Ma Xuefeng, Zhou Weiran, Zheng Hui, Ye Shuer, Yang Bo, Wang Lingxiao, Wang Min, Dong Guang-Heng
Department of Psychology, Yunnan Normal University, Kunming, Yunnan Province 650500, China.
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China.
Psychoradiology. 2023 Nov 27;3:kkad027. doi: 10.1093/psyrad/kkad027. eCollection 2023.
Autism spectrum disorder (ASD) is characterized by social and behavioural deficits. Current diagnosis relies on behavioural criteria, but machine learning, particularly connectome-based predictive modelling (CPM), offers the potential to uncover neural biomarkers for ASD.
This study aims to predict the severity of ASD traits using CPM and explores differences among ASD subtypes, seeking to enhance diagnosis and understanding of ASD.
Resting-state functional magnetic resonance imaging data from 151 ASD patients were used in the model. CPM with leave-one-out cross-validation was conducted to identify intrinsic neural networks that predict Autism Diagnostic Observation Schedule (ADOS) scores. After the model was constructed, it was applied to independent samples to test its replicability (172 ASD patients) and specificity (36 healthy control participants). Furthermore, we examined the predictive model across different aspects of ASD and in subtypes of ASD to understand the potential mechanisms underlying the results.
The CPM successfully identified negative networks that significantly predicted ADOS total scores [ (df = 150) = 0.19, = 0.008 in all patients; (df = 104) = 0.20, = 0.040 in classic autism] and communication scores [ (df = 150) = 0.22, = 0.010 in all patients; (df = 104) = 0.21, = 0.020 in classic autism]. These results were reproducible across independent databases. The networks were characterized by enhanced inter- and intranetwork connectivity associated with the occipital network (OCC), and the sensorimotor network (SMN) also played important roles.
A CPM based on whole-brain resting-state functional connectivity can predicted the severity of ASD. Large-scale networks, including the OCC and SMN, played important roles in the predictive model. These findings may provide new directions for the diagnosis and intervention of ASD, and maybe could be the targets in novel interventions.
自闭症谱系障碍（ASD）的特征是社交和行为缺陷。目前的诊断依赖于行为标准，但机器学习，特别是基于连接组的预测模型（CPM），为发现ASD的神经生物标志物提供了潜力。
本研究旨在使用CPM预测ASD特征的严重程度，并探索ASD亚型之间的差异，以加强对ASD的诊断和理解。
模型使用了151例ASD患者的静息态功能磁共振成像数据。采用留一法交叉验证的CPM来识别预测自闭症诊断观察量表（ADOS）分数的内在神经网络。模型构建完成后，将其应用于独立样本以测试其可重复性（172例ASD患者）和特异性（36名健康对照参与者）。此外，我们在ASD的不同方面和ASD亚型中检验了预测模型，以了解结果背后的潜在机制。
CPM成功识别出显著预测ADOS总分的负性网络[所有患者中（自由度=150）=0.19，=0.008；经典自闭症中（自由度=104）=0.20，=0.040]以及沟通分数[所有患者中（自由度=150）=0.22，=0.010；经典自闭症中（自由度=104）=0.21，=0.020]。这些结果在独立数据库中具有可重复性。这些网络的特征是与枕叶网络（OCC）相关的网络间和网络内连接增强，感觉运动网络（SMN）也发挥了重要作用。
基于全脑静息态功能连接的CPM可以预测ASD的严重程度。包括OCC和SMN在内的大规模网络在预测模型中发挥了重要作用。这些发现可能为ASD的诊断和干预提供新方向，并且可能成为新型干预的靶点。
