Neurodevelopmental spectrum disorders like autism (ASD) are diagnosed, on average, beyond age 4 y, after multiple critical periods of brain development close and behavioral intervention becomes less effective. This raises the urgent need for quantitative, noninvasive, and translational biomarkers for their early detection and tracking. We found that both idiopathic (BTBR) and genetic (CDKL5- and MeCP2-deficient) mouse models of ASD display an early, impaired cholinergic neuromodulation as reflected in altered spontaneous pupil fluctuations. Abnormalities were already present before the onset of symptoms and were rescued by the selective expression of MeCP2 in cholinergic circuits. Hence, we trained a neural network (ConvNetACh) to recognize, with 97% accuracy, patterns of these arousal fluctuations in mice with enhanced cholinergic sensitivity (LYNX1-deficient). ConvNetACh then successfully detected impairments in all ASD mouse models tested except in MeCP2-rescued mice. By retraining only the last layers of ConvNetACh with heart rate variation data (a similar proxy of arousal) directly from Rett syndrome patients, we generated ConvNetPatients, a neural network capable of distinguishing them from typically developing subjects. Even with small cohorts of rare patients, our approach exhibited significant accuracy before (80% in the first and second year of life) and into regression (88% in stage III patients). Thus, transfer learning across species and modalities establishes spontaneous arousal fluctuations combined with deep learning as a robust noninvasive, quantitative, and sensitive translational biomarker for the rapid and early detection of neurodevelopmental disorders before major symptom onset.

在大脑发育的多个关键时期关闭并且行为干预的效果降低后，平均诊断出自闭症（ASD）等神经发育频谱障碍的病史超过4岁。这就迫切需要定量，非侵入性和翻译性生物标记物，以对其进行早期检测和跟踪。我们发现，ASD的特发性（BTBR）和遗传性（CDKL5和MeCP2缺陷型）小鼠模型均显示出早期，受损的胆碱能神经调节，如改变的自发瞳孔波动所反映。在出现症状之前已经存在异常，并通过胆碱能回路中MeCP2的选择性表达得以挽救。因此，我们训练了一个神经网络（ConvNetACh），以97％的准确性识别出 增强胆碱能敏感性（LYNX1缺陷）的小鼠中这些唤醒波动的规律。然后，ConvNetACh成功地在所有MeSD2拯救小鼠中检测到的所有ASD小鼠模型中都检测到了损伤。通过直接使用来自Rett综合征患者的心率变化数据（类似于唤醒的类似信号）仅对ConvNetACh的最后一层进行再培训，我们生成了ConvNetPatients，这是一种能够将其与典型发展中的受试者区分开的神经网络。即使有少量的罕见患者，我们的方法在治疗前（生命的第一和第二年为80％）和回归治疗（III期患者为88％）也显示出显着的准确性。因此，跨物种和形态的转移学习会建立自发的唤醒波动，并结合深度学习作为一种强大的无创，定量，