Epigenetic modifications and related resting state connectivity predict ADHD diagnosis and outcome
Poster No:
309
Submission Type:
Abstract Submission
Authors:
Valentine Chirokoff1, Jo Wrigglesworth1, Peter Daniel Fransquet2, Yen Ting Wong3, Jeffrey Craig3, Timothy Silk4
Institutions:
1Deakin University, Burwood, Victoria, 2Deakin University, Melbourne, Victoria, 3Deakin University, Geelong, Victoria, 4Deakin University, Melbourne, VIC
First Author:
Co-Author(s):
Introduction:
Epigenetic offers promising avenues to unravel how biological and environmental factors interact to shape clinical outcomes in Attention-Deficit Hyperactivity Disorder (ADHD)(Silk et al., 2022). This study adopts a longitudinal approach to investigate how epigenetic modifications during development is associated with brain functional connectivity patterns, contributing to ADHD diagnosis or presentation.
Methods:
Data from 150 controls and ADHD participants were collected across three waves, including MRI, DNA methylation profiles (Illumina EPIC from saliva samples), and clinical assessments. ADHD diagnosis and subtype classification (persistent, remittent, or control) were determined using Conners 3 ADHD Index and the DISC-IV diagnostic interview (Conners, 2008; Shaffer et al., 2000). Resting-state fMRI data were acquired with multiband-accelerated EPI sequences (TR=1500ms, voxel size=2.5mm³, acquisition time=6:33min), alongside T1-weighted anatomical scans (TR=2530ms, voxel size=0.9mm³). Preprocessing was conducted using fMRIPrep (Esteban et al., 2019) with visual and automated quality control. Brain connectivity was analysed via independent component analysis (ICA) in the Conn toolbox (Nieto-Castanon & Whitfield-Gabrieli, 2021), identifying 20 components. Genome-wide methylation analyses were performed using linear mixed models in R (R Core Team, 2021) to identify CpG sites linked to ADHD diagnosis while adjusting for sex, batch effects, and epithelial cell count. Associations between significant baseline methylation sites (M values) and brain connectivity were tested using general linear models with random field theory (RFT) correction (Worsley et al., 1996). Significant clusters were used as seeds in seed-to-voxel analyses with similar contrast and correction, and logistic regression models assessed associations between connectivity patterns and diagnostic status. Multinomial regression was employed to evaluate predictions of diagnostic subgroups at follow-up.
Results:
After exclusion, analyses included 134 participants at baseline (88 males, mean age 10.4, 48 ADHD diagnosis) and follow up (mean age 13.2, 60 ADHD diagnosis including 35 remittent). DNA methylation level at site cg11124426 was associated with ADHD diagnosis (p=0.034, FDR-corrected) while methylation at site cg26901352 (p=0.002, FDR-corrected) and cg17657037 (p=0.013, FDR-corrected) showed significant outcomes x time interactions. No associations were found between methylation level at two sites and brain connectivity at baseline. Methylation level at site cg11124426 was linked to the connectivity of two clusters while adjusting for age and sex: (1) the right lateral occipital cortex (inferior) and temporo-occipital regions (middle and inferior temporal gyri), and (2) the left lateral occipital cortex (inferior). Seed-to-voxel analyses revealed connectivity patterns with seven regions in the frontal (anterior cingulate, precentral gyrus, and subcallosal), parietal (precuneus, postcentral gyrus), and occipital (cuneal, lateral superior) cortices (Figure 1). Connectivity between left fronto-parietal cluster (precuneus, posterior cingulate, postcentral, precentral gyri) and right temporo-occipital cluster was associated with higher odds of ADHD diagnosis at current wave (odds ratio= 13.73, p=0.008) and with persistent ADHD classification at follow-up compared to remittent (odds ratio= 0.338, p=0.001) or control groups (odds ratio= 0.578, p=0.023) while adjusting for age, sex and methylation level at cg11124426.
·Figure 1: Left, Anterior and Posterior views of clusters linked to methylation level in site cg11124426 at baseline while controlling for Age and Sex
Conclusions:
Our findings revealed epigenetic state linked to clinical outcomes at site cg11124426, annotated with the protein tyrosine phosphatase non-receptor type 18 (PTPN18) gene which has been associated with autistic behaviour, language and intellectual abilities (Lyulcheva-Bennett et al., 2023). Related connectivity patterns in dorsal attentional and visual networks also hold predictive value for long-term outcomes, distinguishing persistent ADHD from remittent or control groups.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Genetics:
Genetics Other
Modeling and Analysis Methods:
fMRI Connectivity and Network Modeling 2
Keywords:
Attention Deficit Disorder
MRI
Multivariate
Phenotype-Genotype
Other - epigenetic
1|2Indicates the priority used for review
Abstract Information
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Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., & others. (2019). fMRIPrep: A robust preprocessing pipeline for functional MRI. Nature Methods, 16(1), 111–116.
Lyulcheva-Bennett, E., Genomics England Research Consortium, & Bennett, D. (2023). A retrospective analysis of phosphatase catalytic subunit gene variants in patients with rare disorders identifies novel candidate neurodevelopmental disease genes. Frontiers in Cell and Developmental Biology, 11, 1107930. https://doi.org/10.3389/fcell.2023.1107930
Nieto-Castanon, A., & Whitfield-Gabrieli, S. (2021). CONN functional connectivity toolbox (RRID: SCR_009550), Version 21. Series CONN Functional Connectivity Toolbox (RRID: SCR_009550), Version, 21.
R Core Team. (2021). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. https://www.R-project.org/
Shaffer, D., Fisher, P., Lucas, C. P., Dulcan, M. K., & Schwab-Stone, M. E. (2000). NIMH Diagnostic Interview Schedule for Children Version IV (NIMH DISC-IV): Description, differences from previous versions, and reliability of some common diagnoses. Journal of the American Academy of Child & Adolescent Psychiatry, 39(1), 28–38.
Silk, T., Dipnall, L., Wong, Y. T., & Craig, J. M. (2022). Epigenetics and ADHD. In S. C. Stanford & E. Sciberras (Eds.), New Discoveries in the Behavioral Neuroscience of Attention-Deficit Hyperactivity Disorder (Vol. 57, pp. 269–289). Springer International Publishing. https://doi.org/10.1007/7854_2022_339
Worsley, K. J., Marrett, S., Neelin, P., Vandal, A. C., Friston, K. J., & Evans, A. C. (1996). A unified statistical approach for determining significant signals in images of cerebral activation. Human Brain Mapping, 4(1), 58–73.