Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set

Tsung-Hao Hsieh; Fu-Zen Shaw; Chun-Chia Kung; Sheng-Fu Liang

doi:10.3389/fnhum.2023.1082722

Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set

Front Hum Neurosci. 2023 Sep 12:17:1082722. doi: 10.3389/fnhum.2023.1082722. eCollection 2023.

Authors

Tsung-Hao Hsieh¹, Fu-Zen Shaw², Chun-Chia Kung², Sheng-Fu Liang³

Affiliations

¹ Department of Computer Science, Tunghai University, Taichung City, Taiwan.
² Department of Psychology, National Cheng Kung University, Tainan, Taiwan.
³ Department of Computer Science and Information, National Cheng Kung University, Tainan, Taiwan.

Abstract

Background: Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information.

Method: Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation.

Result: The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds.

Conclusion: This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.

Keywords: ADHD; ADHD-200; regional homogeneity (ReHo) analysis; rs-fMRI; seed-based correlations analysis.

Grants and funding

This research was funded by the Ministry of Science and Technology of Taiwan through grants MOST 111-2634-F-006-003, 111-2740-H-006-003-RE3, the Science and Technology Council of Taiwan through grant NSTC112-2410-H-006-094-MY2, as well as the Higher Education Sprout Project of National Cheng Kung University and the Center Project of the Ministry of Education, Taiwan.