Artificial Intelligence Applications and Innovations - 15th IFIP WG 12.5 International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24-26, 2019, Proceedings
Artificial Intelligence Applications and Innovations - 15th IFIP WG 12.5 International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24-26, 2019, Proceedings
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人工智能应用与创新 - 第 15 届 IFIP WG 12.5 国际会议，AIAI 2019，希腊克里特岛赫索尼索斯，2019 年 5 月 24-26 日，会议记录
DOI：
10.1007/978-3-030-19823-7_40
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发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Azevedo T
中科院分区：
文献类型：
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作者：
Azevedo T
预测认知特征的变异性是一个有吸引力和挑战性的研究领域，其中不同的方法和数据集已经实现了混合的结果。以前使用的一些强大的机器学习算法很难解释，而其他算法很容易解释，但可能没有那么强大。为了更好地理解人类的个体认知差异，我们利用了机器学习的最新发展，其中强大的预测模型可以自信地解释。我们使用了从人类连接组项目（HCP）获得的905人的神经成像数据和各种行为，认知，情感和健康指标。作为本文的主要贡献，我们展示了如何解释认知的神经解剖学基础，我们认为最近的方法尚未在该领域充分探索。通过将神经图像减少到从基于表面的形态测量和皮质髓磷脂估计生成的一组特征良好的特征，我们使这些模型的解释更容易，因为每个特征都是不言自明的。此工具中使用的代码可在公共存储库中获得： https://github.com/tjiagoM/interpreting-cognition-paper-2019 .
Predicting variability in cognition traits is an attractive and challenging area of research, where different approaches and datasets have been implemented with mixed results. Some powerful Machine Learning algorithms employed before are difficult to interpret, while other algorithms are easy to interpret but might not be as powerful. To improve understanding of individual cognitive differences in humans, we make use of the most recent developments in Machine Learning in which powerful prediction models can be interpreted with confidence. We used neuroimaging data and a variety of behavioural, cognitive, affective and health measures from 905 people obtained from the Human Connectome Project (HCP). As a main contribution of this paper, we show how one could interpret the neuroanatomical basis of cognition, with recent methods which we believe are not yet fully explored in the field. By reducing neuroimages to a well characterised set of features generated from surface-based morphometry and cortical myelin estimates, we make the interpretation of such models easier as each feature is self-explanatory. The code used in this tool is available in a public repository: https://github.com/tjiagoM/interpreting-cognition-paper-2019 .