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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Associations of morphometric and metabolic biomarkers with cognitive impairment in Alzheimer's disease and Lewy body dementias
Nedelská, Zuzana ; Hort, Jakub (advisor) ; Nevrlý, Martin (referee) ; Dušek, Petr (referee)
Associations of morphometric and metabolic biomarkers with cognitive impairment in Alzheimer's disease and Lewy body dementias Abstract Dementia has become one of the major health care and socio-economic challenges. Alzheimer's disease (AD) is the most common dementia whereas dementia with Lewy bodies (DLB) is the second most common neurodegenerative after AD. However, both dementias exist in a quite heterogeneous contiua that can overlap with each other. Approaches that allow for the identification of individuals at risk of developing AD in preclinical or prodromal stages are of major interest to apply the symptomatic and newly introduced biological therapies and non- pharmacological interventions that are more effective early on. Similar efforts are undertaken in the DLB field although no causal treatment for DLB is available yet. A prerequisite for an efficacious and targeted intervention is a selection of individuals who would benefit the most from this intervention. This process includes the timely and accurate diagnosis, differential diagnosis, prognostication, and management of treatable comorbidities. This dissertation has two parts. Part one is an overview of AD and DLB. The second part summarizes author's research work. The main research aims corroborated in this thesis are three-fold: First, to...
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Analysis of speech disorders in patients with a high risk of developing Lewy body diseases
Novotný, Kryštof ; Kováč, Daniel (referee) ; Mekyska, Jiří (advisor)
Lewy bodies diseases (one of the most common neurodegenerative disorders) have the same pathological basis, but the individual representatives differ in their clinical manifestations. Different diseases affect the mental or physical side of the patient to a greater or lesser extent. This work assumes that thanks to the acoustic analysis of speech, it is possible to distinguish individual diseases from one another, because the disorders of the cognitive and motor aspects of a patient reflect in speech in specific ways. The thesis aims to describe the clinical features of the main representatives of the Lewy bodies diseases, to investigate their impact on speech, to propose characterizing acoustic parameters and then to compare their discriminative power. Speech recordings from the CoBeN and preLBD databases are used as input data for the proposed algorithm. Descriptive statistics, Mann-Whitney U test, FDR correction and XGBoost machine learning model using stratified cross-validation and balanced accuracy are used for subsequent evaluation. The result are scripts for the automated calculation of speech parameters from the database and their evaluation. The results of the analysis prove that the selected diseases can really be distinguished from each other and from a healthy control based on the manifestations in speech, already in the prodromal stages.
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Functional connectivity and brain structure assessment in patients at risk of synucleinopathies
Klobušiaková, Patrícia ; Gajdoš, Martin (referee) ; Mekyska, Jiří (advisor)
Synucleinopathy is a neurodegenerative disorder characterized by the presence of pathological protein -synuclein in neurons. So far, treatment that could heal or permanently stop this disease is not known. The aim of this work is to identify prodromal stages of synucleinopathies using functional connectivity processed applying graph metrics and assessing cortical thickness and subcortical structures volumes from magnetic resonance imaging data, and to verify specificity and sensitivity of combinations of parameters that sufficiently differentiate patients in risk of synucleinopathies. To accomplish this goal, we collected data from patients in the risk of synucleinopathy (preDLB, n = 27) and healthy controls (HC, n = 28). We found reduced volume of right pallidum and increased hippocampal volume to cortical volume ratio, increased normalised clustering coefficient and higher modularity in the preDLB group in comparison to HC. These four parameters were modeled using machine learning. The resulting model differentiated preDLB and HC with balanced accuracy of 88 %, specificity of 89 % and sensitivity of 86 %. The findings of this thesis can serve as the basis for further studies searching for specific MRI markers of prodromal stage of synucleinopathy that could be targeted with therapy in the future.
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Identification Of Patients At The Risk Of Lewy Body Diseases Based On Acoustic Analysis Of Speech
FernándezMartínez, Andrea
Lewy body diseases (LBDs) is a group of neurodegenerative diseases that consists of Parkinson’s disease and dementia with Lewy bodies, that are generally very crucial to be diagnosed in their prodromal state. In the frame of this study we proposed a multivariate logistic regression model that identifies people in a high risk of LBDs based on their articulatory and prosodic characteristics. More specifically, the model has 80 % specificity and 85 % sensitivity based on quantification of rigidity of tongue/jaw, monoloudness, and inappropriate pausing.
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