ADHD dynamic dysregulation of dorsal attention, subcortical, and visual networks with Hyperband-fMRI
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Poster No:
1204
Submission Type:
Abstract Submission
Authors:
Ric John Ombid1, Paul Condron2, Gil Newburn2, Alan Wang1,3, Karen Waldie4,5, Samantha Holdsworth2,3,5, Hesamoddin Jahanian6, Justin Fernandez1,2,7
Institutions:
1Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand, 2Mātai Medical Research Institute, Gisborne, New Zealand, 3Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand, 4Faculty of Science, University of Auckland, Auckland, New Zealand, 5Centre for Brain Research, University of Auckland, Auckland, New Zealand, 6University of Washington, Seattle, WA, USA, 7Department of Engineering Science and Biomedical Engineering, Faculty of Engineering, University of, Auckland, New Zealand
First Author:
Co-Author(s):
Alan Wang
Auckland Bioengineering Institute, University of Auckland|Faculty of Medical and Health Sciences, University of Auckland
Auckland, New Zealand|Auckland, New Zealand
Auckland Bioengineering Institute, University of Auckland|Faculty of Medical and Health Sciences, University of Auckland
Auckland, New Zealand|Auckland, New Zealand
Karen Waldie
Faculty of Science, University of Auckland|Centre for Brain Research, University of Auckland
Auckland, New Zealand|Auckland, New Zealand
Faculty of Science, University of Auckland|Centre for Brain Research, University of Auckland
Auckland, New Zealand|Auckland, New Zealand
Samantha Holdsworth
Mātai Medical Research Institute|Faculty of Medical and Health Sciences, University of Auckland|Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand|Auckland, New Zealand
Mātai Medical Research Institute|Faculty of Medical and Health Sciences, University of Auckland|Centre for Brain Research, University of Auckland
Gisborne, New Zealand|Auckland, New Zealand|Auckland, New Zealand
Justin Fernandez
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Department of Engineering Science and Biomedical Engineering, Faculty of Engineering, University of
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Auckland Bioengineering Institute, University of Auckland|Mātai Medical Research Institute|Department of Engineering Science and Biomedical Engineering, Faculty of Engineering, University of
Auckland, New Zealand|Gisborne, New Zealand|Auckland, New Zealand
Introduction:
Attention Deficit Hyperactivity Disorder (ADHD) is characterized by inattention, hyperactivity, and impulsivity, and studies suggest that ADHD has altered functional connectivity in key brain networks. While prior research has predominantly focused on the three networks, such as the default mode (DMN), salience (SAL), and central executive (CEN) networks (Fair et al., 2010; Sidlauskaite et al., 2016; Tomasi & Volkow, 2012), other networks remain underexplored in the context of ADHD. Additionally, most studies have employed static analyses, limiting insights into the temporal dynamics of connectivity. This study addresses these gaps by utilizing Hyperband fMRI to investigate both static and dynamic within-network connectivity strengths between ADHD and neurotypical (NT) groups across multiple brain networks.
Methods:
We collected resting-state fMRI data from 31 ADHD and 26 NT participants using a 3T SIGNA Premier MRI scanner (TR=0.43 s, TE=0.02 s, flip angle=80°, voxel size=1.88×1.88 mm², multiband factor=8). High-resolution T1-weighted images were obtained for anatomical localization. Preprocessing included unwarping, motion correction, spatial normalization, smoothing, bandpass filtering (0.01–0.25 Hz) (Jahanian et al., 2019), and linear regression using the CONN software (Whitfield-Gabrieli & Nieto-Castanon, 2012). Functional connectivity analyses were conducted using the Pearson correlation between regions of interest defined by the Power atlas (Power et al., 2011) in Figure 1A. For static analysis, we derived the within and between network strengths correlation matrix from the Fisher Z-transformed Pearson correlation matrix (Figure 1B). Dynamic analysis was assessed using a sliding window approach (window length = 70 TRs, step = 1 TR, Gaussian = 3TR) (Allen et al., 2014; Hutchison et al., 2013). Afterwards, we derived the within network strength across window per subject and then counted for the positive within network values (Figure 1C). Group differences were analyzed using Welch's t-test (p < 0.05).
Results:
Static groupwise analysis of within-network connectivity strengths revealed no significant differences between ADHD and NT participants (Figure 2A). In contrast, dynamic analysis revealed significant temporal dysregulation in ADHD, characterized by reduced mean counts of within-network connectivity in the dorsal attention (DAN; p = 0.006), subcortial (SUB; p = 0.029), and visual (VIS; p = 0.046) networks (Figure 2B). These results aligned with Shappell et al. (2021), that Children with ADHD spend less time in anticorrelated states involving the default mode network and task-relevant networks. Moreover, Sidlauskaite et al. (2016) reported that SAL shows no within-network connectivity difference between groups and that DAN is greater for NT than ADHD. In addition, our results show significant dynamic disruptions in the SUB and VIS networks, highlighting the importance of studying how connectivity changes over time (Cai et al., 2018; Shappell et al., 2021). While previous research has predominantly focused on the DMN, SAL, and CEN networks, this study expands the investigation to other networks. Exploring these networks in dynamic contexts deepens our understanding of ADHD-related functional connectivity and may contribute to identifying novel biomarkers, improving disease characterization, and uncovering potential therapeutic targets.
Conclusions:
This study underscores the potential of dynamic rs-fMRI analysis in uncovering novel insights into pathological network changes associated with ADHD. Static analysis revealed consistent connectivity patterns between groups. In contrast, dynamic analysis captured temporal variability, showing that ADHD has dynamic dysregulation in the DAN, SUB, and VIS networks compared to NT participants. This reflects impaired flexibility in attention and sensory processing. Our approach offers a promising avenue for developing network-specific biomarkers for more accurate diagnoses of ADHD.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism) 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling
Keywords:
FUNCTIONAL MRI
Other - Attention Deficit Hyperactivity Disorder; Dynamic Analysis;
1|2Indicates the priority used for review
Abstract Information
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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.
Cai, W., et al., (2018). Aberrant Time-Varying Cross-Network Interactions in Children With Attention-Deficit/Hyperactivity Disorder and the Relation to Attention Deficits. Biological Psychiatry. Cognitive Neuroscience and Neuroimaging, 3(3), 263–273. https://doi.org/10.1016/J.BPSC.2017.10.005
Fair, D. A., et al., (2010). Atypical Default Network Connectivity in Youth with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 68(12), 1084–1091. https://doi.org/10.1016/j.biopsych.2010.07.003
Hutchison, R. M., et al., (2013). Dynamic functional connectivity: Promise, issues, and interpretations. NeuroImage, 80, 360–378. https://doi.org/10.1016/j.neuroimage.2013.05.079
Jahanian, H., et al., (2019). Advantages of short repetition time resting-state functional MRI enabled by simultaneous multi-slice imaging. Journal of Neuroscience Methods, 311, 122–132. https://doi.org/10.1016/j.jneumeth.2018.09.033
Power, J. D., et al., (2011). Functional Network Organization of the Human Brain. Neuron, 72(4), 665–678. https://doi.org/10.1016/j.neuron.2011.09.006
Shappell, H. M., et al., (2021). Children with attention-deficit/hyperactivity disorder spend more time in hyperconnected network states and less time in segregated network states as revealed by dynamic connectivity analysis. NeuroImage, 229, 117753. https://doi.org/10.1016/j.neuroimage.2021.117753
Sidlauskaite, J., et al., (2016). Altered intrinsic organisation of brain networks implicated in attentional processes in adult attention-deficit/hyperactivity disorder: a resting-state study of attention, default mode and salience network connectivity. European Archives of Psychiatry and Clinical Neuroscience, 266(4), 349–357. https://doi.org/10.1007/s00406-015-0630-0
Tomasi, D., & Volkow, N. D. (2012). Abnormal Functional Connectivity in Children with Attention-Deficit/Hyperactivity Disorder. Biological Psychiatry, 71(5), 443–450. https://doi.org/10.1016/j.biopsych.2011.11.003
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012). Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks. Brain Connectivity, 2(3), 125–141. https://doi.org/10.1089/brain.2012.0073