He Changchun, Cortes Jesus M, Ding Yi, Shan Xiaolong, Zou Maoyang, Chen Heng, Chen Huafu, Wang Xianmin, Duan Xujun
College of Artificial Intelligence (CUIT Shuangliu Industrial College), Chengdu University of Information Technology, Chengdu, 610225, China.
Sichuan Provincial Women's and Children's Hospital, Affiliated Women's and Children's Hospital of Chengdu Medical College, Chengdu, 610045, PR China.
Brain Imaging Behav. 2025 Jun 4. doi: 10.1007/s11682-025-01026-5.
Accumulating neuroimaging evidence suggests that abnormal functional and structural brain connectivity plays a cardinal role in the pathophysiology of autism spectrum disorder (ASD). Here, we constructed brain networks of functional, structural, and morphological connectivity using data from functional magnetic resonance imaging (fMRI), diffusion tensor imaging (DTI), and structural magnetic resonance imaging (sMRI), respectively. The neuroimaging data from a cohort of 50 individuals with ASD and 47 age-, gender- and handedness-matched TDC (age range: 5-18 years) were selected from the Autism Brain Image Data Exchange database. The combination of the fMRI, sMRI and DTI modalities connectivity features resulted in a classification accuracy of 82.69% for differentiating individuals with ASD from TDC. This accuracy surpassed that of any single modality or combination of fMRI and DTI modalities previously examined. Among the fMRI, sMRI and DTI modalities, the most distinguishing connectivity features were observed in the temporal, parietal, and occipital lobes from the DTI modality, the prefrontal and parietal lobes from the fMRI modality, and the temporal lobe from the sMRI modality. In addition, we also found that these distinguishing connectivity features can predict abnormal social interaction behaviours in ASD. These results highlight the complementary information provided by multimodal approaches, further emphasizing the pivotal role of multimodal connectivity patterns in unravelling the intricate mechanisms involved in the pathophysiology of ASD.
越来越多的神经影像学证据表明，大脑功能和结构连接异常在自闭症谱系障碍（ASD）的病理生理学中起着关键作用。在此，我们分别使用功能磁共振成像（fMRI）、扩散张量成像（DTI）和结构磁共振成像（sMRI）的数据构建了功能、结构和形态连接的脑网络。从自闭症脑图像数据交换数据库中选取了50名ASD个体和47名年龄、性别和利手匹配的典型发育儿童（TDC，年龄范围：5 - 18岁）的神经影像学数据。fMRI、sMRI和DTI模态连接特征的组合在区分ASD个体和TDC方面的分类准确率达到了82.69%。这一准确率超过了之前所研究的任何单一模态或fMRI与DTI模态的组合。在fMRI、sMRI和DTI模态中，最具区分性的连接特征分别在DTI模态的颞叶、顶叶和枕叶，fMRI模态的前额叶和顶叶，以及sMRI模态的颞叶中观察到。此外，我们还发现这些具有区分性的连接特征可以预测ASD中的异常社交互动行为。这些结果突出了多模态方法所提供的互补信息，进一步强调了多模态连接模式在揭示ASD病理生理学复杂机制中的关键作用。
