Characterizing Heterogeneity in Brain Structural Covariance Network among Children with ADHD
Poster No:
299
Submission Type:
Abstract Submission
Authors:
Yajing Long1, Nanfang Pan2, Qiuxing Chen1, Qiyong Gong1
Institutions:
1West China Hospital of Sichuan University, Chengdu, China, 2Monash University, Melbourne, Victoria
First Author:
Co-Author(s):
Introduction:
The heterogeneity of attention-deficit/hyperactivity disorder (ADHD) poses challenges to precision medicine. ADHD is associated with abnormal inter-regional structural covariance indicating the disruption of brain maturation. However, previous research focused on group-level structural covariance abnormalities, neglecting interindividual variability. Therefore, we aim to identify individualized structural covariance abnormalities using an individualized differential structural covariance network (IDSCN) analysis (Han, Xu, et al., 2023; Han, Zheng, et al., 2023; Liu et al., 2021), and to cluster ADHD children based on IDSCN.
Methods:
T1-weighted MRI data from six sites, including four from the publicly available ADHD-200 Consortium (http://fcon_1000.projects.nitrc.org/indi/adhd200/) dataset and the Autism Brain Imaging Data Exchange (ABIDE) I and II collections (https://fcon_1000.projects.nitrc.org/indi/abide/), involved 460 children and adolescents with ADHD and 718 typically developing (TD) controls. The voxel-based morphometry processing of T1-weighted structural data was performed using the CAT12 (https://neuro-jena.github.io/cat/). The automated anatomical labeling-90 (AAL-90) atlas was used to identify brain regions and assign them to six anatomical classifications (including frontal, prefrontal, parietal, temporal, subcortical, and occipital lobes). An IDSCN was constructed to quantify how each patient's structural covariance edges deviated from those of the controls. Specifically, the reference structural covariance network (SCN) was created by calculating Pearson correlations of gray matter volumes between all pairs of brain regions in the control group, while a perturbed SCN was constructed by adding each ADHD subject to the control group. For each ADHD subject, the difference between the perturbed and reference SCN was converted to a Z-score, forming individual IDSCN tested for significance (P < 0.05, Bonferroni-corrected). Hierarchical clustering identified ADHD subtypes based on the most shared differential covariance edges among patients (the top 100 differential edges) (Han, Zheng, et al., 2023). To further inquire about the reproducibility of subtyping, we adopted another brain atlas (Desikan Killiany atlas, 68 parcels) to perform the same IDSCN and clustering analysis. An overview of the analytic approach is shown in Figure 1.
·Figure 1. Flowchart for Individual Differential Structural Covariance Network (IDSCN) analysis.
Results:
Children and adolescents with ADHD showed substantial heterogeneity in the distribution of differential structural covariance edges where the top 100 differential edges connected prefrontal and temporal/subcortical regions, and regions within the prefrontal/temporal cortices (Figure 2a&b). The most affected brain regions included the right orbitofrontal cortex, bilateral middle temporal gyrus, left pallidum, left insula, and left putamen. Clustering using the top 100 differential edges as features identified two robust subtypes of ADHD (n = 368 for subtype 1, n = 92 for subtype 2) with significant differences in hyperactivity/impulsivity scores (p = .013, t = 2.503). Of the top 100 differential edges, 66 varied significantly between subtypes (Figure 2c). The subtype with more severe hyperactivity and impulsivity symptoms exhibited widespread decreases in within- and between-systems covariance, compared to another subtype. The validation analysis using another brain atlas identified two robust subtypes of ADHD with similar subgroup difference patterns in IDSCNs and hyperactivity/impulsivity scores (p = 0.002, t = -3.140; Figure 2d), confirming the reproducibility.
·Figure 2. Children and adolescents with ADHD cluster into two subtypes based on IDSCN.
Conclusions:
This study, leveraging a large multi-center sample, reveals individualized structural covariance aberrance primarily in the prefronto-temporal and prefronto-subcortical circuits in ADHD. The identification of two distinct ADHD subtypes highlights the clinical heterogeneity of this disorder and its underlying neurobiological mechanisms, which may pave the way for precision medicine.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural)
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
Novel Imaging Acquisition Methods:
Anatomical MRI 2
Keywords:
Attention Deficit Disorder
Data analysis
Machine Learning
STRUCTURAL MRI
1|2Indicates the priority used for review
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Provide references using APA citation style.
Han, S., et al. (2022). Resolving heterogeneity in depression using individualized structural covariance network analysis. Psychological Medicine, 53(11), 5312-5321.
Liu, Z. W., et al. (2021). Resolving heterogeneity in schizophrenia through a novel systems approach to brain structure: individualized structural covariance network analysis. Molecular Psychiatry, 26(12), 7719-7731.