Brain connectivity differences between typically developed and ADHD subjects using Energy Landscape Analysis of resting-state fMRI data

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https://laccei.org/LACCEI2022-BocaRaton/student_papers/SP484.pdf

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http://hdl.handle.net/11603/26554

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection
UMBC Student Collection

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https://orcid.org/0000-0001-9613-6110

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2022-07-18

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conference papers and proceedings
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Abstract

Functional magnetic resonance imaging (fMRI) is an effective tool used to study neural systems and functional connectivity patterns within brain networks. Using resting-state fMRI data, we can uncover the functional connectivity differences in people with typically developed brains and brains of people with attention deficit hyperactivity disorder (ADHD). Segmenting the human brain into networks and analyzing the internetwork connectivity can help us identify which brain network regions are engaged and if they are working together. In this study, we used energy landscape analysis, a method that calculates and interprets multivariate time series data, such as resting-state fMRI, to investigate brain activity differences in typically developed, ADHD-Hyperactive/Impulsive, ADHD-Inattentive, and ADHDCombined subjects. The functional connectivity differences between the subgroups, analyzed separately, could be attributed to internetwork activity, and can possibly help identify biomarkers of ADHD. The internetwork connections consisted of the auditory network (AUD), attention network (ATN), default-mode network (DMN), frontoparietal network (FPN), salience network (SAN), sensorimotor network (SSM), and visual network (VIS). The activity patterns and disconnectivity graphs are obtained for each subject and the differences between groups are compared. Results suggest that DMN and VIS are strongly coupled for females with ADHD, whereas FPN and SAN are strongly coupled for males with ADHD. These cognitive differences may attribute to neural deficits and cognitive dysfunction in ADHD, such as trouble paying attention and inability to control behavior. The energy landscape analysis technique is a powerful tool for identifying differences between typically developed and ADHD subjects, which could help validate and encourage treatment options.