Nucleus Accumbens Functional Connectivity is Associated with Impulsivity in Over 8000 Children
Poster No:
329
Submission Type:
Abstract Submission
Authors:
Gaon Kim1, Chloe Retika1, Paul Thompson2, Katherine Lawrence1
Institutions:
1University of Southern California, Marina Del Rey, CA, 2University of Southern California, Los Angeles, CA
First Author:
Co-Author(s):
Introduction:
Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental condition characterized by core symptoms of inattention, impulsivity, and hyperactivity (Plichta, 2014). The Research Domain Criteria (RDoC) framework, focusing on continuous symptom dimensions across the population, offers a new perspective on brain-based disorders including ADHD (Insel, 2010). Previous rs-fMRI studies have linked ADHD diagnoses to altered brain reward networks, but the connection between dimensional ADHD symptoms across diagnostic groups and reward circuitry is less well understood. Here we analyzed data from the Adolescent Brain Cognitive Development (ABCD) study, a large population-based study, to investigate associations between ADHD symptoms and functional connectivity (FC) of the reward system (Jernigan, 2018). Specifically, we examined the resting-state FC of the nucleus accumbens (NAcc), a key brain region in the reward network, and ADHD symptoms, as quantified by the CBCL and UPPS-P scales.
Methods:
We analyzed data from 8,612 participants (9-10 years; 49.87% female) from the ABCD study, which was collected across 21 sites (Casey, 2018). ADHD symptoms were assessed using CBCL Attention subscale T-scores and the UPPS-P, which evaluates five dimensions of impulsivity: positive urgency (PU), negative urgency, sensation seeking, lack of planning, and lack of perseverance (LPe) (Barch, 2018; Achenbach, 2009). Participants were assessed at baseline with a total of 15 to 20 minutes of rs-fMRI acquisition acquired on 3T scanners (Hagler, 2019). rs-fMRI data were processed according to standard protocols as described in Hagler et al. (2019), including motion scrubbing and averaging time courses in ROIs from the Gordon parcellation (Hagler, 2019; Gordon, 2016). A total of 13 cortical resting-state networks were extracted based on the Gordon parcellation: auditory, cingulo-opercular, cingulo-parietal, default mode (DMN), dorsal attention, fronto-parietal, none, retrosplenial, salience (SA), sensorimotor mouth (SMM), sensorimotor hand (SMH), ventral attention, visual (Gordon, 2016). For our analyses, we considered each network's FC with the NAcc when averaging across the left and right NAcc. We assessed the association between these measures of NAcc resting-state FC and our measures of ADHD symptoms by using linear mixed-effects models. Standard fixed effects nuisance covariates included age, sex, household income, and parental education. Random effects accounted for relatedness and scanner. The false discovery rate (FDR) was used to correct for multiple comparisons across the number of brain networks (Benjamini, 1995).
Results:
We found no significant associations between CBCL Attention scores and NAcc FC. Several significant associations were found for the UPPS-P subscales. Specifically, higher PU scores were linked with less NAcc-DMN FC (p=0.04, β=-0.03) and less NAcc-SMH FC (p=0.0004, β=-0.04). We also observed a negative association between the LPe subscale and NAcc-SA FC (p=0.002, β=-0.04), as well as a positive association between LPe and NAcc-SMM FC (p=0.04, β=0.03). In sum, we found that impulsivity was related to NAcc connectivity with both higher-order (DMN, SA) and lower-order (SMH, SMM) networks.
Conclusions:
Impulsivity is a key symptom of ADHD, and our findings indicate that it is related to altered FC of the NAcc, a core reward structure. The PU and LPe subscales of the UPPS-P were linked to changes in NAcc connectivity across both higher- and lower-order networks. PU reflects impulsivity triggered by positive emotions, while LPe involves difficulty focusing on boring tasks (Zapolski, 2010). The FC differences observed here thus suggest that impulsivity is associated with complex interactions between brain regions, and that these patterns depend on the specific type of impulsivity. As a whole, these analyses robustly demonstrate how resting-state FC of the reward system is associated with impulsivity, a core symptom of ADHD, in a late childhood sample.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Attention Deficit Disorder
Other - impulsivity; resting-state fMRI; nucleus accumbens; functional connectivity
1|2Indicates the priority used for review
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Provide references using APA citation style.
Barch, D. M. (2018). Demographic, physical and mental health assessments in the adolescent brain and cognitive development study: Rationale and description. Developmental Cognitive Neuroscience, 32, 55–66. https://doi.org/10.1016/j.dcn.2017.10.010
Benjamini, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289-300.
Casey, B. J. (2018). The Adolescent Brain Cognitive Development (ABCD) study: Imaging acquisition across 21 sites. Developmental Cognitive Neuroscience, 32, 43–54. https://doi.org/10.1016/j.dcn.2018.03.001
Gordon, E. M. (2016). Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cerebral cortex (New York, N.Y. : 1991), 26(1), 288–303. https://doi.org/10.1093/cercor/bhu239
Hagler, D. J. (2019). Image processing and analysis methods for the Adolescent Brain Cognitive Development Study. NeuroImage, 202, 116091. https://doi.org/10.1016/j.neuroimage.2019.116091
Insel, T. (2010). Research domain criteria (RDoC): toward a new classification framework for research on mental disorders. The American Journal of Psychiatry, 167(7), 748–751. https://doi.org/10.1176/appi.ajp.2010.09091379
Jernigan, T. L. (2018). Introduction. Developmental Cognitive Neuroscience, 32, 1–3. https://doi.org/10.1016/j.dcn.2018.02.002
Plichta, M. M (2014). Ventral-striatal responsiveness during reward anticipation in ADHD and its relation to trait impulsivity in the healthy population: a meta-analytic review of the fMRI literature. Neuroscience and Biobehavioral Reviews, 38, 125–134. https://doi.org/10.1016/j.neubiorev.2013.07.012
Zapolski, T. C. (2010). The measurement of dispositions to rash action in children. Assessment, 17(1), 116–125. https://doi.org/10.1177/1073191109351372