Alexsavio own bibLatex

@incollection{savio_classification_2009, title = {Classification Results of Artificial Neural Networks for Alzheimer’s Disease Detection}, url = {http://dx.doi.org/10.1007/978-3-642-04394-9_78}, abstract = {Detection of Alzheimer’s disease on brain Magnetic Resonance Imaging ({MRI}) is a highly sought goal in the Neurosciences. We used four different models of Artificial Neural Networks ({ANN}): Backpropagation ({BP}), Radial Basis Networks ({RBF}), Learning Vector Quantization Networks ({LVQ}) and Probabilistic Neural Networks ({PNN}) to perform classification of patients of mild Alzheimer’s disease vs. control subjects. Features are extracted from the brain volume data using Voxel-based Morphometry ({VBM}) detection clusters. The voxel location detection clusters given by the {VBM} were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed from the {GM} segmentation volumes using the {VBM} clusters as voxel selection masks. The study has been performed on {MRI} volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies ({OASIS}) database, which is a large number of subjects compared to current reported studies.}, pages = {641-648}, booktitle = {Intelligent Data Engineering and Automated Learning - {IDEAL} 2009}, author = {Savio, Alexandre and García-Sebastián, Maite and Hernández, Carmen and Graña, Manuel and Villanúa, Jorge}, urldate = {2010-01-13}, date = {2009}, file = {SpringerLink Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/KPQDVTNZ/8q670qm0g4377822.html:text/html} } @inproceedings{savio_results_2009, location = {Santiago de Compostela, Spain}, title = {Results of an Adaboost Approach on Alzheimer's Disease Detection on {MRI}}, isbn = {978-3-642-02266-1}, url = {http://portal.acm.org/citation.cfm?id=1574369}, abstract = {In this paper we explore the use of the Voxel-based Morphometry ({VBM}) detection clusters to guide the feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging ({MRI}). The voxel location detection clusters given by the {VBM} were applied to select the voxel values upon which the classification features were computed. We have evaluated feature vectors computed over the data from the original {MRI} volumes and from the {GM} segmentation volumes, using the {VBM} clusters as voxel selection masks. We use the Support Vector Machine ({SVM}) algorithm to perform classification of patients with mild Alzheimer's disease vs. control subjects. We have also considered combinations of isolated cluster based classifiers and an Adaboost strategy applied to the {SVM} built on the feature vectors. The study has been performed on {MRI} volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies ({OASIS}) database, which is a large number of subjects compared to current reported studies. Results are moderately encouraging, as we can obtain up to 85\% accuracy with the Adaboost strategy in a 10-fold cross-validation.}, pages = {114-123}, booktitle = {Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part {II}: Bioinspired Applications in Artificial and Natural Computation}, publisher = {Springer-Verlag}, author = {Savio, Alexandre and García-Sebastián, Maite and Graña, Manuel and Villanúa, Jorge}, urldate = {2010-01-13}, date = {2009}, note = {Editors: Marios Polycarpou, André C. P. L. F. de Carvalho, Jeng-Shyang Pan, Michał Woźniak, Héctor Quintian, Emilio Corchado}, file = {ACM Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/TNTTW7NV/citation.html:text/html} } @inproceedings{garcia-sebastian_use_2009, location = {Salamanca, Spain}, title = {On the Use of Morphometry Based Features for Alzheimer's Disease Detection on {MRI}}, isbn = {978-3-642-02477-1}, url = {http://portal.acm.org/citation.cfm?id=1573045}, abstract = {We have studied feature extraction processes for the detection of Alzheimer's disease on brain Magnetic Resonance Imaging ({MRI}) based on Voxel-based morphometry ({VBM}). The clusters of voxel locations detected by the {VBM} were applied to select the voxel intensity values upon which the classification features were computed. We have explored the use of the data from the original {MRI} volumes and the {GM} segmentation volumes. In this paper, we apply the Support Vector Machine ({SVM}) algorithm to perform classification of patients with mild Alzheimer's disease vs. control subjects. The study has been performed on {MRI} volumes of 98 females, after careful demographic selection from the Open Access Series of Imaging Studies ({OASIS}) database, which is a large number of subjects compared to current reported studies.}, pages = {957-964}, booktitle = {Proceedings of the 10th International Work-Conference on Artificial Neural Networks: Part I: Bio-Inspired Systems: Computational and Ambient Intelligence}, publisher = {Springer-Verlag}, author = {García-Sebastián, Maite and Savio, Alexandre and Graña, Manuel and Villanúa, Jorge}, urldate = {2010-01-13}, date = {2009}, file = {ACM Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/AIF49A4M/citation.html:text/html} } @inproceedings{garcia-sebastian_comments_2008, title = {Comments on an evolutionary intensity inhomogeneity correction algorithm}, doi = {10.1109/CEC.2008.4631363}, abstract = {{\textless}p{\textgreater}We discuss some aspects of a well known algorithm for inhomogeneity intensity correction in Magnetic Resonance Imaging ({MRI}), the parametric bias correction ({PABIC}) algorithm. In this approach, the intensity inhomogeneity is modelled by a linear combination of 2D or 3D Legengre polynomials (computed as outer products of 1D polynomials). The model parameter estimation process proposed in the original paper is similar to a (1+1) Evolution Strategy, with some small and subtle differences. In this paper we discuss some features of the algorithm elements, trying to uncover sources of undesired behaviors and the limits to its applicability. We study the energy function proposed in the original paper and its relation to the image formation model. We also discuss the original minimization algorithm behavior. We think that this detailed discussion is needed because of the high impact that the original paper had in the literature, leading to an implementation into the well known {ITK} library, which means that it has become a de facto standard.{\textless}/p{\textgreater}}, eventtitle = {Evolutionary Computation, 2008. {CEC} 2008. ({IEEE} World Congress on Computational Intelligence). {IEEE} Congress on}, pages = {4146-4150}, booktitle = {Evolutionary Computation, 2008. {CEC} 2008. ({IEEE} World Congress on Computational Intelligence). {IEEE} Congress on}, author = {Garcia-Sebastian, M. and Savio, A.M. and Grana, M.}, date = {2008}, keywords = {biomedical {MRI}, energy function, evolutionary computation, evolutionary intensity inhomogeneity correction algorithm, evolution strategy, image formation model, {ITK} library, Legendre polynomials, Legengre polynomials, magnetic resonance imaging, medical image processing, minimization algorithm, parameter estimation, parameter estimation process, parametric bias correction}, file = {IEEE Xplore PDF:/home/alexandre/Dropbox/Documents/zotero/storage/TW3GSN6F/Garcia-Sebastian et al. - 2008 - Comments on an evolutionary intensity inhomogeneit.pdf:application/pdf} } @incollection{savio_deformation_2011, title = {Deformation Based Features for Alzheimer’s Disease Detection with Linear {SVM}}, rights = {©2011 Springer Berlin Heidelberg}, isbn = {978-3-642-21221-5, 978-3-642-21222-2}, url = {http://link.springer.com/chapter/10.1007/978-3-642-21222-2_41}, series = {Lecture Notes in Computer Science}, abstract = {Detection of Alzheimer’s disease over brain Magnetic Resonance Imaging ({MRI}) data is a priority goal in the Neurosciences. In previous works we have studied the accuracy of feature vectors obtained from {VBM} studies of the {MRI} data. In this paper we report results working on deformation based features, obtained from the deformation vectors computed by non-linear registration processes. Feature selection is based on the correlation between the scalar values computed from the deformation maps and the control variable. Results with linear kernel {SVM} reach accuracies comparable to previous best results.}, pages = {336-343}, number = {6679}, booktitle = {Hybrid Artificial Intelligent Systems}, publisher = {Springer Berlin Heidelberg}, author = {Savio, Alexandre and Grana, Manuel and Villanúa, Jorge}, editor = {Corchado, Emilio and Kurzyński, Marek and Woźniak, Michał}, urldate = {2013-09-12}, date = {2011-01-01}, keywords = {Algorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), Computation by Abstract Devices, Database Management, Information Storage and Retrieval, Information Systems Applications (incl.Internet)}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/UF4QA6DG/10.html:text/html} } @article{savio_neural_2010, title = {Neural classifiers for Schizophrenia diagnostic support on Diffusion imaging data}, volume = {20}, issn = {1210-0552}, abstract = {Abstract: Diagnostic support for psychiatric disorders is a very interesting goal because of the... {\textbar} Article from Neural Network World January 1, 2010}, pages = {935-949}, journaltitle = {Neural Network World}, author = {Savio, Alexandre and Charpentier, Juliette and Termenón, Maite and Shinn, Ann K. and Graña, Manuel}, urldate = {2013-09-13}, date = {2010-01-01}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/H9JTSDK2/1P3-2340740301.html:text/html} } @incollection{manuel_gorriz_machine_2012, title = {Machine Learning Approach for Myotonic Dystrophy Diagnostic Support from {MRI}}, isbn = {9781608052189}, url = {http://www.eurekaselect.com/node/53989}, pages = {141-148}, booktitle = {Recent Advances in Biomedical Signal Processing}, publisher = {{BENTHAM} {SCIENCE} {PUBLISHERS}}, author = {Bentham Science Publisher, Bentham Science Publisher}, editor = {Manuel Górriz, Juan and W. Lang, Elmar and Ramírez, Javier}, urldate = {2013-09-12}, date = {2012-03-29} } @incollection{savio_ensemble_2013, title = {An Ensemble of Classifiers Guided by the {AAL} Brain Atlas for Alzheimer’s Disease Detection}, rights = {©2013 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-38681-7, 978-3-642-38682-4}, url = {http://link.springer.com/chapter/10.1007/978-3-642-38682-4_13}, series = {Lecture Notes in Computer Science}, abstract = {Detection of Alzheimer’s disease based on Magnetic Resonance Imaging ({MRI}) still is one of the most sought goals in the neuroscientific community. Here, we evaluate a ensemble of classifiers each independently trained with disjoint data extracted from a partition of the brain data volumes performed according to the 116 regions of the Anatomical Automatic Labeling ({AAL}) brain atlas. Grey-matter probability values from 416 subjects (316 controls and 100 patients) of the {OASIS} database are estimated, partitioned into {AAL} regions, and summary statistics per region are computed to create the feature sets. Our objective is to discriminate between control subjects and Alzheimer’s disease patients. For validation we performed a leave-one-out process. Elementary classifiers are linear Support Vector Machines ({SVM}) with model parameter estimated by grid search. The ensemble is composed of one {SVM} per {AAL} region, and we test 6 different methods to make the collective decision. The best performance achieved with this approach is 83.6\% accuracy, 91.0\% sensitivity, 81.3\% specificity and 0.86 of area under the {ROC} curve. Most discriminant regions for some of the collective decision methods are also provided.}, pages = {107-114}, number = {7903}, booktitle = {Advances in Computational Intelligence}, publisher = {Springer Berlin Heidelberg}, author = {Savio, Alexandre and Graña, Manuel}, editor = {Rojas, Ignacio and Joya, Gonzalo and Cabestany, Joan}, urldate = {2013-09-12}, date = {2013-01-01}, keywords = {Artificial Intelligence (incl. Robotics), Bioinformatics, Computational Biology/Bioinformatics, Data Mining and Knowledge Discovery, Models and Principles, pattern recognition}, file = {Full Text PDF:/home/alexandre/Dropbox/Documents/zotero/storage/4H3U6CTA/Savio and Graña - 2013 - An Ensemble of Classifiers Guided by the AAL Brain.pdf:application/pdf;Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/ESZW753K/10.html:text/html} } @article{savio_deformation_2013, title = {Deformation based feature selection for Computer Aided Diagnosis of Alzheimer’s Disease}, volume = {40}, issn = {0957-4174}, url = {http://www.sciencedirect.com/science/article/pii/S0957417412010639}, doi = {10.1016/j.eswa.2012.09.009}, abstract = {Deformation-based Morphometry ({DBM}) allows detection of significant morphological differences of brain anatomy, such as those related to brain atrophy in Alzheimer’s Disease ({AD}). {DBM} process is as follows: First, performs the non-linear registration of a subject’s structural {MRI} volume to a reference template. Second, computes scalar measures of the registration’s deformation field. Third, performs across volume statistical group analysis of these scalar measures to detect effects. In this paper we use the scalar deformation measures for Computer Aided Diagnosis ({CAD}) systems for {AD}. Specifically this paper deals with feature extraction methods over five such scalar measures. We evaluate three supervised feature selection methods based on voxel site significance measures given by Pearson correlation, Bhattacharyya distance and Welch’s t-test, respectively. The {CAD} system discriminating between healthy control subjects ({HC}) and {AD} patients consists of a Support Vector Machine ({SVM}) classifier trained on the {DBM} selected features. The paper reports experimental results on structural {MRI} data from the cross-sectional {OASIS} database. Average 10-fold cross-validation classification results are comparable or improve the state-of-the-art results of other approaches performing {CAD} from structural {MRI} data. Localization in the brain of the most discriminant deformation voxel sites is in agreement with findings reported in the literature.}, pages = {1619-1628}, number = {5}, journaltitle = {Expert Systems with Applications}, shortjournal = {Expert Systems with Applications}, author = {Savio, Alexandre and Graña, Manuel}, urldate = {2014-02-13}, date = {2013-04}, note = {00006}, keywords = {Alzheimer’s disease, Computed Aided Diagnosis, Feature selection, magnetic resonance imaging, Suppor Vector Machines} } @incollection{savio_computer_2014, title = {Computer Aided Diagnosis of Schizophrenia Based on Local-Activity Measures of Resting-State {fMRI}}, rights = {©2014 Springer International Publishing Switzerland}, isbn = {978-3-319-07616-4, 978-3-319-07617-1}, url = {http://link.springer.com/chapter/10.1007/978-3-319-07617-1_1}, series = {Lecture Notes in Computer Science}, abstract = {Resting state functional Magnetic Resonance Imaging (rs-{fMRI}) is increasingly used for the identification of image biomarkers of brain diseases or psychiatric conditions, such as Schizophrenia. One approach is to perform classification experiments on the data, using feature extraction methods that allow to localize the discriminant locations in the brain, so that further studies may assess the clinical value of such locations. The classification accuracy results ensure that the located brain regions have some relation to the disease. In this paper we explore the discriminant value of brain local activity measures for the classification of Schizophrenia patients. The extensive experimental work, carried out on a publicly available database, provides evidence that local activity measures such as Regional Homogeneity ({ReHo}) may be useful for such purposes.}, pages = {1-12}, number = {8480}, booktitle = {Hybrid Artificial Intelligence Systems}, publisher = {Springer International Publishing}, author = {Savio, Alexandre and Chyzhyk, Darya and Graña, Manuel}, editor = {Polycarpou, Marios and Carvalho, André C. P. L. F. de and Pan, Jeng-Shyang and Woźniak, Michał and Quintian, Héctor and Corchado, Emilio}, urldate = {2014-07-12}, date = {2014-01-01}, langid = {english}, note = {00000}, keywords = {Algorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), Computation by Abstract Devices, Image Processing and Computer Vision, Information Systems Applications (incl. Internet), pattern recognition}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/EPEM7AER/10.html:text/html} } @article{chyzhyk_evolutionary_2014, title = {Evolutionary {ELM} wrapper feature selection for Alzheimer's disease {CAD} on anatomical brain {MRI}}, volume = {128}, issn = {0925-2312}, url = {http://www.sciencedirect.com/science/article/pii/S0925231213010096}, doi = {10.1016/j.neucom.2013.01.065}, abstract = {This paper proposes an evolutionary wrapper feature selection using Extreme Learning Machines ({ELM}) as the base classifier training algorithm, comprising a Genetic Algorithm ({GA}) exploring the space of feature combinations. {GA} fitness function is the mean accuracy of a cross-validation evaluation of each individual feature selection. The marginal distribution of the classification accuracy corresponding to a feature is used to measure feature saliency. The raw features are extracted as a voxel selection from anatomical brain magnetic resonance imaging ({MRI}). Voxel selection is provided by Voxel Based Morphometry ({VBM}) which finds statistically significant clusters of voxels that have differences across {MRI} volumes on a paired dataset of Alzheimer's Disease ({AD}) and healthy controls.}, pages = {73-80}, journaltitle = {Neurocomputing}, shortjournal = {Neurocomputing}, author = {Chyzhyk, Darya and Savio, Alexandre and Graña, Manuel}, urldate = {2014-07-12}, date = {2014-03-27}, note = {00002}, keywords = {Alzheimer's disease, Evolutionary wrapper feature selection, Extreme learning machine, Neuroimaging}, file = {ScienceDirect Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/NHFNFXDD/S0925231213010096.html:text/html} } @incollection{ayerdi_meta-ensembles_2013, title = {Meta-ensembles of Classifiers for Alzheimer’s Disease Detection Using Independent {ROI} Features}, rights = {©2013 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-38621-3, 978-3-642-38622-0}, url = {http://link.springer.com/chapter/10.1007/978-3-642-38622-0_13}, series = {Lecture Notes in Computer Science}, abstract = {Due to its growing social impact, prodromal detection of Alzheimer’s disease is of paramount importance. Biomarkers based on Magnetic Resonance Imaging ({MRI}) are one of the most sought results in the neuroscience community. In this paper we evaluate several ensembles of classifiers trained and tested in a two level ensemble scheme as follows: the 116 regions of interest ({ROI}) of the Anatomical Automatic Labeling ({AAL}) brain atlas are used to compute disjoint feature sets from the Grey-matter probability maps from the segmentation of the T1 weighted {MRI} of each subject; {ROI} features are the summary statistics inside this {ROI}; one ensemble of classifiers is trained on each independent {ROI} feature data set; the final classification of each subject is given by the combination of the classifications of each {ROI}, as meta-ensemble classifier. Experiments are performed on the 416 subjects (316 controls and 100 patients) of the {OASIS} database. We perform a hold-out of the 20\% of the data for model selection, computing a leave-one-out validation on the 80\% remaining data. Results are computed without circularity. Tested classifiers are the Extreme Learning Machines ({ELM}), Bootstrapped Dendritic Computing ({BDC}), Hybrid Extreme Random Forest ({HERF}) and Random Forest ({RF}). The best performance achieved is 80.8\% accuracy, 77.1\% specificity and 92.5\% specificity with {BDC}. We also report the most discriminant {ROIs} obtained in the model selection phase.}, pages = {122-130}, number = {7931}, booktitle = {Natural and Artificial Computation in Engineering and Medical Applications}, publisher = {Springer Berlin Heidelberg}, author = {Ayerdi, Borja and Savio, Alexandre and Graña, Manuel}, editor = {Vicente, José Manuel Ferrández and Sánchez, José Ramón Álvarez and López, Félix de la Paz and Moreo, Fco Javier Toledo}, urldate = {2014-06-14}, date = {2013-01-01}, note = {00000}, keywords = {Algorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), Computational Biology/Bioinformatics, Computation by Abstract Devices, Information Systems Applications (incl. Internet), pattern recognition}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/IB8G65NN/10.html:text/html} } @article{dacosta-aguayo_prognostic_2014, title = {Prognostic value of changes in resting-state functional connectivity patterns in cognitive recovery after stroke: A 3T {fMRI} pilot study}, rights = {Copyright © 2014 The Authors. Human Brain Mapping published by Wiley Periodicals, Inc., This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.}, issn = {1097-0193}, url = {http://onlinelibrary.wiley.com/doi/10.1002/hbm.22439/abstract}, doi = {10.1002/hbm.22439}, shorttitle = {Prognostic value of changes in resting-state functional connectivity patterns in cognitive recovery after stroke}, abstract = {Resting-state studies conducted with stroke patients are scarce. First objective was to explore whether patients with good cognitive recovery showed differences in resting-state functional patterns of brain activity when compared to patients with poor cognitive recovery. Second objective was to determine whether such patterns were correlated with cognitive performance. Third objective was to assess the existence of prognostic factors for cognitive recovery. Eighteen right-handed stroke patients and eighteen healthy controls were included in the study. Stroke patients were divided into two groups according to their cognitive improvement observed at three months after stroke. Probabilistic independent component analysis was used to identify resting-state brain activity patterns. The analysis identified six networks: frontal, fronto-temporal, default mode network, secondary visual, parietal, and basal ganglia. Stroke patients showed significant decrease in brain activity in parietal and basal ganglia networks and a widespread increase in brain activity in the remaining ones when compared with healthy controls. When analyzed separately, patients with poor cognitive recovery (n = 10) showed the same pattern as the whole stroke patient group, while patients with good cognitive recovery (n = 8) showed increased activity only in the default mode network and fronto-temporal network, and decreased activity in the basal ganglia. We observe negative correlations between basal ganglia network activity and performance in Semantic Fluency test and Part A of the Trail Making Test for patients with poor cognitive recovery. A reverse pattern was observed between frontal network activity and the abovementioned tests for the same group. Hum Brain Mapp, 2014. © 2014 Wiley Periodicals, Inc.}, pages = {n/a-n/a}, journaltitle = {Human Brain Mapping}, shortjournal = {Hum. Brain Mapp.}, author = {Dacosta-Aguayo, R. and Graña, M. and Savio, A. and Fernández-Andújar, M. and Millán, M. and López-Cancio, E. and Cáceres, C. and Bargalló, N. and Garrido, C. and Barrios, M. and Clemente, I. C. and Hernández, M. and Munuera, J. and Dávalos, A. and Auer, T. and Mataró, M.}, urldate = {2014-06-14}, date = {2014-02-01}, langid = {english}, note = {00000}, keywords = {cognitive recovery, {fMRI}, interhemispheric balance, ischemic stroke, probabilistic independent component analysis, Resting state}, file = {Full Text PDF:/home/alexandre/Dropbox/Documents/zotero/storage/5BJJD2VN/Dacosta-Aguayo et al. - 2014 - Prognostic value of changes in resting-state funct.pdf:application/pdf;Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/DA9X8BGN/abstract.html:text/html} } @inproceedings{savio_supervised_2012, title = {Supervised classification using deformation-based features for Alzheimer's disease detection on the {OASIS} cross-sectional database}, volume = {243}, isbn = {978-1-61499-104-5}, url = {http://www.booksonline.iospress.nl/Content/View.aspx?piid=32161}, series = {Frontiers in Artificial Intelligence and Applications}, pages = {2191-2200}, booktitle = {Advances in Knowledge-Based and Intelligent Information and Engineering Systems - 16th Annual {KES} Conference, San Sebastian, Spain, 10-12 September 2012}, publisher = {{IOS} Press}, author = {Savio, Alexandre}, editor = {Graña, Manuel and Toro, Carlos and Posada, Jorge and Howlett, Robert J. and Jain, Lakhmi C.}, urldate = {2014-07-12}, date = {2012}, note = {00002} } @article{jbabdi_model-based_2012, title = {Model-based analysis of multishell diffusion {MR} data for tractography: How to get over fitting problems}, volume = {68}, rights = {Copyright © 2012 Wiley Periodicals, Inc.}, issn = {1522-2594}, url = {http://onlinelibrary.wiley.com/doi/10.1002/mrm.24204/abstract}, doi = {10.1002/mrm.24204}, shorttitle = {Model-based analysis of multishell diffusion {MR} data for tractography}, abstract = {In this article, we highlight an issue that arises when using multiple b-values in a model-based analysis of diffusion {MR} data for tractography. The non-monoexponential decay, commonly observed in experimental data, is shown to induce overfitting in the distribution of fiber orientations when not considered in the model. Extra fiber orientations perpendicular to the main orientation arise to compensate for the slower apparent signal decay at higher b-values. We propose a simple extension to the ball and stick model based on a continuous gamma distribution of diffusivities, which significantly improves the fitting and reduces the overfitting. Using in vivo experimental data, we show that this model outperforms a simpler, noise floor model, especially at the interfaces between brain tissues, suggesting that partial volume effects are a major cause of the observed non-monoexponential decay. This model may be helpful for future data acquisition strategies that may attempt to combine multiple shells to improve estimates of fiber orientations in white matter and near the cortex. Magn Reson Med, 2012. © 2012 Wiley Periodicals, Inc.}, pages = {1846-1855}, number = {6}, journaltitle = {Magnetic Resonance in Medicine}, shortjournal = {Magn Reson Med}, author = {Jbabdi, Saad and Sotiropoulos, Stamatios N. and Savio, Alexander M. and Graña, Manuel and Behrens, Timothy E. J.}, urldate = {2014-07-12}, date = {2012-12-01}, langid = {english}, note = {00024}, keywords = {Diffusion {MRI}, multishell, Tractography}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/GUS9IQI3/abstract.html:text/html} } @article{chyzhyk_hybrid_2012, title = {Hybrid Dendritic Computing with kernel-{LICA} Applied to Alzheimer's Disease Detection in {MRI}}, volume = {75}, issn = {0925-2312}, url = {http://dx.doi.org/10.1016/j.neucom.2011.02.024}, doi = {10.1016/j.neucom.2011.02.024}, abstract = {Dendritic computing has been proved to produce perfect approximation of any data distribution. This result guarantees perfect accuracy training. However, we have found great performance degradation when tested on conventional k-fold cross-validation schemes. In this paper we propose to use Lattice Independent Component Analysis ({LICA}) and the Kernel transformation of the data as an appropriate feature extraction that improves the generalization of dendritic computing classifiers. We obtain a big increase in classification performance applying with this schema over a database of features extracted from Magnetic Resonance Imaging ({MRI}) including Alzheimer's disease ({AD}) patients and control subjects.}, pages = {72–77}, number = {1}, journaltitle = {Neurocomput.}, author = {Chyzhyk, Darya and Graña, Manuel and Savio, Alexandre and Maiora, Josu}, urldate = {2014-07-12}, date = {2012-01}, note = {00024}, keywords = {Alzheimer's disease, Dendritic computing, Kernel method, Lattice computing, Lattice Independent Component Analysis} } @article{grana_computer_2011, title = {Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation}, volume = {502}, issn = {0304-3940}, url = {http://www.sciencedirect.com/science/article/pii/S0304394011011268}, doi = {10.1016/j.neulet.2011.07.049}, abstract = {The aim of this paper is to obtain discriminant features from two scalar measures of Diffusion Tensor Imaging ({DTI}) data, Fractional Anisotropy ({FA}) and Mean Diffusivity ({MD}), and to train and test classifiers able to discriminate Alzheimer's Disease ({AD}) patients from controls on the basis of features extracted from the {FA} or {MD} volumes. In this study, support vector machine ({SVM}) classifier was trained and tested on {FA} and {MD} data. Feature selection is done computing the Pearson's correlation between {FA} or {MD} values at voxel site across subjects and the indicative variable specifying the subject class. Voxel sites with high absolute correlation are selected for feature extraction. Results are obtained over an on-going study in Hospital de Santiago Apostol collecting anatomical T1-weighted {MRI} volumes and {DTI} data from healthy control subjects and {AD} patients. {FA} features and a linear {SVM} classifier achieve perfect accuracy, sensitivity and specificity in several cross-validation studies, supporting the usefulness of {DTI}-derived features as an image-marker for {AD} and to the feasibility of building Computer Aided Diagnosis systems for {AD} based on them.}, pages = {225-229}, number = {3}, journaltitle = {Neuroscience Letters}, shortjournal = {Neuroscience Letters}, author = {Graña, M. and Termenon, M. and Savio, A. and Gonzalez-Pinto, A. and Echeveste, J. and Pérez, J. M. and Besga, A.}, urldate = {2014-07-12}, date = {2011-09-20}, note = {00000}, keywords = {Alzheimer's disease, Classification, Diffusion tensor imaging ({DTI}), Feature selection, Fractional anisotropy, image registration, Mean diffusivity, support vector machines}, file = {ScienceDirect Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/QT6E79J5/S0304394011011268.html:text/html} } @article{savio_neurocognitive_2011, title = {Neurocognitive disorder detection based on feature vectors extracted from {VBM} analysis of structural {MRI}}, volume = {41}, issn = {1879-0534}, doi = {10.1016/j.compbiomed.2011.05.010}, abstract = {Dementia is a growing concern due to the aging process of the western societies. Non-invasive detection is therefore a high priority research endeavor. In this paper we report results of classification systems applied to the feature vectors obtained by a feature extraction method computed on structural magnetic resonance imaging ({sMRI}) volumes for the detection of two neurological disorders with cognitive impairment: myotonic dystrophy of type 1 ({MD}1) and Alzheimer disease ({AD}). The feature extraction process is based on the voxel clusters detected by voxel-based morphometry ({VBM}) analysis of {sMRI} upon a set of patient and control subjects. This feature extraction process is specific for each kind of disease and is grounded on the findings obtained by medical experts. The 10-fold cross-validation results of several statistical and neural network based classification algorithms trained and tested on these features show high specificity and moderate sensitivity of the classifiers, suggesting that the approach is better suited for rejecting than for detecting early stages of the diseases.}, pages = {600-610}, number = {8}, journaltitle = {Computers in Biology and Medicine}, shortjournal = {Comput. Biol. Med.}, author = {Savio, A. and García-Sebastián, M. T. and Chyzyk, D. and Hernandez, C. and Graña, M. and Sistiaga, A. and de Munain, A. López and Villanúa, J.}, date = {2011-08}, note = {00023 {PMID}: 21621760}, keywords = {Adult, Algorithms, Alzheimer Disease, Brain, Cluster Analysis, Female, Humans, Image Processing, Computer-Assisted, magnetic resonance imaging, Male, Middle Aged, Myotonic Dystrophy, Neural Networks (Computer), Reproducibility of Results} } @article{grana_lattice_2010, title = {A lattice computing approach for on-line {fMRI} analysis}, volume = {28}, issn = {0262-8856}, url = {http://www.sciencedirect.com/science/article/pii/S0262885609002182}, doi = {10.1016/j.imavis.2009.10.004}, series = {Online pattern recognition and machine learning techniques for computer-vision: Theory and applications}, abstract = {We introduce an approach to {fMRI} analysis based on the Endmember Induction Heuristic Algorithm ({EIHA}). This algorithm uses the Lattice Associative Memory ({LAM}) to detect Lattice Independent vectors, which can be assumed to be Affine Independent, and therefore candidates to be the endmembers of the data. Induced endmembers are used to compute the activation levels of voxels as result of an unmixing process. The endmembers correspond to diverse activation patterns, one of these activation patterns corresponds to the resting state of the neuronal tissue. The on-line working of the algorithm does not need neither a previous training process nor a priori models of the data. Results on a case study compare with the results given by the state of art {SPM} software.}, pages = {1155-1161}, number = {7}, journaltitle = {Image and Vision Computing}, shortjournal = {Image and Vision Computing}, author = {Graña, Manuel and Savio, Alexandre M. and García-Sebastián, Maite and Fernandez, Elsa}, urldate = {2014-07-12}, date = {2010-07}, note = {00023}, keywords = {{fMRI}, Lattice Associative Memories, Lattice computing, Linear mixing model}, file = {ScienceDirect Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/IXT3UEDV/S0262885609002182.html:text/html} } @incollection{chyzyk_comparison_2010, title = {A Comparison of {VBM} Results by {SPM}, {ICA} and {LICA}}, rights = {©2010 Springer-Verlag Berlin Heidelberg}, isbn = {978-3-642-13802-7, 978-3-642-13803-4}, url = {http://link.springer.com/chapter/10.1007/978-3-642-13803-4_53}, series = {Lecture Notes in Computer Science}, abstract = {Lattice Independent Component Analysis ({LICA}) approach consists of a detection of independent vectors in the morphological or lattice theoretic sense that are the basis for a linear decomposition of the data. We apply it in this paper to a Voxel Based Morphometry ({VBM}) study on Alzheimer’s disease ({AD}) patients extracted from a well known public database. The approach is compared to {SPM} and Independent Component Analysis results.}, pages = {429-435}, number = {6077}, booktitle = {Hybrid Artificial Intelligence Systems}, publisher = {Springer Berlin Heidelberg}, author = {Chyzyk, Darya and Termenon, Maite and Savio, Alexandre}, editor = {Corchado, Emilio and Romay, Manuel Graña and Savio, Alexandre Manhaes}, urldate = {2014-07-12}, date = {2010-01-01}, langid = {english}, note = {00002}, keywords = {Algorithm Analysis and Problem Complexity, Artificial Intelligence (incl. Robotics), Computation by Abstract Devices, Database Management, Information Storage and Retrieval, Information Systems Applications (incl.Internet)}, file = {Snapshot:/home/alexandre/Dropbox/Documents/zotero/storage/SDRST9QG/10.html:text/html} }