Identification and localization of human brain activity patterns using particle swarm optimization

Classifier-based multivariate pattern recognition techniques have in recent years enabled highly sensitive mapping of brain regions where mind states can be decoded from functional magnetic resonance imaging (fMRI) data. The ¿searchlight¿ mapping approach, where the brain volume is exhaustively scanned with a fixed-size search volume, is highly appealing in terms of sensitivity but also exceedingly time consuming. We therefore propose an efficient, easily-implemented particle swarm optimization (PSO) brain mapping method, where fixed-size, fixed-shape (spherical) particles search the brain volume for informative regions where a classifier can decode the mind states. Particle positions and velocities are encoded in Cartesian coordinates, and niching techniques are used to identify multiple informative brain regions. We demonstrate the versatility of the algorithm in combination with linear discriminant analysis (LDA) and linear and non-linear support vector machines (SVMs) to decode brain states on simulated as well as authentic fMRI data. The PSO method outperformed the conventional general linear model (GLM) method in terms of mapping sensitivity, and compared favorably with the ¿searchlight¿ algorithm - for a dramatic reduction in time requirements (e.g. 6.7 min compared to 9 h for a minute reduction in mapping sensitivity). On the authentic fMRI dataset, expected brain regions were identified. The PSO algorithm is a promising highly efficient multivariate alternative for functional brain mapping and brain state decoding.