Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm.

Scott J Peltier; Thad A Polk; Douglas C Noll

doi:10.1002/hbm.10144

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Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm.

Scott J Peltier·Thad A Polk·Douglas C Noll

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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.

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Scott J Peltier, Thad A Polk, Douglas C Noll. 2003. Detecting low-frequency functional connectivity in fMRI using a self-organizing map (SOM) algorithm.. https://doi.org/10.1002/hbm.10144

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