Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder.
Toward a more informative representation of the fetal-neonatal brain connectome using variational autoencoder.
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DOI：
10.7554/elife.80878
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发表时间：
2023-05-15
期刊：
影响因子：
7.7
通讯作者：
Limperopoulos C
中科院分区：
文献类型：
--
作者：
Kim JH;De Asis-Cruz J;Krishnamurthy D;Limperopoulos C
功能性磁共振成像（fMRI）的最新进展有助于阐明以前无法实现的早期生命，产前和新生儿大脑发育的轨迹。迄今为止，胎儿-新生儿fMRI数据的解释依赖于线性分析模型，类似于成人神经影像数据。然而，与成人大脑不同的是，胎儿和新生儿的大脑发育非常迅速，远远超过生命周期中任何其他大脑发育时期。因此，传统的线性计算模型可能无法充分捕捉这些加速和复杂的神经发育轨迹在这个关键时期的大脑发育沿着产前-新生儿连续。为了获得对胎儿-新生儿大脑发育（包括非线性生长）的细致入微的理解，我们首次使用称为变分自动编码器（VAE）的无监督深度生成模型，在大样本（>500）胎儿，早产儿和足月新生儿中开发了大脑活动的定量，全系统表示，该模型先前被证明在表示健康成人的复杂静息状态数据方面优于线性模型上级。在这里，我们证明了非线性大脑特征，即潜在变量，来自于在成人rsfMRI上预训练的VAE，携带重要的个体神经特征，与线性模型相比，改善了产前-新生儿大脑成熟模式的表现，并在新生儿队列中进行了更准确和稳定的年龄预测。使用VAE解码器，我们还揭示了跨越感觉和默认模式网络的不同功能的大脑网络。使用VAE，我们能够可靠地捕获和量化复杂的，非线性的胎儿-新生儿功能神经连接。这将为详细绘制起源于胎儿生命的健康和异常功能性大脑特征奠定关键基础。
Recent advances in functional magnetic resonance imaging (fMRI) have helped elucidate previously inaccessible trajectories of early-life prenatal and neonatal brain development. To date, the interpretation of fetal–neonatal fMRI data has relied on linear analytic models, akin to adult neuroimaging data. However, unlike the adult brain, the fetal and newborn brain develops extraordinarily rapidly, far outpacing any other brain development period across the life span. Consequently, conventional linear computational models may not adequately capture these accelerated and complex neurodevelopmental trajectories during this critical period of brain development along the prenatal-neonatal continuum. To obtain a nuanced understanding of fetal–neonatal brain development, including nonlinear growth, for the first time, we developed quantitative, systems-wide representations of brain activity in a large sample (>500) of fetuses, preterm, and full-term neonates using an unsupervised deep generative model called variational autoencoder (VAE), a model previously shown to be superior to linear models in representing complex resting-state data in healthy adults. Here, we demonstrated that nonlinear brain features, that is, latent variables, derived with the VAE pretrained on rsfMRI of human adults, carried important individual neural signatures, leading to improved representation of prenatal-neonatal brain maturational patterns and more accurate and stable age prediction in the neonate cohort compared to linear models. Using the VAE decoder, we also revealed distinct functional brain networks spanning the sensory and default mode networks. Using the VAE, we are able to reliably capture and quantify complex, nonlinear fetal–neonatal functional neural connectivity. This will lay the critical foundation for detailed mapping of healthy and aberrant functional brain signatures that have their origins in fetal life.