by Joao Ricardo Sato, Chang Chiann, Eduardo Hiromassa Taniguchi, Emerson Gomes dos Santos, Paula Ricci Arantes, Maria Lucia Mourao, Edson Amaro Junior and Pedro Alberto Morettin

Journal of Data Science, v.6, no.1, 89-103

Abstract

Functional magnetic resonance imaging (fMRI) has, since its description fifteen years ago, become the most common in-vivo neuroimaging technique. FMRI allows the identification of brain areas which are related to specific tasks, by statistical analysis of the BOLD (blood oxigenation level dependent) signal. Classically, the observed BOLD signal is compared to an expected haemodynamic response function (HRF) using a general linear model (GLM). However, the results of GLM rely on the HRF specification, which is usually determined in an ad hoc fashion. For periodic experimental designs, we propose a multisubject frequency domain brain mapping, which requires only the stimulation frequency, and consequently avoids subjective choices of HRF. We present some computational simulations, which demonstrate a good performance of the proposed approach in short length time series. In addition, an application to real fMRI datasets is also presented.

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