Error estimation must be used to find the accuracy of a designed classifier, an issue that is critical in biomarker discovery for disease diagnosis and prognosis in genomics and proteomics. This dissertation is concerned with the analytical formulation of the joint distribution of the true error of misclassification and two of its commonly used estimators, resubstitution and leave-one-out, as well as their marginal and mixed moments, in the context of the Linear Discriminant Analysis (LDA) classification rule. In the first part of this dissertation, we obtain the joint sampling distribution of the actual and estimated errors under a general parametric Gaussian assumption. Exact results are provided in the univariate case and an accurate approximation is obtained in the multivariate case. We show how these results can be applied in the computation of conditional bounds and the regression of the actual error, given the observed error estimate. In practice the unknown parameters of the Gaussian distributions, which figure in the expressions, are not known and need to be estimated. Using the usual maximum-likelihood estimates for such parameters and plugging them into the theoretical exact expressions provides a sample-based approximation to the joint distribution, and also sample-based methods to estimate upper conditional bounds. In the second part of this dissertation, exact analytical expressions for the bias, variance, and Root Mean Square (RMS) for the resubstitution and leave-one-out error estimators in the univariate Gaussian model are derived. All probabilistic characteristics of an error estimator are given by the knowledge of its joint distribution with the true error. Partial information is contained in their mixed moments, in particular, their second mixed moment. Marginal information regarding an error estimator is contained in its marginal moments, in particular, its mean and variance. Since we are interested in estimator accuracy and wish to use the RMS to measure that accuracy, we desire knowledge of the second-order moments, marginal and mixed, with the true error. In the multivariate case, using the double asymptotic approach with the assumption of knowing the common covariance matrix of the Gaussian model, analytical expressions for the first moments, second moments, and mixed moment with the actual error for the resubstitution and leave-one-out error estimators are derived. The results provide accurate small sample approximations and this is demonstrated in the present situation via numerical comparisons. Application of the results is discussed in the context of genomics.

While all mammals sleep, the functions and implications of sleep are not well understood, and are a strong area of investigation in the research community. Mice are utilized in many sleep studies, with electroencephalography (EEG) signals widely used for data acquisition and analysis. However, since EEG electrodes must be surgically implanted in the mice, the method is high cost and time intensive. This work presents an extension of a previously researched high throughput, low cost, non-invasive method for mouse behavior detection and classification. A novel hierarchical classifier is presented that classifies behavior states including NREM and REM sleep, as well as active behavior states, using data acquired from a Signal Solutions (Lexington, KY) piezoelectric cage floor system. The NREM/REM classification system presented an 81% agreement with human EEG scorers, indicating a useful, high throughput alternative to the widely used EEG acquisition method.

The purpose of this investigation was to examine the cultural and motivational perspectives associated with the occupational or vocational decision-related processes of senior high school students. Two theoretical frameworks were developed to guide the investigation by integrating theories from the culture, motivation, decision-making, and vocational decision-making literatures. One theoretical framework investigated the roles of culture and motivation in the vocational decisions made by senior high school students, while the other examined the vocational decision-related processes of senior high school students in terms of the extent to which they may be amotivated about choosing a future occupation. A mixed methods approach (incorporating a cross-sectional and correlational research design for the quantitative component) was implemented using a specially developed questionnaire. In the first phase of the investigation, the questionnaire was administered to 492 Year 11 students attending a stratified random sample of six Independent high schools located in the Sydney metropolitan area. In the second and main phase, a refined version of the questionnaire was administered to 566 Year 11 students attending a stratified random sample of 16 government high schools located in the Sydney metropolitan area. Structural equation modelling, discriminant analyses, and qualitative techniques were used to analyse the data collected in the two phases. The major findings of the investigation included the development and confirmation (after modifications) of two new theoretically-justifiable models of vocational decision-related processes. One model provided empirical support for relationships between cultural orientation, values, and attitudes/intentions toward occupations, while the other identified relationships between amotivation, indecision toward occupations, expectancy-value variables, and influences from the family. Multiple themes that were identified in the qualitative data analyses supplemented and partially supported elements of the two empirical models, and enabled a richer understanding of the issues surrounding the vocational decision. The findings of the investigation may be used by career advisors, psychologists, educators, and families to advise and assist senior high school students faced with the vocational decision. The investigation may contribute to reducing the gap in the literature on the roles of culture and motivation in the vocational decision-related processes of senior high school students.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

Sleep disordered breathing (SDB) is a highly prevalent but an under-diagnosed disease. Among adults in the ages between 30 to 60 years, 24% of males and 9% of females show conditions of SDB, while 82% of men and 93% of women with moderate to severe SDB remain undiagnosed. Polysomnography (PSG) is the reference diagnostic test for SDB. During PSG, a number of physiological signals are recorded during an overnight sleep and then manually scored for sleep/wake stages and SDB events to obtain the reference diagnosis. The manual scoring of SDB events is an extremely time consuming and cumbersome task with high inter- and intra-rater variations. PSG is a labour intensive, expensive and patient inconvenient test. Further, PSG facilities are limited leading to long waiting lists. There is an enormous clinical need for automation of PSG scoring and an alternative automated ambulatory method suitable for screening the population. During the work of this thesis, we focus (1) on implementing a framework that enables more reliable scoring of SDB events which also lowers manual scoring time, and (2) implementing a reliable automated screening procedure that can be used as a patient-friendly home based study. The recordings of physiological measurements obtained during patients’ sleep of- ten suffer from data losses, interferences and artefacts. In a typical sleep scoring session, artifact-corrupted signal segments are visually detected and removed from further consideration. We developed a novel framework for automated artifact detection and signal restoration, based on the redundancy among respiratory flow signals. The signals focused on are the airflow (thermistor sensors) and nasal pressure signals that are clinically significant in detecting respira- tory disturbances. We treat the respiratory system as a dynamical system, and use the celebrated Takens embedding theorem as the theoretical basis for sig- nal prediction. In this study, we categorise commonly occurring artefacts and distortions in the airflow and nasal pressure measurements into several groups and explore the efficacy of the proposed technique in detecting/recovering them. Results we obtained from a database of clinical PSG signals indicated that theproposed technique can detect artefacts/distortions with a sensitivity >88% and specificity >92%. This work has the potential to simplify the work done by sleep scoring technicians, and also to improve automated sleep scoring methods. During the next phase of the thesis we have investigated the diagnostic ability of single – and dual–channel respiratory flow measuring devices. Recent studies have shown that single channel respiratory flow measurements can be used for automated diagnosis/screening for sleep disordered breathing (SDB) diseases. Improvements for reliable home-based monitoring for SDB may be achieved with the use of predictors based on recurrence quantification analysis (RQA). RQA essentially measures the complex structures present in a time series and are relatively independent of the nonlinearities present in the respiratory measurements such as those due to breathing nonlinearities and sensor movements. The nasal pressure, thermistor-based airflow, abdominal movement and thoracic movement measurements obtained during Polysomnography, were used in this study to implement an algorithm for automated screening for SDB diseases. The algorithm predicts SDB-affected measurement segments using twelve features based on RQA, body mass index (BMI) and neck circumference using mixture discriminant analysis (MDA). The rate of SDB affected segments of data per hour of recording (RDIS) is used as a measure for the diagnosis of SDB diseases. The operating points to be chosen were the prior probability of SDB affected data segments (π1) and the RDIS threshold value, above which a patient is predicted to have a SDB disease. Cross-validation with five-folds, stratified based on the RDI values of the recordings, was used in estimating the operating points. Sensitivity and specificity rates for the final classifier were estimated using a two-layer assessment approach with the operating points chosen at the inner layer using five-fold cross-validation and the choice assessed at the outer layer using repeated learning-testing. The nasal pressure measurement showed higher accuracy compared to other respiratory measurements when used alone. The nasal pressure and thoracic movement measurements were identified as the best pair of measurements to be used in a dual channel device. The estimated sensitivity and specificity (standard error) in diagnosing SDB disease (RDI ≥ 15) are 90.3(3.1)% and 88.3(5.5)% when nasal pressure is used alone and together with the thoracic movement it was 89.5(3.7)% and 100.0(0.0)%. Present results suggest that RQA of a single respiratory measurement has potential to be used in an automated SDB screening device, while with dual-channel more reliable accuracy can be expected. Improvements may be possible by including other RQA based features and optimisation of the parameters.

The purpose of this investigation was to examine the cultural and motivational perspectives associated with the occupational or vocational decision-related processes of senior high school students. Two theoretical frameworks were developed to guide the investigation by integrating theories from the culture, motivation, decision-making, and vocational decision-making literatures. One theoretical framework investigated the roles of culture and motivation in the vocational decisions made by senior high school students, while the other examined the vocational decision-related processes of senior high school students in terms of the extent to which they may be amotivated about choosing a future occupation. A mixed methods approach (incorporating a cross-sectional and correlational research design for the quantitative component) was implemented using a specially developed questionnaire. In the first phase of the investigation, the questionnaire was administered to 492 Year 11 students attending a stratified random sample of six Independent high schools located in the Sydney metropolitan area. In the second and main phase, a refined version of the questionnaire was administered to 566 Year 11 students attending a stratified random sample of 16 government high schools located in the Sydney metropolitan area. Structural equation modelling, discriminant analyses, and qualitative techniques were used to analyse the data collected in the two phases. The major findings of the investigation included the development and confirmation (after modifications) of two new theoretically-justifiable models of vocational decision-related processes. One model provided empirical support for relationships between cultural orientation, values, and attitudes/intentions toward occupations, while the other identified relationships between amotivation, indecision toward occupations, expectancy-value variables, and influences from the family. Multiple themes that were identified in the qualitative data analyses supplemented and partially supported elements of the two empirical models, and enabled a richer understanding of the issues surrounding the vocational decision. The findings of the investigation may be used by career advisors, psychologists, educators, and families to advise and assist senior high school students faced with the vocational decision. The investigation may contribute to reducing the gap in the literature on the roles of culture and motivation in the vocational decision-related processes of senior high school students.

Made available in DSpace on 2015-03-26T13:32:13Z (GMT). No. of bitstreams: 1 texto completo.pdf: 1211050 bytes, checksum: ef3f5f575d905c3bcac49eef49b02816 (MD5) Previous issue date: 2012-02-13 / This study aimed to assess, as the consistency of the grouping, hierarchical clustering methods UPGMA and Ward and optimization Tocher and modified Tocher by application of Fisher discriminant analysis in groups obtained with each method in the study of genetic divergence among garlic accessions, also identifying the most dissimilar access. The groupings were based on the Mahalanobis distance, which also allowed to quantify the relative importance of characters. The accessions with the highest dissimilarity accesses were 13 (BGH 4505) and 61 (BGH 5958), especially in relation to the average weight of the bulb and productivity. Modified Tocher methods, UPGMA and Ward algorithm presented results agree with each other and form groups. However, the Fisher discriminant analysis applied to groups of hierarchical methods (UPGMA and Ward) showed the lowest apparent error, therefore, more consistent methods for studying the genetic diversity of garlic accessions. / Este estudo teve por objetivo avaliar, quanto a consistência do agrupamento, os métodos de agrupamentos hierárquicos UPGMA e Ward e os de otimização de Tocher e Tocher modificado, pela aplicação da análise discriminante de Fisher aos grupos obtidos com cada método, em estudo da divergência genética entre acessos de alho, identificando também os acessos mais dissimilares. Os agrupamentos foram realizados com base na distância generalizada de Mahalanobis, que também permitiu quantificar a importância relativa dos caracteres. Os acessos que apresentaram maior dissimilaridade foram os acessos 13 (BGH 4505) e 61 (BGH 5958), principalmente em relação ao peso médio do bulbo e produtividade. Os métodos de Tocher modificado, UPGMA e algoritmo de Ward apresentaram resultados concordantes entre si quanto a formação dos grupos. No entanto, pela análise discriminante de Fisher aplicada aos grupos dos métodos hierárquicos (UPGMA e Ward) observou-se as menores taxas de erro aparente, sendo, portanto, os métodos mais consistentes para o estudo da diversidade genética de acessos de alho.

A Doença de Machado Joseph(DMJ) é uma doença genética autossômica dominante de início na vida adulta que afeta a coordenação motora e cursa com sintomas neurodegenerativos. É causada por uma expansão da repetição CAG no gene ATXN3. Há várias hipóteses a respeito da sua fisiopatogenia, e uma delas envolve a resposta inflamatória. O objetivo deste estudo foi descrever as concentrações séricas das citocinas em indivíduos sintomáticos, assintomáticos e compará-los com os controles saudáveis. Após a confirmação molecular dos pacientes e controles pareados por sexo e idade, os indivíduos foram convidados a participar do estudo. A idade de início e a duração da doença foram obtidas, e as escalas clínicas Scale for the Assessment and Rating of Ataxia (SARA), Neurological Examination Score for Spinocerebellar Ataxias (NESSCA), SCA Functional Index (SCAFI), and Composite Cerebellar Functional Score (CCFS), aplicadas. O soro dos indivíduos foi coletado e um painel de citocina foi realizado, incluindo a Eotaxina, GM-CSF, IFN-a, IFN-γ, IL-1b, IL-1Ra, IL-2, IL-2R, IL-4, IL- 5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-13, IL-15, IL-17, IP-10, MCP-1, MIG, MIP1a, MIP1b, RANTES e O TNF-a. Entre os indivíduos sintomáticos, o painel foi repetido após 90 e 360 dias. O perfil das citocinas no baseline foi estudado por análise discriminante. Aquelas que apresentaram alterações relevantes entre os grupos tiveram seus níveis sérico reavaliados após 90 e 360 dias e estes dados foram avaliados pela equação de estimação generalizada (GEE). Sessenta e seis sintomáticos, 13 assintomáticos e 43 controles foram estudados. Quando comparados os sintomáticos e assintomáticos com seus respectivos controles saudáveis, não se observou diferenças nos padrões das citocinas. No entanto, apenas uma citocina teve destaque: os níveis séricos de Eotaxina foram significativamente mais elevados em assintomáticos (p = 0,001, ANCOVA) e entre os sintomáticos seus níveis foram menores após 360 dias do que naquelas obtidas no início do estudo (p = 0,039, GEE). A idade, a duração da doença, a expansão CAG, e as escalas NESSCA e SARA não se correlacionaram com os níveis das citocinas. O padrão relativamente benigno de citocinas em portadores sintomáticos sugere que a ativação do micróglia não seja primordial na DMJ. Entretanto, os níveis de eotaxina, um peptídeo secretado por astrócitos para repelir as células imunes circulantes, foram elevados no grupo assintomático, o que sugere que uma resposta específica destas células pode estar relacionada com a ausência de sintomas e/ou que a perda de astrócitos estaria relacionada à progressão da doença em DMJ. / Machado Joseph Disease (MJD) is an autosomal dominant genetic disease of adulthood which affects motor coordination and progresses with neurodegenerative symptoms. It is caused by an expansion of the CAG repeat at ATXN3 gene. There are several hypotheses about its pathogenesis, and one of them involves the inflammatory response. The aim of the present study is to describe the serum concentrations of a broad spectrum of cytokines in symptomatic and asymptomatic carriers of Machado Joseph disease (SCA3/MJD) CAG expansions. Molecularly confirmed carriers and controls were studied. Age at onset, disease duration, and clinical scales Scale for the Assessment and Rating of Ataxia (SARA), Neurological Examination Score for Spinocerebellar Ataxias (NESSCA), SCA Functional Index (SCAFI), and Composite Cerebellar Functional Score (CCFS) were obtained from the symptomatic carriers. Serum was obtained from all individuals and a cytokine panel consisted of eotaxin, granulocyte-macrophage colony-stimulating factor (GM-CSF), interferon (IFN)-α, IFN-γ, interleukin (IL)-1β, IL-1RA, IL-2, IL-2R, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL- 12, IL-13, IL-15, IL-17, interferon gamma-induced protein (IP)-10, monocyte chemoattractant protein (MCP)-1, monokine induced by gamma interferon (MIG), macrophage inflammatory protein (MIP)-a, MIP-b, regulated on activation, normal T cell expressed and secreted (RANTES) and tumor necrosis factor (TNF)-α was analyzed. In a subgroup of symptomatic carriers, the cytokine panel was repeated after 90 and 360 days. Cytokine distribution among groups was studied by discriminant analysis; changes in serum levels after 90 and 360 days were studied by generalized estimation equation. Sixty-six symptomatic carriers, 13 asymptomatic carriers, and 43 controls were studied. No differences in cytokine patterns were found between controls and carriers of the CAG expansions or between controls and symptomatic carriers only. In contrast, eotaxin concentrations were significantly higher in asymptomatic than in symptomatic carriers or in controls (p = 0.001, ANCOVA). Eotaxin did not correlate with age, disease duration, CAG expansion, NESSCA score, and SARA score. Among symptomatic carriers, eotaxin dropped after 360 days (p = 0.039, GEE). SCA3/ MJD patients presented a benign pattern of serum cytokines. In contrast, levels of eotaxin, a peptide secreted by astrocytes, were elevated in the asymptomatic carriers, suggesting that a specific response of these cells can be related to symptom progression, in SCA3/MJD.

Orientador: Prof. Dr. João Ricardo Sato / Dissertação (mestrado) - Universidade Federal do ABC, Programa de Pós-Graduação em Neurociência e Cognição, 2016. / Diversos estudos reportaram alterações cerebrais ao longo do curso da esquizofrenia. Até mesmo nos estágios incipientes, como no Primeiro Episódio Psicótico (PEP). Métodos de aprendizagem de máquina podem ser utilizados para análise multivariada de dados de neuroimagem, porém a grande maioria dos estudos os emprega principalmente para previsões entre grupos, como discriminar pacientes com esquizofrenia de controles saudáveis. No presente estudo, foi aplicado o método maximum entropy linear discriminant analysis (MLDA) com o objetivo de buscar um melhor entendimento dos estágios da esquizofrenia. Foram analisados dados neuro-volumétricos provenientes de imagens de ressonância magnética de 143 pacientes crônicos com esquizofrenia, 32 pacientes PEP e 82 controles saudáveis. O método projeta as características multivariadas de um sujeito em um sub-espaço discriminante univariado, provendo um "escore de esquizofrenia". Inicialmente, a performance do MLDA na tarefa de discriminação entre pacientes com esquizofrenia de controles foi avaliada e foram identificados as regiões cerebrais que mais contribuíram para a classificação. Por fim, foram utilizados os escores provenientes do MLDA para realizar uma comparação entre os padrões volumétricos de pacientes PEP e pacientes com esquizofrenia e controles saudáveis. A classificação atingiu uma acurácia balanceada de 72.96%. O grupo PEP apresentou uma distribuição de escores mais similar aos pacientes com esquizofrenia em comparação aos controles saudáveis. Após repetição das análises excluindo as regiões afetadas por medicação anti-psicótica, a acurácia permaneceu aproximadamente a mesma (73.66%), porém os escores do PEP se tornaram mais similares ao grupo controle. Os resultados do presente estudo sugerem que as primeiras estruturas alteradas no PEP podem ser as regiões afetadas por anti-psicóticos. Entre as estruturas mais discriminantes na classificação se encontravam, principalmente, estruturas relacionadas ao sistema límbico e a circuiteria envolvida em comportamentos orientados a objetivos. Em conclusão, nossos resultados sugerem a importância de considerar os efeitos dos anti-psicóticos, a fim de entender os substratos neurais envolvidos na esquizofrenia. / Several studies reported brain changes along the course of the schizophrenia. Even in the early stages, such as first episode psychosis (FEP). Machine learning methods can be applied for multivariate analysis of neuroimaging data, however, they have been employed in most of the studies with main concern in group prediction, such as discriminating schizophrenic patients from healthy controls. In the present study we applied the maximum entropy linear discriminant analysis (MLDA) aiming to a better comprehension of the schizophrenia stages. We analysed brain structures volumetric data from MRI images of 143 patients with chronic schizophrenia, 32 FEP patients and 82 healthy controls. The method projects the multivariate characteristics of a subject onto a univariate discriminant subspace, providing a "schizophrenia score". First, the performance of MLDA in the discrimination task between schizophrenia patients from controls was evaluated and we identified the brain regions that most contribuited to the classification. Finally, we utilized the scores provided by MLDA to make a comparison among the volumetric patterns of FEP patients and schizophrenic patients and healthy controls. The classification achieved a balanced accuracy of 72.96%. We found that the FEP group had a score distribution more similar to patients with schizophrenia in comparison with healthy subjects. After the exclusion of regions affected by antipsychotic medication and repeating MLDA analysis, the accuracy remained approximately the same (73.66%), but the FEP scores became more similar to control group. Our results suggest that the first structures altered in FEP might be the regions affected by antipsychotics. Structures related to the limbic system and the circuitry involved in goal-directed behaviours were the most discriminant regions in the classification. In conclusion, our results suggest the importance of taking into account the brain structural effects of antipsychotic drugs in order to understand the neural substrates involved in schizophrenia.