Atypical brain functional gradient in children with attention-deficit/hyperactivity disorder
Poster No:
267
Submission Type:
Abstract Submission
Authors:
Weiyan Yin1, Weili Lin1
Institutions:
1UNC Chapel Hill, Chapel Hill, NC
First Author:
Co-Author:
Introduction:
Attention-deficit/hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in children in the United States (Danielson et al., 2024), and individuals with ADHD often struggle with controlling their behavior and maintaining attention. Developing effective methods to identify children at high risk for ADHD is therefore crucial. Recent connectome studies have identified a principal sensorimotor-association (S-A) gradient in brain functional organization, with sensory-motor regions and default network regions positioned at opposite ends, reflecting a functional spectrum from sensory-motor functions to abstract cognitive processes (Margulies et al., 2016). Notably, individuals with ADHD often exhibit sensory and cognitive impairments, along with altered interactions among the default, attention, and control networks (Pereira-Sanchez & Castellanos, 2021), suggesting potential alterations in the S-A gradient. We hypothesized that the S-A gradient is altered in children with ADHD and that these alterations may be associated with ADHD severity.
Methods:
The ADHD-200 dataset (Peking University site), comprising 92 children with ADHD and 131 typically developing children (TDC), was analyzed. Clinical assessments were conducted using the ADHD Rating Scale IV. Resting-state fMRI data were processed using a standard pipeline (Yin et al., 2022) and normalized to standard space with ANTS registration. Mean time series data for each cortical region were extracted using the Schaefer1000 atlas (Schaefer et al., 2018).
Gradient analysis was conducted following the procedure outlined by Margulies et al. Specifically, a functional connectivity (FC) matrix was generated for each subject. The top 10% of connections for each region were retained, and cosine similarity between regions was computed. Diffusion map embedding was then applied to identify functional gradient components. The resulting gradient maps were aligned across individuals using iterative Procrustes rotation.
Three global metrics were calculated: gradient explanation ratio (the dominance of the gradient in the FC matrix), gradient range (the difference between the highest and lowest values), and spatial variation (the standard deviation of gradient scores across regions) (Xia et al., 2022). Statistical differences in these global metrics and regional gradients between the ADHD and TDC groups were evaluated using a linear regression model. Additionally, correlations between gradient scores and the ADHD symptom index were analyzed for each brain region. Both analyses controlled for age and sex.
Gradient analysis was conducted following the procedure outlined by Margulies et al. Specifically, a functional connectivity (FC) matrix was generated for each subject. The top 10% of connections for each region were retained, and cosine similarity between regions was computed. Diffusion map embedding was then applied to identify functional gradient components. The resulting gradient maps were aligned across individuals using iterative Procrustes rotation.
Three global metrics were calculated: gradient explanation ratio (the dominance of the gradient in the FC matrix), gradient range (the difference between the highest and lowest values), and spatial variation (the standard deviation of gradient scores across regions) (Xia et al., 2022). Statistical differences in these global metrics and regional gradients between the ADHD and TDC groups were evaluated using a linear regression model. Additionally, correlations between gradient scores and the ADHD symptom index were analyzed for each brain region. Both analyses controlled for age and sex.
Results:
Fig. 1a shows the principal S-A gradient maps for the TDC and ADHD groups, demonstrating high global similarity but notable spatial differences. Case-control analyses revealed that the ADHD group had a significantly lower explanation ratio, narrower gradient range, and reduced spatial variation compared to the TDC group (Fig. 1b), suggesting a less differentiated topology between primary and association regions in ADHD. Regionally, 164 areas exhibited significant differences between the groups (p < 0.05, FDR corrected). Specifically, the ADHD group showed lower gradient scores in regions of the default and visual networks but higher scores in regions of the somatomotor and ventral attention networks (Fig. 2a). Additionally, functional gradient scores in 56 regions were significantly correlated with the ADHD symptom index (Fig. 2b, p < 0.05, FDR corrected). Positive associations were primarily observed in regions of the ventral attention network, while negative associations were found in regions of the default network.
Conclusions:
This study highlights alterations in the principal S-A gradient in children with ADHD and its potential association with symptom severity, providing new insights into the hierarchical network mechanisms underlying functional impairments and suggesting potential biomarkers for the diagnosis and monitoring of disease severity.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Keywords:
Attention Deficit Disorder
Computational Neuroscience
Data analysis
FUNCTIONAL MRI
PEDIATRIC
Pediatric Disorders
Other - Functional gradient
1|2Indicates the priority used for review
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Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., Jefferies, E., & Smallwood, J. (2016). Situating the default-mode network along a principal gradient of macroscale cortical organization. Proc Natl Acad Sci U S A, 113(44), 12574-12579. https://doi.org/10.1073/pnas.1608282113
Pereira-Sanchez, V., & Castellanos, F. X. (2021). Neuroimaging in attention-deficit/hyperactivity disorder. Curr Opin Psychiatry, 34(2), 105-111. https://doi.org/10.1097/YCO.0000000000000669
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X. N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018). Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex, 28(9), 3095-3114. https://doi.org/10.1093/cercor/bhx179
Xia, Y., Xia, M., Liu, J., Liao, X., Lei, T., Liang, X., Zhao, T., Shi, Z., Sun, L., Chen, X., Men, W., Wang, Y., Pan, Z., Luo, J., Peng, S., Chen, M., Hao, L., Tan, S., Gao, J. H., . . . He, Y. (2022). Development of functional connectome gradients during childhood and adolescence. Sci Bull (Beijing), 67(10), 1049-1061. https://doi.org/10.1016/j.scib.2022.01.002
Yin, W., Li, T., Mucha, P. J., Cohen, J. R., Zhu, H., Zhu, Z., & Lin, W. (2022). Altered neural flexibility in children with attention-deficit/hyperactivity disorder. Molecular Psychiatry, 27(11), 4673-4679. https://doi.org/10.1038/s41380-022-01706-4