Spatially Regularized SVM for the Detection of Brain Areas Associated with Stroke Outcome

Cuingnet, Rémi; Rosso, Charlotte; Lehericy, Stéphane; Dormont, Didier; Benali, Habib; Samson, Yves; Colliot, Olivier

doi:10.1007/978-3-642-15705-9_39

Cited by 12 publications

(9 citation statements)

References 20 publications

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“…Contrary, to other high-dimensional methods applied before that perform feature selection (Davatzikos et al [17]; Vemuri et al [50]), dimension reduction steps (Magnin et al [35]; Teipel et al [45]) or use the kernel approach (Ashburner [1]; Cuingnet et al [12]; Cuingnet et al [14]; Kloppel et al [31]), we approached the sMRI classification problem as a large-scale regularization problem using voxels as input features. We have shown here that classification problems with a number of features approaching 1 million can be solved with levels of accuracy that are very competitive with other methods in the field (Cuingnet et al [13]).…”

Section: Discussionmentioning

confidence: 99%

“…Others resort to complicated procedures consisting of several steps based on downsampling of the images (Vemuri et al [50]) or image processing methods (Fan et al [19]). Recently, Ashburner, Kloppel, Cuignet and colleagues have shown that voxel-based classification using kernel methods is not only feasible but produces very good results when classifying sMRI brain images (Ashburner [1]; Chu [11]; Cuingnet et al [12]; Cuingnet et al [14]; Kloppel et al [31]). In a recent comparison of several of the most successful methods, a linear SVM method (Kloppel et al [31]) was one of the best performers (Cuingnet et al [13]).…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

Casanova

Maldjian

Espeland

2011

Int J Biomed Data Min

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In this work we introduce the use of penalized logistic regression (PLR) to the problem of classification of MRI images and automatic detection of Alzheimer's disease. Classification of sMRI is approached as a large scale regularization problem which uses voxels as input features. We evaluate how differences in sMRI preprocessing steps such as smoothing, normalization, and template selection affect the performance of highdimensional classification methods. In addition, we compared the relative performance of PLR to a different approach based on support vector machines. To study these questions we used data from the Alzheimer Disease Neuroimaging Initiative (ADNI). The ADNI project follows a protocol consisting of acquisition of two images in each session, image correction steps and further evaluation by experts to obtain the optimized images. We evaluated here the impact of this optimization process on the performance of high-dimensional machine learning techniques.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

Casanova

Maldjian

Espeland

2011

Int J Biomed Data Min

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“…It is possible that there are several other solutions with a similar degree of accuracy but that are more "map-like". In the future, analyses might be developed to allow us to find these alternative solutions without much loss of classification accuracy (50).…”

Section: Discussionmentioning

confidence: 99%

Position sensitivity in the visual word form area

Rauschecker

Bowen

Parvizi

et al. 2012

Proc. Natl. Acad. Sci. U.S.A.

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Seeing words involves the activity of neural circuitry within a small region in human ventral temporal cortex known as the visual word form area (VWFA). It is widely asserted that VWFA responses, which are essential for skilled reading, do not depend on the visual field position of the writing (position invariant). Such position invariance supports the hypothesis that the VWFA analyzes word forms at an abstract level, far removed from specific stimulus features. Using functional MRI pattern-classification techniques, we show that position information is encoded in the spatial pattern of VWFA responses. A right-hemisphere homolog (rVWFA) shows similarly position-sensitive responses. Furthermore, electrophysiological recordings in the human brain show position-sensitive VWFA response latencies. These findings show that position-sensitive information is present in the neural circuitry that conveys visual word form information to language areas. The presence of position sensitivity in the VWFA has implications for how word forms might be learned and stored within the reading circuitry.

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“…There are a few classification methodologies that rely on large scale regularization; most are based on SVM (Ashburner, 2007; Kloppel et al, 2008; Cuingnet et al, 2010a) and the kernel approach. For example, Kloppel and colleagues used linear SVM for automatic classification of gray matter (GM) maps combining it with a high dimensional normalization technique called DARTEL (Ashburner, 2007; Kloppel et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

Casanova¹,

Whitlow²,

Wagner³

et al. 2011

Front. Neuroinform.

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In this work we use a large scale regularization approach based on penalized logistic regression to automatically classify structural MRI images (sMRI) according to cognitive status. Its performance is illustrated using sMRI data from the Alzheimer Disease Neuroimaging Initiative (ADNI) clinical database. We downloaded sMRI data from 98 subjects (49 cognitive normal and 49 patients) matched by age and sex from the ADNI website. Images were segmented and normalized using SPM8 and ANTS software packages. Classification was performed using GLMNET library implementation of penalized logistic regression based on coordinate-wise descent optimization techniques. To avoid optimistic estimates classification accuracy, sensitivity, and specificity were determined based on a combination of three-way split of the data with nested 10-fold cross-validations. One of the main features of this approach is that classification is performed based on large scale regularization. The methodology presented here was highly accurate, sensitive, and specific when automatically classifying sMRI images of cognitive normal subjects and Alzheimer disease (AD) patients. Higher levels of accuracy, sensitivity, and specificity were achieved for gray matter (GM) volume maps (85.7, 82.9, and 90%, respectively) compared to white matter volume maps (81.1, 80.6, and 82.5%, respectively). We found that GM and white matter tissues carry useful information for discriminating patients from cognitive normal subjects using sMRI brain data. Although we have demonstrated the efficacy of this voxel-wise classification method in discriminating cognitive normal subjects from AD patients, in principle it could be applied to any clinical population.

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Spatially Regularized SVM for the Detection of Brain Areas Associated with Stroke Outcome

Cited by 12 publications

References 20 publications

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

Evaluating the Impact of Different Factors on Voxel-Based Classification Methods of ADNI Structural MRI Brain Images

Position sensitivity in the visual word form area

High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

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