By Topic

An Evolutionary Approach to the Identification of Informative Voxel Clusters for Brain State Discrimination

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Aberg, M.B. ; Inst. of Neurosci. & Physiol., Univ. of Gothenburg, Gothenburg ; Wessberg, J.

We present a novel multivariate machine learning approach to the identification of voxel clusters containing brain state discriminating information, serving as a potentially more sensitive alternative to univariate activation detection. The proposed method consists of an evolutionary algorithm that, in conjunction with a classifier, extracts voxel clusters with a classification score above a pre-defined, above-chance threshold. The results can be displayed as two- or three-dimensional voxel discrimination relevance maps (VDRMs), indicating where and to what degree brain state classification is possible. When applied to a finger-tapping dataset numerous voxel clusters with impressive classification rates were identified, at best scoring an area under the receiver operating characteristic curve (ROC)-curve (AUC) of 1 within as well as between subjects. The location of high-scoring regions correlated well with functionally relevant areas as defined by the general linear model (GLM). Combining clusters for maximal classification scores as a feature selection approach outperformed the GLM T-map voxel ranking method (e.g., group level AUC of 0.908 compared to 0.785 for one cluster/200 voxels). Moreover, on data from a tactile study we show that the proposed algorithm can produce significant brain state discrimination scores where both the GLM and ROI-based classification fail to detect significantly activated voxels. Finally, we demonstrate that the algorithm can be successfully applied to data with more than two conditions and hence produce multiclass voxel relevance maps. The proposed evolutionary classification scheme has thus proven excellent in identifying voxel clusters that contain information about given brain states, which can be utilized not only for maximal single-volume fMRI classification, but also for multivariate, multiclass, highly sensitive functional brain mapping.

Published in:

Selected Topics in Signal Processing, IEEE Journal of  (Volume:2 ,  Issue: 6 )