title: Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm.
abstract: Low-frequency oscillations (<0.08 Hz) have been detected in functional MRI studies, and appear to be synchronized between functionally related areas. A current challenge is to detect these patterns without using an external reference. Self-organizing maps (SOMs) offer a way to automatically group data without requiring a user-biased reference function or region of interest. Resting state functional MRI data was classified using a self-organizing map (SOM). Functional connectivity between the left and right motor cortices was detected in five subjects, and was comparable to results from a reference-based approach. SOMs are shown to be an attractive option in detecting functional connectivity using a model-free approach. Hum. Brain Mapping 20:220-226, 2003.
authors.affiliation: Department of Applied Physics, University of Michigan, Ann Arbor, Michigan, USA. speltier@bme.emory.edu
authors.forename: Scott J
authors.initials: SJ
authors.lastname: Peltier
authors.forename: Thad A
authors.initials: TA
authors.lastname: Polk
authors.forename: Douglas C
authors.initials: DC
authors.lastname: Noll
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D008279:Magnetic Resonance Imaging; D009044:Motor Cortex / Q000502:physiology*; D009434:Neural Pathways / Q000502:physiology