Kernel regression for fMRI pattern prediction

C Chu, Y Ni, G Tan, C Saunders, J Ashburner

Research output: Contribution to journalArticle (Academic Journal)peer-review

61 Citations (Scopus)


This paper introduces two kernel-based regression schemes to decode or predict brain states from functional brain scans as part of the Pittsburgh Brain Activity Interpretation Competition (PBAIC) 2007, in which our team was awarded first place. Our procedure involved image realignment, spatial smoothing, detrending of low-frequency drifts, and application of multivariate linear and non-linear kernel regression methods: namely kernel ridge regression (KRR) and relevance vector regression (RVR). RVR is based on a Bayesian framework, which automatically determines a sparse solution through maximization of marginal likelihood. KRR is the dual-form formulation of ridge regression, which solves regression problems with high dimensional data in a computationally efficient way. Feature selection based on prior knowledge about human brain function was also used. Post-processing by constrained deconvolution and re-convolution was used to furnish the prediction. This paper also contains a detailed description of how prior knowledge was used to fine tune predictions of specific “feature ratings,” which we believe is one of the key factors in our prediction accuracy. The impact of pre-processing was also evaluated, demonstrating that different pre-processing may lead to significantly different accuracies. Although the original work was aimed at the PBAIC, many techniques described in this paper can be generally applied to any fMRI decoding works to increase the prediction accuracy.
Translated title of the contributionKernel regression for fMRI pattern prediction
Original languageEnglish
VolumeIn Press
Publication statusPublished - Mar 2010

Bibliographical note

Publisher: Elsevier


Dive into the research topics of 'Kernel regression for fMRI pattern prediction'. Together they form a unique fingerprint.

Cite this