Biomedical subjects

Epifanio Bagarinao

Publications and source records attributed to Epifanio Bagarinao.

4 recordsLinked to original sources

PubMed2006

Dynamic monitoring of brain activation under visual stimulation using fMRI--the advantage of real-time fMRI with sliding window GLM analysis.

An fMRI technique based on real-time analysis was applied to evaluate the advantages of dynamic monitoring of the t-statistics based on a general linear model. The temporal change of the t-statistics in V1 and V4 under four conditions of visual stimuli covering different visual fields with or without coloring was estimated using an incremental analysis and a sliding window analysis (SWA). The SWA not only visualized the dynamic change of the activation in response to the task conditions and switching, but also enabled us to evaluate the temporal correlation of the t-statistics among the four visual areas. It was suggested that the activity in the V4 was bilaterally organized, and the altering color stimuli gave stronger stimulation to the V1 than did the black and white stimuli. Although the activation map at each time point represents the brain activity during several task and rest blocks, a SWA will be useful to evaluate the transition of neuronal activation in response to several sequential task conditions. An incremental analysis will be useful to monitor the ongoing activation in real-time during the scan, since it gives a higher t-value according to the accumulation of volume data. These two methods will be complementary.

Adult↗

PubMed2006

Real-time functional MRI: development and emerging applications.

Real-time functional magnetic resonance imaging (fMRI) is an emerging technique for assessing the dynamic and robust changes in brain activation during an ongoing experiment. Real-time fMRI allows measurement of several processes within the brain as they occur. The extracted information can be used to monitor the quality of acquired data sets, serve as the basis for neurofeedback training, and manipulate scans for interactive paradigm designs. Although more work is needed, recent results have demonstrated a variety of potential applications for real-time fMRI for research and clinical use. We discuss these developments and focus on methods enabling real-time analysis of fMRI data sets, novel research applications arising from these approaches, and potential use of real-time fMRI in clinical settings.

Algorithms↗

PubMed2004

Application of independent component analysis to magnetic resonance imaging for enhancing the contrast of gray and white matter.

An application of independent component analysis (ICA) was attempted to develop a method of processing magnetic resonance (MR) images to extract physiologically independent components representing tissue relaxation times and achieve improved visualization of normal and pathologic structures. Anatomical T1-weighted, T2-weighted and proton density images were obtained from 10 normal subjects, 3 patients with brain tumors and 1 patient with multiple sclerosis. The data sets were analyzed using ICA based on the learning rule of Bell and Sejnowski after prewhitening operations. The three independent components obtained from the three original data sets corresponded to (1) short T1 components representing myelin of white matter and lipids, (2) relatively short T1 components representing gray matter and (3) long T2 components representing free water. The involvement of gray or white matter in brain tumor cases and the demyelination in the case of multiple sclerosis were enhanced and visualized in independent component images. ICA can potentially achieve separation of tissues with different relaxation characteristics and generate new contrast images of gray and white matter. With the proper choice of contrast for the original images, ICA may be useful not only for extracting subtle or hidden changes but also for preprocessing transformation before clustering and segmenting the structure of the human brain.

Adolescent↗

PubMed2002

Algorithm for vector autoregressive model parameter estimation using an orthogonalization procedure.

We review the derivation of the fast orthogonal search algorithm, first proposed by Korenberg, with emphasis on its application to the problem of estimating coefficient matrices of vector autoregressive models. New aspects of the algorithm not previously considered are examined. One of these is the application of the algorithm to estimate coefficient matrices of a vector autoregressive process with time-varying coefficients when multiple realizations of the said process are available. Computer simulations were also performed to characterize the statistical properties of the estimates. The results show that even for shorter time series the algorithm works well and obtains good estimates of the time-varying parameters. Statistical characterization indicates that the standard deviation of the estimates decreases as 1 square root N (N being the length of the time series), a typical behavior of least-squares estimators. Another key aspect of the approach, which has previously been considered, is its direct extension to the parameter estimation of vector nonlinear autoregressive models. Nonlinear terms can be added to the model and the same algorithm can be applied to effectively estimate their associated parameters. Using chaotic time series generated from the Lorenz equations, the algorithm produces a model that captures the nonlinear structure of the data and exhibits the same chaotic attractor as that of the original system.

Algorithms↗