Density-based clustering of static and dynamic functional MRI connectivity features obtained from subjects with cognitive impairment.

ABSTRACT: Various machine-learning classification techniques have been employed previously to classify brain states in healthy and disease populations using functional magnetic resonance imaging (fMRI). These methods generally use supervised classifiers that are sensitive to outliers and require labeling of training data to generate a predictive model. Density-based clustering, which overcomes these issues, is a popular unsupervised learning approach whose utility for high-dimensional neuroimaging data has not been previously evaluated. Its advantages include insensitivity to outliers and ability to work with unlabeled data. Unlike the popular k-means clustering, the number of clusters need not be specified. In this study, we compare the performance of two popular density-based clustering methods, D

SUBMITTER: Rangaprakash D 

PROVIDER: S-EPMC7691406 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature