The main challenge in analysing functional magnetic resonance imaging (fMRI) data from extended samples of subject (N > 100) is to extract as much relevant information as possible from big amounts of noisy data. When studying neurodegenerative diseases with resting-state fMRI, one of the objectives is to determine regions with abnormal background activity with respect to a healthy brain and this is often attained with comparative statistical models applied to single voxels or brain parcels within one or several functional networks. In this work, we propose a novel approach based on clustering and stochastic rank aggregation to identify parcels that exhibit a coherent behaviour in groups of subjects affected by the same disorder and apply it to default-mode network independent component maps from resting-state fMRI data sets. Brain voxels are partitioned into parcels through k-means clustering, then solutions are enhanced by means of consensus techniques. For each subject, clusters are ranked according to their median value and a stochastic rank aggregation method, TopKLists, is applied to combine the individual rankings within each class of subjects. For comparison, the same approach was tested on an anatomical parcellation. We found parcels for which the rankings were different among control subjects and subjects affected by Parkinson’s disease and amyotrophic lateral sclerosis and found evidence in literature for the relevance of top ranked regions in default-mode brain activity. The proposed framework represents a valid method for the identification of functional neuromarkers from resting-state fMRI data, and it might therefore constitute a step forward in the development of fully automated data-driven techniques to support early diagnoses of neurodegenerative diseases.

中文翻译：

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随机秩聚合用于功能神经标记物的鉴定。

分析来自扩展样本的功能性磁共振成像（fMRI）数据的主要挑战（N > 100）是从大量的嘈杂数据中提取尽可能多的相关信息。当使用静止状态功能磁共振成像研究神经退行性疾病时，目标之一是确定相对于健康大脑而言背景活动异常的区域，这通常可以通过将比较统计模型应用于一个或多个功能网络中的单个体素或大脑包裹来实现。在这项工作中，我们提出了一种基于聚类和随机等级聚集的新方法，以识别在受同一疾病影响的受试者群体中表现出一致行为的包裹，并将其应用于来自静止状态fMRI数据的默认模式独立于网络的组件图套。通过k均值聚类将大脑体素划分为多个包裹，然后通过共识技术增强解决方案。TopKLists，用于组合每个类别的学科中的个人排名。为了进行比较，对相同的方法在解剖学上进行了测试。我们发现包裹在对照受试者和受帕金森氏病和肌萎缩性侧索硬化症影响的受试者中排名不同的包裹，并在文献中找到证据显示默认模式脑活动中排名最高的区域的相关性。提出的框架代表了一种从静止状态fMRI数据中识别功能性神经标记的有效方法，因此它可能构成了全自动数据驱动技术发展的一步，以支持神经退行性疾病的早期诊断。