Estabilidad del espectro de potencia en resting state: un estudio de fiabilidad con Magnetoencefalografía

La sincronización de las oscilaciones cerebrales se produce incluso en ausencia de tarea, por eso, el resting state está aportando interesantes vías de estudio de los procesos normales y patológicos. Dada la creciente necesidad por utilizar las medidas derivadas de las señales MEG en resting state como biomarcadores clínicos o en la evaluación de tratamientos, es necesario garantizar su fiabilidad. En este estudio se ha investigado por primera vez la fiabilidad de la las medidas espectrales derivadas de registros MEG explorando la estabilidad en resting state de la potencia de 10 sujetos sanos en tres sesiones con un intervalo test-retest de 7 días. A partir de las señales MEG de cada sujeto y sesión se calculó el espectro de potencia de 1 a 100Hz en cada sensor, y como medida de fiabilidad se utilizó el coeficiente de correlación intraclase (ICC). Para explorar cómo afecta la intensidad de la señal a la estabilidad, se registró la señal de la cámara vacía en cada sesión de registro y se calculó la relación señal/ruido (SNR). La potencia espectral en MEG es muy estable en las bandas de frecuencia α, β y θ, y menos estable en δ y γ-2. Con respecto a la distribución de la estabilidad, la señal capturada en la zona frontal del equipo MEG fue la menos estable a través de todas las bandas de frecuencia. La estabilidad mostró cierta tendencia a disminuir conforme disminuye la SNR; este efecto es parcial, ya que los ritmos cerebrales estables mostraron un alto ICC incluso con baja SNR. En conjunto, estos resultados sugieren que las medidas espectrales en resting state con MEG son suficientemente fiables para ser consideradas en futuros estudios longitudinales sobre cambios en la actividad cerebral.

Test-Retest Reliability of Resting-State Magnetoencephalography Power in Sensor and Source Space

Several studies have reported changes in spontaneous brain rhythms that could be used asclinical biomarkers or in the evaluation of neuropsychological and drug treatments in longitudinal studies using magnetoencephalography (MEG). There is an increasing necessity to use these measures in early diagnosis and pathology progression; however, there is a lack of studies addressing how reliable they are. Here, we provide the first test-retest reliability estimate of MEG power in resting-state at sensor and source space. In this study, we recorded 3 sessions of resting-state MEG activity from 24 healthy subjects with an interval of a week between each session. Power values were estimated at sensor and source space with beamforming for classical frequency bands: delta (2–4 Hz), theta (4–8 Hz), alpha (8–13 Hz), low beta (13–20 Hz), high beta (20–30 Hz), and gamma (30–45 Hz). Then, test-retest reliability was evaluated using the intraclass correlation coefficient (ICC). We also evaluated the relation between source power and the within-subject variability. In general, ICC of theta, alpha, and low beta power was fairly high (ICC > 0.6) while in delta and gamma power was lower. In source space, fronto-posterior alpha, frontal beta, and medial temporal theta showed the most reliable profiles. Signal-to-noise ratio could be partially responsible for reliability as low signal intensity resulted inhigh within-subject variability, but also the inherent nature of some brain rhythms in resting-state might be driving these reliability patterns. In conclusion, our results described the reliability of MEG power estimates in each frequency band, which could be considered in disease characterization or clinical trials.

Discovering complex interrelationships between socioeconomic status and health in Europe: A case study applying Bayesian Networks

Studies assume that socioeconomic status determines individuals’ states of health, but how does health determine socioeconomic status? And how does this association vary depending on contextual differences? To answer this question, our study uses an additive Bayesian Networks model to explain the interrelationships between health and socioeconomic determinants using complex and messy data. This model has been used to find the most probable structure in a network to describe the interdependence of these factors in five European welfare state regimes. The advantage of this study is that it offers a specific picture to describe the complex interrelationship between socioeconomic determinants and health, producing a network that is controlled by socio demographic factors such as gender and age. The present work provides a general framework to describe and understand the complex association between socioeconomic determinants and health.