Biomedical subjects

Brandon Whitcher

Publications and source records attributed to Brandon Whitcher.

6 recordsLinked to original sources

PubMed2006

Study-level wavelet cluster analysis and data-driven signal models in pharmacological MRI.

In pharmacological MRI (phMRI) studies tracking signal changes following the acute administration of a compound, the spatiotemporal pattern of response is often unknown a priori. Moreover, when analysed within a general linear model (GLM) framework, the experimental paradigm of a single injection point under-informs the construction of an appropriate signal model, and information from pharmacokinetics or ancillary in vivo studies may be unavailable or insufficient to accurately describe the dynamic signal changes observed following injection of the drug. Here, we extend the application of a data-driven clustering algorithm, wavelet cluster analysis (WCA), to phMRI data from one or more groups of subjects in a study. A WCA decomposition of spatially concatenated time series' provides a compact overview of spatiotemporal response patterns across cohorts, highlighting typical temporal signatures, brain regions implicated in the response and inter-subject variability. Further, we demonstrate the use of regressors based on selected temporal components as suitable signal models in GLM-based analyses, resulting in a close fit to dynamic phMRI signal changes. This approach is illustrated with simulated data and two representative in vivo phMRI studies in the rat (nicotine and apomorphine challenges).

Animals↗

PubMed2006

Co-deposition of salmeterol and fluticasone propionate by a combination inhaler.

The combination of the long-acting beta2-agonist, salmeterol xinafoate (salmeterol) and inhaled corticosteroid, fluticasone propionate (FP) (Seretide/Advair) has shown enhanced efficacy compared with concurrent administration of the two drugs from individual inhalers at the same dose. A possible explanation for this increased effect is a higher degree of co-deposition of the two drugs from the combination (Seretide) inhaler compared with the component drugs administered separately. Raman laser spectroscopy, a technique capable of identifying individual drug particles, has been used with novel statistical methodology that we have developed, to determine whether there is any co-association between drug particles and whether this occurs in the Seretide formulation rather than by chance. Samples from a combined Seretide metered dose inhaler (MDI, 25/50 mcg) and salmeterol (25 mcg) with FP (50 mcg) from separate MDI's taken from Plate 4 of an Anderson Cascade Impactor were analysed. Using a statistical test based on the bootstrap technique, it was found that the co-deposition of FP and salmeterol particles from the combination MDI was significantly greater than from the separate inhalers group (p < 0.001). A higher degree of co-deposition on the same cells of the airways may possibly account for the increased efficacy observed in patients prescribed Seretide MDI.

Adrenergic beta-Agonists↗

PubMed2006

A resilient, low-frequency, small-world human brain functional network with highly connected association cortical hubs.

Small-world properties have been demonstrated for many complex networks. Here, we applied the discrete wavelet transform to functional magnetic resonance imaging (fMRI) time series, acquired from healthy volunteers in the resting state, to estimate frequency-dependent correlation matrices characterizing functional connectivity between 90 cortical and subcortical regions. After thresholding the wavelet correlation matrices to create undirected graphs of brain functional networks, we found a small-world topology of sparse connections most salient in the low-frequency interval 0.03-0.06 Hz. Global mean path length (2.49) was approximately equivalent to a comparable random network, whereas clustering (0.53) was two times greater; similar parameters have been reported for the network of anatomical connections in the macaque cortex. The human functional network was dominated by a neocortical core of highly connected hubs and had an exponentially truncated power law degree distribution. Hubs included recently evolved regions of the heteromodal association cortex, with long-distance connections to other regions, and more cliquishly connected regions of the unimodal association and primary cortices; paralimbic and limbic regions were topologically more peripheral. The network was more resilient to targeted attack on its hubs than a comparable scale-free network, but about equally resilient to random error. We conclude that correlated, low-frequency oscillations in human fMRI data have a small-world architecture that probably reflects underlying anatomical connectivity of the cortex. Because the major hubs of this network are critical for cognition, its slow dynamics could provide a physiological substrate for segregated and distributed information processing.

Adult↗

PubMed2006

Bayesian methods for pharmacokinetic models in dynamic contrast-enhanced magnetic resonance imaging.

This paper proposes a new method for estimating kinetic parameters of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) based on adaptive Gaussian Markov random fields. Kinetic parameter estimates using neighboring voxels reduce the observed variability in local tumor regions while preserving sharp transitions between heterogeneous tissue boundaries. Asymptotic results for standard errors from likelihood-based nonlinear regression are compared with those derived from the posterior distribution using Bayesian estimation with and without neighborhood information. Application of the method to the analysis of breast tumors based on kinetic parameters has shown that the use of Bayesian analysis combined with adaptive Gaussian Markov random fields provides improved convergence behavior and more consistent morphological and functional statistics.

Algorithms↗

PubMed2005

Wavelet-based cluster analysis: data-driven grouping of voxel time courses with application to perfusion-weighted and pharmacological MRI of the rat brain.

MRI time series experiments produce a wealth of information contained in two or three spatial dimensions that evolve over time. Such experiments can, for example, localize brain response to pharmacological stimuli, but frequently the spatiotemporal characteristics of the cerebral response are unknown a priori and variable, and thus difficult to evaluate using hypothesis-based methods alone. Here we used features in the temporal dimension to group voxels with similar time courses based on a nonparametric discrete wavelet transform (DWT) representation of each time course. Applying the DWT to each voxel decomposes its temporal information into coefficients associated with both time and scale. Discarding scales in the DWT that are associated with high-frequency oscillations (noise) provided a straight-forward data reduction step and decreased the computational burden. Optimization-based clustering was then applied to the remaining wavelet coefficients in order to produce a finite number of voxel clusters. This wavelet-based cluster analysis (WCA) was evaluated using two representative classes of MRI neuroimaging experiments. In perfusion-weighted MRI, following occlusion of the middle cerebral artery (MCAO), WCA differentiated healthy tissue and different regions within the ischemic hemisphere. Following an acute cocaine challenge, WCA localized subtle differences in the pharmacokinetic profile of the cerebral response. We conclude that WCA provides a robust method for blind analysis of time series image data.

Animals↗

PubMed2004

Wavelets and functional magnetic resonance imaging of the human brain.

The discrete wavelet transform (DWT) is widely used for multiresolution analysis and decorrelation or "whitening" of nonstationary time series and spatial processes. Wavelets are naturally appropriate for analysis of biological data, such as functional magnetic resonance images of the human brain, which often demonstrate scale invariant or fractal properties. We provide a brief formal introduction to key properties of the DWT and review the growing literature on its application to fMRI. We focus on three applications in particular: (i) wavelet coefficient resampling or "wavestrapping" of 1-D time series, 2- to 3-D spatial maps and 4-D spatiotemporal processes; (ii) wavelet-based estimators for signal and noise parameters of time series regression models assuming the errors are fractional Gaussian noise (fGn); and (iii) wavelet shrinkage in frequentist and Bayesian frameworks to support multiresolution hypothesis testing on spatially extended statistic maps. We conclude that the wavelet domain is a rich source of new concepts and techniques to enhance the power of statistical analysis of human fMRI data.

Algorithms↗