Classification of fMRI independent components using IC- fingerprints and support vector machine classifiers

• Published in: NeuroImage (Orlando, Fla.) Vol. 34; no. 1; pp. 177 - 194
• Main Authors: De Martino, Federico, Gentile, Francesco, Esposito, Fabrizio, Balsi, Marco, Di Salle, Francesco, Goebel, Rainer, Formisano, Elia
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 2007; Elsevier Limited
• Subjects: Algorithms; Brain - anatomy & histology; Classification; Decomposition; Entropy; Humans; Hypotheses; Image Processing, Computer-Assisted; Magnetic Resonance Imaging - classification; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - statistics & numerical data; Methods; Noise; Principal components analysis; Time series

We present a general method for the classification of independent components (ICs) extracted from functional MRI (fMRI) data sets. The method consists of two steps. In the first step, each fMRI-IC is associated with an IC -fingerprint, i.e., a representation of the component in a multidimensional sp...