RESUMO
Abstract: Introduction: Wake-sleep transition is a continuous, gradual process of change. Most studies evaluating electroencephalogram spectral power during this transition have used variance analysis (ANOVA). However, using this type of analysis does not allow one to detect specific changes in the statistical properties of a time series. Objective: To determine whether change point analysis (CPA) makes it possible to identify and characterize electroencephalographic, electromyographic, and cardiac changes during the wake-sleep transition through a cross-sectional study. Method: The study included 18 healthy volunteers (12 women and six men), from which polysomnography data were obtained during a two-minute transition. Heart rate, respiratory sinus arrhythmia, electroencephalogram spectral power, as well as electromyographic median and mean frequency and electromyographic root mean square were calculated in five-second segments. These segments were analyzed using repeated measures ANOVA, and CPA focused individually and for the group as a whole. Results: Repeated measures ANOVA and CPA by group found decreased levels of alpha and beta power and beta/delta index during wakefulness, and increased theta and delta power levels during sleep. CPA by individual found that only alpha power changed in all participants and failed to identify a specific moment when all the variables studied changed simultaneously. Discussion and conclusion: We consider that CPA provides additional information to statistical analyses such as ANOVA for the specific location of physiological changes during sleep-wake transition.
Resumen: Introducción: La transición vigilia-sueño es un proceso de cambio continuo y gradual. Los estudios que han evaluado el poder espectral del electroencefalograma (EEG) durante esta transición han usado principalmente el análisis de varianza (ANOVA). Sin embargo, con este tipo de análisis no se pueden ubicar con precisión los cambios en las propiedades estadísticas de series de tiempo. Objetivo: Evaluar si el análisis de punto de cambio (APC) permite identificar y caracterizar cambios electroencefalográficos, electromiográficos y cardiacos durante la transición vigilia-sueño mediante un estudio transversal descriptivo. Método: Participaron 18 voluntarios sanos (12 mujeres y seis hombres) a los cuales se les realizó una polisomnografía para determinar un periodo de transición de dos minutos. En segmentos de cinco segundos se calcularon la frecuencia cardiaca, arritmia del sinus respiratorio, frecuencia mediana y media cuadrática del electromiograma y poder espectral del EEG. Estos segmentos se analizaron con ANOVA de medidas repetidas y con el APC que se aplicó de forma grupal e individual. Resultados: Con el ANOVA de medidas repetidas y el APC grupal se encontraron disminución de la potencia alfa, beta e índice beta/delta durante la vigilia e incrementos de theta y delta durante el sueño. Con el APC individual no se identificó un momento específico en el que todas las variables estudiadas cambiaran simultáneamente; además, se encontró que sólo la potencia alfa cambió en todos los participantes. Discusión y conclusión: El APC aportó información adicional al ANOVA ya que permitió conocer la ubicación específica de los cambios en las variables fisiológicas estudiadas durante la transición vigilia-sueño.
RESUMO
Deforestation is a primary driver of biodiversity change through habitat loss and fragmentation. Stream biodiversity may not respond to deforestation in a simple linear relationship. Rather, threshold responses to extent and timing of deforestation may occur. Identification of critical deforestation thresholds is needed for effective conservation and management. We tested for threshold responses of fish species and functional groups to degree of watershed and riparian zone deforestation and time since impact in 75 streams in the western Brazilian Amazon. We used remote sensing to assess deforestation from 1984 to 2011. Fish assemblages were sampled with seines and dip nets in a standardized manner. Fish species (n = 84) were classified into 20 functional groups based on ecomorphological traits associated with habitat use, feeding, and locomotion. Threshold responses were quantified using threshold indicator taxa analysis. Negative threshold responses to deforestation were common and consistently occurred at very low levels of deforestation (<20%) and soon after impact (<10 years). Sensitive species were functionally unique and associated with complex habitats and structures of allochthonous origin found in forested watersheds. Positive threshold responses of species were less common and generally occurred at >70% deforestation and >10 years after impact. Findings were similar at the community level for both taxonomic and functional analyses. Because most negative threshold responses occurred at low levels of deforestation and soon after impact, even minimal change is expected to negatively affect biodiversity. Delayed positive threshold responses to extreme deforestation by a few species do not offset the loss of sensitive taxa and likely contribute to biotic homogenization.