Stress in autism (STREAM): A study protocol on the role of circadian activity, sleep quality and sensory reactivity.

Mental health issues are markedly increased in individuals with autism, making it the number one research priority by stakeholders. There is a crucial need to use personalized approaches to understand the underpinnings of mental illness in autism and consequently, to address individual needs. Based on the risk factors identified in typical mental research, we propose the following themes central to mental health issues in autism: sleep difficulties and stress. Indeed, the prevalence of manifold circadian disruptions and sleep difficulties in autism, alongside stress related to sensory overload, forms an integral part of autistic symptomatology. This proof-of-concept study protocol outlines an innovative, individualised approach towards investigating the interrelationships between stress indices, sleep and circadian activation patterns, and sensory sensitivity in autism. Embracing an individualized methodology, we aim to collect 14 days of data per participant from 20 individuals with autism diagnoses and 20 without. Participants' sleep will be monitored using wearable EEG headbands and a sleep diary. Diurnal tracking of heart rate and electrodermal activity through wearables will serve as proxies of stress. Those objective data will be synchronized with subjective experience traces collected throughout the day using the Temporal Experience Tracing (TET) method. TET facilitates the quantification of relevant aspects of individual experience states, such as stress or sensory sensitivities, by providing a continuous multidimensional description of subjective experiences. Capturing the dynamics of subjective experiences phase-locked to neural and physiological proxies both between and within individuals, this approach has the potential to contribute to our understanding of critical issues in autism, including sleep problems, sensory reactivity and stress. The planned strives to provide a pathway towards developing a more nuanced and individualized approach to addressing mental health in autism.

Tracking wakefulness as it fades: Micro-measures of alertness.

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.

Homeostatic responses to palatable food consumption in satiated rats.

OBJECTIVE: Energy intake is regulated by overlapping homeostatic and hedonic systems. Consumption of palatable foods has been implicated in weight gain, but this assumes that homeostatic control systems do not accurately detect this hedonically driven energy intake. This study tested this assumption, hypothesizing that satiated rats would reduce their voluntary food intake and maintain a stable body weight after consuming a palatable food. METHODS: Lean rats or rats previously exposed to an obesogenic diet were schedule-fed with fixed or varying amounts of palatable sweetened condensed milk (SCM) daily, and their voluntary energy intake and body weight were monitored. RESULTS: During scheduled feeding of SCM, rats voluntarily reduced bland food consumption and maintained a stable body weight. This behavior was also seen in rats with access to an obesogenic diet and was independent of the predictability of SCM access. However, lean rats offered large amounts of SCM showed an increase in total energy intake. To test whether a nutrient deficiency drove this under-compensatory behavior, SCM was enriched with protein. However, no effect was seen on voluntary energy intake. CONCLUSIONS: In schedule-fed rats, compensatory reductions in voluntary energy intake were seen, but under-compensation was observed if large amounts of SCM were consumed.

Local inhibitory plasticity tunes macroscopic brain dynamics and allows the emergence of functional brain networks.

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.

On the independence of visual awareness and metacognition: a signal detection theoretic analysis.

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.