De Grupo de Inteligencia Computacional (GIC)


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.


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).


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.


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.


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.


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 subjects who 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.

6. Ewald Moser, PhD Markus Diemling, Richard Baumgartner, Fuzzy Clustering of Gradient-Echo Functional MRI in the Human Visual Cortex. Part II: Quantification, Journal of Magnetic Resonance Imaging (1997); 7: 1102-1 108


Fuzzy cluster analysis (FCA) is a new exploratory method for analyzing fMRI data. Using simulated functional MRI (fMRI) data, the performance of FCA, as implemented in the software package EvIdent, was tested and a quantitative comparison with correlation analysis is presented. Furthermore, the fMRI model fit allows separation and quantification of flow and blood oxygen level dependent [BOLD] contributions in the human visual cortex. In gradient-recalled echo fMRl at 1.5 T (TR= 60 ms, TE = 42 ms, radiofrequency excitation flip angle [a] = lOº-6Oº total signal enhancement in the human visual cortex, ie, flow-enhanced BOLD plus inflow contributions, on average varies from 5% to 10% in or close to the visual cortex [average cerebral blood volume [CBV = 4%) and from 10% to 20% in areas containing medium-sized vessels (ie, average CBV = 12% per voxel), respectively. Inflow enhancement, however, is restricted to intravascular space (= CBV) and increases with increasing radiofrequency (RF) flip angle, whereas BOLD contributions may be obtained from a region up to three times larger and, applying an unspoiled gradient-echo (GRE) sequence, also show a fip angle dependency with a minimum at approximately 30º. This result suggests that a localized hernodynamic response from the microvasculature at 1.5 T may be extracted via fuzzy clustering. In summary, fuzzy clustering of fMRI data, as realized in the EMdent software, is a robust and efficient method to (a) separate functional brain activation from noise or other sources resulting in time-dependent signal changes as proven by simulated fMRI data analysis and in vivo data from the visual cortex, and (b) allows separation of different levels of activation even if the temporal pattern is indistinguishable. Combining fuzzy cluster separation of brain activation with appropriate model calculations allows quantification of flow and (flow-enhanced) BOLD contributions in areas with different vascularization.

7. Christian Windischberger, Markus Barth, Claus Lamm, Lee Schroeder, Herbert Bauer, Ruben C. Gur, Ewald Moser, Fuzzy cluster analysis of high-field functional MRI data, Artificial Intelligence in Medicine 29 (2003) 203–223.


Functional magnetic resonance imaging (fMRI) based on blood–oxygen level dependent (BOLD) contrast today is an established brain research method and quickly gains acceptance for complementary clinical diagnosis. However, neither the basic mechanisms like coupling between neuronal activation and haemodynamic response are known exactly, nor can the various artifacts be predicted or controlled. Thus, modeling functional signal changes is non-trivial and exploratory data analysis (EDA) may be rather useful. In particular, identification and separation of artifacts as well as quantification of expected, i.e. stimulus correlated, and novel information on brain activity is important for both, new insights in neuroscience and future developments in functional MRI of the human brain. After an introduction on fuzzy clustering and very high-field fMRI we present several examples where fuzzy cluster analysis (FCA) of fMRI time series helps to identify and locally separate various artifacts. We also present and discuss applications and limitations of fuzzy cluster analysis in very high-field functional MRI: differentiate temporal patterns in MRI using (a) a test object with static and dynamic parts, (b) artifacts due to gross head motion artifacts. Using a synthetic fMRI data set we quantitatively examine the influences of relevant FCA parameters on clustering results in terms of receiver–operator characteristics (ROC) and compare them with a commonly used model-based correlation analysis (CA) approach. The application of FCA in analyzing in vivo fMRI data is shown for (a) a motor paradigm, (b) data from multi-echo imaging, and (c) a fMRI study using mental rotation of three-dimensional cubes.We found that differentiation of true ‘‘neural’’ from false ‘‘vascular’’ activation is possible based on echo time dependence and specific activation levels, as well as based on their signal time-course. Exploratory data analysis methods in general and fuzzy cluster analysis in particular may help to identify artifacts and add novel and unexpected information valuable for interpretation, classification and characterization of functional MRI data which can be used to design new data acquisition schemes, stimulus presentations, neuro(physio)logical paradigms, as well as to improve quantitative biophysical models.

8. Kai-Hsiang Chuang, Ming-Jang Chiu, Chung-Chih Lin, and Jyh-Horng Chen, Model-Free Functional MRI Analysis Using Kohonen Clustering Neural Network and Fuzzy C-Means , IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 18, NO. 12, DECEMBER 1999.


Conventional model-based or statistical analysis methods for functional MRI (fMRI) suffer from the limitation of the assumed paradigm and biased results. Temporal clustering methods, such as fuzzy clustering, can eliminate these problems but are difficult to find activation occupying a small area, sensitive to noise and initial values, and computationally demanding. To overcome these adversities, a cascade clustering method combining a Kohonen clustering network and fuzzy c means is developed. Receiver operating characteristic (ROC) analysis is used to compare this method with correlation coefficient analysis and t test on a series of testing phantoms. Results show that this method can efficiently and stably identify the actual functional response with typical signal change to noise ratio, from a small activation area occupying only 0.2% of head size, with phase delay, and from other noise sources such as head motion. With the ability of finding activities of small sizes stably, this method can not only identify the functional responses and the active regions more precisely, but also discriminate responses from different signal sources, such as large venous vessels or different types of activation patterns in human studies involving motor cortex activation. Even when the experimental paradigm is unknown in a blind test such that model-based methods are inapplicable, this method can identify the activation patterns and regions correctly.

9. M.J. Fadili, S. Ruan,1 D. Bloyet, and B. Mazoyer, A Multistep Unsupervised Fuzzy Clustering Analysis of fMRI Time Series, Human Brain Mapping 10:160–178(2000).


A paradigm independent multistage strategy based on the Unsupervised Fuzzy Clustering Analysis (UFCA) and its potential for fMRI data analysis are presented. The influence of the fuzziness index is studied using Receiver Operating Characteristics (ROC) methodology and an interval of choice, around the widely used value 2, is shown to yield the best performance. The ill-balanced data problemis also overcome using a pre-processing step to reduce the number of voxels presented to the method. Statistical and anatomical criteria are proposed to exclude some voxels and enhance the UFCAsensitivity. An original postprocessing step aiming at statistically characterizing the obtained clusters is also developed. Two similarity criteria are used: the correlation coefficient on temporal profiles and a novel fuzzy overlap coefficient on membership degree maps. This final step provides a useful analysis tool to study intra-individual reproducibility of the classes across series (stimulation vs. stimulation, noise vs. noise or stimulation vs. noise). Finally, a comparison between this technique and some existing or locally developed postprocessing algorithms is presented using ROC methods. Its sensitivity and robustness is compared to the classical FCA or other techniques as a function of several parameters such as Contrastto- Noise Ratio (CNR) and noise amplitude. Even without knowledge about the paradigm, the hemodynamic response function and the number of clusters, the performances of the proposed strategy are comparable to those of the classical approaches where extensive prior knowledge has to be added.

10. Evgenia Dimitriadou, Markus Barth, Christian Windischberger, Kurt Hornik, Ewald Moser, A quantitative comparison of functional MRI cluster analysis, Artificial Intelligence in Medicine (2004) 31, 57—71.


The aim of this work is to compare the efficiency and power of several cluster analysis techniques on fully artificial (mathematical) and synthesized (hybrid) functional magnetic resonance imaging (fMRI) data sets. The clustering algorithms used are hierarchical, crisp (neural gas, self-organizing maps, hard competitive learning, k-means, maximin-distance, CLARA) and fuzzy (c-means, fuzzy competitive learning). To compare these methods we use two performance measures, namely the correlation coefficient and the weighted Jaccard coefficient (wJC). Both performance coefficients (PCs) clearly show that the neural gas and the k-means algorithm perform significantly better than all the other methods using our setup. For the hierarchical methods the ward linkage algorithm performs best under our simulation design. In conclusion, the neural gas method seems to be the best choice for fMRI cluster analysis, given its correct classification of activated pixels (true positives (TPs)) whilst minimizing the misclassification of inactivated pixels (false positives (FPs)), and in the stability of the results achieved.

11. A. Meyer-Bäse *, A. Saalbach, O. Lange, A. Wismüller, Unsupervised clustering of fMRI and MRI time series, Biomedical Signal Processing and Control 2 (2007) 295–310.


Unsupervised clustering represents a powerful technique for self-organized segmentation of biomedical image time series data describing groups of pixels exhibiting similar properties of local signal dynamics. The theoretical background is presented in the beginning, followed by several medical applications demonstrating the flexibility and conceptual power of these techniques. These applications range from functional MRI data analysis to dynamic contrast-enhanced perfusion MRI and breast MRI. For fMRI, these methods can be employed to identify and separate time courses of interest, along with their associated spatial patterns. When applied to dynamic perfusion MRI, they identify groups of voxels associated with time courses that are clinically informative and straightforward to interpret. In breast MRI, a segmentation of the lesion is achieved and in addition a subclassification is obtained within the lesion with regard to regions characterized by different MRI signal time courses. In the present paper, we conclude that unsupervised clustering techniques provide a robust method for blind analysis of time series image data in the important and current field of functional and dynamic MRI.

--Elsa.fernandez 17:57 15 abr 2008 (CEST)