ijrte0102173175[A Note On Visualization Of Brain Information]

This paper presents a general approach to the analysis of functional MRI timeseries for one or more subjects. The approach seeks a compromise between efficiency, generality, validity, simplicity and execution speed. The main differences between this technique and previous ones are: (i) a simple bias reduction and regularization of voxel wise autoregressive model parameters, (ii) the combination of effects and their estimated standard deviations across different runs/sessions/subjects, and (iii) overcoming the problem of small number of runs/sessions/subjects. The proposed method was applied to real fMRI database acquired at different fMRI acquisition parameters. The ability of the system in detecting neuronal activation was tested using standard statistical tests implemented in statistical parametric mapping (SPM).

Experimental Brain Research, 1998

It is now feasible to create spatial maps of activity in the human brain completely non-invasively using magnetic resonance imaging. Magnetic resonance imaging (MRI) images in which the spin magnetization is refocussed by gradient switching are sensitive to local changes in magnetic susceptibility, which can occur when the oxygenation state of blood changes. Cortical neural activity causes increases in blood flow, which usually result in changes in blood oxygenation. Hence changes of image intensity can be observed, given rise to the socalled Blood Oxygenation Level Dependent (BOLD) contrast technique. Use of echo-planar imaging methods (EPI) allows the monitoring over the entire brain of such changes in real time. A temporal resolution of 1±3 s, and a spatial resolution of 2 mm in-plane, can thus be obtained. Generally in a brain mapping experiment hundred of brain image volumes are acquired at repeat times of 1±6 s, while brain tasks are performed. The data are transformed into statistical maps of image difference, using the technique known as statistical parametric mapping (SPM). This method, based on robust multilinear regression techniques, has become the method of reference for analysis of positron emission tomography (PET) image data. The special characteristics of functional MRI data require some modification of SPM algorithms and strategies, and the MRI data must be gaussianized in time and space to conform to the assumptions of the statistics of Gaussian random fields. The steps of analysis comprise: removal of head movement effects, spatial smoothing, and statistical interference, which includes temporal smoothing and removal by fitting of temporal variations slower than the experimental paradigm. By these means, activation maps can be generated with great flexibility and statistical power, giving probability estimates for activated brain regions based on intensity or spatial extent, or both combined. Recent studies have shown that patterns of activation obtained in human brain for a given stimulus are indepen-dent of the order and spatial orientation with which MRI images are acquired, and hence that inflow effects are not important for EPI data with a TR much longer than T1.

The insight journal, 2006

Shape analysis has become of increasing interest to the neuroimaging community due to its potential to precisely locate morphological changes between healthy and pathological structures. This manuscript presents a comprehensive set of tools for the computation of 3D structural statistical shape analysis. It has been applied in several studies on brain morphometry, but can potentially be employed in other 3D shape problems. Its main limitations is the necessity of spherical topology.The input of the proposed shape analysis is a set of binary segmentation of a single brain structure, such as the hippocampus or caudate. These segmentations are converted into a corresponding spherical harmonic description (SPHARM), which is then sampled into a triangulated surfaces (SPHARM-PDM). After alignment, differences between groups of surfaces are computed using the Hotelling T(2) two sample metric. Statistical p-values, both raw and corrected for multiple comparisons, result in significance maps...

Biomedical Engineering, Trends, Research and Technologies, 2011

Neuroimage, 2006

A major attraction of voxel-based morphometry (VBM) is that it allows researchers to explore large datasets with minimal human intervention. However, the validity and sensitivity of the Statistical Parametric Mapping (SPM2) approach to VBM is the subject of considerable debate. We visually inspected the SPM2 gray matter segmentations for 101 research participants and found a gross inclusion of non-brain tissue surrounding the entire brain as gray matter in five subjects, and focal areas bordering the brain in which non-brain tissue was classified as gray matter in many other subjects. We also found many areas in which the cortical grey matter was incorrectly excluded from the segmentation of the brain. The major source of these errors was the misregistration of individual brain images with the reference T1-weighted brain template. These errors could be eliminated if SPM2 operated on images from which non-brain tissues (scalp, skull, and meninges) are removed (brain-extracted images). We developed a modified SPM2 processing pipeline that used brainextracted images as inputs to test this hypothesis. We describe the modifications to the SPM2 pipeline that allow analysis of brain-extracted inputs. Using brain-extracted inputs eliminated the non-brain matter inclusions and the cortical gray matter exclusions noted above, reducing the residual mean square errors (RMSEs, the error term of the SPM2 statistical analyses) by over thirty percent. We show how this reduction in the RMSEs profoundly affects power analyses. SPM2 analyses of brainextracted images may require sample sizes only half as great as analyses of non-brain extracted images.

In the past a versatile medical image analysis environment has been built, that includes a rich set of visualisation functions for 3D atlases and 3D modality images. Later on, the application has been extended with a novel projection method, which unwraps the complete cortical surface of the brain in a 2D plane without severe geometric distortion. This flat map enables the user to follow the anatomical structures, i.e. the sulci, in their entirety, so that it becomes much easier to recognise them. In order to promote this new visualisation method, a web-based course on cortical brain anatomy has been elaborated on the basis of the material provided by experts in brain anatomy, who identified the anatomical structures in a number of flat maps from different subjects. In addition, these labelled anatomical structures were further processed and represented under the form of a probabilistic atlas. In this paper, we will only briefly discuss the theoretical aspects of this atlas, and show how it can be used by an intelligent labeling tool, meant to assist the students during learning scenarios.

Morphometry is identifying and characterizing differences and correlations between brain shapes among population. Study of brain shape has drawn attention among many researchers on different diseases counting Schizophrenia, dyslexia, autism, Alzheimer and turner's syndrome. Many approaches have been proposed for computer-assisted diagnostic taxonomy. Several significant progressive brain changes occur during aging. Pattern-Based morphometry is a robust application for measuring the change in brain parametric mapping. By reason of bias featuring in image along with the algorithm, have a penalty term and inverse consistency that are necessary to oversight the change in non-biological structure. Morphometric analysis forces a pact between the sensitivity and specificity that has drawn reporting or increasing attention in the field of biological science. A novel technique illustrating the brain tissue aging survey with surface-based and multivariate pattern of morphometric brain change To keep the robustness and specificity contributed by the spatial term and cortical analyses, while maintaining the localization and sensitivity Experimental results propose a greater inter variability within normal aging as well as the generation of more sensitive based morphometry in brain survey.

Proceedings : a conference of the American Medical Informatics Association / ... AMIA Annual Fall Symposium. AMIA Fall Symposium, 1996

The Human Brain Project was initiated with the goal of developing methods for managing and sharing information about the brain. As a prototype Human Brain Project application we are developing a system for organizing, visualizing, integrating and sharing information about human language function. The goal of the brain mapping component of our work, described in this article, is to generate the 3D location and extent of cortical language sites with respect to a uniform, 3D patient coordinate system. The language sites of individual patients can then be combined with or related to other patient data in terms of a Talairach, surface-based, or other deformable coordinate systems. Language site mapping is done by visually comparing an intraoperative photograph with the rendered image (from MRI data). The techniques outlined in this article have been utilized to map cortical language sites of six patients. Preliminary results point to the adequacy of our volume visualizations for language...