Molloy M Fiona, Saygin Zeynep M
Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, United States.
Department of Psychology, The Ohio State University, 1835 Neil Avenue, Columbus, OH 43210, United States.
Neuroimage. 2022 Jun;253:119101. doi: 10.1016/j.neuroimage.2022.119101. Epub 2022 Mar 15.
The adult brain is organized into distinct functional networks, forming the basis of information processing and determining individual differences in behavior. Is this network organization genetically determined and present at birth? And what is the individual variability in this organization in neonates? Here, we use unsupervised learning to uncover intrinsic functional brain organization using resting-state connectivity from a large cohort of neonates (Developing Human Connectome Project). We identified a set of symmetric, hierarchical, and replicable networks: sensorimotor, visual, default mode, ventral attention, and high-level vision. We quantified individual variability across neonates, and found the most individual variability in the ventral attention networks. Crucially, the variability of these networks was not driven by SNR differences or differences from adult networks (Yeo et al., 2011). Finally, differential gene expression provided a potential explanation for the emergence of these distinct networks and identified potential genes of interest for future developmental and individual variability research. Overall, we found neonatal connectomes (even at the voxel-level) can reveal broad individual-specific information processing units. The presence of individual differences in neonates and the framework for personalized parcellations demonstrated here has the potential to improve prediction of behavior and future outcomes from neonatal and infant brain data.
成人大脑被组织成不同的功能网络，构成信息处理的基础并决定行为的个体差异。这种网络组织是由基因决定并在出生时就存在的吗？新生儿这种组织的个体变异性又是什么样的呢？在这里，我们使用无监督学习，通过来自大量新生儿队列（人类连接组发育项目）的静息态连接来揭示大脑的内在功能组织。我们识别出了一组对称、分层且可重复的网络：感觉运动网络、视觉网络、默认模式网络、腹侧注意网络和高级视觉网络。我们量化了新生儿之间的个体变异性，发现腹侧注意网络中的个体变异性最大。至关重要的是，这些网络的变异性并非由信噪比差异或与成人网络的差异所驱动（Yeo等人，2011年）。最后，差异基因表达为这些不同网络的出现提供了一个潜在的解释，并确定了未来发育和个体变异性研究中潜在的感兴趣基因。总体而言，我们发现新生儿连接组（即使在体素水平）可以揭示广泛的个体特异性信息处理单元。新生儿个体差异的存在以及这里展示的个性化脑区划分框架有可能改善从新生儿和婴儿脑数据对行为和未来结果的预测。
