Working-page-elsa-fernandez
Lista articulos de trabajo
1. R. Baumgartner, L. Ryner, W. Richter, R. Summers, M. Jarmasz, R. Somorjai*, Comparison of two exploratory data analysis methods for fMRI: fuzzy clustering vs. principal component analysis. Magnetic Resonance Imaging 18 (2000) 89–94.
Abstract
Exploratory data-driven methods such as Fuzzy clustering analysis (FCA) and Principal component analysis (PCA) may be considered as hypothesis-generating procedures that are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). Here, a comparison between FCA and PCA is presented in a systematic fMRI study, with MR data acquired under the null condition, i.e., no activation, with different noise contributions and simulated, varying “activation.” The contrast-to-noise (CNR) ratio ranged between 1–10. We found that if fMRI data are corrupted by scanner noise only, FCA and PCA show comparable performance. In the presence of other sources of signal variation (e.g., physiological noise), FCA outperforms PCA in the entire CNR range of interest in fMRI, particularly for low CNR values. The comparison method that we introduced may be used to assess other exploratory approaches such as independent component analysis or neural network-based techniques.
2. R. Baumgartner, PhD, R. Somorjai, PhD,* R. Summers, MSc, W. Richter, PhD, L. Ryner, PhD, and M. Jarmasz, PhD, Resampling as a Cluster Validation Technique in fMRI. JOURNAL OF MAGNETIC RESONANCE IMAGING 11:228–231 (2000).
Abstract
Exploratory, data-driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network-based techniques are complementary to hypothesis-led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, ie, “no activation.” We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation-based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions.
3. A. Meyer-Baese, Axel Wismueller, and Oliver Lange, Comparison of Two Exploratory Data Analysis Methods for fMRI: Unsupervised Clustering Versus Independent Component Analysis, IEEE TRANSACTIONS ON INFORMATION TECHNOLOGY IN BIOMEDICINE, VOL. 8, NO. 3, SEPTEMBER 2004.
Abstract
Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesis-generating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between unsupervised clustering and ICA in a systematic fMRI study. The comparative results were evaluated by 1) task-related activation maps, 2) associated time-courses, and 3) receiver operating characteristic analysis. For the fMRI data, a comparative quantitative evaluation between the three clustering techniques, self-organizing map, “neural gas” network, and fuzzy clustering based on deterministic annealing, and the three ICA methods, FastICA, Infomax and topographic ICA was performed. The ICA methods proved to extract features relatively well for a small number of independent components but are limited to the linear mixture assumption. The unsupervised clustering outperforms ICA in terms of classification results but requires a longer processing time than the ICA methods.
'4. O. Lange, A. Meyer-Baese, M. Hurdal, S. Foo, A comparison between neural and fuzzy cluster analysis techniques for functional MRI, Biomedical Signal Processing and Control 1 (2006) 243–252.'
Abstract
Exploratory data-driven methods such as unsupervised clustering and independent component analysis (ICA) are considered to be hypothesisgenerating procedures, and are complementary to the hypothesis-led statistical inferential methods in functional magnetic resonance imaging (fMRI). In this paper, we present a comparison between neural and fuzzy clustering techniques in a systematic fMRI study. For the fMRI data, a comparative quantitative evaluation based on ROC analysis between the Gath–Geva algorithm, the fuzzy n-means algorithm, Kohonen’s selforganizing map, fuzzy n-means algorithm with unsupervised initialization, minimal free energy vector quantizer and the ‘‘neural-gas’’ network was performed. The most important findings in this paper are: (1) SOM is outperformed by all other neural and fuzzy techniques regardless of the chosen number of codebook vectors in terms of detecting small activation areas, (2) the variations among the other techniques are minimal, and (3) a small number of codebook vectors is in general required to obtain consistent task-related activation maps, as determined by the performance evaluation based on cluster validity indices.
5. Mohamed L. Seghier,⁎ Karl J. Friston, and Cathy J. Price, Detecting subject-specific activations using fuzzy clustering,NeuroImage 36 (2007) 594–605.
Abstract
Inter-subject variability in evoked brain responses is attracting attention because it may reflect important variability in structure–function relationships over subjects. This variability could be a signature of degenerate (many-to-one) structure–function mappings in normal subjects or reflect changes that are disclosed by brain damage. In this paper, we describe a non-iterative fuzzy clustering algorithm(FCP: fuzzy clustering with fixed prototypes) for characterizing inter-subject variability in between-subject or second-level analyses of fMRI data. The approach identifies the contribution of each subject to response profiles in voxels surviving a classical F-statistic criterion. The output identifies subjectswho drive activation in specific cortical regions (local effects) or in voxels distributed across neural systems (global effects). The sensitivity of the approach was assessed in 38 normal subjects performing an overt naming task. FCP revealed that several subjects had either abnormally high or abnormally low responses. FCP may be particularly useful for characterizing outlier responses in rare patients or heterogeneous populations. In these cases, atypical activations may not be detected by standard tests, under parametric assumptions. The advantage of using FCP is that it searches all voxels systematically and can identify atypical activation patterns in a quantitative and unsupervised manner.
