Simply correlating a sensory parameter (reflecting input at a neuronal level) with the hemodynamic response will miss significant crosscorrelations that are distributed in time according to the delay and dispersion of the hernodynamic response eter and hemodynamic response, the sensory parameter must first be subject to the same delay and dispersion as that mediating between neuronal activity and hemodynamics. This is important because the correlations between evoked changes in neuronal activity and measured hemodynamics will be displaced and dispersed (smeared) in time. SPMs significant correlations between a time-dependent are treated as smooth multidimensional statistical prosensorimotor or cognitive parameter and measured cesses and thresholded such that the probability of changes in neurophysiology MRI times-series (100 ms-5 s).
ANALYSIS OF FUNCTIONAL MRI TIME SERIES. HUMAN BRAIN MAPPING HOW TO
The problem no significant activations or correlations between senaddressed is how to identify regionally specific and sorimotor parameters and central physiology. The null hypothesis is usually that there are activation studies of the human brain. Key words: functional MRI, time-series, statistical parametric mapping, significance, visual, cross- correlations, autocorrelations INTRODUCTION Statistical parametric maps (SPMs) are images whose voxel values are distributed, under the null hypothThis article is about the analysis of functional mag- esis, according to some known probability density netic resonance imaging (MRI) data obtained during function. These autocorrelations are necessarily present, due to the hemodynamic response function or temporal point spread function. To lend the approach statistical validity, it is brought into the framework of statistical parametric mapping by using a measure of cross-correlations between sensory input and hemodynamic response that is valid in the presence of intrinsic autocorrelations. This estimate is obtained without reference to any assumed input. The method involves testing for correlations between sensory input and the hemodynamic response after convolving the sensory input with an estimate of the hernodynamic response function. Turner The Neurosciences Institute, La Jolla, California (K.J.F.) and Laboratory of Cardiac Energetics, NHLBI, NIH, Bethesda, Ma ryland (P.J., R.T.) Abstract: A method for detecting significant and regionally specific correlations between sensory input and the brain's physiologicalresponse, as measured with functional magnetic resonance imaging (MRI), is presented in this paper. Analysis of functional MRI time‐series Analysis of functional MRI time‐series
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