Kampel Nikolas, Abdellatif Farah, Shah N Jon, Neuner Irene, Dammers Jürgen
Institute of Neuroscience and Medicine (INM-4), Forschungszentrum Jülich GmbH, Jülich, Germany.
Faculty of Medicine, RWTH Aachen University, Aachen, Germany.
Front Nucl Med. 2024 Nov 13;4:1332747. doi: 10.3389/fnume.2024.1332747. eCollection 2024.
Neural fingerprinting is a technique used to identify individuals based on their unique brain activity patterns. While deep learning techniques have been demonstrated to outperform traditional correlation-based methods, they often require retraining to accommodate new subjects. Furthermore, the limited availability of samples in neuroscience research can impede the quick adoption of deep learning methods, presenting a challenge for their broader application in neural fingerprinting.
This study addresses these challenges by using contrastive learning to eliminate the need for retraining with new subjects and developing a data augmentation methodology to enhance model robustness in limited sample size conditions. We utilized the LEMON dataset, comprising 3 Tesla MRI and resting-state fMRI scans from 138 subjects, to compute functional connectivity as a baseline for fingerprinting performance based on correlation metrics. We adapted a recent deep learning model by incorporating data augmentation with short random temporal segments for training and reformulated the fingerprinting task as a contrastive problem, comparing the efficacy of contrastive triplet loss against conventional cross-entropy loss.
The results of this study confirm that deep learning methods can significantly improve fingerprinting performance over correlation-based methods, achieving an accuracy of about 98% in identifying a single subject out of 138 subjects utilizing 39 different functional connectivity profiles.
The contrastive method showed added value in the "leave subject out" scenario, demonstrating flexibility comparable to correlation-based methods and robustness across different data sizes. These findings suggest that contrastive learning and data augmentation offer a scalable solution for neural fingerprinting, even with limited sample sizes.
神经指纹识别是一种基于个体独特的大脑活动模式来识别个体的技术。虽然深度学习技术已被证明优于传统的基于相关性的方法，但它们通常需要重新训练以适应新的受试者。此外，神经科学研究中样本的有限可用性可能会阻碍深度学习方法的快速采用，这对其在神经指纹识别中的更广泛应用提出了挑战。
本研究通过使用对比学习来消除对新受试者进行重新训练的需求，并开发一种数据增强方法来提高在有限样本量条件下模型的鲁棒性，从而应对这些挑战。我们利用LEMON数据集（包括来自138名受试者的3特斯拉MRI和静息态功能磁共振成像扫描），基于相关性指标计算功能连接性，作为指纹识别性能的基线。我们通过将数据增强与短随机时间片段相结合进行训练，对最近的深度学习模型进行了调整，并将指纹识别任务重新表述为一个对比问题，比较了对比三元组损失与传统交叉熵损失的效果。
本研究结果证实，深度学习方法在指纹识别性能上可比基于相关性的方法有显著提高，在利用39种不同的功能连接性概况从138名受试者中识别单个受试者时，准确率达到约98%。
对比方法在“留一受试者法”场景中显示出附加值，表现出与基于相关性的方法相当的灵活性以及在不同数据量下的鲁棒性。这些发现表明，即使样本量有限，对比学习和数据增强也为神经指纹识别提供了一种可扩展解决方案。
