Hsieh Tsung-Hao, Shaw Fu-Zen, Kung Chun-Chia, Liang Sheng-Fu
Department of Computer Science, Tunghai University, Taichung City, Taiwan.
Department of Psychology, National Cheng Kung University, Tainan, Taiwan.
Front Hum Neurosci. 2023 Sep 12;17:1082722. doi: 10.3389/fnhum.2023.1082722. eCollection 2023.
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.
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.
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.
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.
注意力缺陷多动障碍（ADHD）是一种多因素发病机制的神经发育障碍，常伴有多个脑功能连接功能障碍。静息态功能磁共振成像已用于ADHD研究，并被提议作为诊断信息的一种可能生物标志物。本研究的主要目的是提供一种有效的种子相关性分析程序，以研究静息态脑网络中作为诊断信息的可能生物标志物。
分析了149例儿童ADHD的静息态功能磁共振成像（rs-fMRI）数据。在本研究中，我们提出了一种两步分层分析方法来提取功能连接特征，并通过线性分类器和随机抽样验证进行评估。
数据驱动方法ReHo提供了四个脑区（内侧前额叶皮质、颞极、运动区和壳核），其区域同质性存在差异，作为二级种子用于分析远距离脑区之间的功能连接差异。该程序降低了在估计脑连接时种子选择（位置、形状和大小）的难度，提高了寻找有效种子的效率；我们研究中提出的特征在通过随机抽样（将25%留作测试集，其余数据为训练集）验证（使用简单线性分类器）来识别ADHD患者方面成功率达到83.24%，超过了使用传统种子的情况。
这项初步研究通过分析来自ADHD-200纽约大学数据集的静息态fMRI数据，检验了诊断ADHD的可行性。数据驱动模型提供了一种找到可靠种子的精确方法。数据驱动模型为找到可靠种子提供了精确方法，并且在不同数据集上都是可行的。此外，这种现象可能表明，使用数据驱动方法构建特定于单个数据集的模型可能比组合多个数据并创建通用模型更好。
