Spatiotemporal Denoising and Clustering of fMRI Data

Magnetic Resonance Imaging

Chen

2014

Self Cite

Resting state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of specific functional tasks and to capture changes in the connectivity due to neurological diseases. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI non-stationarity, the threshold cannot adapt to variation of data characteristics across sessions and subjects, and generates unreliable mapping results. In this study, a new method is presented for resting state fMRI data analysis. Specifically, the resting state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected instead of a threshold. Multiple features for resting state analysis were extracted and examined using a SVM-based feature selection method, and the most representative features were identified. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting state quantitative fMRI studies.

Section: Methodsmentioning

confidence: 99%

A SVM-based quantitative fMRI method for resting-state functional network detection

Magnetic Resonance Imaging

Chen

2014

Self Cite

“…Then the data are spatially smoothed using a wavelet domain Bayesian noise removal method [13], and low-pass filtered at a cut-off frequency of 0.1 Hz to extract low frequency fluctuations of interest in resting state. All fMRI data were normalized to zero mean and unit variance.…”

Section: B Preprocessingmentioning

confidence: 99%

Resting state fMRI data analysis using support vector machines

2013 IEEE Signal Processing in Medicine and Biology Symposium (SPMB)

Chen

2013

Self Cite

Resting state functional magnetic resonance imaging (fMRI) aims to measure baseline neuronal connectivity independent of functional tasks. Most existing network detection methods rely on a fixed threshold to identify functionally connected voxels under the resting state. Due to fMRI nonstationarity, a fixed threshold cannot adapt to inter-session and inter-subject variation. In this work, a new method is proposed for resting state fMRI data analysis. Specifically, the resting state network mapping is formulated as an outlier detection process that is implemented using one-class support vector machine (SVM). The results are refined by using a spatial-feature domain prototype selection method and two-class SVM reclassification. The final decision on each voxel is made by comparing its probabilities of functionally connected and unconnected. The proposed method was evaluated using synthetic and experimental fMRI data. A comparison study was also performed with independent component analysis (ICA) and correlation analysis. The experimental results show that the proposed method can provide comparable or better network detection performance than ICA and correlation analysis. The method is potentially applicable to various resting state quantitative fMRI studies.

“…The noise in DI is approximately Gaussian [6], and was removed using the method in [20]. A set of features were extracted for each voxel from the denoised DI, including: the maximum magnitude and p value of the t-test for its time course (TC), the average, and maximum correlation coefficients(cc) between its TC and other voxels' TCs within its 2nd-order neighbor, the cc value between its TC and the paradigm, the signed extreme value and its delay in the cross correlation function between the TC and the paradigm [10], and a temporal self-correlation measure computed by averaging correlation coefficients between all pairs of TCs of this voxel.…”

Section: Preprocessingmentioning

confidence: 99%

One-class Machine Learning for Brain Activation Detection

2007 IEEE Conference on Computer Vision and Pattern Recognition

Iordanescu

Wyrwicz

2007

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Machine learning methods, such as support vector machine (SVM), have been applied to fMRI data analysis, where most studies focus on supervised detection and classification of cognitive states. In this work, we study the general fMRI activation detection using SVM in an unsupervised way instead of the classification of cognitive states. Specifically, activation detection is formulated as an outlier (activated voxels) detection problem of the one-class support vector machine (OCSVM). An OCSVM implementation, ν-SVM, is used where parameter ν controls the outlier ratio, and is usually unknown. We propose a detection method that is not sensitive to ν randomly set within a range known a priori. In cases that this range is also unknown, we consider ν estimation using geometry and texture features. Results from both synthetic and experimental data demonstrate the effectiveness of the proposed methods.