A pattern recognition approach to the detection of single-trial event-related fMRI.

Abstract

Functional magnetic resonance imaging (FMRI) is an imaging technique for determining which regions of the brain are activated in response to a stimulus or event. Early FMRI experiment paradigms were based upon those used in positron emission tomography (PET), i.e. employing a block design consisting of extended periods of `on? against `off ? activations. More recent experiments were based on event-related FMRI, harnessing the fact that very short stimuli trains or single events can generate robust responses. FMRI data suffer from low signal-to-noise ratios, and typical event-related experiment paradigms employ selective averaging over many trials before using statistical methods for determining active brain regions. The paper reports a pattern recognition approach to the detection of single-trial FMRI responses without recourse to averaging and at modest ?eld strengths (1.5 T). Linear discrimi- nant analysis (LDA) was applied in conjunction with different feature extraction techniques. Use of the unprocessed data samples as features resulted in single- trial events being classi?ed with an accuracy of 61.0 ? 9.5% over ?ve subjects. To improve classi?cation accuracy, knowledge of the ideal template haemodynamic response was used in the feature extraction stage. A novel application of parametric modelling yielded an accuracy of 69.8 ? 6.3%, and a matched ?ltering approach yielded an accuracy of 71.9 ? 5.4%. Single-trial detection of event-related FMRI may yield new ways of examining the brain by facilitating new adaptive experiment designs and enabling tight integration with other single-trial electrophysiological methods.epi