Kyeong Sunghyon, Park Seonjeong, Cheon Keun-Ah, Kim Jae-Jin, Song Dong-Ho, Kim Eunjoo
Department of Psychiatry and Institute of Behavioral Science in Medicine, Yonsei University College of Medicine, Seoul, Republic of Korea; Brain Korea 21 PLUS Project for Medical Science, Yonsei University, Seoul, Republic of Korea; Division of Mathematical Models, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
Division of Mathematical Models, National Institute for Mathematical Sciences, Daejeon, Republic of Korea.
PLoS One. 2015 Sep 9;10(9):e0137296. doi: 10.1371/journal.pone.0137296. eCollection 2015.
Attention-deficit/hyperactivity disorder (ADHD) is currently diagnosed by a diagnostic interview, mainly based on subjective reports from parents or teachers. It is necessary to develop methods that rely on objectively measureable neurobiological data to assess brain-behavior relationship in patients with ADHD. We investigated the application of a topological data analysis tool, Mapper, to analyze the brain functional connectivity data from ADHD patients.
To quantify the disease severity using the neuroimaging data, the decomposition of individual functional networks into normal and disease components by the healthy state model (HSM) was performed, and the magnitude of the disease component (MDC) was computed. Topological data analysis using Mapper was performed to distinguish children with ADHD (n = 196) from typically developing controls (TDC) (n = 214).
In the topological data analysis, the partial clustering results of patients with ADHD and normal subjects were shown in a chain-like graph. In the correlation analysis, the MDC showed a significant increase with lower intelligence scores in TDC. We also found that the rates of comorbidity in ADHD significantly increased when the deviation of the functional connectivity from HSM was large. In addition, a significant correlation between ADHD symptom severity and MDC was found in part of the dataset.
The application of HSM and topological data analysis methods in assessing the brain functional connectivity seem to be promising tools to quantify ADHD symptom severity and to reveal the hidden relationship between clinical phenotypic variables and brain connectivity.
注意力缺陷多动障碍（ADHD）目前通过诊断访谈进行诊断，主要基于家长或教师的主观报告。有必要开发依赖于可客观测量的神经生物学数据的方法，以评估ADHD患者的脑-行为关系。我们研究了一种拓扑数据分析工具Mapper在分析ADHD患者脑功能连接数据中的应用。
为了使用神经影像学数据量化疾病严重程度，通过健康状态模型（HSM）将个体功能网络分解为正常和疾病成分，并计算疾病成分的大小（MDC）。使用Mapper进行拓扑数据分析，以区分ADHD儿童（n = 196）和正常发育对照（TDC）儿童（n = 214）。
在拓扑数据分析中，ADHD患者和正常受试者的部分聚类结果显示在一个链状图中。在相关性分析中，TDC中MDC随智力得分降低而显著增加。我们还发现，当功能连接与HSM的偏差较大时，ADHD的共病率显著增加。此外，在部分数据集中发现ADHD症状严重程度与MDC之间存在显著相关性。
HSM和拓扑数据分析方法在评估脑功能连接方面的应用似乎是量化ADHD症状严重程度以及揭示临床表型变量与脑连接之间隐藏关系的有前景的工具。
