Functional connectivity describes neural activity from resting-state functional magnetic resonance imaging (rs-fMRI). This noninvasive modality is a promising imaging biomark-er of neurodegenerative diseases, such as Alzheimer's disease (AD), where the connectome can be an indicator to assess and to understand the pathology. However, it only provides noisy measurements of brain activity. As a consequence, it has shown fairly limited discrimination power on clinical groups. So far, the reference functional marker of AD is the fluorodeoxyglucose positron emission tomography (FDG-PET). It gives a reliable quantification of metabolic activity, but it is costly and invasive. Here, our goal is to analyze AD populations solely based on rs-fMRI, as functional connectivity is correlated to metabolism. We introduce transmodal learning: leveraging a prior from one modality to improve results of another modality on different subjects. A metabolic prior is learned from an independent FDG-PET dataset to improve functional connectivity-based prediction of AD. The prior acts as a regularization of connectivity learning and improves the estimation of discriminative patterns from distinct rs-fMRI datasets. Our approach is a two-stage classification strategy that combines several seed-based connectivity maps to cover a large number of functional networks that identify AD physiopathology. Experimental results show that our transmodal approach increases classification accuracy compared to pure rs-fMRI approaches, without resorting to additional invasive acquisitions. The method successfully recovers brain regions known to be impacted by the disease.

中文翻译：

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用于阿尔茨海默病预测的功能网络的跨模态学习


功能连接描述了静息态功能磁共振成像 (rs-fMRI) 的神经活动。这种非侵入性方式是一种很有前途的神经退行性疾病成像生物标志物，例如阿尔茨海默病（AD），其中连接组可以作为评估和理解病理学的指标。然而，它只提供大脑活动的噪声测量。因此，它对临床群体的歧视能力相当有限。迄今为止，AD的参考功能标志物是氟脱氧葡萄糖正电子发射断层扫描（FDG-PET）。它可以对代谢活动进行可靠的量化，但成本高昂且具有侵入性。在这里，我们的目标是仅基于 rs-fMRI 分析 AD 人群，因为功能连接与新陈代谢相关。我们引入跨模态学习：利用一种模态的先验来改进另一种模态在不同主题上的结果。从独立的 FDG-PET 数据集中学习代谢先验，以改进基于功能连接的 AD 预测。先验作为连通性学习的正则化，并改进了对不同 rs-fMRI 数据集的判别模式的估计。我们的方法是一种两阶段分类策略，结合了多个基于种子的连接图来覆盖大量识别 AD 病理生理学的功能网络。实验结果表明，与纯 rs-fMRI 方法相比，我们的跨模态方法提高了分类准确性，而无需采取额外的侵入性采集。该方法成功地恢复了已知受该疾病影响的大脑区域。