Stochastic Rank Aggregation for the Identification of Functional Neuromarkers

• Published in: Neuroinformatics (Totowa, N.J.) Vol. 17; no. 4; pp. 479 - 496
• Main Authors: Galdi, Paola, Fratello, Michele, Trojsi, Francesca, Russo, Antonio, Tedeschi, Gioacchino, Tagliaferri, Roberto, Esposito, Fabrizio
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.10.2019; Springer Nature B.V
• Subjects: Adult; Aged; Aged, 80 and over; Amyotrophic lateral sclerosis; Bioinformatics; Biomedical and Life Sciences; Biomedicine; Brain - diagnostic imaging; Brain mapping; Brain Mapping - methods; Cluster Analysis; Cohort Studies; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Female; Functional magnetic resonance imaging; Humans; Information processing; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Male; Mathematical models; Middle Aged; Neurodegenerative diseases; Neurodegenerative Diseases - diagnostic imaging; Neuroimaging; Neurology; Neurosciences; Original Article; Rest; Statistical analysis; Stochastic Processes; Default mode network; Clustering; Stochastic rank aggregation; fMRI data analysis; Independent component analysis
• ISSN: 1539-2791; 1559-0089; 1559-0089
• DOI: 10.1007/s12021-018-9412-y

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.