Applications of fMRI for Brain Mapping (original) (raw)
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Introduction to the Issue on fMRI Analysis for Human Brain Mapping
IEEE Journal of Selected Topics in Signal Processing, 2000
Functional magnetic resonance imaging (fMRI), one of the most recently developed forms of neuroimaging technology, allows noninvasive assessment of brain activity and has been aptly called "our window into the human brain". By enabling researchers to study temporal and spatial changes in both the healthy and the diseased brain as a function of various stimuli, fMRI has contributed significantly to our understanding of the brain, and its study has been one of the most active areas of research. The study of fMRI data is highly interdisciplinary due to its unique nature and particular challenges. Between the two main groups-the developers of the technology and the ultimate end users-there is a major shift and increasing recognition of the role signal processing plays for extracting, processing, analyzing and modeling fMRI data for human brain mapping. As a result, fMRI analysis for human brain mapping has been gaining importance and momentum within the signal processing community. This special issue aims to underline this major current trend and bring together a diverse but complementary set of contributions to address the current brain mapping challenges and the solutions where signal processing plays an important role.
Analysis of Fmri Data for Statistical Activation Mapping
Functional Magnetic Resonance Imaging (fMRI) is a non-invasive technique which shows great promise in providing neurological information on healthy subjects and clinical patients by mapping functional activation within the brain. The functional structure of human brain, the correlation between neural activities and the Blood-Oxygen-Level Dependent (BOLD) signal and fMRI experimental design techniques were studied in this work. The 2D and 3D anatomical high resolution and 3D + Time series (4D) low resolution functional images have been reconstructed and normalized. Spin echo-Echo Planar Imaging (EPI) technique has been used for obtaining fMRI data acquisition with spatially high resolution activation map overlaid on EPI image with the reduction of image acquisition time. The steps in the analysis of fMRI data were described and two statistical techniques, e.g., t-statistic and correlation analysis for data from single events have been proposed. The effect of Hemodynamic Response (HDR...
Exploring brain function with magnetic resonance imaging
European Journal of Radiology, 1999
Since its invention in the early 1990s, functional magnetic resonance imaging (fMRI) has rapidly assumed a leading role among the techniques used to localize brain activity. The spatial and temporal resolution provided by state-of-the-art MR technology and its non-invasive character, which allows multiple studies of the same subject, are some of the main advantages of fMRI over the other functional
Functional magnetic resonance imaging of the human brain: Data acquisition and analysis
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.
Neuroscience and Behavioral Physiology, 2000
Magnetic resonance tomography as a method is well established in the diagnostic algorithms for investigating patients with central nervous system diseases . However, the process of developing new applications for this methodology continues, and new results continue to accumulate, in neurology and neurosurgery and other fields . Neuroradiological methods have gradually entered practice, including magnetic resonance spectroscopy, diffusionweighted and perfusion-weighted magnetic resonance tomography and, finally, functional magnetic resonance tomography, which is based on contrast determined by the degree of blood oxygen saturation .
Pattern Classification and Analysis of Brain Maps through fMRI data with Multiple Methods
International Journal of Computer Applications, 2010
The activity patterns in functional Magnetic Resonance Imaging (fMRI) data are unique and located in specific location in the brain. The main aim of analyzing these datasets is to localize the areas of the brain that have been activated by a predefined stimulus [1]. The basic analysis involves carrying out a statistical test for activation at thousands of locations in the brain. The analysis is based on fMRI brain activation maps generated using the Statistical Parametric Mapping (SPM) approach. The use of individually generated activation maps with SPM allows for better scalability to very large subject pools and it has the potential to integrate data at the activation map level that would be technically difficult to combine at the raw data level. The fMRI data is huge, dimensionally dissimilar for different orientation data and also show a lot of variation in the data acquired for different subjects for similar activities. The variations are so obvious that there are variations in the data of same subject for different trails. In this context we have explored the possibility of different Pattern Recognition Technique on same data to choose the best option. The comparison of classification efficiency of two methods implemented: The Back Propagation Neural Network Technique and The Naïve Bayesian Technique show that the two are efficient in classification of the fMRI Patterns under different contexts.
Magnetic resonance imaging of human brain function
Surgical Neurology, 1996
BACKGROUND Previously the exclusive domain of the technology of positron emission tomography, functional MRl is now proving capable of mapping functional regions of the human cortex in near real time during specific task activations or in response to any hemodynamic stress. Of particular interest is the opportunity to observe secondary cortical responses, activation due to imagined tasks, memory function, time-resolved pathways through cortical regions, and activation in sub-cortical structures. METHODS AND RESULTS One method of functional MRl uses blood oxygenation changes, which can be imaged continuously while functional centers are being stimulated. Image intensity can become darker if there is more deoxygenated blood and brighter if more oxygenated blood enters the brain. This concept works in all perfused tissues in the body, and allows use of the blood oxygenation mechanism to image neuronal activation. A second method takes advantage of the fact that the protons within the MRf slice are always partially saturated by the rapid rate of imaging. As blood flow delivers unsaturated blood water protons into an imaged slice, these arterially-delivered protons will appear very bright in the image. Visualization of this effect is accomplished by simple image subtraction or by comparison of intensity changes as a function of the paradigm application frequency. Using either approach leads directly to a functional map. CONCLUSIONS At present, clinical applications are rapidly moving toward routine non-invasive mapping of distortions of the functional motor and somatosensory cortex and other cortical regions as a result of brain tumors. Other clinical applications include the observation of the effect of degenerative diseases such as multiple sclerosis, Alzheimer's disease, stroke, migraine, epilepsy, and other diseases causing neuronal loss and Parkinsonism. Functional MRI and its applications will continue to grow exponentially throughout the decade.
Biomedical engineering online, 2004
Functional magnetic resonance imaging (fMRI) is recently developing as imaging modality used for mapping hemodynamics of neuronal and motor event related tissue blood oxygen level dependence (BOLD) in terms of brain activation. Image processing is performed by segmentation and registration methods. Segmentation algorithms provide brain surface-based analysis, automated anatomical labeling of cortical fields in magnetic resonance data sets based on oxygen metabolic state. Registration algorithms provide geometric features using two or more imaging modalities to assure clinically useful neuronal and motor information of brain activation. This review article summarizes the physiological basis of fMRI signal, its origin, contrast enhancement, physical factors, anatomical labeling by segmentation, registration approaches with examples of visual and motor activity in brain. Latest developments are reviewed for clinical applications of fMRI along with other different neurophysiological and...