Feng Aichen, Zhi Dongmei, Feng Yuan, Jiang Rongtao, Fu Zening, Xu Ming, Zhao Min, Yu Shan, Stevens Michael, Sun Li, Calhoun Vince, Sui Jing
Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
EClinicalMedicine. 2024 Oct 10;77:102876. doi: 10.1016/j.eclinm.2024.102876. eCollection 2024 Nov.
Attention deficit hyperactivity disorder (ADHD) is one prevalent neurodevelopmental disorder with childhood onset, however, there is no clear correspondence established between clinical ADHD subtypes and primary medications. Identifying objective and reliable neuroimaging markers for categorizing ADHD biotypes may lead to more individualized, biotype-guided treatment.
Here we proposed a graph convolution network for biological subtype detection (GCN-BSD) using both functional network connectivity (FNC) and non-imaging phenotypic data for ADHD biotype. We applied GCN-BSD to ADHD patients from the ABCD study as the discovery dataset and a validation ADHD dataset with longitudinal medication treatment from Peking University Sixth Hospital.
We identified two biotypes based on 1069 ADHD patients selected from Adolescent Brain and Cognitive Development (ABCD) study, which were validated on independent ADHD adolescents undergoing longitudinal medication treatment (n = 130). Interestingly, in addition to differences in cognitive performance and hyperactivity/impulsivity symptoms, biotype 1 demonstrated a significantly better recovery rate in psychosomatic problems score (p < 0.05, baseline symptom score adjusted) when treated with methylphenidate than with atomoxetine.
Our results suggested that such an imaging-driven, biotype-guided approaches hold promise for facilitating personalized treatment of ADHD and exploring possible boundaries through innovative deep learning algorithms to improve medication treatment effectiveness.
Science and Technology Innovation 2030 Major Projects, the National Natural Science Foundation of China, the Startup Funds for Talents at Beijing Normal University, China Postdoctoral Science Foundation, and the National Institutes of Health.
注意力缺陷多动障碍（ADHD）是一种常见的儿童期起病的神经发育障碍，然而，ADHD临床亚型与一线药物之间尚未建立明确的对应关系。识别用于ADHD生物型分类的客观可靠的神经影像学标志物可能会带来更个体化的、基于生物型的治疗。
在此，我们提出了一种用于生物亚型检测的图卷积网络（GCN-BSD），使用功能网络连接性（FNC）和ADHD生物型的非影像学表型数据。我们将GCN-BSD应用于来自青少年大脑认知发展（ABCD）研究的ADHD患者作为发现数据集，并应用于北京大学第六医院具有纵向药物治疗的ADHD验证数据集。
我们基于从青少年大脑认知发展（ABCD）研究中选取的1069例ADHD患者确定了两种生物型，并在接受纵向药物治疗的独立ADHD青少年（n = 130）中进行了验证。有趣的是，除了认知表现和多动/冲动症状的差异外，生物型1在使用哌甲酯治疗时，与使用托莫西汀相比，在调整基线症状评分后，心身问题评分的恢复率显著更高（p < 0.05）。
我们的结果表明，这种成像驱动、生物型导向的方法有望促进ADHD的个性化治疗，并通过创新的深度学习算法探索可能的界限，以提高药物治疗效果。
科技创新2030重大项目、国家自然科学基金、北京师范大学人才引进启动资金、中国博士后科学基金和美国国立卫生研究院。
