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Stochastic Rank Aggregation for the Identification of Functional Neuromarkers

机译：随机排名聚集用于鉴定功能性神经标志物

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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.
机译：分析来自对象（N> 100）的扩展样本的功能磁共振成像（FMRI）数据的主要挑战是从大量嘈杂数据中提取尽可能多的相关信息。在用休息状态FMRI研究神经变性疾病时，其中一个目的是确定具有相对于健康大脑的异常背景活动的区域，并且通常在一个或多个功能网络中施加到单体素或大脑包裹的比较统计模型常用。在这项工作中，我们提出了一种基于聚类和随机等级聚合的新方法，以识别由受同一混乱影响的受试者组中表现出相干行为的包裹，并将其应用于resting-state fmri数据的默认模式网络独立组件映射套。通过K-Means聚类将脑体素分配到包裹中，然后通过共识技术提高解决方案。对于每个受试者，群集根据其位值和随机排名聚集方法排序，以将各类受试者中的个体排名组合在一起。为了比较，在解剖局上测试了相同的方法。我们发现了由帕金森病的控制受试者和受试者不同的地块，受帕金森病的疾病和肌萎缩的外侧硬化，并在文献中发现了默认模式大脑活动中的顶部排名区域的文献证据。所提出的框架代表了一种有效的方法，用于鉴定来自静息状态的FMRI数据的功能性神经标志物，因此它可能构成了全自动数据驱动技术的发展中的前进，以支持神经变性疾病的早期诊断。

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