Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution

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 HRFs. Results: ForWaRD is applied to fMRI time signals, after removing low-frequency trends by a wavelet-based method, and the output of ForWaRD is a time series of volumes, containing the HRF in each voxel. Compared to more complex methods, this extraction algorithm requires few assumptions (separability of signal and noise in the frequency and wavelet domains and the general linear model) and it is fast (HRF extraction from a single fMRI data set takes about the same time as spatial resampling). The extraction method is tested on simulated event-related activation signals, contaminated with noise from a time series of real MRI images. An application for HRF data is demonstrated in a simple event-related experiment: data are extracted from a region with significant effects of interest in a first time series. A continuous-time HRF is obtained by fitting a nonlinear function to the discrete HRF coeffcients, and is then used to analyse a later time series. Conclusion: With the parameters used in this paper, the extraction method presented here is very robust to changes in signal properties. Comparison of analyses with fitted HRFs and with a canonical HRF shows that a subject-specific, regional HRF significantly improves detection power. Sensitivity and specificity increase not only in the region from which the HRFs are extracted, but also in other regions of interest.

BOLD Noise Assumptions in fMRI.

This paper discusses the assumption of Gaussian noise in the blood-oxygenation-dependent (BOLD) contrast for functional MRI (fMRI). In principle, magnitudes in MRI images follow a Rice distribution. We start by reviewing differences between Rician and Gaussian noise. An analytic expression is derived for the null (resting-state) distribution of the difference between two Rician distributed images. This distribution is shown to be symmetric, and an exact expression for its standard deviation is derived. This distribution can be well approximated by a Gaussian, with very high precision for high SNR, and high precision for lower SNR. Tests on simulated and real MR images show that subtracting the time-series mean in fMRI yields asymmetrically distributed temporal noise. Subtracting a resting-state time series from the first results in symmetric and nearly Gaussian noise. This has important consequences for fMRI analyses using standard statistical tests.Peer Reviewe

DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites

Background: A typical step in the analysis of gene expression data is the determination of clusters of genes that exhibit similar expression patterns. Researchers are confronted with the seemingly arbitrary choice between numerous algorithms to perform cluster analysis. Results: We developed an exploratory application that benchmarks the results of clustering methods using functional annotations. In addition, a de novo DNA motif discovery algorithm is integrated in our program which identifies overrepresented DNA binding sites in the upstream DNA sequences of genes from the clusters that are indicative of sites of transcriptional control. The performance of our program was evaluated by comparing the original results of a time course experiment with the findings of our application. Conclusion: DISCLOSE assists researchers in the prokaryotic research community in systematically evaluating results of the application of a range of clustering algorithms to transcriptome data. Different performance measures allow to quickly and comprehensively determine the best suited clustering approach for a given dataset.

GSWO: A Programming Model for GPU-enabled Parallelization of Sliding Window Operations in Image Processing

Sliding Window Operations (SWOs) are widely used in image processing applications. They often have to be performed repeatedly across the target image, which can demand significant computing resources when processing large images with large windows. In applications in which real-time performance is essential, running these filters on a CPU often fails to deliver results within an acceptable timeframe. The emergence of sophisticated graphic processing units (GPUs) presents an opportunity to address this challenge. However, GPU programming requires a steep learning curve and is error-prone for novices, so the availability of a tool that can produce a GPU implementation automatically from the original CPU source code can provide an attractive means by which the GPU power can be harnessed effectively. This paper presents a GPUenabled programming model, called GSWO, which can assist GPU novices by converting their SWO-based image processing applications from the original C/C++ source code to CUDA code in a highly automated manner. This model includes a new set of simple SWO pragmas to generate GPU kernels and to support effective GPU memory management. We have implemented this programming model based on a CPU-to-GPU translator (C2GPU). Evaluations have been performed on a number of typical SWO image filters and applications. The experimental results show that the GSWO model is capable of efficiently accelerating these applications, with improved applicability and a speed-up of performance compared to several leading CPU-to- GPU source-to-source translators

HRFs extracted from the random-ISI experiment by : whole-volume (a) and region-specific (b)

×: extracted HRF coeffcients. Dashed lines: function HRFfitted to ×. 95% prediction intervals for the fitted functions are shown as error bars.<p><b>Copyright information:</b></p><p>Taken from "Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution"</p><p>http://www.biomedcentral.com/1471-2342/8/7</p><p>BMC Medical Imaging 2008;8():7-7.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2409308.</p><p></p

HRFs extracted from the fixed-ISI data by selective averaging (top row) and (bottom row)

Left: whole-volume, right: region-specific. The extracted coeffcient are the × at each TR. Dotted lines: fits of HRFto the coeffcients. Error bars show the 95% confidence intervals for the fitted function.<p><b>Copyright information:</b></p><p>Taken from "Data-driven haemodynamic response function extraction using Fourier-wavelet regularised deconvolution"</p><p>http://www.biomedcentral.com/1471-2342/8/7</p><p>BMC Medical Imaging 2008;8():7-7.</p><p>Published online 10 Apr 2008</p><p>PMCID:PMC2409308.</p><p></p