Distinct Control Energy Alterations in Two ADHD Subtypes Linked to Working Memory
Poster No:
291
Submission Type:
Abstract Submission
Authors:
Hang Yang1,2, Xiaoyu Xu1,2,3, Zaixu Cui1,2
Institutions:
1Beijing Institute for Brain Research, Chinese Academy of Medical, Beijing, China, 2Chinese Institute for Brain Research, Beijing, China, 3State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
First Author:
Hang Yang
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Co-Author(s):
Xiaoyu Xu
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research|State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University
Beijing, China|Beijing, China|Beijing, China
Zaixu Cui
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Beijing Institute for Brain Research, Chinese Academy of Medical|Chinese Institute for Brain Research
Beijing, China|Beijing, China
Introduction:
Children with attention-deficit/hyperactivity disorder (ADHD) experience not only attention difficulties but also impairments in executive functions like working memory (Kasper, 2012). These behavioral deficits are thought to be associated with alterations in brain networks (Parlatini, 2023). However, how the structural brain network's capacity to support working memory is disrupted in children with ADHD remains relatively unknown. In this study, we applied network control theory (Gu, 2017; Cui, 2020) to a large dataset of children with ADHD and typically developing children to address this question.
Methods:
We included 2,629 typically developing (TD) children and 677 children with ADHD from the baseline Adolescent Brain Cognitive Development (ABCD) study. Diffusion magnetic resonance imaging (dMRI) data were preprocessed using qsiprep (Cieslak, 2021), and probabilistic tractography was used to construct a structural connectivity matrix for 252 brain regions. Edge weight was defined as the streamline count scaled by the inverse of the node volumes. Each brain region was mapped to an a priori network (Yeo, 2011). The group-level activation map for the n-back task was obtained from (Chaarani, 2021). Based on the structural connectivity network of each participant, we evaluated the control energy required to move each brain region from a baseline state to a target state with [2-back − 0-back] activation (Fig. 1a), and harmonized the control energy values using ComBat to reduce site effects (Pomponio, 2020).
The TD group's energy cost was used as a reference to construct a normative model. The test set consisted of 677 ADHD and 677 matched TD children, with the remaining 1,952 TD children as the training set. W-scores were calculated for each region in the test set to evaluate deviations from the normative model (Tetreault, 2020), adjusting for age, sex, handedness, head motion, total brain volume, and network strength (Fig. 1b). Next, we used spectral clustering to identify ADHD subtypes with distinct energy deviations across the whole brain. Finally, we compared each ADHD subtype with the TD test group at group, network, and regional levels, with Cohen's d used to calculate effect sizes for group differences.
The TD group's energy cost was used as a reference to construct a normative model. The test set consisted of 677 ADHD and 677 matched TD children, with the remaining 1,952 TD children as the training set. W-scores were calculated for each region in the test set to evaluate deviations from the normative model (Tetreault, 2020), adjusting for age, sex, handedness, head motion, total brain volume, and network strength (Fig. 1b). Next, we used spectral clustering to identify ADHD subtypes with distinct energy deviations across the whole brain. Finally, we compared each ADHD subtype with the TD test group at group, network, and regional levels, with Cohen's d used to calculate effect sizes for group differences.
Results:
We first compared all children with ADHD to the TD test group and found no significant differences (Fig. 2a−c). The largest effect size for the regional difference was Cohen's d = 0.15, which is consistent with previous findings from the ENIGMA-ADHD sample, which also reported a small effect size (Hoogman, 2019), suggesting that ADHD might be highly heterogeneous. Spectral clustering revealed two ADHD subtypes: Subtype 1 (N = 335) exhibited an overall decreased energy cost (Fig. 2d), whereas Subtype 2 (N = 342) showed the opposite pattern (Fig. 2g). Specifically, ADHD-subtype1 demonstrated reduced control energy in the somatomotor, dorsal attention, ventral attention, fronto-parietal, and subcortical networks, but increased energy in limbic and default mode networks (Fig. 2e, P < 0.05, FDR adjusted), while ADHD-subtype2 exhibited the inverse pattern (Fig. 2h). Additionally, ADHD-subtype2, with increased energy cost, also had more severe attention problems (t = 2.04, P = 0.042) and externalizing problems (t = 3.25, P = 0.001) compared to ADHD-subtype1.
Conclusions:
We identified two ADHD subtypes with opposing energy deviation patterns related to working memory, as well as distinct attention and externalizing problems. These findings suggest that the structural network in children with ADHD is heterogeneous, highlighting the need for individualized treatment in ADHD and other neurodevelopmental and psychiatric disorders.
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
Data analysis
White Matter
Other - structural brain network, network control theory
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cieslak, M. (2021). QSIPrep: an integrative platform for preprocessing and reconstructing diffusion MRI data. Nature Methods, 18(7), 775-778.
Cui, Z. (2020). Optimization of energy state transition trajectory supports the development of executive function during youth. elife, 9, e53060.
Gu, S. (2017). Optimal trajectories of brain state transitions. NeuroImage, 148, 305-317.
Hoogman, M. (2019). Brain Imaging of the Cortex in ADHD: A Coordinated Analysis of Large-Scale Clinical and Population-Based Samples. American Journal of Psychiatry, 176(7), 531-542.
Kasper, L.J. (2012). Moderators of working memory deficits in children with attention-deficit/hyperactivity disorder (ADHD): a meta-analytic review. Clinical Psychology Review, 32(7), 605-617.
Parlatini, V. (2023). White matter alterations in Attention-Deficit/Hyperactivity Disorder (ADHD): a systematic review of 129 diffusion imaging studies with meta-analysis. Molecular Psychiatry, 28(10), 4098-4123.
Pomponio, R. (2020). Harmonization of large MRI datasets for the analysis of brain imaging patterns throughout the lifespan. NeuroImage, 208, 116450.
Tetreault, A.M. (2020). Network localization of clinical, cognitive, and neuropsychiatric symptoms in Alzheimer's disease. Brain, 143(4), 1249-1260.
Yeo, B.T. (2011). The organization of the human cerebral cortex estimated by intrinsic functional connectivity. Journal of Neurophysiology, 106(3), 1125-1165.