Neuronal avalanches change from wakefulness to deep sleep - a study of intracranial depth recordings in humans

Neuronal dynamics differs between wakefulness and sleep stages, so does the cognitive state. In contrast, a single attractor state, called self-organized critical (SOC), has been proposed to govern human brain dynamics for its optimal information coding and processing capabilities. Here we address two open questions: First, does the human brain always operate in this computationally optimal state, even during deep sleep? Second, previous evidence for SOC was based on activity within single brain areas, however, the interaction between brain areas may be organized differently. Here we asked whether the interaction between brain areas is SOC. ..

A receptor-based analysis of local ecosystems in the human brain.

BackgroundAs a complex system, the brain is a self-organizing entity that depends on local interactions among cells. Its regions (anatomically defined nuclei and areas) can be conceptualized as cellular ecosystems, but the similarity of their functional profiles is poorly understood. The study used the Allen Human Brain Atlas to classify 169 brain regions into hierarchically-organized environments based on their expression of 100 G protein-coupled neurotransmitter receptors, with no a priori reference to the regions' positions in the brain's anatomy or function. The analysis was based on hierarchical clustering, and multiscale bootstrap resampling was used to estimate the reliability of detected clusters.ResultsThe study presents the first unbiased, hierarchical tree of functional environments in the human brain. The similarity of brain regions was strongly influenced by their anatomical proximity, even when they belonged to different functional systems. Generally, spatial vicinity trumped long-range projections or network connectivity. The main cluster of brain regions excluded the dentate gyrus of the hippocampus. The nuclei of the amygdala formed a cluster irrespective of their striatal or pallial origin. In its receptor profile, the hypothalamus was more closely associated with the midbrain than with the thalamus. The cerebellar cortical areas formed a tight and exclusive cluster. Most of the neocortical areas (with the exception of some occipital areas) clustered in a large, statistically well supported group that included no other brain regions.ConclusionsThis study adds a new dimension to the established classifications of brain divisions. In a single framework, they are reconsidered at multiple scales-from individual nuclei and areas to their groups to the entire brain. The analysis provides support for predictive models of brain self-organization and adaptation

Controllability of structural brain networks.

Cognitive function is driven by dynamic interactions between large-scale neural circuits or networks, enabling behaviour. However, fundamental principles constraining these dynamic network processes have remained elusive. Here we use tools from control and network theories to offer a mechanistic explanation for how the brain moves between cognitive states drawn from the network organization of white matter microstructure. Our results suggest that densely connected areas, particularly in the default mode system, facilitate the movement of the brain to many easily reachable states. Weakly connected areas, particularly in cognitive control systems, facilitate the movement of the brain to difficult-to-reach states. Areas located on the boundary between network communities, particularly in attentional control systems, facilitate the integration or segregation of diverse cognitive systems. Our results suggest that structural network differences between cognitive circuits dictate their distinct roles in controlling trajectories of brain network function

Evolution of cognitive function via redeployment of brain areas

The creative re-use of existing cognitive capacities may have played a significant role in the evolutionary development of the brain. There are obvious evolutionary advantages to such redeployment, and the data presented here confirm three important empirical predictions of this account of the development of cognition: (1) a typical brain area will be utilized by many cognitive functions in diverse task categories, (2) evolutionarily older brain areas will be deployed in more cognitive functions and (3) more recent cognitive functions will utilize more, and more widely scattered brain areas. These findings have implications not just for our understanding of the evolutionary origins of cognitive function, but also for the practice of both clinical and experimental neuroscience

Structure and stimulus familiarity: A study of memory in chess-players with functional magnetic resonance imaging.

A grandmaster and an international chess master were compared with a group of novices in a memory task with chess and non-chess stimuli, varying the structure and familiarity of the stimuli, while functional magnetic resonance images were acquired. The pattern of brain activity in the masters was different from that of the novices. Masters showed no differences in brain activity when different degrees of structure and familiarity where compared; however, novices did show differences in brain activity in such contrasts. The most important differences were found in the contrast of stimulus familiarity with chess positions. In this contrast, there was an extended brain activity in bilateral frontal areas such as the anterior cingulate and the superior, middle, and inferior frontal gyri; furthermore, posterior areas, such as posterior cingulate and cerebellum, showed great bilateral activation. These results strengthen the hypothesis that when performing a domain-specific task, experts activate different brain systems from that of novices. The use of the expertsversus- novices paradigm in brain imaging contributes towards the search for brain systems involved in cognitive processes