Personalized functional network mapping for autism spectrum disorder and attention-deficit/hyperactivity disorder.
Jiang ZhangZhiwei ZhangHui SunYingzi MaJia YangKexuan ChenXiaohui YuTianwei QinTianyu ZhaoJingyue ZhangCongying ChuJiaojian WangPublished in: Translational psychiatry (2024)
Autism spectrum disorder (ASD) and Attention-deficit/hyperactivity disorder (ADHD) are two typical neurodevelopmental disorders that have a long-term impact on physical and mental health. ASD is usually comorbid with ADHD and thus shares highly overlapping clinical symptoms. Delineating the shared and distinct neurophysiological profiles is important to uncover the neurobiological mechanisms to guide better therapy. In this study, we aimed to establish the behaviors, functional connectome, and network properties differences between ASD, ADHD-Combined, and ADHD-Inattentive using resting-state functional magnetic resonance imaging. We used the non-negative matrix fraction method to define personalized large-scale functional networks for each participant. The individual large-scale functional network connectivity (FNC) and graph-theory-based complex network analyses were executed and identified shared and disorder-specific differences in FNCs and network attributes. In addition, edge-wise functional connectivity analysis revealed abnormal edge co-fluctuation amplitude and number of transitions among different groups. Taken together, our study revealed disorder-specific and -shared regional and edge-wise functional connectivity and network differences for ASD and ADHD using an individual-level functional network mapping approach, which provides new evidence for the brain functional abnormalities in ASD and ADHD and facilitates understanding the neurobiological basis for both disorders.
Keyphrases
- attention deficit hyperactivity disorder
- resting state
- autism spectrum disorder
- functional connectivity
- intellectual disability
- magnetic resonance imaging
- mental health
- working memory
- high resolution
- computed tomography
- mass spectrometry
- physical activity
- blood brain barrier
- machine learning
- network analysis
- mental illness
- bone marrow
- magnetic resonance
- deep learning
- smoking cessation