A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network
A Machine Learning Framework for Accurate Functional Connectome Fingerprinting and an Application of a Siamese Network
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DOI：
10.1007/978-3-030-32391-2_9
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发表时间：
2019-10
期刊：
影响因子：
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通讯作者：
A. Shojaee;K. Li;G. Atluri
中科院分区：
文献类型：
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作者：
A. Shojaee;K. Li;G. Atluri
功能连接组（FC）指纹识别的目标是根据被试的功能连接组来唯一地识别被试。近年来，随着人们努力了解影响指纹识别准确性的因素和开发更有效的方法，人们对这个问题的兴趣大大增加。在这项工作中，我们开发了一种新的FC指纹机器学习框架。具体来说，虽然现有的方法基于两个FC之间的相关性评分来匹配查询FC和参考FC，但我们的框架采用了机器学习模型来确定两个FC是否相似。这使我们能够从fc中捕获更复杂的特征，并捕获fc之间可能存在的非线性相似性。我们探索了多种机器学习算法，包括暹罗神经网络和几种分类算法。从我们的实验中，我们观察到Siamese网络优于其他分类模型，其FC指纹识别准确率为。
The goal of functional connectome (FC) fingerprinting is to uniquely identify subjects based on their functional connectome. In recent years, interest in this problem has increased substantially with efforts made to understand the factors that affect the accuracy of fingerprinting and to develop more effective approaches. In this work, we developed a novel machine learning framework for FC fingerprinting. Specifically, while existing approaches match a query FC with a reference FC based on a correlation score between the two FCs, our framework employed a machine learning model to determine if two FCs are similar. This allowed us to capture more complex features from FCs and also to capture non-linear similarities that may exist among FCs. We explored multiple machine learning algorithms that include a Siamese neural network and several classification algorithms. From our experiments, we observed that the Siamese network outperformed other classification models, with an FC fingerprinting accuracy of.