Adaptive Cluster Analysis Approach for Functional Localization Using Magnetoencephalography

• Main Authors: Hooman eAlikhanian, J. Douglas eCrawford, Joseph F X DeSouza, Douglas eCheyne, Gunnar eBlohm
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2013-05-01
• Series: Frontiers in Neuroscience
• Subjects: machine learning; Cluster analysis; Magnetoencephalography (MEG); localization of function; beamforming
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fnins.2013.00073/full

In this paper we propose an agglomerative hierarchical clustering Ward’s algorithm in tandem with the Affinity Propagation algorithm to reliably localize active brain regions from magnetorencephalography (MEG) brain signals. Reliable localization of brain areas with MEG has been difficult due to variations in signal strength, and the spatial extend of the reconstructed activity. The proposed approach to resolve this difficulty is based on adaptive clustering on reconstructed beamformer images to find locations that are consistently active across different participants and experimental conditions with high spatial resolution. Using data from a human reaching task, we show that the method allows more accurate and reliable localization from MEG data alone without using functional magnetic resonance imaging (fMRI) or any other imaging techniques.