Biomedical subjects

V D Calhoun

Publications and source records attributed to V D Calhoun.

6 recordsLinked to original sources

PubMed2002

Independent component analysis of fMRI data in the complex domain.

In BOLD fMRI a series of MR images is acquired and examined for task-related amplitude changes. These functional changes are small, so it is important to maximize detection efficiency. Virtually all fMRI processing strategies utilize magnitude information and ignore the phase, resulting in an unnecessary loss of efficiency. As the optimum way to model the phase information is not clear, a flexible modeling technique is useful. To analyze complex data sets, independent component analysis (ICA), a data-driven approach, is proposed. In ICA, the data are modeled as spatially independent components multiplied by their respective time-courses. There are thus three possible approaches: 1) the time-courses can be complex-valued, 2) the images can be complex-valued, or 3) both the time-courses and the images can be complex-valued. These analytic approaches are applied to data from a visual stimulation paradigm, and results from three complex analysis models are presented and compared with magnitude-only results. Using the criterion of the number of contiguous activated voxels at a given threshold, an average of 12-23% more voxels are detected by complex-valued ICA estimation at a threshold of /Z/ > 2.5. Additionally, preliminary results from the complex models reveal a phase modulation similar to the magnitude time-course in some voxels, and oppositely modulated in other voxels.

Brain↗

PubMed2002

Rapid method for correction of CSF partial volume in quantitative proton MR spectroscopic imaging.

Partial volume effects with cerebrospinal fluid (CSF), if uncorrected, can lead to underestimation of metabolite concentrations in quantitative proton magnetic resonance spectroscopic imaging (MRSI) of the brain. A rapid method for the correction of CSF partial volume effects is described based on selective CSF imaging using long echo time (TE) fast spin echo (FSE) magnetic resonance imaging (MRI). In order to achieve maximum suppression of signal from brain parenchyma, the FSE sequence is coupled with an inversion recovery (IR) pulse. Scan time is minimized using single shot (SS) IR-FSE. The method is validated against a current "gold standard" for the determination of CSF volumes, namely, segmented 3D spoiled gradient-echo (SPGR) scans. Excellent agreement in CSF percentage determined by the two methods was found (linear regression analysis: slope = 0.99 +/- 0.02, intercept = 2.08 +/- 0.45; mean +/- standard errors, R = 0.93) in pooled data from four healthy subjects. An example of the use of SS-IR-FSE for partial volume correction in a leukodystrophy patient with T(2) hyperintense lesions is demonstrated. SS-IR-FSE is a simple and rapid method for applying partial volume corrections in quantitative proton MRSI, which may be of particular value in the clinical environment when time constraints do not allow longer, perhaps more accurate segmentation methods to be used.

Adult↗

PubMed2001

Spatial and temporal independent component analysis of functional MRI data containing a pair of task-related waveforms.

Independent component analysis (ICA) is a technique that attempts to separate data into maximally independent groups. Achieving maximal independence in space or time yields two varieties of ICA meaningful for functional MRI (fMRI) applications: spatial ICA (SICA) and temporal ICA (TICA). SICA has so far dominated the application of ICA to fMRI. The objective of these experiments was to study ICA with two predictable components present and evaluate the importance of the underlying independence assumption in the application of ICA. Four novel visual activation paradigms were designed, each consisting of two spatiotemporal components that were either spatially dependent, temporally dependent, both spatially and temporally dependent, or spatially and temporally uncorrelated, respectively. Simulated data were generated and fMRI data from six subjects were acquired using these paradigms. Data from each paradigm were analyzed with regression analysis in order to determine if the signal was occurring as expected. Spatial and temporal ICA were then applied to these data, with the general result that ICA found components only where expected, e.g., S(T)ICA "failed" (i.e., yielded independent components unrelated to the "self-evident" components) for paradigms that were spatially (temporally) dependent, and "worked" otherwise. Regression analysis proved a useful "check" for these data, however strong hypotheses will not always be available, and a strength of ICA is that it can characterize data without making specific modeling assumptions. We report a careful examination of some of the assumptions behind ICA methodologies, provide examples of when applying ICA would provide difficult-to-interpret results, and offer suggestions for applying ICA to fMRI data especially when more than one task-related component is present in the data.

Algorithms↗

PubMed2001

A method for making group inferences from functional MRI data using independent component analysis.

Independent component analysis (ICA) is a promising analysis method that is being increasingly applied to fMRI data. A principal advantage of this approach is its applicability to cognitive paradigms for which detailed models of brain activity are not available. Independent component analysis has been successfully utilized to analyze single-subject fMRI data sets, and an extension of this work would be to provide for group inferences. However, unlike univariate methods (e.g., regression analysis, Kolmogorov-Smirnov statistics), ICA does not naturally generalize to a method suitable for drawing inferences about groups of subjects. We introduce a novel approach for drawing group inferences using ICA of fMRI data, and present its application to a simple visual paradigm that alternately stimulates the left or right visual field. Our group ICA analysis revealed task-related components in left and right visual cortex, a transiently task-related component in bilateral occipital/parietal cortex, and a non-task-related component in bilateral visual association cortex. We address issues involved in the use of ICA as an fMRI analysis method such as: (1) How many components should be calculated? (2) How are these components to be combined across subjects? (3) How should the final results be thresholded and/or presented? We show that the methodology we present provides answers to these questions and lay out a process for making group inferences from fMRI data using independent component analysis.

Algorithms↗

PubMed2001

fMRI activation in a visual-perception task: network of areas detected using the general linear model and independent components analysis.

The Motor-Free Visual Perception Test, revised (MVPT-R), provides a measure of visual perceptual processing. It involves different cognitive elements including visual discrimination, spatial relationships, and mental rotation. We adapted the MVPT-R to an event-related functional MRI (fMRI) environment to investigate the brain regions involved in the interrelation of these cognitive elements. Two complementary analysis methods were employed to characterize the fMRI data: (a) a general linear model SPM approach based upon a model of the time course and a hemodynamic response estimate and (b) independent component analysis (ICA), which does not constrain the specific shape of the time course per se, although we did require it to be at least transiently task-related. Additionally, we implemented ICA in a novel way to create a group average that was compared with the SPM group results. Both methods yielded similar, but not identical, results and detected a network of robustly activated visual, inferior parietal, and frontal eye-field areas as well as thalamus and cerebellum. SPM appeared to be the more sensitive method and has a well-developed theoretical approach to thresholding. The ICA method segregated functional elements into separate maps and identified additional regions with extended activation in response to presented events. The results demonstrate the utility of complementary analyses for fMRI data and suggest that the cerebellum may play a significant role in visual perceptual processing. Additionally, results illustrate functional connectivity between frontal eye fields and prefrontal and parietal regions.

Attention↗

PubMed1997

A finite element model for predicting the distribution of drugs delivered intracranially to the brain.

Drug therapy to the central nervous system is complicated by the presence of the blood-brain barrier. The development of new drug delivery techniques to overcome this obstacle will be aided by a clear understanding of the transport processes in the brain. A rigorous theoretical framework of the transport of drugs delivered locally to the parenchyma has been developed using the finite element method. Magnetic resonance imaging has been used to track the transport of paramagnetic contrast markers in the brain. The information obtained by postprocessing spin-echo, T1-weighted, and proton density images has been used to refine the mathematical model that includes realistic brain geometry and salient anatomic features and allows for two-dimensional transport of chemical species, including both diffusive and convective contributions. In addition, the effects of regional differences in tissue properties, ventricular boundary, and edema on the transport have been considered. The model has been used to predict transport of interleukin-2 in the brain and study the major determinants of transport, at both early and late times after drug delivery.

Animals↗