论文标题
从连接组到任务引起的指纹：静止状态功能连接的任务对比的个性化预测
From Connectomic to Task-evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-state Functional Connectivity
论文作者
论文摘要
静止状态功能性MRI（RSFMRI）产生的功能连接组可以用作个体的认知指纹。已证明连接指纹在许多机器学习任务中有用，例如预测特定于主题的行为特征或任务引起的活动。在这项工作中，我们提出了一个基于表面的卷积神经网络（Brainsurfcnn）模型，以预测其静止状态指纹对比的个别任务。我们引入了重建对抗性损失，该损失可以实施模型输出的主体特定性，同时最大程度地减少预测误差。所提出的方法显着提高了预测的对比度的准确性，而对比良好的基线。此外，Brainsurfcnn的预测还超过了主题识别任务中的测试基准。
Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many machine learning tasks, such as predicting subject-specific behavioral traits or task-evoked activity. In this work, we propose a surface-based convolutional neural network (BrainSurfCNN) model to predict individual task contrasts from their resting-state fingerprints. We introduce a reconstructive-contrastive loss that enforces subject-specificity of model outputs while minimizing predictive error. The proposed approach significantly improves the accuracy of predicted contrasts over a well-established baseline. Furthermore, BrainSurfCNN's prediction also surpasses test-retest benchmark in a subject identification task.
