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Accessing Brain Network in ADHD Using Relative Phase Analysis

Poster No:

417

Submission Type:

Abstract Submission

Authors:

Younghwa Cha¹^,2, Joon-Young Moon¹^,2

Institutions:

¹Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Republic of Korea, ²Sungkyunkwan University, Suwon, Republic of Korea

First Author:

Younghwa Cha
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Republic of Korea|Suwon, Republic of Korea

Co-Author:

Joon-Young Moon
Center for Neuroscience Imaging Research, Institute for Basic Science|Sungkyunkwan University
Suwon, Republic of Korea|Suwon, Republic of Korea

E-Poster

Introduction:

Attention-deficit hyperactivity disorder (ADHD) is a psychological disorder that produces difficulties in focusing and controlling attention and behavior in a daily life of the affected individuals. To address the challenges caused by ADHD, it is crucial to receive an accurate diagnosis and appropriate treatment at their respective stages. However, despite many studies on the disorder, developing accessible and accurate quantitative diagnostic methods presents significant challenges[1]. Furthermore, the underlying causes of the disorder remain relatively unknown. As an attempt to develop effective diagnostic tools and understand the mechanism of the disorder, we analyzed the relative phase patterns of brain waves across the whole brain network to examine the unique properties of the ADHD groups against the control group using their electroencephalography (EEG) data.

Methods:

To effectively capture the differences in brain information processing between the ADHD group and the control group, we focused on the phase relationship. In a system, the phase of each signal refers to the specific timing or position of the signal within a periodic waveform. We hypothesized that differences in the phase directionality during resting states between the two groups can assist in diagnosing ADHD and uncovering its characteristics. To examine phase directionality, we calculated the relative phase by subtracting the global mean phase from each electrode's phase within the whole brain area. Relative phase demonstrates the phase-lead and -lag relationships of EEG signals at each time point, thus revealing temporal dynamics in the brain[2, 3]. We applied relative phase analysis to eyes closed and eyes open resting state EEG data from the Healthy Brain Network Biobank from the Child Mind Institute, constituting a total of 44 ADHD inattentive patients and 66 control individuals aged 11 and above[4].

Results:

we observe a robust switching pattern in the brain networks of both groups between top-down mode (where the higher-order hub regions phase-lead the peripheral sensory regions) and bottom-up mode (where the peripheral regions phase-lead hub regions). To compare the switching patterns between the two groups, we conducted k-means clustering. Based on our analysis, we found significant differences between the ADHD and the control group in mode 4 (the higher-order hub regions phase-lead), both in the eyes closed and eyes open states, at the p-value level of 0.001 from the student-t test (see Fig 1). In the ADHD group, the ratio of mode 4 is 7.98% higher and the dwelling time of mode 4 is 24.30% longer during the eyes closed state compared to the control group.
To examine the transition frequency of each mode, we created a transition matrix illustrating the average transition frequency (Hz) between modes (see Fig 2). In both the eyes-open and eyes-closed states, the ADHD group exhibited 57.92% and 32.05% more transitions from mode 4 to mode 4 (staying at mode 4), respectively, compared to the control group. Furthermore, the ADHD group showed fewer instances of staying in mode 2 and mode 3, which serve as pathways connecting mode 1 (the peripheral regions phase-leading) and mode 4 (the higher-order hub regions phase-leading). Our findings suggest that inattentive characteristics of the group may arise from the slower information processing represented by lower switching frequency between the modes.

Conclusions:

When considering the differences in ratio, dwelling time, and transition frequency between the two groups in mode 4, we can identify the potential utility of relative phase analysis as a physiological diagnostic tool for the ADHD inattentive subtype. In addition, it can be postulated that the prolonged stay at mode 4 is linked with the underlying mechanisms of ADHD-inattentive type. For future studies, the phase-lead/lag relationship across different brain areas for children under 10 years old and different ADHD subtypes will be completed.

Disorders of the Nervous System:

Neurodevelopmental/ Early Life (eg. ADHD, autism) ¹

Modeling and Analysis Methods:

EEG/MEG Modeling and Analysis ²

Methods Development

Task-Independent and Resting-State Analysis

Keywords:

Attention Deficit Disorder

Computational Neuroscience

Data analysis

Electroencephaolography (EEG)

^1|2Indicates the priority used for review

Provide references using author date format

[1] Brandeis, D., Loo, S. K., McLoughlin, G., Heinrich, H., & Banaschewski, T. (2018). Neurophysiology. Oxford textbook of attention deficit hyperactivity disorder, 82-93.
[2] Moon, J. Y., Lee, U., Blain-Moraes, S., & Mashour, G. A. (2015). 'General relationship of global topology, local dynamics, and directionality in large-scale brain networks'. PLoS computational biology, 11(4), e1004225.
[3] Moon, J. Y., Kim, J., Ko, T. W., Kim, M., Iturria-Medina, Y., Choi, J. H., ... & Lee, U. (2017). Structure shapes dynamics and directionality in diverse brain networks: mathematical principles and empirical confirmation in three species. Scientific reports, 7(1), 46606.
[4] Alexander, L. M., Escalera, J., Ai, L., Andreotti, C., Febre, K., Mangone, A., ... & Milham, M. P. (2017). An open resource for transdiagnostic research in pediatric mental health and learning disorders. Scientific data, 4(1), 1-26.