Marcadores de imagen cerebral de diferentes trayectorias de envejecimiento

La enfermedad de Alzheimer (EA) es la demencia neurodegenerativa con mayor incidencia a partir de los 65 años. Esta enfermedad progresa desde fases preclínicas (asintomáticas) hacia fases prodrómicas (deterioro cognitivo leve) y clínicas (sintomáticas), mostrando cada uno de estos estadios una caracterización neuropatológica determinada. Actualmente, existen diferentes propuestas de biomarcadores para cada una de las fases de la EA, aunque se desconoce cómo estos biomarcadores interactúan para retroalimentar la progresión de la enfermedad o qué biomarcadores son capaces de detectar trayectorias de envejecimiento vulnerable en personas mayores asintomáticas. A lo largo de la presente exposición, revisaremos los diferentes biomarcadores que caracterizan a cada una de las fases de la EA, centrándonos especialmente en aquellos basados en la imagen cerebral. También mostraremos algunos resultados de neuroimagen que confirman el daño colinérgico en el deterioro cognitivo leve y sus relaciones con las alteraciones del sueñ. Finalmente, discutiremos algunos resultados recientes que nos permiten detectar vulnerabilidad cerebral en personas mayores asintomáticas, a partir de la combinación de marcadores genéticos, bioquímicos y de neuroimagen.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Stability of neural encoding moderates the contribution of sleep and repeated testing to memory consolidation

There is evidence suggesting that online consolidation during retrieval-mediated learning interacts with offline consolidation during subsequent sleep to transform memory. Here we investigate whether this interaction persists when retrieval-mediated learning follows post-training sleep and whether the direction of this interaction is conditioned by the quality of encoding resulting from manipulation of the amount of sleep on the previous night. The quality of encoding was determined by computing the degree of similarity between EEG-activity patterns across restudy of face pairs in two groups of young participants, one who slept the last 4 h of the pre-training night, and another who slept 8 h. The offline consolidation was assessed by computing the degree of coupling between slow oscillations (SOs) and spindles (SPs) during post-training sleep, while the online consolidation was evaluated by determining the degree of similarity between EEG-activity patterns recorded during the study phase and during repeated recognition of either the same face pair (i.e., specific similarity) or face pairs sharing sex and profession (i.e., categorical similarity) to evaluate differentiation and generalization, respectively. The study and recognition phases were separated by a night of normal sleep duration. Mixed-effects models revealed that the stability of neural encoding moderated the relationship between sleep- and retrieval-mediated consolidation processes over left frontal regions. For memories showing lower encoding stability, the enhanced SO-SP coupling was associated with increased reinstatement of category-specific encoding-related activity at the expense of content-specific activity, whilst the opposite occurred for memories showing greater encoding stability. Overall, these results suggest that offline consolidation during post-training sleep interacts with online consolidation during retrieval the next day to favor the reorganization of memory contents, by increasing specificity of stronger memories and generalization of the weaker ones.Laboratory of Functional Neuroscience, Universidad Pablo de Olavide, Seville 41013, SpainCIBERNED, Network Center for Biomedical Research in Neurodegenerative Diseases, Spai

Proyecto Coaster: un programa para generar prácticas interactivas basado en simulaciones matemáticas y aplicaciones multimedia

COASTER es un proyecto de la Unión Europea desarrollado por 6 universidades y 4 centros asociados de diferentes paises cuyo objetivo es realizar, demostrar y evaluar pedagógicamente la eficacia de un programa informático para generar simulaciones interactivas de prácticas de elevada calidad científica, pedagógica y técnica, basadas en experimentos reales de diferentes áreas de conocimiento como una alternativa al alto coste y a las limitaciones de los laboratorios experimentales para atender a una docencia práctica de calidad. COASTER es capaz de simular procesos biológicos y físicos e introduce aplicaciones multimedia para mejorar la comprensión de los contenidos por los estudiantes. Además, COASTER permite que un docente o un estudiante con conocimientos informáticos estándares pueda realizar experimentos basados en modelos matemáticos creados por los modelizadores, diseñados para recrear cualquier proceso biológico o físico siempre que se disponga de una descripción matemática del mismo

Spreadsheet for the simulation of artificial neural networks (ANNs)

La utilización de Redes de Neuronas Artificiales (RNA) en problemas de predicción de series de tiempo, clasificación y reconocimiento de patrones ha aumentado considerablemente en los últimos años. Programas informáticos de matemáticas de propósito general tales como MATLAB, MATHCAD y aplicaciones estadísticas como SPSS y S-PLUS incorporan herramientas que permiten implementar RNAs. A esta oferta de software hay que añadir programas específicos como NeuralWare, EasyNN o Neuron. Desde un punto de vista educativo, el acceso de los estudiantes a estos programas puede ser difícil dado que no están pensadas como herramientas didácticas. Por otro lado, las hojas de cálculo como Excel y Gnumeric incorporan utilidades que permiten implementar RNAs y son de fácil acceso para los estudiantes. El objetivo de este trabajo es proporcionar un pequeño tutorial sobre la utilización de Excel para implementar una RNA que nos permita ajustar los valores de una serie de tiempo correspondiente a actividad cerebral alfa y que permita al alumno entender el funcionamiento de estos dispositivos de cálculo.In recent years, the use of Artificial Neural Networks or ANNs has increased considerably to solve prediction problems in time series, classification and recognition of patterns. General-purpose mathematical programs such as MATLAB, MATHCAD and mathematical and statistical programs such as SPSS and S-PLUS incorporate tools that allow the implementation of ANNs. In addition to these, specific programs such as NeuralWare, EasyNN, or Neuron, complete the software offer using ANNs. From an educational point of view, an aspect that concerns the authors of this work, student access to these programs can be expensive or, in sorne case, unadvisable given the few possibilities they provide as didactic instruments. These programs are usually easy to use but do not facilitate the understanding of the technique used. On the other hand, spreadsheets like Excel or Gnumeric incorporate tools that allow all of the necessary calculations to implement an ANN. These programs are user-friendly to the point that they are used by university laboratories, as well as psychology, economic science, and engineering students, to mention a few. This paper provides a small tutorial on the use of a spreadsheet, specifically Excel, to implement an ANN to adjust the values of a time series corresponding to cerebral alpha activity

Caracterización electrofisiológica de la actividad alfa en tres estados de activación cerebral en sujetos humanos vigilia relajada, somnolencia y fase REM

La actividad alfa puede ser detectada de forma claramente observable en los estados de vigilia relajada, somnolencia y fase REM en sujetos humanos. Con este trabajo se caracterizó electrofisiológicamente dicha actividad cerebral en función del estado dón

Assessment of plasma chitotriosidase activity, CCL18/PARC concentration and NP-C suspicion index in the diagnosis of Niemann-Pick disease type C : A prospective observational study

Niemann-Pick disease type C (NP-C) is a rare, autosomal recessive neurodegenerative disease caused by mutations in either the NPC1 or NPC2 genes. The diagnosis of NP-C remains challenging due to the non-specific, heterogeneous nature of signs/symptoms. This study assessed the utility of plasma chitotriosidase (ChT) and Chemokine (C-C motif) ligand 18 (CCL18)/pulmonary and activation-regulated chemokine (PARC) in conjunction with the NP-C suspicion index (NP-C SI) for guiding confirmatory laboratory testing in patients with suspected NP-C. In a prospective observational cohort study, incorporating a retrospective determination of NP-C SI scores, two different diagnostic approaches were applied in two separate groups of unrelated patients from 51 Spanish medical centers (n = 118 in both groups). From Jan 2010 to Apr 2012 (Period 1), patients with ≥2 clinical signs/symptoms of NP-C were considered 'suspected NP-C' cases, and NPC1/NPC2 sequencing, plasma chitotriosidase (ChT), CCL18/PARC and sphingomyelinase levels were assessed. Based on findings in Period 1, plasma ChT and CCL18/PARC, and NP-C SI prediction scores were determined in a second group of patients between May 2012 and Apr 2014 (Period 2), and NPC1 and NPC2 were sequenced only in those with elevated ChT and/or elevated CCL18/PARC and/or NP-C SI ≥70. Filipin staining and 7-ketocholesterol (7-KC) measurements were performed in all patients with NP-C gene mutations, where possible. In total across Periods 1 and 2, 10/236 (4%) patients had a confirmed diagnosis o NP-C based on gene sequencing (5/118 [4.2%] in each Period): all of these patients had two causal NPC1 mutations. Single mutant NPC1 alleles were detected in 8/236 (3%) patients, overall. Positive filipin staining results comprised three classical and five variant biochemical phenotypes. No NPC2 mutations were detected. All patients with NPC1 mutations had high ChT activity, high CCL18/PARC concentrations and/or NP-C SI scores ≥70. Plasma 7-KC was higher than control cut-off values in all patients with two NPC1 mutations, and in the majority of patients with single mutations. Family studies identified three further NP-C patients. This approach may be very useful for laboratories that do not have mass spectrometry facilities and therefore, they cannot use other NP-C biomarkers for diagnosis