Neonatal brain connectivity outliers identify over forty percent of IQ outliers at 4 years of age.
Neonatal brain connectivity outliers identify over forty percent of IQ outliers at 4 years of age.
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DOI：
10.1002/brb3.1846
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发表时间：
2020-12
影响因子：
3.1
通讯作者：
Gilmore JH
中科院分区：
文献类型：
--
作者：
Gao W;Chen Y;Cornea E;Goldman BD;Gilmore JH
定义可靠的大脑标记来预测异常行为结果仍然是神经科学研究中紧迫但极具挑战性的任务。鉴于婴儿期的大脑和行为生长最为显着，这对于婴儿研究尤其重要。在这项研究中，我们通过将个体新生儿的全脑功能连接模式抽象为三个离群值（Triple O），提出了一种新颖的预测方案，并测试了基于 Triple O 识别为“大脑离群值”的新生儿更有可能在 4 岁时发展为智商离群值的假设。 Triple O 不需要使用行为数据进行训练，它代表了一种新颖的概念验证方法，可以根据新生儿大脑数据预测以后的智商结果。 Triple O 在 175 名不同足月、双胞胎和母体疾病状态的新生儿组成的混合队列中正确识别出 42.1% 的真实智商异常值。 Triple O 也达到了高水平的特异性 (96.2%) 和总体准确性 (90.3%)。增强的 Triple O+ 进一步纳入人口统计信息指标，可以进一步区分 4 年 IQ 异常值的高低。针对七个独立参考样本的验证测试显示出高度一致的结果，并且最小样本量约为 50，以实现稳健的性能。考虑到出生后大脑生长和各种环境因素也可能影响 4 岁智商，Triple O 纯粹基于新生儿功能连接数据，可以识别 > 40% 的 4 岁智商异常值，这一事实令人震惊。加上非常高的特异性，Triple O 预测的每个异常值都代表着有意义的风险，但未来需要努力探索识别其余异常值的方法。总体而言，由于无需训练、高鲁棒性和对样本量的最低要求，所提出的 Triple O 方法展示了使用新生儿功能连接数据预测后期外围智商表现的巨大潜力。发展研究最期望的目标之一是能够尽早根据大脑生物标志物预测儿童行为结果，以促进及时干预。然而，这个问题是出了名的具有挑战性，部分原因是大脑功能的复杂性以及据报道的大脑与行为关系的薄弱。在这项研究中，我们得出了一个简单的、免训练的预测方案，将新生儿全脑功能连接模式抽象为三个异常值测量（Triple O），并表明它可以识别超过 40% 的 4 年 IQ 表现异常值，并且对不同参考样本具有高度的鲁棒性。这项研究的结果代表了基于大脑异常值预测后期行为结果这一新方向的第一步。
Defining reliable brain markers for the prediction of abnormal behavioral outcomes remains an urgent but extremely challenging task in neuroscience research. This is particularly important for infant studies given the most dramatic brain and behavioral growth during infancy. In this study, we proposed a novel prediction scheme through abstracting individual newborn's whole‐brain functional connectivity pattern to three outlier measures (Triple O) and tested the hypothesis that neonates identified as “brain outliers” based on Triple O were more likely to develop as IQ outliers at 4 years of age. Without need for training with behavioral data, Triple O represents a novel proof‐of‐concept approach to predict later IQ outcomes based on neonatal brain data. Triple O correctly identified 42.1% true IQ outliers among a mixed cohort of 175 newborns with different term, twin, and maternal disorder statuses. Triple O also reached a high level of specificity (96.2%) and overall accuracy (90.3%). Further incorporating a demographic information indicator, the enhanced Triple O+ could further differentiate between high and low 4YR IQ outliers. Validation tests against seven independent reference samples revealed highly consistent results and a minimum sample size of ~50 for robust performance. Considering that postnatal brain growth and various environmental factors likely also contribute to 4YR IQ, the fact that Triple O, based purely on neonatal functional connectivity data, could identify >40% of 4YR IQ outliers is striking. Together with the very high level of specificity, each outlier predicted by Triple O represents a meaningful risk but future efforts are needed to explore ways to identify the rest of outliers. Overall, with no need for training, a high level of robustness, and a minimal requirement on sample size, the proposed Triple O approach demonstrates great potential to predict later outlying IQ performances using neonatal functional connectivity data. One of the most desired goals of developmental research is to be able to predict childhood behavioral outcomes based on brain biomarkers as early as possible to facilitate in‐time intervention. However, this problem is notoriously challenging, partly due to the complex nature of brain function and reportedly weak brain–behavioral relationships. In this study, we derived a simple, training‐free prediction scheme to abstract the neonatal whole‐brain functional connectivity pattern to three outlier measures (Triple O) and showed that it could identify over forty percent of 4‐year IQ performance outliers with a high level of robustness against different reference samples. Results in this study represent a first step in this novel direction of brain outlier‐based prediction of later behavioral outcomes.
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影响因子：
4.3
作者：
Achard S;Bullmore E
通讯作者：
Bullmore E
DOI：
10.1038/nrn.2018.1
发表时间：
2018-02-16
期刊：
Nature reviews. Neuroscience
影响因子：
--
作者：
Gilmore JH;Knickmeyer RC;Gao W
通讯作者：
Gao W
影响因子：
3.7
作者：
Fransson, Peter;Aden, Ulrika;Lagercrantz, Hugo
通讯作者：
Lagercrantz, Hugo
影响因子：
3.7
作者：
Eriksen, Hanne-Lise Falgreen;Kesmodel, Ulrik Schioler;Mortensen, Erik Lykke
通讯作者：
Mortensen, Erik Lykke
影响因子：
2.9
作者：
Grewen K;Salzwedel AP;Gao W
通讯作者：
Gao W