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• W2804787249 abstract "Abstract Background and aims 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. Methods 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. Results 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. Conclusions 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." @default.
• W2804787249 created "2018-06-01" @default.
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• W2804787249 date "2018-05-24" @default.
• W2804787249 modified "2023-09-23" @default.
• W2804787249 title "Stochastic rank aggregation for the identification of functional neuromarkers" @default.
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• W2804787249 doi "https://doi.org/10.1101/329383" @default.
• W2804787249 hasPublicationYear "2018" @default.
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