Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer's patients from controls in the Nun Study

<p>Abstract</p> <p>Background</p> <p>Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old.</p> <p>The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis</p> <p>Methods</p> <p>The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts.</p> <p>Results</p> <p>By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same.</p> <p>Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus.</p> <p>Conclusion</p> <p>The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls.</p

Neuropathological findings processed by artificial neural networks (ANNs) can perfectly distinguish Alzheimer\u27s patients from controls in the Nun Study

BACKGROUND: Many reports have described that there are fewer differences in AD brain neuropathologic lesions between AD patients and control subjects aged 80 years and older, as compared with the considerable differences between younger persons with AD and controls. In fact some investigators have suggested that since neurofibrillary tangles (NFT) can be identified in the brains of non-demented elderly subjects they should be considered as a consequence of the aging process. At present, there are no universally accepted neuropathological criteria which can mathematically differentiate AD from healthy brain in the oldest old. The aim of this study is to discover the hidden and non-linear associations among AD pathognomonic brain lesions and the clinical diagnosis of AD in participants in the Nun Study through Artificial Neural Networks (ANNs) analysis. METHODS: The analyses were based on 26 clinically- and pathologically-confirmed AD cases and 36 controls who had normal cognitive function. The inputs used for the analyses were just NFT and neuritic plaques counts in neocortex and hippocampus, for which, despite substantial differences in mean lesions counts between AD cases and controls, there was a substantial overlap in the range of lesion counts. RESULTS: By taking into account the above four neuropathological features, the overall predictive capability of ANNs in sorting out AD cases from normal controls reached 100%. The corresponding accuracy obtained with Linear Discriminant Analysis was 92.30%. These results were consistently obtained in ten independent experiments. The same experiments were carried out with ANNs on a subgroup of 13 non severe AD patients and on the same 36 controls. The results obtained in terms of prediction accuracy with ANNs were exactly the same. Input relevance analysis confirmed the relative dominance of NFT in neocortex in discriminating between AD patients and controls and indicated the lesser importance played by NP in the hippocampus. CONCLUSION: The results of this study suggest that: a) cortical NFT represent the key variable in AD neuropathology; b) the neuropathologic profile of AD subjects is complex, however, c) ANNs can analyze neuropathologic features and differentiate AD cases from controls

PREDICTION OF OPTIMAL WARFARIN MAINTENANCE DOSE USING ADVANCED ARTIFICIAL NEURAL NETWORKS

Introduction. The individual response to vitamin K antagonists (VKA) is highly variable, being influenced by clinical factors and genetic variants of enzymes that are involved in the metabolism of VKA (CYP2C)) and vitamin K (VKORC1). Currently, the dose of VKA is adjusted based on measurements of the prothrombin time. In the last years, mathematical algorithms were developed for estimating the appropriate VKA dose, based on different mathematical approaches working on clinical and genetic data. Artificial Neural Networks (ANN) are computerized algorithms resembling interactive processes of the human brain, which allow to study very complex non-linear phenomena like biological systems. Aim. To evaluate the performance of new generation ANN on a large data base of patients on chronic VKA treatment. Methods. Clinical and genetic data from 377 patients (186 m; 191 f) treated with a VKA (warfarin) average weekly maintenance dose (WMD) of 23.7 mg (11.5 SD) were used to create a dose algorithm. Forty-eight variables, including demographic, clinical and genetic data (5 CYP2C9 and 3 VKORC1 genetic variants) were entered into Twist\uae system, which can select fundamental variables during their evolution in search for the best predictive model. The final model, based on 23 variables expressed a functional approximation of the actual dose within a validation protocol based on a tripartite division of the data set (training, testing, validation). Results. In the validation cohort, the pharmacogenetic algorithm reached high accuracy, with an average absolute error of 5.7 mg WMD. In the subset of patients requiring 6421 mg (45 % of the cohort) and 21-49 mg (51 % of the cohort) the absolute error was 3.86 mg and 5.45 with a high percentage of subjects being correctly identified (72%, 74% respectively). Conclusion. ANN can be applied successfully for VKA maintenance dose prediction and represent a robust basis for a prospective multicentre clinical trial of the efficacy of genetically informed dose estimation for patients who require VKA

Métodos computacionais para a determinação da correlação entre atrofias cerebrais e disfunções cognitivas

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Ciência da ComputaçãoO Mal de Alzheimer tornou-se, nos tempos modernos, um grande desafio para a saúde pública, visto que a média de idade nos países industrializados tem crescido gradativamente. A cura para essa doença ainda não foi encontrada, e o desenvolvimento de novos tratamentos têm se tornado um tópico de grande interesse na área de pesquisa. Diagnósticos imparciais, bem fundamentados e precoces são indispensáveis a fim de permitir uma imediata observação médica e intervenção. Este trabalho visa propor uma seqüência de passos de análise de imagens obtidas através de ressonância magnética de maneira totalmente automatizada para se precisar o estágio de desenvolvimento da atrofia do cérebro. Foi testada a utilidade da análise para fornecer informações diagnósticas nos casos de Mal de Alzheimer e Comprometimento Cognitivo Moderado, um período transitório de deficiência cognitiva que é considerado um pré-estágio do mal de Alzheimer. Os resultados foram validados num grupo misto de 68 indivíduos através da distinção entre indivíduos com Mal de Alzheimer, Comprometimento Cognitivo Moderado e indivíduos de controle saudáveis, com a utilização de classificadores lineares e Redes Neurais Artificiais. O melhor classificador identificou com acuidade pacientes com o Mal de Alzheimer em 80% dos casos, e indivíduos saudáveis do grupo de controle em 85% dos casos. Reconhecendo mais de 8 em cada 10 indivíduos em um grupo de saudáveis e com Comprometimento Cognitivo Moderado, o método também valida uma indicação precoce de Mal de Alzheimer. A capacidade discernente desta simples, porém tão poderosa análise é capaz de competir com outras metodologias semi-automáticas - e que por isso se tornam mais demoradas. Plenamente aplicável e eficiente em estações de trabalho tradicionais, a análise de imagens pode ser aplicada à pratica clínica diária sem a necessidade de novos investimentos. A sua aceitação clínica pode ser facilitada pela compreensibilidade intuitiva dos cálculos aplicados. Se os resultados puderem ser revalidados por estudos clínicos posteriores, a análise incrementará a diagnose e o tratamento do Mal de Alzheimer, principalmente em seus estágios iniciais, além de servir como uma ferramenta de medição do progresso dos tratamentos