Investigating diffusion MRI biomarkers and structural connectivity in adults with ADHD
Poster No:
345
Submission Type:
Abstract Submission
Authors:
Emma Uren1, Maryam Tayebi2, Xirui Zhao1, Eryn Kwon3, Christian John Saludar4, Justin Fernandez5, Vickie Shim3
Institutions:
1Auckland Bioengineering Institute, University of Auckland, Auckland, Outside US, 2Mātai Medical Research Institute, Gisborne, Outside America, 3Auckland Bioengineering Institute, University of Auckland, Auckland, Outside America, 4University of Auckland, Auckland City, Outside America, 5Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand
First Author:
Co-Author(s):
Vickie Shim, PhD
Auckland Bioengineering Institute, University of Auckland
Auckland, Outside America
Auckland Bioengineering Institute, University of Auckland
Auckland, Outside America
Introduction:
Attention Deficit and Hyperactivity Disorder (ADHD) affects 5.9% of youth and 2.5% of adults worldwide (Faraone, 2021) and is associated with an increased risk of adverse life outcomes (Fairbank, 2023). This study aims to deepen our understanding of its neurobiological mechanisms to aid with early diagnosis.
Methods:
The study recruited individuals diagnosed with ADHD (n=22, 33.6±8.5 y) and neurotypical controls (n=21, 23.3±8.1 y). Brain scans were acquired on a 3.0T GE MRI scanner. Multi-shell diffusion MRI data was collected (b-values (gradient directions) = 0,1000,2000,3000(4,15,15,20); isotropic voxel size 2mm3).Diffusion scans were preprocessed and tensor maps were generated using FSL (Smith, 2023). Tractography was performed with TractSeg. Tract-based spatial statistics (TBSS) and the randomise tool were used to compare diffusion maps between groups (unpaired t-tests, 5000 permutations, corrected for family-wise error rate, controlled for age and sex).
Next, mesh morphing (Tayebi, 2024) and principal component analysis (PCA) were applied on identified significant tracts. Combining multiple DTI maps with tract meshes allows for advanced statistical testing and enhanced identification of individual differences.Connectivity analysis was performed using FreeSurfer and MRtrix3 to explore graph theory network metrics in the global, topological, and nodal level. A Welch-t-test was used to compared group-wise metrics.
Next, mesh morphing (Tayebi, 2024) and principal component analysis (PCA) were applied on identified significant tracts. Combining multiple DTI maps with tract meshes allows for advanced statistical testing and enhanced identification of individual differences.Connectivity analysis was performed using FreeSurfer and MRtrix3 to explore graph theory network metrics in the global, topological, and nodal level. A Welch-t-test was used to compared group-wise metrics.
Results:
Compared to neurotypicals, TBSS revealed significant clusters (p<0.05) where ADHD individuals showed lower fractional anisotropy (FA), higher mean diffusivity (MD), and higher radial diffusivity (RD) (Figure 1A), primarily in the left hemisphere.Plotting PC1 against PC2 for significant tracts demonstrated clustering of ADHD and control groups (Figure 1B), with PC1-4 explaining >80% of variation, suggesting distinct patterns of white matter microstructure in ADHD. The most significant tracts included the left inferior fronto-occipital fasciculus (FA: 15.70%, MD: 24.00%, RD: 28.27% of tract; cohen's d: -0.40, 0.61, 0.34), left thalamo-prefrontal tract (FA: 15.96%, MD: 2.05%, RD: 24.84% of tract; cohen's d: -0.28, 0.34, 0.24), and arcuate fascicle (FA: 9.44%, MD: 18.89%, RD: 27.01% of tract; cohen's d: -0.44, 0.60, 0.37), with medium effect sizes (Figure 1C).
Global connectivity analysis showed no overall difference between groups (Figure 2A); however, ADHD had typically lower self-connectivity, local efficiency and local clustering, while node strength was typically higher (Figure 2C). There were no significant differences in cortical-cortical or subcortical-subcortical connectivity, but cortical-subcortical connectivity approached significance (p=0.07, Figure 2B).
Global connectivity analysis showed no overall difference between groups (Figure 2A); however, ADHD had typically lower self-connectivity, local efficiency and local clustering, while node strength was typically higher (Figure 2C). There were no significant differences in cortical-cortical or subcortical-subcortical connectivity, but cortical-subcortical connectivity approached significance (p=0.07, Figure 2B).
Conclusions:
Results show that ADHD individuals exhibit significant alterations in white matter microstructure, especially in the left hemisphere. Lower FA and higher MD and RD suggest disrupted collinearity of white matter in key tracts for functions associated with ADHD like visuospatial attention, executive control and emotion regulation. Left hemisphere dominance supports theories of neural lateralisation in ADHD (Helfer, 2020). Lower FA is a frequently observed tract alteration in ADHD (Parlatini, 2023; ElShahawi, 2021; Wu, 2016; Onnink, 2015). PCA clustering of ADHD and NT underscores the future potential to identify individual differences. Combining interdependent diffusion variables enhances the detection of subtle microstructural abnormalities, offering a more robust assessment of white matter organisation.
The consistent reduction in local clustering, efficiency, and self-connectivity in parietal, visual, and sensorimotor regions suggests disrupted local network organization in ADHD. These findings highlight local network inefficiencies as a key neural basis underlying core symptoms of ADHD, including inattention, distractibility, and hyperactivity, particularly in brain regions involved in attention, perception, and motor control.
The consistent reduction in local clustering, efficiency, and self-connectivity in parietal, visual, and sensorimotor regions suggests disrupted local network organization in ADHD. These findings highlight local network inefficiencies as a key neural basis underlying core symptoms of ADHD, including inattention, distractibility, and hyperactivity, particularly in brain regions involved in attention, perception, and motor control.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
Diffusion MRI Modeling and Analysis 2
Novel Imaging Acquisition Methods:
Diffusion MRI
Physiology, Metabolism and Neurotransmission:
Physiology, Metabolism and Neurotransmission Other
Keywords:
Attention Deficit Disorder
Data analysis
Morphometrics
MRI
STRUCTURAL MRI
WHITE MATTER IMAGING - DTI, HARDI, DSI, ETC
1|2Indicates the priority used for review
Abstract Information
By submitting your proposal, you grant permission for the Organization for Human Brain Mapping (OHBM) to distribute your work in any format, including video, audio print and electronic text through OHBM OnDemand, social media channels, the OHBM website, or other electronic publications and media.
The Open Science Special Interest Group (OSSIG) is introducing a reproducibility challenge for OHBM 2025. This new initiative aims to enhance the reproducibility of scientific results and foster collaborations between labs. Teams will consist of a “source” party and a “reproducing” party, and will be evaluated on the success of their replication, the openness of the source work, and additional deliverables. Click here for more information. Propose your OHBM abstract(s) as source work for future OHBM meetings by selecting one of the following options:
Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
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.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Fairbank, R. American Psychological Association. (2023). An ADHD diagnosis in adulthood comes with challenges and benefits. Monitor on Psychology. https://www.apa.org/monitor/2023/03/adult-adhd-diagnosis
Faraone, S. V. (2021). The World Federation of ADHD International Consensus Statement: 208 evidence-based conclusions about the disorder. Neuroscience & Biobehavioral Reviews, 128, 789–818. https://doi.org/10.1016/j.neubiorev.2021.01.022
Helfer, B. (2020). Lateralization of attention in adults with ADHD: Evidence of pseudoneglect. European Psychiatry, 63(1). https://doi.org/10.1192/j.eurpsy.2020.68
Onnink, A. M. (2015). Deviant white matter structure in adults with attention-deficit/hyperactivity disorder points to aberrant myelination and affects neuropsychological performance. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 63, 14–22. https://doi.org/10.1016/j.pnpbp.2015.04.008
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. https://doi.org/10.1038/s41380-023-02173-1
Smith, S. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23. https://doi.org/10.1016/j.neuroimage.2004.07.051
Tayebi, M. (2024). Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts. Brain Communications, 6(2). https://doi.org/10.1093/braincomms/fcae027
Wu, Z.-M. (2016). White matter microstructural alterations in children with ADHD: Categorical and dimensional perspectives. Neuropsychopharmacology, 42(2), 572–580. https://doi.org/10.1038/npp.2016.223