Successful object encoding induces increased directed connectivity in presymptomatic early-onset Alzheimer's disease

Background: Recent studies report increases in neural activity in brain regions critical to episodic memory at preclinical stages of Alzheimer’s disease (AD). Although electroencephalography (EEG) is widely used in AD studies, given its non-invasiveness and low cost, there is a need to translate the findings in other neuroimaging methods to EEG. Objective: To examine how the previous findings using functional magnetic resonance imaging (fMRI) at preclinical stage in presenilin-1 E280A mutation carriers could be assessed and extended, using EEG and a connectivity approach. Methods: EEG signals were acquired during resting and encoding in 30 normal cognitive young subjects, from an autosomal dominant early-onset AD kindred from Antioquia, Colombia. Regions of the brain previously reported as hyperactive were used for connectivity analysis. Results: Mutation carriers exhibited increasing connectivity at analyzed regions. Among them, the right precuneus exhibited the highest changes in connectivity. Conclusion: Increased connectivity in hyperactive cerebral regions is seen in individuals, genetically-determined to develop AD, at preclinical stage. The use of a connectivity approach and a widely available neuroimaging technique opens the possibility to increase the use of EEG in early detection of preclinical AD.Postprint (author's final draft

Identificación de biomarcadores en la enfermedad de Alzheimer usando análisis de conectividad efectiva a partir de registros de electroencefalografía

RESUMEN: La enfermedad de Alzheimer (EA) es la causa más común de demencia, la cual afecta generalmente a personas después de los 65 años de edad. Algunas mutaciones genéticas inducen la aparición temprana de EA ayudando a monitorear la evolución de los síntomas y los cambios fisiológicos en diferentes etapas de la enfermedad. En Colombia existe un gran grupo familiar con la mutación PSEN1 E280A, con una edad media de aparición de los síntomas de 46,8 años. La EA ha sido definida como un síndrome de desconexión; en consecuencia, enfoques de redes podrían ayudar a capturar diferentes características de la enfermedad. El objetivo del presente trabajo es identificar un biomarcador en la EA que permita realizar el seguimiento del proceso neurodegenerativo. Se registró una electroencefalografía (EEG) durante la codificación de información visual en cuatro grupos de sujetos: portadores de la mutación PSEN1 E280A asintomáticos y con deterioro cognitivo leve y dos grupos control de no portadores. Para cada sujeto se estimó la conectividad efectiva utilizando la Función de Transferencia Directa dirigida y se extrajeron tres medidas de grafos: fuerza de entrada, fuerza de salida y fuerza total. Se calculó una relación entre el estado cognitivo y la edad de los participantes con las características de conectividad. Para aquellas medidas de conectividad que tuvieran una relación con la edad o la escala clínica, se evaluó su desempeño como variable de diagnóstico. Se encontró que la fuerza de conectividad saliente en la región parieto-occipital derecha está relacionada con la edad del grupo de los portadores (r = −0,54, p = 0,0036), y que tiene alta sensibilidad y especificidad para distinguir entre portadores y no portadores (67 % de sensibilidad y 80 % de especificidad en casos asintomáticos, y 83 % de sensibilidad y 67 % de especificidad en casos sintomáticos). Esta relación indica que la fuerza de conectividad saliente podría estar relacionada con el proceso neurodegenerativo de la enfermedad y podría ayudar a realizar un seguimiento de la conversión desde la etapa asintomática hacia la demencia.ABSTRACT: Alzheimer’s disease (AD) is the most common cause of dementia, which generally affects people over 65 years old. Some genetic mutations induce early onset of AD and help to track the evolution of the symptoms and the physiological changes at different stages of the disease. In Colombia there is a large family group with the PSEN1 E280A mutation with a median age of 46,8 years old for onset of symptoms. AD has been defined as a disconnection syndrome; consequently, network approaches could help to capture different features of the disease. The aim of the current work is to identify a biomarker in AD that helps in the tracking of the neurodegenerative process. Electroencephalography (EEG) was recorded during the encoding of visual information for four groups of individuals: asymptomatic and mild cognitive impairment carriers of the PSEN1 E280A mutation, and two non-carrier control groups. For each individual, the effective connectivity was estimated using the direct Directed Transfer Function and three measurements from graph theory were extracted: input strength, output strength and total strength. A relation between the cognitive status and age of the participants with the connectivity features was calculated. For those connectivity measures in which there is a relation with the age or the clinical scale, the performance as a diagnostic feature was evaluated. We found that output strength connectivity in the right occipito-parietal region is related to age of the carrier groups (r = −0,54, p = 0,0036) and has a high sensitivity and high specificity to distinguish between carriers and non-carriers (67 % sensitivity and 80 % specificity in asymptomatic cases, and 83 % sensitivity and 67 % specificity in symptomatic cases). This relationship indicates that output strength connectivity could be related to the neurodegenerative process of the disease and could help to track the conversion from the asymptomatic stage to dementia

Módulo para el control sin contacto de imágenes diagnósticas en la sala de cirugía con el sistema Leap Motion y el Software 3D Slicer

RESUMEN: Durante los procedimientos quirúrgicos es importante que el personal (cirujanos, residentes o asistentes) interactúe con el paciente, evitando cualquier contacto físico con equipo y materiales que pudieron no ser esterilizados apropiadamente. Esto se hace con el fin de evitar al paciente infecciones y complicaciones posteriores a la cirugía. Con el aumento de la disponibilidad de imágenes diagnósticas esta herramienta se ha hecho cada vez más indispensable en los quirófanos, pero no siempre es posible mantener el control de asepsia de los equipos informáticos en los cuales se ejecutan los programas de visualización, factor que dificulta el acceso al personal asistencial a la información contenida en las imágenes. En este trabajo se presenta el desarrollo de un sistema que permite manipular un programa de visualización de imágenes diagnósticas mediante gestos evitando que el cirujano tenga contacto directo con la computadora. El sistema, que requiere una computadora con el software 3D-Slicer y el dispositivo Leap Motion, permite mediante gestos realizados con las manos acceder a operaciones básicas como el movimiento entre cortes de un volumen, cambio del tamaño de la imagen y cambio del plano anatómico de visualización, operaciones que para el cirujano son esenciales para la ubicación espacial y la toma de decisiones.ABSTRACT: During surgical procedures, it is important that the personnel (surgeon, residents, or assistants) interact with the patient avoiding any physical contact with equipment and materials that might have not been appropriately sterilized. This is done in order to prevent patient’s infections and complications after surgery. With the increased availability of diagnostic images, this technology has become indispensable in operating rooms but to maintain asepsis control of computer equipment on which the visualization programs are executed is not always possible, hindering access to personnel to information contained in the images. This paper describes the development of a system that allows the personnel to manipulate a medical imaging display program using gestures, avoiding the surgeon or the nurse to have a direct contact with the computer. The system, which requires a computer with 3D-Slicer software and Leap Motion (LM) device, allows through gestures made with the hands, to access the basic operations such as the movement between sections of a volume, to change the image size and the anatomical plane visualization; operations that are essential to the surgeon for the spatial location and decision making

Smart: sistemas multi-agente robótico

El siguiente artículo busca dar una visión global de los Sistemas Multi-Agentes Robóticos (MARS) mediante una explicación de las áreas relacionadas con el tema para luego presentar el Sistema Multi-Agente Robótico (SMART). SMART es un enjambre inteligente conformado por un robot nodriza y tres robot tipo baliza (guías) que navegan de manera colaborativa un escenario estructurado

Evaluación de estrategias basadas en Wavelet-ICA e ICLabel para la corrección de artefactos sobre registros EEG

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built.En la electroencefalografía cuantitativa es de vital importancia la eliminación de componentes no neuronales, ya que estos pueden conducir a un análisis erróneo de las señales adquiridas, limitando su uso al diagnóstico y otras aplicaciones clínicas. Dado este inconveniente, en la década de 2000 se propusieron flujos de preprocesamiento basados ​​en el uso conjunto de la Transformada Wavelet y la técnica de Análisis de Componentes Independientes (wICA). Recientemente, con la llegada de los métodos basados en datos, se desarrollaron modelos de aprendizaje profundo para el etiquetado automático de componentes independientes, lo que generó una oportunidad para la optimización de las técnicas basadas en ICA. En este estudio, se añadió ICLabel, uno de estos modelos de aprendizaje profundo, a la metodología de wICA para explorar su mejora. Para evaluar la utilidad de este enfoque, se comparó con diferentes flujos que muestran el uso de wICA e ICLabel de forma independiente y en su ausencia. El impacto de cada flujo se midió mediante su capacidad para resaltar diferencias estadísticas conocidas entre los portadores asintomáticos de la mutación PSEN-1 E280A y un grupo de control sano. Se calcularon específicamente el tamaño del efecto entre grupos y los valores P para comparar los flujos. Los resultados muestran que el uso de ICLabel para la eliminación de artefactos puede mejorar el tamaño del efecto (ES) y que, al aprovecharlo con wICA, se puede construir un enfoque de suavizado de artefactos menos susceptible a la pérdida de información neuronal

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Evaluation of Strategies Based on Wavelet-ICA and ICLabel for Artifact Correction in EEG Recordings

In quantitative electroencephalography, it is of vital importance to eliminate non-neural components, as these can lead to an erroneous analysis of the acquired signals, limiting their use in diagnosis and other clinical applications. In light of this drawback, preprocessing pipelines based on the joint use of the Wavelet Transform and the Independent Component Analysis technique (wICA) were proposed in the 2000s. Recently, with the advent of data-driven methods, deep learning models were developed for the automatic labeling of independent components, which constitutes an opportunity for the optimization of ICA-based techniques. In this paper, ICLabel, one of these deep learning models, was added to the wICA methodology in order to explore its improvement. To assess the usefulness of this approach, it was compared to different pipelines which feature the use of wICA and ICLabel independently and a lack thereof. The impact of each pipeline was measured by its capacity to highlight known statistical differences between asymptomatic carriers of the PSEN-1 E280A mutation and a healthy control group. Specifically, the between-group effect size and the P-values were calculated to compare the pipelines. The results show that using ICLabel for artifact removal can improve the effect size (ES) and that, by leveraging it with wICA, an artifact smoothing approach that is less prone to the loss of neural information can be built

Resting-state EEG alpha/theta ratio related to neuropsychological testperformance in Parkinson’s Disease

Q2Q1Objective To determine possible associations of hemispheric-regional alpha/theta ratio (α/θ) with neuropsychological test performance in Parkinson’s Disease (PD) non-demented patients. Methods 36 PD were matched to 36 Healthy Controls (HC). The α/θ in eight hemispheric regions was computed from the relative power spectral density of the resting-state quantitative electroencephalogram (qEEG). Correlations between α/θ and performance in several neuropsychological tests were conducted, significant findings were included in a moderation analysis. Results The α/θ in all regions was lower in PD than in HC, with larger effect sizes in the posterior regions. Right parietal, and right and left occipital α/θ had significant positive correlations with performance in Judgement of Line Orientation Test (JLOT) in PD. Adjusted moderation analysis indicated that right, but not left, occipital α/θ influenced the JLOT performance related to PD. Conclusions Reduction of the occipital α/θ, in particular on the right side, was associated with visuospatial performance impairment in PD. Significance Visuospatial impairment in PD, which is highly correlated with the subsequent development of dementia, is reflected in α/θ in the right posterior regions. The right occipital α/θ may represent a useful qEEG marker for evaluating the presence of early signs of cognitive decline in PD and the subsequent risk of dementia.https://orcid.org/0000-0001-5832-0603Revista Internacional - Indexad

Estrategias de publicidad social. Coyunturas sociales como oportunidad de mejoramiento de valor responsable

Este libro invita a un grupo de autores, pertenecientes a tres grupos de investigación de distintas universidades, a enriquecer la reflexión sobre el papel que desempeña la publicidad social en las estrategias de responsabilidad social empresarial y cómo esto puede redundar en el mejoramiento del valor de marca. Las apreciaciones se encuadran en el escenario de posguerra o posconflicto que vive el País desde la firma de los Acuerdos de la Habana, entre el Estado colombiano y las FARC-EP