc. The false discovery rate was used to correct for multiple comparisons. In the regression, fixed effects included sex, household income, highest parental education, age, and total SRS scores. Scanner was designated as a random effect to account for between scanner variation.
Results:
Higher total scores on the SRS were associated with greater functional connectivity between the NAc and the DMN (corrected p=0.0069, β=0.039) (Figure 1). There were no significant associations between SRS scores and NAc connectivity with any other examined cortical network.
·Total scores on the Social Responsiveness Scale (SRS) are significantly associated with functional connectivity between the nucleus accumbens (NAc) and default mode network (DMN).
Conclusions:
We found that individual differences in social functioning across the population during childhood were related to nuanced individual alterations in the functional connectivity of a key reward structure, the NAc. These findings lay a groundwork for beginning to understand how subtle individual differences in the reward system are related to human behavior across health and disease.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling
Task-Independent and Resting-State Analysis 1
Keywords:
Autism
FUNCTIONAL MRI
PEDIATRIC
Other - rsfMRI; Functional Connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
2. Hagler, D. J. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage, 202, 116091.
3. Power, J.D. (2014). Methods to detect, characterize, and remove motion artifact in resting state fMRI. NeuroImage, 84, 320-341.
4. Van Dijk, K.R. (2010). Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of Neurophysiology, 103, 297-321.