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P L Purdon

Publications and source records attributed to P L Purdon.

4 recordsLinked to original sources
PubMed2001

Locally regularized spatiotemporal modeling and model comparison for functional MRI.

In this work we treat fMRI data analysis as a spatiotemporal system identification problem and address issues of model formulation, estimation, and model comparison. We present a new model that includes a physiologically based hemodynamic response and an empirically derived low-frequency noise model. We introduce an estimation method employing spatial regularization that improves the precision of spatially varying noise estimates. We call the algorithm locally regularized spatiotemporal (LRST) modeling. We develop a new model selection criterion and compare our model to the SPM-GLM method. Our findings suggest that our method offers a better approach to identifying appropriate statistical models for fMRI studies.

Arousal↗
PubMed1998

Effect of temporal autocorrelation due to physiological noise and stimulus paradigm on voxel-level false-positive rates in fMRI.

Statistical mapping within a binary hypothesis testing framework is the most widely used analytical method in functional MRI of the brain. A common assumption in this kind of analysis is that the fMRI time series are independent and identically distributed in time, yet we know that fMRI data can have significant temporal correlation due to low-frequency physiological fluctuation (Weisskoff et al. [1993]; Proc Soc Magn Reson Med 9:7; Biswal et al. [1995]: Mag Reson Med 34:537-541). Furthermore, since the signal-to-noise ratio will vary with imaging rate, we should expect that the degree of correlation will vary with imaging rate. In this paper, we investigate the effect of temporal correlation and experimental paradigm on false-positive rates (type I error rates), using data synthesized through a simple autoregressive plus white-noise model whose parameters were estimated from real data over a range of imaging rates. We demonstrate that actual false-positive rates can be biased far above or below the assumed significance level alpha when temporal autocorrelation is ignored in a way that depends on both the degree of correlation as well as the paradigm frequency. Furthermore, we present a simple method, based on the noise model described above, for correcting such distortions, and relate this method to the extended general linear model of Worsley and Friston ([1995]: Neuroimage 2:173-181).

Acoustic Stimulation↗
PubMed1996

Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations.

Fractal dynamics were recently detected in the apparently "noisy" variations in the stride interval of human walking. Dynamical analysis of these step-to-step fluctuations revealed a self-similar pattern: fluctuations at one time scale are statistically similar to those at multiple other time scales, at least over hundreds of steps, while healthy subjects walk at their normal rate. To study the stability of this fractal property, we analyzed data obtained from healthy subjects who walked for 1 h at their usual, slow, and fast paces. The stride interval fluctuations exhibited long-range correlations with power-law decay for up to 1,000 strides at all 3 walking rates. In contrast, during metronomically paced walking, these long-range correlations disappeared; variations in the stride interval were random (uncorrelated) and nonfractal. The long-range correlations observed during spontaneous walking were not affected by removal of drifts in the time series. Thus the fractal dynamics of spontaneous stride interval are normally quite robust and intrinsic to the locomotor system. Furthermore, this fractal property of neural output may be related to the higher nervous centers responsible for the control of walking rhythm.

Adult↗