Three simple ideas for predicting progression to Alzheimer's disease

International audienceIn spite of the amount of research done in the prediction of the progression of mild cognitive impaired (MCI) subjects to Alzheimer's disease (AD), there is still room for further improvement. Sophisticated methods have been proposed, some reaching classification accuracies of up to 85%. In the present paper, we propose a combination of simple ideas to determine if they allow to obtain similar accuracies when predicting MCI to AD conversion. We present three approaches making use of ADNI database. We set a performance baseline using only demographic and clinical data (gender, education level, APOE4, MMSE, CDR sum of boxes, ADASCog) that provides a balanced accuracy of 76% (AUC of 0.84). When using imaging data, an important finding is that when an SVM is trained for discriminating between cognitive normal (CN) subjects and AD patients, and the resulting classifier is applied to MCI subjects to predict conversion, performance using FDG PET data improves to 76% of balanced accuracy and an AUC of 0.82. The third approach, consisting of multimodal data, namely the combination of the scores obtained from SVM for T1w and FDG PET data, and the demographic and clinical data, provided the best prediction results (80% balanced accuracy, AUC of 0.88). These prediction accuracies, resulting from the combination simple ideas, are in line with state-of-the-art results, and provide a new baseline to compare more sophisticated methods against. All the code of the framework and the experiments will be publicly available at https://gitlab.icm-institute.org/aramislab/AD-ML

Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study

International audienceVarious machine learning approaches have been developed for predicting progression to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET data. Objective comparison of these approaches is nearly impossible because of differences at all steps, from data management to image processing and evaluation procedures. Moreover, with a few exceptions, these papers rarely compare their results to that obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of neuroimaging in this context. We previously proposed a framework for the reproducible evaluation of ML algorithms for AD classification. This framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and FDG PET). Here, we extend our previous work to the combination of multimodal clinical and neuroimaging data for predicting progression to AD among MCI patients. All the code is publicly available at: https://github.com/aramis-lab/AD-ML

First insights into the genetic diversity of Mycobacterium tuberculosis isolates from HIV-infected Mexican patients and mutations causing multidrug resistance

<p>Abstract</p> <p>Background</p> <p>The prevalence of infections with <it>Mycobacterium tuberculosis </it>(MTb) and nontuberculous mycobacteria (NTM) species in HIV-infected patients in Mexico is unknown. The aims of this study were to determine the frequency of MTb and NTM species in HIV-infected patients from Mexico City, to evaluate the genotypic diversity of the <it>Mycobacterium tuberculosis </it>complex strains, to determine their drug resistance profiles by colorimetric microplate Alamar Blue assay (MABA), and finally, to detect mutations present in <it>kat</it>G, <it>rpo</it>B and <it>inh</it>A genes, resulting in isoniazid (INH) and rifampin (RIF) resistance.</p> <p>Results</p> <p>Of the 67 mycobacterial strains isolated, 48 were identified as MTb, 9 as <it>M. bovis</it>, 9 as <it>M. avium </it>and 1 as <it>M. intracellulare</it>. IS<it>6110</it>-RFLP of 48 MTb strains showed 27 profiles. Spoligotyping of the 48 MTb strains yielded 21 patterns, and 9 <it>M. bovis </it>strains produced 7 patterns. Eleven new spoligotypes patterns were found. A total of 40 patterns were produced from the 48 MTb strains when MIRU-VNTR was performed. Nineteen (39.6%) MTb strains were resistant to one or more drugs. One (2.1%) multidrug-resistant (MDR) strain was identified. A novel mutation was identified in a RIF-resistant strain, GAG → TCG (Glu → Ser) at codon 469 of <it>rpo</it>B gene.</p> <p>Conclusions</p> <p>This is the first molecular analysis of mycobacteria isolated from HIV-infected patients in Mexico, which describe the prevalence of different mycobacterial species in this population. A high genetic diversity of MTb strains was identified. New spoligotypes and MIRU-VNTR patterns as well as a novel mutation associated to RIF-resistance were found. This information will facilitate the tracking of different mycobacterial species in HIV-infected individuals, and monitoring the spread of these microorganisms, leading to more appropriate measures for tuberculosis control.</p

Comparison of DTI Features for the Classification of Alzheimer's Disease: A Reproducible Study

International audienc

A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI

International audienc

Rendimiento académico del ingeniero en formación de la universidad de la costa CUC frente a las competencias de la prueba saber Pro 2012 - 2013

Especialización en Estudios PedagógicosThe current workshop, entitled Rendimiento Académico del Ingeniero en Formación de la Universidad de la Costa frente a las Competencias de las Pruebas Saber Pro 2012 - 2013 (CUC), studied the academical behavior of the Engineering´s Faculty students, in function of their academical performance. For this purpose, an educational instrument was made to know the quantitatively level of basic and specific skills of engineering students, taking as sample students of the first, fifth and tenth semester. This instrument was performed under the standard developed by the State, the Saber Pro and Abet. Moreover, this workshop consists of seven chapters, divided into the introduction, followed by the problem statement, justification, objectives, framework, definition, methodology, analysis of results and conclusions, respectively. The authors wish that the present material is of pleasure to the reader, and also a basis for future research in the area.En el presente trabajo, titulado Rendimiento Académico del Ingeniero en Formación de la Universidad de la Costa frente a las Competencias de las Pruebas Saber Pro 2012 - 2013 (CUC), se realizó el estudio del comportamiento de académico de los estudiantes de la Facultad de Ingeniería, en virtud de su rendimiento. Para tal efecto, se elaboró un instrumento pedagógico que permitiera valorar cuantitativamente el nivel de ompetencias básicas y específicas de los estudiantes de Ingeniería, tomando como muestra a los jóvenes de primero, quinto y décimo semestre; éstas, se realizaron bajo el estándar de las pruebas de Estado, Saber Pro, y Abet. Así también, el presente consta de siete capítulos, los cuales están divididos en la parte introductoria, seguido del planteamiento del problema, justificación, objetivos, marco teórico, delimitación, metodología, análisis de resultados y conclusiones, de manera respectiva. Los autores desean que el actual material sea de agrado para el lector, así también una base de futuras investigaciones en el área

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Reproducible evaluation of methods for predicting progression to Alzheimer's disease from clinical and neuroimaging data

International audienceVarious machine learning methods have been proposed for predicting progression of patients with mild cognitive impairment (MCI) to Alzheimer's disease (AD) using neuroimaging data. Even though the vast majority of these works use the public dataset ADNI, reproducing their results is complicated because they often do not make available elements that are essential for reproducibility, such as selected participants and input data, image preprocessing and cross-validation procedures. Comparability is also an issue. Specially, the influence of different components like preprocessing, feature extraction or classification algorithms on the performance is difficult to evaluate. Finally, these studies rarely compare their results to models built from clinical data only, a critical aspect to demonstrate the utility of neuroimaging. In our previous work, 1, 2 we presented a framework for reproducible and objective classification experiments in AD, that included automatic conversion of ADNI database into the BIDS community standard, image preprocessing pipelines and machine learning evaluation. We applied this framework to perform unimodal classifications of T1 MRI and FDG-PET images. In the present paper, we extend this work to the combination of multimodal clinical and neuroimaging data. All experiments are based on standard approaches (namely SVM and random forests). In particular, we assess the added value of neuroimaging over using only clinical data. We first demonstrate that using only demographic and clinical data (gender, education level, MMSE, CDR sum of boxes, ADASCog) results in a balanced accuracy of 75% (AUC of 0.84). This performance is higher than that of standard neuroimaging-based classifiers. We then propose a simple trick to improve the performance of neuroimaging-based classifiers: training from AD patients and controls (rather than from MCI patients) improves the performance of FDG-PET classification by 5 percent points, reaching the level of the clinical classifier. Finally, combining clinical and neuroimaging data, prediction results further improved to 80% balanced accuracy and an AUC of 0.88). These prediction accuracies, obtained in a reproducible way, provide a base to develop on top of it and, to compare against, more sophisticated methods. All the code of the framework and the experiments is publicly available at https://github.com/aramis-lab/AD-M

Predicting progression to Alzheimer’s disease from clinical and imaging data: a reproducible study

International audienceVarious machine learning approaches have been developed for predicting progression to Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI) from MRI and PET data. Objective comparison of these approaches is nearly impossible because of differences at all steps, from data management to image processing and evaluation procedures. Moreover, with a few exceptions, these papers rarely compare their results to that obtained with clinical/cognitive data only, a critical point to demonstrate the practical utility of neuroimaging in this context. We previously proposed a framework for the reproducible evaluation of ML algorithms for AD classification. This framework was applied to AD classification using unimodal neuroimaging data (T1 MRI and FDG PET). Here, we extend our previous work to the combination of multimodal clinical and neuroimaging data for predicting progression to AD among MCI patients. All the code is publicly available at: https://github.com/aramis-lab/AD-ML

Three simple ideas for predicting progression to Alzheimer's disease

International audienceIn spite of the amount of research done in the prediction of the progression of mild cognitive impaired (MCI) subjects to Alzheimer's disease (AD), there is still room for further improvement. Sophisticated methods have been proposed, some reaching classification accuracies of up to 85%. In the present paper, we propose a combination of simple ideas to determine if they allow to obtain similar accuracies when predicting MCI to AD conversion. We present three approaches making use of ADNI database. We set a performance baseline using only demographic and clinical data (gender, education level, APOE4, MMSE, CDR sum of boxes, ADASCog) that provides a balanced accuracy of 76% (AUC of 0.84). When using imaging data, an important finding is that when an SVM is trained for discriminating between cognitive normal (CN) subjects and AD patients, and the resulting classifier is applied to MCI subjects to predict conversion, performance using FDG PET data improves to 76% of balanced accuracy and an AUC of 0.82. The third approach, consisting of multimodal data, namely the combination of the scores obtained from SVM for T1w and FDG PET data, and the demographic and clinical data, provided the best prediction results (80% balanced accuracy, AUC of 0.88). These prediction accuracies, resulting from the combination simple ideas, are in line with state-of-the-art results, and provide a new baseline to compare more sophisticated methods against. All the code of the framework and the experiments will be publicly available at https://gitlab.icm-institute.org/aramislab/AD-ML