A least angle regression method for fMRI activation detection in phase-encoded experimental designs

• Published in: NeuroImage (Orlando, Fla.) Vol. 52; no. 4; pp. 1390 - 1400
• Main Authors: Li, Xingfeng, Coyle, Damien, Maguire, Liam, McGinnity, Thomas M, Watson, David R, Benali, Habib
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.10.2010; Elsevier Limited
• Subjects: Algorithms; Brain - physiology; Brain activation detection; Brain Mapping - methods; Data analysis; Data Interpretation, Statistical; Dictionaries; Evoked Potentials - physiology; Fast orthogonal search; fMRI time series; Humans; Hypotheses; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; Least angle regression; Magnetic Resonance Imaging - methods; Model selection; NMR; Nuclear magnetic resonance; Population; Regression Analysis; Reproducibility of Results; Sensitivity and Specificity; fMRI time series; Model selection; Brain activation detection; Least angle regression; Fast orthogonal search

This paper presents a new regression method for functional magnetic resonance imaging (fMRI) activation detection. Unlike general linear models (GLM), this method is based on selecting models for activation detection adaptively which overcomes the limitation of requiring a predefined design matrix i...