Automatic classification of attention-deficit/hyperactivity disorder using brain activation

Nowadays, there is an active fi eld of research in neuroscience trying to fi nd relations between neurofunctional abnormalities of brain structures and neurological disorders. Previous statistical studies on brain functional Magnetic Resonance Images (fMRI) have found Attention Defi cit Hyperactivity Disorder (ADHD) patients are characterized by reduced activity in the inferior frontal gyrus (IFG) during response inhibition tasks and in the Ventral Striatum (VStr) during reward anticipation tasks. Interpreting brain image experiments using fMRI requires analysis of complex data and diff erent univariate or multivariate approaches can be chosen. Recently, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to discriminate abnormal behavior or other variables of interest from fMRI data. The purpose of this work is to apply machine learning techniques to perform fMRI group analysis in an adult population. We propose a multivariate classifi er using diff erent discriminative features. Furthermore, we show how temporal information of fMRI data can be taken into account to improve the discrimination. We demonstrate that our new approach is able to diagnose the ADHD characteristics based on the activation in the executive functions. Previous results on the response inhibition task did not find di fferences between activation responses. Opposite to these results, we achieve accurate classifi cation performance for this task. Moreover, in this case, we show that classi fication rates can be signi cantly improved by incorporating temporal information into the classi fier

An end-to-end framework for intima media measurement and atherosclerotic plaque detection in the carotid artery

Background and objectives: The detection and delineation of atherosclerotic plaque are usually manually performed by medical experts on the carotid artery. Evidence suggests that this manual process is subject to errors and has a large variability between experts, equipment, and datasets. This paper proposes a robust end-to-end framework for automatic atherosclerotic plaque detection. Methods: The proposed framework is composed of: (1) a semantic segmentation model based on U-Net, with EfficientNet as the backbone, that obtains a segmentation mask with the carotid intima-media region; and (2) a convolutional neural network designed using Bayesian optimization that simultaneously performs a regression to get the average and maximum carotid intima media thickness, and a classification to determine the presence of plaque. Results: Our approach improves the state-of-the-art in both co and bulb territories in the REGICOR database, with more than 8000 images, while providing predictions in real-time. The correlation coefficient was 0.89 in the common carotid artery and 0.74 for bulb region, and the F1 score for atherosclerotic plaque detecting was 0.60 and 0.59, respectively. The experimentation carried out includes a comparison with other fully automatic methods for carotid intima media thickness estimation found in the literature. Additionally, we present an extensive experimental study to evaluate the robustness of our proposal, as well as its suitability and efficiency compared to different versions of the framework. Conclusions: The proposed end-to-end framework significantly improves the automatic characterization of atherosclerotic plaque. The generation of the segmented mask can be helpful for practitioners since it allows them to evaluate and interpret the model's results by visual inspection. Furthermore, the proposed framework overcomes the limitations of previous research based on ad-hoc post-processing, which could lead to overestimations in the case of oblique forms of the carotid artery

Polyvascular Subclinical Atherosclerosis: Correlation Between Ankle Brachial Index and Carotid Atherosclerosis in a Population-Based Sample

We assessed the correlation between the biomarkers of lower limb atherosclerosis (eg, ankle-brachial index [ABI]) and of carotid atherosclerosis (eg, common carotid intima-media thickness (IMT) and presence of atherosclerotic plaque) in a population-based cohort from Girona (Northwest Spain) recruited in 2010. Ankle-brachial index and carotid ultrasound were performed in all participants. Generalized additive multivariable models were used to adjust a regression model of common carotid IMT on ABI. Logistic regression multivariable models were adjusted to assess the probability of carotid plaque in individuals with peripheral artery disease. We included 3307 individuals (54.2% women), mean age 60 years (standard deviation 11). Two patterns of association were observed between subclinical biomarkers of atherosclerosis at the lower limb and carotid artery. Ankle-brachial index and common carotid IMT showed a linear trend in men [beta coefficient (95% confidence interval) =-.068 (-.123; -.012); P = .016]. Women with peripheral artery disease presented with high risk of atherosclerotic plaque at the carotid artery [Odds ratio (95% confidence interval) = 2.61, (1.46; 4.69); P = .001]. Men showed a significant linear association between ABI levels and common carotid IMT values. Women with peripheral artery disease presented with high risk of atherosclerotic plaque at the carotid artery

Do individuals with autoimmune disease have increased risk of subclinical carotid atherosclerosis and stiffness?

To explore the role of chronic inflammation inherent to autoimmune diseases in the development of subclinical atherosclerosis and arterial stiffness, this study recruited two population-based samples of individuals with and without autoimmune disease (ratio 1:5) matched by age, sex, and education level and with a longstanding (≥6 years) diagnosis of autoimmune disease. Common carotid intima media thickness (IMT) and arterial distensibility and compliance were assessed with carotid ultrasound. Multivariable linear and logistic regression models were adjusted for 10-year cardiovascular risk. In total, 546 individuals with and without autoimmune diseases (91 and 455, respectively) were included. Mean age was 66 years (standard deviation 12), and 240 (43.9%) were women. Arterial stiffness did not differ according to presence of autoimmune diseases. In men, the diagnosis of autoimmune diseases significantly increased common carotid IMT [beta-coefficient (95% confidence interval): 0.058 (0.009; 0.108); p-value=0.022] and the percentage having IMT ≥ percentile 75 [1.012 (0.145; 1.880); p-value=0.022]. Women without autoimmune disease were more likely to have IMT ≥ percentile 75 [-2.181 (-4.214; -0.149); p-value=0.035] but analysis of IMT as a continuous variable did not yield significant results. In conclusion, subclinical carotid atherosclerosis, but not arterial stiffness, was higher in men with autoimmune diseases. Women did not show significant differences in any of these carotid features. Sex was an effect modifier in the association between common carotid IMT values and the diagnosis of autoimmune diseases

Propiedades psicométricas del Shared Decision-Making Questionnaire (SDM-Q-9) en oncología

Antecedentes/Objetivo: Este estudio analiza las propiedades psicométricas del cuestionario Shared Decision-Making (SDM-Q-9) en pacientes con cáncer resecado, no metastásico y elegibles para quimioterapia adyuvante. Método: Estudio multicéntrico, prospectivo, transversal en el que se reclutaron 568 pacientes que respondieron al SDM-Q-9 después de visitar a su oncólogo quien, a su vez, completó el SDM-Q-versión médico. Se estudiaron la fiabilidad, la estructura factorial [análisis factorial exploratorio (EFA), análisis factorial confirmatorio (CFA)] y la validez convergente de las puntuaciones del SDMQ-9. Resultados: La escala SDM-Q-9 mostró una estructura factorial clara, compatible con un factor general fuerte y replicable, y un factor de grupo secundario. La puntuación del factor general mostró una buena fiabilidad en términos de coeficiente omega: 0,90. La asociación entre la percepción del médico y del paciente en la SDM fue débil y no logró alcanzar significación estadística. Los hombres y los pacientes mayores de 60 años mostraron mayor satisfacción con la toma de decisiones compartidas con el oncólogo. Conclusiones: El cuestionario SDM-Q-9 puede ayudar en la evaluación de la toma de decisiones compartidas desde la perspectiva de los pacientes con cáncer y como indicador del grado de calidad y satisfacción en la relación médico-paciente

Automatic classification of attention-deficit/hyperactivity disorder using brain activation

Nowadays, there is an active fi eld of research in neuroscience trying to fi nd relations between neurofunctional abnormalities of brain structures and neurological disorders. Previous statistical studies on brain functional Magnetic Resonance Images (fMRI) have found Attention Defi cit Hyperactivity Disorder (ADHD) patients are characterized by reduced activity in the inferior frontal gyrus (IFG) during response inhibition tasks and in the Ventral Striatum (VStr) during reward anticipation tasks. Interpreting brain image experiments using fMRI requires analysis of complex data and diff erent univariate or multivariate approaches can be chosen. Recently, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to discriminate abnormal behavior or other variables of interest from fMRI data. The purpose of this work is to apply machine learning techniques to perform fMRI group analysis in an adult population. We propose a multivariate classifi er using diff erent discriminative features. Furthermore, we show how temporal information of fMRI data can be taken into account to improve the discrimination. We demonstrate that our new approach is able to diagnose the ADHD characteristics based on the activation in the executive functions. Previous results on the response inhibition task did not find di fferences between activation responses. Opposite to these results, we achieve accurate classifi cation performance for this task. Moreover, in this case, we show that classi fication rates can be signi cantly improved by incorporating temporal information into the classi fier

Automatic classification of attention-deficit/hyperactivity disorder using brain activation

Nowadays, there is an active fi eld of research in neuroscience trying to fi nd relations between neurofunctional abnormalities of brain structures and neurological disorders. Previous statistical studies on brain functional Magnetic Resonance Images (fMRI) have found Attention Defi cit Hyperactivity Disorder (ADHD) patients are characterized by reduced activity in the inferior frontal gyrus (IFG) during response inhibition tasks and in the Ventral Striatum (VStr) during reward anticipation tasks. Interpreting brain image experiments using fMRI requires analysis of complex data and diff erent univariate or multivariate approaches can be chosen. Recently, one analysis approach that has grown in popularity is the use of machine learning algorithms to train classifiers to discriminate abnormal behavior or other variables of interest from fMRI data. The purpose of this work is to apply machine learning techniques to perform fMRI group analysis in an adult population. We propose a multivariate classifi er using diff erent discriminative features. Furthermore, we show how temporal information of fMRI data can be taken into account to improve the discrimination. We demonstrate that our new approach is able to diagnose the ADHD characteristics based on the activation in the executive functions. Previous results on the response inhibition task did not find di fferences between activation responses. Opposite to these results, we achieve accurate classifi cation performance for this task. Moreover, in this case, we show that classi fication rates can be signi cantly improved by incorporating temporal information into the classi fier

Propiedades psicométricas del Shared Decision-Making Questionnaire (SDM-Q-9) en oncología

Antecedentes/Objetivo: Este estudio analiza las propiedades psicométricas del cuestionario Shared Decision-Making (SDM-Q-9) en pacientes con cáncer resecado, no metastásico y elegibles para quimioterapia adyuvante. Método: Estudio multicéntrico, prospectivo, transversal en el que se reclutaron 568 pacientes que respondieron al SDM-Q-9 después de visitar a su oncólogo quien, a su vez, completó el SDM-Q-versión médico. Se estudiaron la fiabilidad, la estructura factorial [análisis factorial exploratorio (EFA), análisis factorial confirmatorio (CFA)] y la validez convergente de las puntuaciones del SDMQ-9. Resultados: La escala SDM-Q-9 mostró una estructura factorial clara, compatible con un factor general fuerte y replicable, y un factor de grupo secundario. La puntuación del factor general mostró una buena fiabilidad en términos de coeficiente omega: 0,90. La asociación entre la percepción del médico y del paciente en la SDM fue débil y no logró alcanzar significación estadística. Los hombres y los pacientes mayores de 60 años mostraron mayor satisfacción con la toma de decisiones compartidas con el oncólogo. Conclusiones: El cuestionario SDM-Q-9 puede ayudar en la evaluación de la toma de decisiones compartidas desde la perspectiva de los pacientes con cáncer y como indicador del grado de calidad y satisfacción en la relación médico-paciente

Ordenadores en las aulas : la clave es la metodología

Bibliografía al final de los capítulos. Resumen basado en el de la publicaciónSe exponen los cambios de contenidos, metodologías, herramientas y recursos tecnológicos, más significativos y de los que dependen principalmente de los docentes. Se tratan: los contenidos desde una mirada competencial; las metodologías que han de favorecer el pensamiento científico, la creatividad y la solidaridad; y las tecnologías de la información y la comunicación (TIC) que han de permitir atender a la diversidad y abrir la escuela al mundo. Las metodologías tienen en común el trabajo cooperativo, la atención a la diversidad, la investigación, la construcción del conocimiento, la creatividad, la interdisciplinariedad, etc. Por último, se facilitan las herramientas que se deberían poner en manos de los alumnos para que hagan sus creaciones, sus tareas; para que ellos mismos transformen la información y construyan su conocimiento de manera autónoma.CataluñaBiblioteca de Educación del Ministerio de Educación, Cultura y Deporte; Calle San Agustín 5 -3 Planta; 28014 Madrid; Tel. +34917748000; [email protected]