Abstract: Particularly with the fundamental works of the Leipzig school of experimental psychophysiology (between the 1850s and 1880s), the modern neurosciences witnessed an increasing interest in attempts to objectify “pain” as a bodily signal and physiological value. This development has led to refined psychological test repertoires and new clinical measurement techniques, which became progressively paired with imaging approaches and sophisticated theories about neuropathological pain etiology. With the advent of electroencephalography since the middle of the 20th century, and through the use of brain stimulation technologies and modern neuroimaging, the chosen scientific route towards an ever more refined “objectification” of pain phenomena took firm root in Western medicine. This article provides a broad overview of landmark events and key imaging technologies, which represent the long developmental path of a field that could be called “algesiogenic pathology.”
Abstract: Emotional meta-memory can be defined as the knowledge people have about the strategies and monitoring processes that they can use to remember their emotionally charged memories. Although meta-memory per se has been studied in many cognitive laboratories for many years, fewer studies have explicitly focused on meta-memory for emotionally charged or valenced information. In this brief review, we analyzed a series of behavioral and neuroimaging studies that used different meta-memory tasks with valenced information in order to foster new research in this direction, especially in terms of commonalities/peculiarities of the emotion and meta-memory interaction. In addition, results further support meta-cognitive models that take emotional factors into account when defining meta-memory per se.
Abstract: Cognitive development is influenced by maturational changes in processing speed, a construct reflecting the rapidity of executing cognitive operations. Although cognitive ability and processing speed are linked to spindles and sigma power in the sleep electroencephalogram (EEG), little is known about such associations in early childhood, a time of major neuronal refinement. We calculated EEG power for slow (10–13 Hz) and fast (13.25–17 Hz) sigma power from all-night high-density electroencephalography (EEG) in a cross-sectional sample of healthy preschool children (n = 10, 4.3 ± 1.0 years). Processing speed was assessed as simple reaction time. On average, reaction time was 1409 ± 251 ms; slow sigma power was 4.0 ± 1.5 μV2; and fast sigma power was 0.9 ± 0.2 μV2. Both slow and fast sigma power predominated over central areas. Only slow sigma power was correlated with processing speed in a large parietal electrode cluster (p < 0.05, r ranging from −0.6 to −0.8), such that greater power predicted faster reaction time. Our findings indicate regional correlates between sigma power and processing speed that are specific to early childhood and provide novel insights into the neurobiological features of the EEG that may underlie developing cognitive abilities.
Abstract: One of the unique features of prenatal alcohol exposure in humans is impaired cognitive and behavioral function resulting from damage to the central nervous system (CNS), which leads to a spectrum of impairments referred to as fetal alcohol spectrum disorder (FASD). Human FASD phenotypes can be reproduced in the rodent CNS following prenatal ethanol exposure. Several mechanisms are expected to contribute to the detrimental effects of prenatal alcohol exposure on the developing fetus, particularly in the developing CNS. These mechanisms may act simultaneously or consecutively and differ among a variety of cell types at specific developmental stages in particular brain regions. Studies have identified numerous potential mechanisms through which alcohol can act on the fetus. Among these mechanisms are increased oxidative stress, mitochondrial damage, interference with the activity of growth factors, glia cells, cell adhesion molecules, gene expression during CNS development and impaired function of signaling molecules involved in neuronal communication and circuit formation. These alcohol-induced deficits result in long-lasting abnormalities in neuronal plasticity and learning and memory and can explain many of the neurobehavioral abnormalities found in FASD. In this review, the author discusses the mechanisms that are associated with FASD and provides a current status on the endocannabinoid system in the development of FASD.
Abstract: Caffeine is the most commonly ingested psychoactive drug worldwide with increasing consumption rates among young individuals. While caffeine leads to decreased sleep quality in adults, studies investigating how caffeine consumption affects children’s and adolescents’ sleep remain scarce. We explored the effects of regular caffeine consumption on sleep behavior and the sleep electroencephalogram (EEG) in children and adolescents (10–16 years). While later habitual bedtimes (Caffeine 23:14 ± 11.4, Controls 22:17 ± 15.4) and less time in bed were found in caffeine consumers compared to the control group (Caffeine 08:10 ± 13.3, Controls 09:03 ± 16.1), morning tiredness was unaffected. Furthermore, caffeine consumers exhibited reduced sleep EEG slow-wave activity (SWA, 1–4.5 Hz) at the beginning of the night compared to controls (20% ± 9% average reduction across all electrodes and subjects). Comparable reductions were found for alpha activity (8.25–9.75 Hz). These effects, however, disappeared in the morning hours. Our findings suggest that caffeine consumption in adolescents may lead to later bedtimes and reduced SWA, a well-established marker of sleep depth. Because deep sleep is involved in recovery processes during sleep, further research is needed to understand whether a caffeine-induced loss of sleep depth interacts with neuronal network refinement processes that occur during the sensitive period of adolescent development.
Abstract: Ecumenically, the fastest growing segment of Big Data is human biology-related data and the annual data creation is on the order of zetabytes. The implications are global across industries, of which the treatment of brain related illnesses and trauma could see the most significant and immediate effects. The next generation of health care IT and sensory devices are acquiring and storing massive amounts of patient related data. An innovative Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian factor analysis algorithms that can distinguish distinct thought actions using magneto encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90% positive performance in MEG mid- and post-movement activity. We describe a driver that substitutes the actions of the BCI as mouse button presses for real-time use in visual simulations. This process has been added into a flight visualization demonstration. By thinking left or right, the user experiences the aircraft turning in the chosen direction. The driver components of the BCI can be compiled into any software and substitute a user’s intent for specific keyboard strikes or mouse button presses. The BCI’s data analytics OPEN ACCESS Brain. Sci. 2015, 5 420 of a subject’s MEG brainwaves and flight visualization performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data warehouse.