Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging

Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging

Manel Martínez-Ramón (Universidad Carlos III de Madrid, Spain), Vladimir Koltchinskii (Georgia Institute of Technology, USA), Gregory L. Heileman (University of New Mexico, USA) and Stefan Posse (University of New Mexico, USA)
Copyright: © 2007 |Pages: 26
DOI: 10.4018/978-1-59904-042-4.ch005
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Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in neuroscience. It is intended to automatically identify differences in distributed neural substrates resulting from cognitive tasks. Previous works in fMRI classification revealed that information is organized in coarse areas in the neural tissues rather than in small neural microstructures. This fact opens a field of study of the functional areas of the brain from the multivariate analysis of the rather coarse images provided by fMRI. Nevertheless, reliable pattern classification is challenging due to the high dimensionality of fMRI data, the small number of available data sets, interindividual differences, and dependence on the acquisition methodology. The application of kernel methods and, in particular, SVMs, to pattern recognition of fMRI is a reasonable approach to deal with these difficulties and has given reasonable results in accuracy and generalization ability. Some of the most relevant fMRI classification studies using SVMs are analyzed in this chapter. All of them were applied in individual subjects using ad hoc techniques to isolate small brain areas in order to reduce the dimensionality of the problem. Some of them included blind techniques for feature selection; others used the previous knowledge of the human brain to isolate the areas in which the information is presumed to lie. Nevertheless, these methods do not explicitly address the dimensionality, small data sets, or cross-subject classification issues. We present an approach to improve multiclass classification across groups of subjects, field strengths, and fMRI methods. We use an approach based on the segmentation of the brain in functional areas using a neuroanatomical atlas, and each map is classified separately using local classifiers. A single multiclass output is applied using an Adaboost aggregation of the classifier’s outputs. This Adaboost combined the region-specific classifiers to achieve improved classification accuracy with respect to conventional techniques without previous ad hoc area or voxel selection.

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Table of Contents
Bernhard Schölkopf
Gustavo Camps-Valls, José Luis Rojo-Álvarez, Manel Martínez-Ramón
Gustavo Camps-Valls, José Luis Rojo-Álvarez, Manel Martínez-Ramón
List of Reviewers
Chapter 1
Nello Cristianini, John Shawe-Taylor, Craig Saunders
During the past decade, a major revolution has taken place in pattern-recognition technology with the introduction of rigorous and powerful... Sample PDF
Kernel Methods: A Paradigm for Pattern Analysis
Chapter 2
Jean-Philippe Vert
Support vector machines and kernel methods are increasingly popular in genomics and computational biology due to their good performance in... Sample PDF
Kernel Methods in Genomics and Computational Biology
Chapter 3
Nathalie L.M.M. Pochet, Fabian Ojeda, Frank De Smet, Tijl De Bie, Johan A.K. Suykens
Clustering techniques like k-means and hierarchical clustering have shown to be useful when applied to microarray data for the identification of... Sample PDF
Kernel Clustering for Knowledge Discovery in Clinical Microarray Data Analysis
Chapter 4
Stanislaw Osowski, Tomasz Markiewicz
This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow... Sample PDF
Support Vector Machine for Recognition of White Blood Cells of Leukaemia
Chapter 5
Manel Martínez-Ramón, Vladimir Koltchinskii, Gregory L. Heileman, Stefan Posse
Pattern recognition in functional magnetic resource imaging (fMRI) is a novel technique that may lead to a quantity of discovery tools in... Sample PDF
Classification of Multiple Interleaved Human Brain Tasks in Functional Magnetic Resonance Imaging
Chapter 6
José Luis Rojo-Álvarez, Manel Martínez-Ramón, Gustavo Camps-Valls, Carlos E. Martínez-Cruz, Carlos Figuera
Digital signal processing (DSP) of time series using SVM has been addressed in the literature with a straightforward application of the SVM kernel... Sample PDF
Discrete Time Signal Processing Framework with Support Vector Machines
Chapter 7
M. Julia Fernández-Getino García, José Luis Rojo-Álvarez, Víctor P. Gil-Jiménez, Felipe Alonso-Atienza, Ana García-Armada
Most of the approaches to digital communication applications using support vector machines (SVMs) rely on the conventional classification and... Sample PDF
A Complex Support Vector Machine Approach to OFDM Coherent Demodulation
Chapter 8
Christos Christodoulou, Manel Martínez-Ramón
Support vector machines (SVMs) are a good candidate for the solution of antenna array processing problems such as beamforming, detection of the... Sample PDF
Comparison of Kernel Methods Applied to Smart Antenna Array Processing
Chapter 9
Joseph Picone, Aravind Ganapathiraju, Jon Hamaker
Automated speech recognition is traditionally defined as the process of converting an audio signal into a sequence of words. Over the past 30 years... Sample PDF
Applications of Kernel Theory to Speech Recognition
Chapter 10
Vincent Wan
This chapter describes the adaptation and application of kernel methods for speech processing. It is divided into two sections dealing with speaker... Sample PDF
Building Sequence Kernels for Speaker Verification and Word Recognition
Chapter 11
Blaž Fortuna, Nello Cristianini, John Shawe-Taylor
We present a general method using kernel canonical correlation analysis (KCCA) to learn a semantic of text from an aligned multilingual collection... Sample PDF
A Kernel Canonical Correlation Analysis for Learning the Semantics of Text
Chapter 12
Gökhan Bakir, Bernhard Schölkopf, Jason Weston
In this chapter, we are concerned with the problem of reconstructing patterns from their representation in feature space, known as the pre-image... Sample PDF
On the Pre-Image Problem in Kernel Methods
Chapter 13
Juan Gutiérrez, Gabriel Gómez-Perez, Jesús Malo, Gustavo Camps-Valls
Support vector machine (SVM) image coding relies on the ability of SVMs for function approximation. The size and the profile of the e-insensitivity... Sample PDF
Perceptual Image Representations for Support Vector Machine Image Coding
Chapter 14
Francesca Odone, Alessandro Verri
In this chapter we review some kernel methods useful for image classification and retrieval applications. Starting from the problem of constructing... Sample PDF
Image Classification and Retrieval with Kernel Methods
Chapter 15
Daniel Cremers, Timo Kohlberger
We present a method of density estimation that is based on an extension of kernel PCA to a probabilistic framework. Given a set of sample data, we... Sample PDF
Probabilistic Kernel PCA and its Application to Statistical Modeling and Inference
Chapter 16
Lorenzo Bruzzone, Luis Gomez-Chova, Mattia Marconcini, Gustavo Camps-Valls
The information contained in hyperspectral images allows the characterization, identification, and classification of land covers with improved... Sample PDF
Hyperspectral Image Classification with Kernels
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