Rich, spontaneous brain activity has been observed across a range of different temporal and spatial scales. These dynamics are thought to be important for efficient neural functioning. A range of experimental evidence suggests that these neural dynamics are maintained across a variety of different cognitive states, in response to alterations of the environment and to changes in brain configuration (e.g., across individuals, development and in many neurological disorders). This suggests that the brain has evolved mechanisms to maintain rich dynamics across a broad range of situations. Several mechanisms based around homeostatic plasticity have been proposed to explain how these dynamics emerge from networks of neurons at the microscopic scale. Here we explore how a homeostatic mechanism may operate at the macroscopic scale: in particular, focusing on how it interacts with the underlying structural network topology and how it gives rise to well-described functional connectivity networks. We use a simple mean-field model of the brain, constrained by empirical white matter structural connectivity where each region of the brain is simulated using a pool of excitatory and inhibitory neurons. We show, as with the microscopic work, that homeostatic plasticity regulates network activity and allows for the emergence of rich, spontaneous dynamics across a range of brain configurations, which otherwise show a very limited range of dynamic regimes. In addition, the simulated functional connectivity of the homeostatic model better resembles empirical functional connectivity network. To accomplish this, we show how the inhibitory weights adapt over time to capture important graph theoretic properties of the underlying structural network. Therefore, this work presents suggests how inhibitory homeostatic mechanisms facilitate stable macroscopic dynamics to emerge in the brain, aiding the formation of functional connectivity networks.

Classically, visual awareness and metacognition are thought to be intimately linked, with our knowledge of the correctness of perceptual choices (henceforth metacognition) being dependent on the level of stimulus awareness. Here we used a signal detection theoretic approach involving a Gabor orientation discrimination task in conjunction with trial-by-trial ratings of perceptual awareness and response confidence in order to gauge estimates of type-1 (perceptual) orientation sensitivity and type-2 (metacognitive) sensitivity at different levels of stimulus awareness. Data from three experiments indicate that while the level of stimulus awareness had a profound impact on type-1 perceptual sensitivity, the awareness effect on type-2 metacognitive sensitivity was far lower by comparison. The present data pose a challenge for signal detection theoretic models in which both type-1 (perceptual) and type-2 (metacognitive) processes are assumed to operate on the same input. More broadly, the findings challenge the commonly held view that metacognition is tightly coupled to conscious states.

A major problem in psychology and physiology experiments is drowsiness: around a third of participants show decreased wakefulness despite being instructed to stay alert. In some non-visual experiments participants keep their eyes closed throughout the task, thus promoting the occurrence of such periods of varying alertness. These wakefulness changes contribute to systematic noise in data and measures of interest. To account for this omnipresent problem in data acquisition we defined criteria and code to allow researchers to detect and control for varying alertness in electroencephalography (EEG) experiments under eyes-closed settings. We first revise a visual-scoring method developed for detection and characterization of the sleep-onset process, and adapt the same for detection of alertness levels. Furthermore, we show the major issues preventing the practical use of this method, and overcome these issues by developing an automated method (micro-measures algorithm) based on frequency and sleep graphoelements, which are capable of detecting micro variations in alertness. The validity of the micro-measures algorithm was verified by training and testing using a dataset where participants are known to fall asleep. In addition, we tested generalisability by independent validation on another dataset. The methods developed constitute a unique tool to assess micro variations in levels of alertness and control trial-by-trial retrospectively or prospectively in every experiment performed with EEG in cognitive neuroscience under eyes-closed settings.

16A major problem in psychology and physiology experiments is drowsiness: around a third of 17 participants show decreased wakefulness despite being instructed to stay alert. In some non-18 visual experiments participants keep their eyes closed throughout the task, thus promoting the 19 occurrence of such periods of varying alertness. These wakefulness changes contribute to 20 systematic noise in data and measures of interest. To account for this omnipresent problem in 21 data acquisition we defined criteria and code to allow researchers to detect and control for 22 varying alertness in electroencephalography (EEG) experiments. We first revise a visual-scoring 23 method developed for detection and characterization of the sleep-onset process, and adapt the 24 same for detection of alertness levels. Furthermore, we show the major issues preventing the 25 practical use of this method, and overcome these issues by developing an automated method 26 based on frequency and sleep graphoelements, which is capable of detecting micro variations in 27 alertness. The validity of the automated method was verified by training and testing the algorithm 28 using a dataset where participants are known to fall asleep. In addition, we tested generalizability 29 by independent validation on another dataset. The methods developed constitute a unique tool 30 to assess micro variations in levels of alertness and control trial-by-trial retrospectively or 31 prospectively in every experiment performed with EEG in cognitive neuroscience. 32