Brain Activity Dynamics in Children with and without Attention-Deficit Hyperactivity Disorder (ADHD)
Poster No:
427
Submission Type:
Abstract Submission
Authors:
Marie Hédo1, Louisa Schilling2, Parker Singleton3, Keith Jamison2, Amy Kuceyeski2
Institutions:
1Weill Cornell Medicine, Ithaca, NY, 2Weill Cornell Medicine, New York City, NY, 3Weill Cornell Medicine, New York, NY
First Author:
Co-Author(s):
Introduction:
Attention-deficit/Hyperactivity Disorder (ADHD) is one of the most common neurodevelopmental conditions, however achieving conclusive evidence on neural correlates of ADHD has proven challenging. Additionally, the disorder disproportionally affects boys compared to girls, and there exist sex differences in their symptom profile, behavioral expression, and risk of comorbidities. However, it is unclear whether sex-specific neural correlates underlie sex differences in the disorder presentation. Here, the associations between functional brain activity dynamics and energy landscapes and ADHD symptoms across boys and girls were assessed in a population-based sample of children using network control theory (NCT) tools and multimodal imaging data. Finally, we studied whether comorbid internalizing and/or externalizing symptoms alter the energy landscape.
Methods:
We used data from a subset of individuals (N = 2226, 1200 girls, age = 10-11 years) from the Adolescent Brain Cognitive Development (ABCD) study. Pre-processed resting-state fMRI time-series were parcellated into 86 regions by combining the Desikan-Killiany gyral atlas (68 regions; Desikan et al., 2006) and 18 subcortical structures (Fischl et al., 2002). K-means clustering was applied to identify four distinct recurring patterns of brain activity in the time series (called brain states). An average 86-region structural connectome was reconstructed based on diffusion-weighted imaging through probabilistic tractography. We used NCT to calculate the minimum transition energy (TE) required to transition between the four states as described previously (Cornblath et al. (2020) and Singleton et al. (2022)). Average TEs across all state transitions were computed at regional and functional network (Yeo et al., 2011) levels. Network TEs were computed by averaging the regional TEs of all the regions assigned to that network. We used scores on the attention-deficit/ hyperactivity problems DSM-oriented scale from the child behavior checklist completed by parents or guardians to assess ADHD symptoms. The associations between TEs, ADHD symptoms, and sex were analyzed by general linear models (GLMs) with sex, ADHD scores, and their interaction as outcome predictors and age, stimulant use, handedness, family socioeconomic status, scanner type, and frame-wise displacement as covariates. False discovery rate correction was applied to all p-values. We grouped the participants into high and low ADHD (cutoff score of 5) and conducted a principal component analysis (PCA) on all six DSM-oriented problem scales to assess comorbid symptomology profiles and their effect on energy demands. Associations between the principal component (PC) scores of behavior and TE were analyzed by additional GLMs.
Results:
The GLM for network-level TE revealed that girls had higher TEs in the dorsal attention, ventral attention, and limbic network. ADHD was not associated with differences in TEs in any of the networks. However, a positive association with ADHD was found in the left banks of the superior temporal sulcus in the regional TE analysis.
The PCA of behavioral scores resulted in three components, summarized as both high externalizing and internalizing symptoms (PC1), high externalizing symptoms (PC2), and high internalizing symptoms (PC3). PC3 was positively associated with TE in default mode network (DMN) regions. No associations between PC1 and PC2 and network, or regional TE were observed.
The PCA of behavioral scores resulted in three components, summarized as both high externalizing and internalizing symptoms (PC1), high externalizing symptoms (PC2), and high internalizing symptoms (PC3). PC3 was positively associated with TE in default mode network (DMN) regions. No associations between PC1 and PC2 and network, or regional TE were observed.
Conclusions:
The control energy required to move through the brain's state space differs at a network level between girls and boys and highlights the importance of sex-specific analysis. Additionally, ADHD symptomatology is associated with region-specific increases in control energy. Only the internalizing symptoms in children with high ADHD scores were associated with an increase in TE in the DMN, indicating that the general psychopathology symptom profile has some implications in brain dynamics and energetic needs.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Lifespan Development:
Early life, Adolescence, Aging
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Attention Deficit Disorder
Computational Neuroscience
Data analysis
Development
DISORDERS
FUNCTIONAL MRI
Open Data
Pediatric Disorders
Tractography
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Provide references using author date format
Desikan, R. S. (2006), ‘An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest’, NeuroImage, 31(3), 968–980.
Salerno, L. (2020), ‘Neuromodulation in Attention- Deficit/Hyperactivity Disorder: Toward a Precision Psychiatry approach’, In Springer eBooks (pp. 107–122).
Fischl, B. (2002), ‘Whole brain segmentation: Automated Labeling of Neuroanatomical Structures in the Human Brain’, Neuron, 33(3), 341–355.
Murphy, A. B. (2017), ’A network neuroscience of neurofeedback for clinical translation’, Current Opinion in Biomedical Engineering, 1, 63–70.
Singleton, S. (2022), ‘Receptor-informed network control theory links LSD and psilocybin to a flattening of the brain’s control energy landscape’, Nature Communications, 13(1).
Yeo, B. T. (2011), ‘The organization of the human cerebral cortex estimated by intrinsic functional connectivity’, Journal of Neurophysiology, 106(3), 1125–1165.