Darya Chyzhyk

Hybrid dendritic computing with kernel-LICA applied to Alzheimer's disease detection in MRI

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 Alzheimers disease (AD) patients and control subjects.

Lattice independent component analysis for functional magnetic resonance imaging

We introduce a lattice independent component analysis (LICA) unsupervised scheme to functional magnetic resonance imaging (fMRI) data analysis. LICA is a non-linear alternative to independent component analysis (ICA), such that ICAs statistical independent sources correspond to LICAs lattice independent sources. In this paper, LICA uses an incremental lattice source induction algorithm (ILSIA) to induce the lattice independent sources from the input dataset. The ILSIA computes a set of Strongly Lattice Independent vectors using properties of lattice associative memories regarding Lattice Independence and Chebyshev best approximation. The lattice independent sources constitute a set of Affine Independent vectors that define a simplex covering the input data. LICA carries out data linear unmixing based on the lattice independent sources basis. Therefore, LICA is a hybrid combination of a non-linear lattice based component and a linear unmixing component. The principal advantage over ICA is that LICA does not impose any probabilistic model assumptions on the data sources. We compare LICA with ICA in two case studies. Firstly, including simulated fMRI data, LICA discovers the spatial location of meaningful sources with less ambiguity than ICA. Secondly, including real data from an auditory stimulation experiment, LICA improves over some state of the art ICA variants discovering the activation patterns detected by Statistical Parametric Mapping (SPM) on the same data.

Evolutionary ELM wrapper feature selection for Alzheimer's disease CAD on anatomical brain MRI

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 Alzheimers Disease (AD) and healthy controls.

Discrimination of Schizophrenia Auditory Hallucinators by Machine Learning of Resting-State Functional MRI

Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschls gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschls gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that computational intelligence approaches can provide robust tools for uncovering subtleties in complex neuroimaging data, and have the potential to advance the search for more neuroscience-based criteria for classifying mental illness in psychiatry research.

Computer aided diagnosis of schizophrenia on resting state fMRI data by ensembles of ELM

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. This paper deals with the application of ensembles of Extreme Learning Machines (ELM) to build Computer Aided Diagnosis systems on the basis of features extracted from the activity measures computed over rs-fMRI data. The power of ELM to provide quick but near optimal solutions to the training of Single Layer Feedforward Networks (SLFN) allows extensive exploration of discriminative power of feature spaces in affordable time with off-the-shelf computational resources. Exploration is performed in this paper by an evolutionary search approach that has found functional activity map features allowing to achieve quite successful classification experiments, providing biologically plausible voxel-site localizations.

Discrimination between Alzheimer’s Disease and Late Onset Bipolar Disorder Using Multivariate Analysis

Background Late onset bipolar disorder (LOBD) is often difficult to distinguish from degenerative dementias, such as Alzheimer disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence in the elder population is not negligible and it is increasing. Both pathologies share pathophysiological neuroinflammation features. Improvements in differential diagnosis of LOBD and AD will help to select the best personalized treatment. Objective The aim of this study is to assess the relative significance of clinical observations, neuropsychological tests, and specific blood plasma biomarkers (inflammatory and neurotrophic), separately and combined, in the differential diagnosis of LOBD versus AD. It was carried out evaluating the accuracy achieved by classification-based computer-aided diagnosis (CAD) systems based on these variables. Materials A sample of healthy controls (HC) (n = 26), AD patients (n = 37), and LOBD patients (n = 32) was recruited at the Alava University Hospital. Clinical observations, neuropsychological tests, and plasma biomarkers were measured at recruitment time. Methods We applied multivariate machine learning classification methods to discriminate subjects from HC, AD, and LOBD populations in the study. We analyzed, for each classification contrast, feature sets combining clinical observations, neuropsychological measures, and biological markers, including inflammation biomarkers. Furthermore, we analyzed reduced feature sets containing variables with significative differences determined by a Welch’s t-test. Furthermore, a battery of classifier architectures were applied, encompassing linear and non-linear Support Vector Machines (SVM), Random Forests (RF), Classification and regression trees (CART), and their performance was evaluated in a leave-one-out (LOO) cross-validation scheme. Post hoc analysis of Gini index in CART classifiers provided a measure of each variable importance. Results Welch’s t-test found one biomarker (Malondialdehyde) with significative differences (p < 0.001) in LOBD vs. AD contrast. Classification results with the best features are as follows: discrimination of HC vs. AD patients reaches accuracy 97.21% and AUC 98.17%. Discrimination of LOBD vs. AD patients reaches accuracy 90.26% and AUC 89.57%. Discrimination of HC vs LOBD patients achieves accuracy 95.76% and AUC 88.46%. Conclusion It is feasible to build CAD systems for differential diagnosis of LOBD and AD on the basis of a reduced set of clinical variables. Clinical observations provide the greatest discrimination. Neuropsychological tests are improved by the addition of biomarkers, and both contribute significantly to improve the overall predictive performance.

Active Learning with Bootstrapped Dendritic Classifier applied to medical image segmentation

We perform the segmentation of medical images following an Active Learning approach that allows quick interactive segmentation minimizing the requirements for intervention of the human operator. The basic classifier is the Bootstrapped Dendritic Classifier (BDC), which combine the output of an ensemble of weak Dendritic Classifiers by majority voting. Weak Dendritic Classifiers are trained on bootstrapped samples of the train data setting a limit on the number of dendrites. We validate the approach on the segmentation of the thrombus in 3D Computed Tomography Angiography (CTA) data of Abdominal Aortic Aneurysm (AAA) patients simulating the human oracle by the provided ground truth. The generalization results in terms of accuracy and true positive ratio of the classification of the entire volume by the classifier trained on one slice confirm that the approach is worth its consideration for clinical practice.

Optimal Hyperbox Shrinking in Dendritic Computing Applied to Alzheimer’s Disease Detection in MRI

The artificial neural networks are an imitation of human brain architecture. Dendritic Computing is based on the concept that dendrites are the basic building blocks for a wide range of nervous systems. 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 modify the basic strategy of hyperbox definition in DC introducing a factor of reduction of these hyperboxes.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.

Image Understanding Applications of Lattice Autoassociative Memories

Multivariate mathematical morphology (MMM) aims to extend the mathematical morphology from gray scale images to images whose pixels are high-dimensional vectors, such as remote sensing hyperspectral images and functional magnetic resonance images (fMRIs). Defining an ordering over the multidimensional image data space is a fundamental issue MMM, to ensure that ensuing morphological operators and filters are mathematically consistent. Recent approaches use the outputs of two-class classifiers to build such reduced orderings. This paper presents the applications of MMM built on reduced supervised orderings based on lattice autoassociative memories (LAAMs) recall error measured by the Chebyshev distance. Foreground supervised orderings use one set of training data from a foreground class, whereas background/foreground supervised orderings use two training data sets, one for each relevant class. The first case study refers to the realization of the thematic segmentation of the hyperspectral images using spatial-spectral information. Spectral classification is enhanced by a spatial processing consisting in the spatial correction guided by a watershed segmentation computed by the LAAM-based morphological operators. The approach improves the state-of-the-art hyperspectral spatial-spectral thematic map building approaches. The second case study is the analysis of resting state fMRI data, working on a data set of healthy controls, schizophrenia patients with and without auditory hallucinations. We perform two experiments: 1) the localization of differences in brain functional networks on population-dependent templates and 2) the classification of subjects into each possible pair of cases. In this data set, we find that the LAAM-based morphological features improve over the conventional correlation-based graph measure features often employed in fMRI data classification.

Hybrid multivariate morphology using lattice auto-associative memories for resting-state fMRI network discovery

Analysis of fMRI data, specifically resting-state fMRI data, is performed here from the point of view of a hybrid Multivariate Mathematical Morphology induced by a supervised h-ordering defined on the fMRI time series by the response of Lattice Auto-associative Memories built from specific fMRI voxels. The supervised h-ordering values and the results of morphological filters, i.e. a morphological top-hat, allow to identify some brain networks depending on the seed voxel value. Results on a set of resting state fMRI images of schizophrenia patients and healthy controls show that these networks can be dependent on the subject class, thus providing discriminant findings that may be useful for machine learning approaches.