Resting state fMRI functional connectivity of inhibitory control of attention and ADHD in children
Poster No:
301
Submission Type:
Abstract Submission
Authors:
Kelsey Harkness1, Matthias Wilms1, Kate Godfrey1, Signe Bray1, Kara Murias1
Institutions:
1University of Calgary, Calgary, Alberta
First Author:
Co-Author(s):
Introduction:
Inhibitory control of attention (the ability to attend to target stimuli and inhibit attention to non-target stimuli) presents along a spectrum of ability but is commonly dysregulated in individuals with neurodevelopmental conditions such as attention deficit/hyperactivity disorder ADHD (Mueller et al. 2017). Impairments in inhibitory attention have been associated with poorer academic, occupational, and personal outcomes (Boxhoorn et al. 2018). Therefore, better understanding the neural correlates of inhibitory attention and with symptoms may be important in understanding ADHD and treatment.
This study assessed whether the relationship between FC and inhibitory attention (as measured by the Flanker task; Zelazo et al. 2013) is consistent across children with or without ADHD, or if there is a significant interaction between ADHD diagnosis and inhibitory attention skills when predicting network level connectivity.
This study assessed whether the relationship between FC and inhibitory attention (as measured by the Flanker task; Zelazo et al. 2013) is consistent across children with or without ADHD, or if there is a significant interaction between ADHD diagnosis and inhibitory attention skills when predicting network level connectivity.
Methods:
Neuroimaging, Flanker t-scores, ADHD diagnosis (from Kiddie Score for Affective Disorders and Schizophrenia), and demographic information was acquired from the Adolescent Brain Cognitive Development (ABCD) database. Preprocessed resting state fMRI network interconnectivity and intraconnectivity (between and within respectively) for twelve brain networks (based in parcellation described by Gordon et. al), as obtained from the ABCD release 4.0 (https://nda.nih.gov/abcd/abcd-annual-releases). After exclusions participants (aged 9-10 years; N=7030) were divided into groups with (n=494) and without (n=6536) ADHD. Additional analysis was conducted utilizing a composite measure of ADHD symptom severity as described by Cordova et al. (2022).
Analysis: Mixed effects models (MEMs) were used to evaluate the relationship between network-level FC and the main effect of Flanker scores and ADHD diagnosis separately, controlling for the fixed effects of age, sex, parental education, in-scanner movement, and the nested random effects of site and family group. Then MEMs were performed including both the main effects of Flanker scores and ADHD diagnosis, plus an interaction between Flanker scores and ADHD diagnosis (same covariates as above.) This was repeated using ADHD symptom severity score. Models were created with each intra- and inter-network FC value as the outcome, totaling 78 models per hypothesis (Bonferroni corrected alpha=0.00065).
Analysis: Mixed effects models (MEMs) were used to evaluate the relationship between network-level FC and the main effect of Flanker scores and ADHD diagnosis separately, controlling for the fixed effects of age, sex, parental education, in-scanner movement, and the nested random effects of site and family group. Then MEMs were performed including both the main effects of Flanker scores and ADHD diagnosis, plus an interaction between Flanker scores and ADHD diagnosis (same covariates as above.) This was repeated using ADHD symptom severity score. Models were created with each intra- and inter-network FC value as the outcome, totaling 78 models per hypothesis (Bonferroni corrected alpha=0.00065).
Results:
FC between the retrosplenial-temporal network and the cingulo-parietal, sensorimotor-mouth, visual, and default mode network (DMN) was significantly associated with inhibitory attention. There were also significant associations within the cingulo-parietal network, and between the sensorimotor-mouth network and both the fronto-parietal and sensorimotor-hand networks (figure 1A). ADHD group was significantly associated with FC between the auditory and salience networks; and cingulo-opercular and ventral attention networks (figure 1B). The dimensional ADHD symptom measure showed significant associations with FC between the visual and cingulo-parietal networks; DMN and cingulo-parietal networks; and dorsal attention and DMN(figure 1C).
The interaction between Flanker scores and ADHD (group or symptom severity) was not significant associated with FC between or within networks in any of the MEMs.
The interaction between Flanker scores and ADHD (group or symptom severity) was not significant associated with FC between or within networks in any of the MEMs.
·Figure 1
Conclusions:
We found that there were network-level FC associations with inhibitory attention, current ADHD diagnosis, and ADHD symptom severity. However, the networks implicated were different. Furthermore, network level FC is not significantly associated with the interaction between inhibitory attention and ADHD diagnosis, or inhibitory attention and ADHD symptom severity. This supports the hypothesis that inhibitory attention lies along a spectrum of ability and likely has similar neural correlates in children with and without an ADHD (Kelly et al. 2008, Vandewouw et al. 2023). This has implications for understanding inhibitory attention ability, as well as the neural correlates of ADHD symptoms.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Higher Cognitive Functions:
Executive Function, Cognitive Control and Decision Making 2
Keywords:
Attention Deficit Disorder
Cognition
Development
FUNCTIONAL MRI
PEDIATRIC
Pediatric Disorders
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
Provide references using APA citation style.
Cordova MM, Antovich DM, Ryabinin P, Neighbor C, Mooney MA, Dieckmann NF, Miranda-Dominguez O, Nagel BJ, Fair DA, Nigg JT. Attention-Deficit/Hyperactivity Disorder: Restricted Phenotypes Prevalence, Comorbidity, and Polygenic Risk Sensitivity in the ABCD Baseline Cohort. J Am Acad Child Adolesc Psychiatry. 2022 Oct;61(10):1273-1284. doi: 10.1016/j.jaac.2022.03.030.
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016). Generation and Evaluation of a Cortical Area Parcellation from Resting-State Correlations. Cerebral Cortex, 26(1), 288–303. https://doi.org/10.1093/cercor/bhu239
Kelly, A. M. C., Uddin, L. Q., Biswal, B. B., Castellanos, F. X., & Milham, M. P. (2008). Competition between functional brain networks mediates behavioral variability. NeuroImage, 39(1), 527–537. https://doi.org/10.1016/j.neuroimage.2007.08.008
Mueller, A., Hong, D. S., Shepard, S., & Moore, T. (2017). Linking ADHD to the Neural Circuitry of Attention. Trends in Cognitive Sciences, 21(6), 474–488. https://doi.org/10.1016/j.tics.2017.03.009
Vandewouw, M. M., Brian, J., Crosbie, J., Schachar, R. J., Iaboni, A., Georgiades, S., Nicolson, R., Kelley, E., Ayub, M., Jones, J., Taylor, M. J., Lerch, J. P., Anagnostou, E., & Kushki, A. (2023). Identifying Replicable Subgroups in Neurodevelopmental Conditions Using Resting-State Functional Magnetic Resonance Imaging Data. JAMA Network Open, 6(3). https://doi.org/10.1001/jamanetworkopen.2023.2066
Zelazo, P. D., Anderson, J. E., Richler, J., Wallner-Allen, K., Beaumont, J. L., & Weintraub, S. (2013). NIH toolbox cognition battery (CB): Measuring executive function and attention. Monogr Soc Res Child Dev, 78(4), 16–33. https://doi.org/10.1111/mono.12032