Biomedical subjects

T Adali

Publications and source records attributed to T Adali.

8 recordsLinked to original sources

PubMed2003

Comparison of penalties resulting from first-order and all-order polarization mode dispersion distortions in optical fiber transmission systems.

We compare the eye-opening penalty from a first-order polarization mode dispersion (PMD) model with that from an all-order PMD model in optical fiber transmission systems. Evaluating the performance by taking into account only first-order PMD produces a good approximation of the true eye-opening penalty of uncompensated systems when the penalty is low. However, when the penalties are high, this model overestimates the penalty for outage probabilities in the range of interest for systems designers, which is typically approximately 10(-5) to 10(-6).

Journal Article↗

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↗

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↗

PubMed2001

Magnetic resonance image analysis by information theoretic criteria and stochastic site models.

Quantitative analysis of magnetic resonance (MR) images is a powerful tool for image-guided diagnosis, monitoring, and intervention. The major tasks involve tissue quantification and image segmentation where both the pixel and context images are considered. To extract clinically useful information from images that might be lacking in prior knowledge, we introduce an unsupervised tissue characterization algorithm that is both statistically principled and patient specific. The method uses adaptive standard finite normal mixture and inhomogeneous Markov random field models, whose parameters are estimated using expectation-maximization and relaxation labeling algorithms under information theoretic criteria. We demonstrate the successful applications of the approach with synthetic data sets and then with real MR brain images.

Algorithms↗

PubMed2000

A weighted least-squares algorithm for estimation and visualization of relative latencies in event-related functional MRI.

The properties of the hemodynamic latencies in functional maps have been relatively unexplored. Accurate methods of estimating hemodynamic latencies are needed to take advantage of this feature of fMRI. A fully automated, weighted least-squares (WLS) method for estimating temporal latencies is reported. Using a weighted linear model, the optimal latency and amplitude of the fMRI response can be determined for those voxels that pass a detection threshold. There is evidence from previous studies that the hemodynamic response may be time-locked to the stimulus within certain limits, less variable earlier in its evolution, and able to resolve information about relative hemodynamic timing. This information can be used to test hypotheses about the sequence and spatial distribution of neural activity. The method can be used to weight the earliest evolution of the hemodynamic response more heavily and decrease bias resulting from the hemodynamic response function. Additionally, the WLS method can control for varying response shapes across the brain and improve latency comparisons between brain regions. The WLS method was developed to study the properties of hemodynamic latencies, which may be increasingly important as event-related fMRI continues to be advanced.

Algorithms↗

PubMed2000

Automatic detection of foreign objects in computed radiography.

This paper presents an effective two-step scheme for automatic object detection in computed radiography (CR) images. First, various structure elements of the morphological filters, designed by incorporating available morphological features of the objects of interest including their sizes and rough shape descriptions, are used to effectively distinguish the foreign object candidates from the complex background structures. Second, since the boundaries of the objects are the key features in reflecting object characteristics, active contour models are employed to accurately outline the morphological shapes of the suspicious foreign objects to further reduce the rate of false alarms. The actual detection scheme is accomplished by jointly using these two steps. The proposed methods are tested with a database of 50 hand-wrist computed radiographic images containing various types of foreign objects. Our experimental results demonstrate that the combined use of morphological filters and active contour models can provide an effective automatic detection of foreign objects in CR images achieving good sensitivity and specificity, and the accurate descriptions of the object morphological characteristics.

Algorithms↗