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Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level.

Zarogianni E, Moorhead TW, Lawrie SM - Neuroimage Clin (2013)

Bottom Line: Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia.However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings.We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

View Article: PubMed Central - PubMed

Affiliation: Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, Scotland, UK.

ABSTRACT
Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

No MeSH data available.


Related in: MedlinePlus

Representation of a linear, binary SVM classifier. The optimal separating hyperplane is the one with the largest margin of separation between the two groups and is described as a function of f(x) = w ∗ x + b, where w is a weight vector that is normal to the hyperplane, b is an offset and b///w// is the distance from the hyperplane to the origin. Points in the dashed lines represent the support vectors. During the training phase, the SVM classifier computes the optimal decision function f(x) and in the testing phase, this decision boundary is applied to new data instances.
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f0005: Representation of a linear, binary SVM classifier. The optimal separating hyperplane is the one with the largest margin of separation between the two groups and is described as a function of f(x) = w ∗ x + b, where w is a weight vector that is normal to the hyperplane, b is an offset and b///w// is the distance from the hyperplane to the origin. Points in the dashed lines represent the support vectors. During the training phase, the SVM classifier computes the optimal decision function f(x) and in the testing phase, this decision boundary is applied to new data instances.

Mentions: SVM is one of the most popular supervised machine learning methods used in neuroimaging settings, partly because it can deal effectively with high-dimensional data and provide good classification results. The aim of a SVM classifier is to find a decision surface that would optimally distinguish between classes and based on that surface assign new, previously unseen data instances into the groups. In the training phase, the classifier computes the optimal decision surface expressed in the form f(x) = w·x + b only by a subset of the original training set D = <xi, yi> called the support vectors. Support vectors are data points that lie closest to the optimal separating hyperplane and hence are the most difficult patterns to classify (see Fig. 1). The optimal hyperplane is determined by maximizing the margin of separation between the two classes (which is equal to 2///w/). Equally, the problem of finding the optimal hyperplane, thus, becomes an optimization problem where we need to: min //w// subject to yi (xi·w + b) − 1 ≥ 0. The constraint part of the quadratic problem ensures that no data points can lie in the margin.


Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level.

Zarogianni E, Moorhead TW, Lawrie SM - Neuroimage Clin (2013)

Representation of a linear, binary SVM classifier. The optimal separating hyperplane is the one with the largest margin of separation between the two groups and is described as a function of f(x) = w ∗ x + b, where w is a weight vector that is normal to the hyperplane, b is an offset and b///w// is the distance from the hyperplane to the origin. Points in the dashed lines represent the support vectors. During the training phase, the SVM classifier computes the optimal decision function f(x) and in the testing phase, this decision boundary is applied to new data instances.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814947&req=5

f0005: Representation of a linear, binary SVM classifier. The optimal separating hyperplane is the one with the largest margin of separation between the two groups and is described as a function of f(x) = w ∗ x + b, where w is a weight vector that is normal to the hyperplane, b is an offset and b///w// is the distance from the hyperplane to the origin. Points in the dashed lines represent the support vectors. During the training phase, the SVM classifier computes the optimal decision function f(x) and in the testing phase, this decision boundary is applied to new data instances.
Mentions: SVM is one of the most popular supervised machine learning methods used in neuroimaging settings, partly because it can deal effectively with high-dimensional data and provide good classification results. The aim of a SVM classifier is to find a decision surface that would optimally distinguish between classes and based on that surface assign new, previously unseen data instances into the groups. In the training phase, the classifier computes the optimal decision surface expressed in the form f(x) = w·x + b only by a subset of the original training set D = <xi, yi> called the support vectors. Support vectors are data points that lie closest to the optimal separating hyperplane and hence are the most difficult patterns to classify (see Fig. 1). The optimal hyperplane is determined by maximizing the margin of separation between the two classes (which is equal to 2///w/). Equally, the problem of finding the optimal hyperplane, thus, becomes an optimization problem where we need to: min //w// subject to yi (xi·w + b) − 1 ≥ 0. The constraint part of the quadratic problem ensures that no data points can lie in the margin.

Bottom Line: Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia.However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings.We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

View Article: PubMed Central - PubMed

Affiliation: Division of Psychiatry, School of Clinical Sciences, University of Edinburgh, The Royal Edinburgh Hospital, Morningside Park, Edinburgh EH10 5HF, Scotland, UK.

ABSTRACT
Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

No MeSH data available.


Related in: MedlinePlus