Darya Chyzhyk

• PostDoc Position at University of Florida

Background With increasing data sizes and more easily available computational methods, neurosciences rely more and more on predictive modeling with machine learning, e.g., to extract disease biomarkers. Yet, a successful prediction may capture a confounding effect correlated with the outcome instead of brain features specific to the outcome of inte...

Functional connectomes reveal biomarkers of individual psychological or clinical traits. However, there is great variability in the analytic pipelines typically used to derive them from rest-fMRI cohorts. Here, we consider a specific type of studies, using predictive models on the edge weights of functional connectomes, for which we highlight the b...

Hallucinations are an elusive phenomena that has been associated with psychotic behavior, but which has a high prevalence in healthy population. Some models of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The current study aims to obtain measurement of effective connectivity on rs-fMRI...

Background: Late Onset Bipolar Disorder (LOBD) is the development of Bipolar Disorder (BD) at an age above 50 years old. It is often difficult to differentiate from other aging dementias, such as Alzheimer's Disease (AD), because they share cognitive and behavioral impairment symptoms. Objectives: We look for WM tract voxel clusters showing signifi...

Automated detection of white matter hyperintensities (WHM) may have a broad clinical use, because WHM appear in several brain diseases. Deep learning architectures have been recently very successful for the segmentation of brain lesions, such as ictus or tumour lesions. We propose a Convolutional Neural Network composed of four parallel data paths...

White matter hyperintensities (WHM) are characteristics of various brain diseases, so automated detection tools have a broad clinical spectrum. Deep learning architectures have been recently very successful for the segmentation of brain lesions, such as ictus or tumour lesions. We propose a Convolutional Neural Network composed of four parallel dat...

Hallucinations are elusive phenomena that have been associated with psychotic behavior, but that have a high prevalence in healthy population. Some generative mechanisms of Auditory Hallucinations (AH) have been proposed in the literature, but so far empirical evidence is scarce. The most widely accepted generative mechanism hypothesis nowadays con...

The aging of population is increasing the prevalence of some previously rare neuropathological condition, such as the Late Onset of Bipolar Disorder (LOBD). Bipolar Disorder appears at youth or even earlier in life, so that its appearance at sixty years or later is a rare event that can be confused with degenerative diseases such as Alzheimer’s Dis...

White matter (WM) lesions are a phenomena perceived in magnetic resonance imaging (MRI) which is prevalent in many different brain pathologies, hence the general interest in automated methods for lesion segmentation (LS). We provide a short review of some commonly used state-of-the-art approaches. The article is focused on the machine learning tech...

This special issue editorial begins with a brief discussion on the current trends of innovations in healthcare and medicine driven by the evolution of sensing devices as well as the information processing techniques, and the social media revolution. This discussion aims to set the stage for the actual papers accepted for the special issue which are...

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 featu...

Background: Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to pop...

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...

Hallucinations, and more specifically auditory hallucinations (AH), are a perplexing phenomena experienced by many people. Though they are a clinical symptom in some mental diseases, such as Schizophrenia, they are also experienced by normal, healthy persons. There are several models of the mechanics happening in the brain leading to hallucinations...

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 extr...

Currently there are many efforts to find neurological biomarkers that can be extracted from resting state fMRI data. In this paper we concentrate on a study about the discrimination between schizophrenia patients and healthy control, as well as the discrimination of subpopulations of schizophrenia patients with and without auditory hallucinations....

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 m...

Resting state fMRI has growing number of studies with diverse aims, always centered on some kind of functional connectivity biomarker obtained from correlation regarding seed regions, or by analytical decomposition of the signal towards the localization of the spatial distribution of functional connectivity patterns. In general, studies are computa...

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...

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 d...

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 voti...

Resting state fMRI data can be used to find biomarkers of specific neurological conditions, such as schizophrenia. In this paper we report results on the discrimination between schizophrenia patients and healthy control, as well as the discrimination of subpopulations of schizophrenia patients with and without auditory hallucinations. Data features...

We work on the definition of Lattice Computing approach to identify functional networks in resting state fMRI data (rsfMRI) looking for biomarkers of cognitive or neurodegenerative diseases. The approach uses Lattice Auto-Associative Memories (LAAM) to compute a reduced ordering h-function that can be thresholded or processed by morphological opera...

In this paper, we present a method to discriminate cocaine dependent patients and healthy subjects using features computed from structural magnetic resonance imaging (MRI). After image preprocessing, we compute voxel based morphometry (VBM) applying Gaussian smoothing with three different full width at half maximum (FWHM) kernel sizes. VBM clusters...

This paper presents an intelligent approach to classification analysis of Alzheimer disease patients. Bootstrap technique is chosen to get rid of weak point of Dendritic Classifiers (DC), which is low Specificity and improve the Accuracy at all. Dendritic Classifiers (BDC) is an ensemble of weak DC trained combining their output by majority voting....

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 resul...

We present a novel hetero-associative memory based on dendritic neural computation. The computations in this model are based on lattice group operations. The proposed model does not suffer from the usual storage capacity problem and is extremely robust in the presence of various types of noise and data corruption.

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 Kerne...

Bootstrapped Dendritic Classifiers (BDC) is an ensemble of weak Dendritic Classifiers trained combining their output by majority voting to obtain improved classification generalization performance. Weak Dendritic Classifiers are trained on bootstrapped samples of the train data setting a limit on the number of dendrites. There is no additional data...

Lattice Independent Component Analysis (LICA) approach consists of a detection of lattice independent vectors (endmembers) that are used as a basis for a linear decomposition of the data (unmixing). In this paper we explore the network detections obtained with LICA in resting state fMRI data from healthy controls and schizophrenic patients. We comp...

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 ICA’s statistical independent sources correspond to LICA’s lattice independent sources. In this paper, LICA uses an increm...

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. Ho...

An international students' project is presented focused on application of Open Office and Excel spreadsheets for modeling of projectile-motion type dynamical systems. Variation of the parameters of plotted and animated families of jets flowing at different angles out of the holes in the wall of water-filled reservoir [1,2] revealed unexpected pecul...

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 da...