Deep learning identifies partially overlapping subnetworks in the human social brain.
Deep learning identifies partially overlapping subnetworks in the human social brain.
复制标题
深度学习识别人类社会脑中有部分重叠的子网络。
DOI：
10.1038/s42003-020-01559-z
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发表时间：
2021-01-14
影响因子：
5.9
通讯作者：
Bzdok D
中科院分区：
文献类型：
--
作者：
Kiesow H;Spreng RN;Holmes AJ;Chakravarty MM;Marquand AF;Yeo BTT;Bzdok D
复杂的社会相互作用是人类物种的一个定义属性。在社会神经科学中，许多实验试图首先定义“观点采择”、“移情”和其他心理学概念，然后将其定位于特定的大脑回路。很少有自下而上的研究首先确定大脑变异的解释模式，然后将其与心理概念联系起来;这可能是由于缺乏大型人口数据集。本着这种精神，我们对社会大脑形态进行了系统的解构，将其分解为基本的构建模块，涉及约10，000名英国生物银行参与者。我们在最近的社会大脑图谱中探索了人口规模的结构协变的连贯表示，通过翻译来自深度学习的自动编码器神经网络。学习的子网络揭示了社会性大脑区域之间结构关系的基本模式，核心是脑桥核、内侧前额叶皮层和颞顶连接。一些未被发现的子网络有助于预测一般的社会特征，而其他子网络则有助于预测社会功能的特定方面，例如社交孤立的经历。根据我们在人群水平上的证据，社会脑的空间重叠子系统可能与日常社会生活中的个体间差异有关。Kiesow等人使用深度学习来识别人类社会大脑中在人群水平上部分重叠的子网络。他们还证明，学习的子网络表示可以用来预测社会特征。
Complex social interplay is a defining property of the human species. In social neuroscience, many experiments have sought to first define and then locate ‘perspective taking’, ‘empathy’, and other psychological concepts to specific brain circuits. Seldom, bottom-up studies were conducted to first identify explanatory patterns of brain variation, which are then related to psychological concepts; perhaps due to a lack of large population datasets. In this spirit, we performed a systematic de-construction of social brain morphology into its elementary building blocks, involving ~10,000 UK Biobank participants. We explored coherent representations of structural co-variation at population scale within a recent social brain atlas, by translating autoencoder neural networks from deep learning. The learned subnetworks revealed essential patterns of structural relationships between social brain regions, with the nucleus accumbens, medial prefrontal cortex, and temporoparietal junction embedded at the core. Some of the uncovered subnetworks contributed to predicting examined social traits in general, while other subnetworks helped predict specific facets of social functioning, such as the experience of social isolation. As a consequence of our population-level evidence, spatially overlapping subsystems of the social brain probably relate to interindividual differences in everyday social life. Kiesow et al. use deep learning to identify partially overlapping subnetworks in the human social brain at the population level. They also demonstrate that the learned subnetwork representations can be used to predict social traits.
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影响因子：
4.3
作者：
Bzdok D
通讯作者：
Bzdok D
DOI：
10.1098/rstb.2016.0244
发表时间：
2017-08-19
期刊：
Philosophical transactions of the Royal Society of London. Series B, Biological sciences
影响因子：
--
作者：
Dunbar RIM;Shultz S
通讯作者：
Shultz S
影响因子：
3.5
作者：
Abraham A;Pedregosa F;Eickenberg M;Gervais P;Mueller A;Kossaifi J;Gramfort A;Thirion B;Varoquaux G
通讯作者：
Varoquaux G
影响因子：
5.7
作者：
Bzdok, Danilo;Yeo, B. T. Thomas
通讯作者：
Yeo, B. T. Thomas
影响因子：
19.9
作者：
Cacioppo, John T.;Hawkey, Louise C.
通讯作者：
Hawkey, Louise C.