BACKGROUND We present a simple, data-driven method to extract haemodynamic response functions (HRF) from functional magnetic resonance imaging (fMRI) time series, based on the Fourier-wavelet regularised deconvolution (ForWaRD) technique. HRF data are required for many fMRI applications, such as defining region-specific HRFs, effciently representing a general HRF, or comparing subject-specific ...

Brain activity is accompanied by changes in cerebral blood flow (CBF) and the differential blood oxygenation that are detectable using functional magnetic resonance imaging (fMRI). The process of identifying brain activation regions can be facilitated by estimating the hemodynamic response function (HRF). There have been some remarkable new developments in statistics to handle this problem. In ...

The functional Magnetic Resonance imaging (fMRI) is currently the most prominent method used for functional brain imaging, and it is a big step forward in the process of answering the main question asked to all the functional imaging methods: What are the brain regions involved in mediating a specific brain function? And thought the fMRI’s obvious qualities have allowed for its fast acceptance ...

Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM) and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of...

Introduction The hemodynamic response function (HRF) of fMRI data is known to vary across subjects and brain regions [1]. Since HRF variability may be dictated, in part, by nonneuronal considerations, it has the potential to confound inferences about directional neuronal influences obtained from Granger causality (GC) analysis of fMRI data [2]. However, a systematic investigation of this confou...

Near-infrared spectroscopy (NIRS) is susceptible to signal artifacts caused by relative motion between NIRS optical fibers and the scalp. These artifacts can be very damaging to the utility of functional NIRS, particularly in challenging subject groups where motion can be unavoidable. A number of approaches to the removal of motion artifacts from NIRS data have been suggested. In this paper we ...

As near-infrared spectroscopy (NIRS) broadens its application area to different age and disease groups, motion artifacts in the NIRS signal due to subject movement is becoming an important challenge. Motion artifacts generally produce signal fluctuations that are larger than physiological NIRS signals, thus it is crucial to correct for them before obtaining an estimate of stimulus evoked hemody...

INTRODUCTION Over the past five years, functional magnetic resonance imaging (fMRI) of the human spinal cord has been developed and proven effective for localizing areas of neuronal activity within the spine in response to sensory stimuli [1]. However, fMRI of the spinal cord is currently not as reliable as fMRI of the brain because of several spine-specific issues. Breathing and heart-rate, as...

Estimation and inferences for the hemodynamic response functions (HRF) using multi-subject fMRI data are considered. Within the context of the General Linear Model, two new nonparametric estimators for the HRF are proposed. The first is a kernel-smoothed estimator, which is used to construct hypothesis tests on the entire HRF curve, in contrast to only summaries of the curve as in most existing...

A semi-parametric model for estimating hemodynamic response function (HRF) from multi-subject fMRI data is introduced within the context of the General Linear Model. The new model assumes that the HRFs for a fixed brain voxel under a given stimulus share the same unknown functional form across subjects, but differ in height, time to peak, and width. A nonparametric spline-smoothing method is de...