title: Unsupervised learning of brain states from fMRI data.
abstract: The use of multivariate pattern recognition for the analysis of neural representations encoded in fMRI data has become a significant research topic, with wide applications in neuroscience and psychology. A popular approach is to learn a mapping from the data to the observed behavior. However, identifying the instantaneous cognitive state without reference to external conditions is a relatively unexplored problem and could provide important insights into mental processes. In this paper, we present preliminary but promising results from the application of an unsupervised learning technique to identify distinct brain states. The temporal ordering of the states were seen to be synchronized with the experimental conditions, while the spatial distribution of activity in a state conformed with the expected functional recruitment.
authors.affiliation: Dept. of Computer Science, The Ohio State University, USA.
authors.forename: F
authors.initials: F
authors.lastname: Janoos
authors.forename: R
authors.initials: R
authors.lastname: Machiraju
authors.forename: S
authors.initials: S
authors.lastname: Sammet
authors.forename: M V
authors.initials: MV
authors.lastname: Knopp
authors.forename: I A
authors.initials: IA
authors.lastname: Mórocz
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000502:physiology*; D001931:Brain Mapping / Q000379:methods*; D003071:Cognition / Q000502:physiology*; D005071:Evoked Potentials / Q000502:physiology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity