ADHD Classifiers Based on GM-WM Structural Connectivity Couplings and Transcriptional Signatures
Presented During:
Tuesday, June 25, 2024: 12:00 PM - 1:15 PM
COEX
Room:
Hall D 2
Poster No:
332
Submission Type:
Abstract Submission
Authors:
Nanfang Pan1, Yajing Long1, Ying Chen1, Qiyong Gong1
Institutions:
1West China Hospital of Sichuan University, Chengdu, China
First Author:
Co-Author(s):
Introduction:
Attention-Deficit/Hyperactivity Disorder (ADHD) stands as a complex neurodevelopmental disorder, drawing considerable focus in the realm of neuroimaging psychiatry. While aberrations in the neural mechanisms of both brain gray matter and white matter have been extensively pinpointed, the intricate patterns of their structural connectivity coupling and the concurrent gene expression profiles continue to elude comprehensive understanding. Herein, we established machine-learning classifiers based on Gray-White Matter Structural Connectivity Coupling (GWSC) patterns, with a parallel exploration to unravel the underlying transcriptomes.
Methods:
T1-weighted and diffusion-weighted MRI data were obtained from a cohort of children with ADHD (n = 83) and typically developing children (n = 89). Gray matter covariance networks and white matter connectivity networks were constructed using the Kullback-Leibler divergence similarity measure and probabilistic tractography respectively. We computed the strength of their regional coupling as we termed GWSC coupling. To individually classify ADHD children from typically developing controls, we established the machine-learning pipeline in pursuit of clinical applicability. Four configure learning algorithms, namely linear support vector machine (SVM), Gaussian-kernel SVM, k-nearest neighbors, and decision tree were employed to build up fitting models. Finally, we extracted gene expression data from the Allen Human Brain Atlas and performed partial least squares regression analysis to bridge the gap between abnormal GWSC coupling patterns and microarray-based transcriptomes, and gene enrichment analysis was conducted to interpret the inference of enriched gene ontology biological processes.
Results:
All four classifiers we employed distinguished children with ADHD with more than 75% accuracy, wherein the Gaussian-kernel SVM enables the highest accuracy of 82.6% (95%CI: 78.4%-86.8%). Sensitivity and specificity for the discrimination were 79.5% and 85.4% respectively. In this model, the GWSC couplings in the ventromedial prefrontal cortex provided the greatest contribution to the classifier. After correcting for enrichment terms (pFDR<.05) and discarding discrete enrichment clusters, the top significant gene ontology biological process is "neuron projection development".
Conclusions:
By constructing GWSC coupling patterns in ADHD, we developed machine-learning classifiers with acceptable predictive performance, with the ventromedial prefrontal cortex severed as a central substrate. Our transcriptional findings reveal the involvement of neuron projection in the psychopathological processes of GWSC patterns in ADHD. We uncovered GWSC coupling phenotypes in ADHD and identified their transcriptional signatures, facilitating a more comprehensive understanding of ADHD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Classification and Predictive Modeling
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
White Matter Anatomy, Fiber Pathways and Connectivity
Keywords:
Attention Deficit Disorder
Computational Neuroscience
Machine Learning
STRUCTURAL MRI
Other - Brain Connectome
1|2Indicates the priority used for review
·Figure 1. Schematic Overview of the Analytical Procedures for GWSC Coupling and Following Analyses.
·Figure 2. Transcriptional Profiles Underlying Abnormal GWSC Coupling Patterns.
Provide references using author date format
2. Gu Z, (2021). Heritability and interindividual variability of regional structure-function coupling. Nat Commun. 12:1–12.
3. Baum GL, (2020). Development of structure–function coupling in human brain networks during youth. Proc Natl Acad Sci USA. 117:771–778.
4. Koutsouleris N, (2018). Prediction Models of Functional Outcomes for Individuals in the Clinical High-Risk State for Psychosis or with Recent-Onset Depression: A Multimodal, Multisite Machine Learning Analysis. JAMA Psychiatry. 75:1156–1172.
5. Li J, (2021). Cortical structural differences in major depressive disorder correlate with cell type-specific transcriptional signatures. Nat Commun. 12.