Cardiac arrhythmia and epilepsy genetic variants in sudden unexpected death in epilepsy.

Introduction: Sudden Unexpected Death in Epilepsy (SUDEP) is the leading epilepsy-related cause of death, affecting approximately 1 per 1,000 individuals with epilepsy per year. Genetic variants that affect autonomic function, such as genes associated with cardiac arrhythmias, may predispose people with epilepsy to greater risk of both sudden cardiac death and SUDEP. Advances in next generation sequencing allow for the exploration of gene variants as potential biomarkers.

Predictors of harsh parenting practices and inter-partner conflict during the COVID-19 pandemic in Ontario, Canada: a cross-sectional analysis from the Ontario Parent Survey.

Objectives: Guided by the bioecological model, the purpose of this study was to examine the associations of (1) individual level factors (sociodemographic, health behaviour and mental health), (2) family (micro) level COVID-19 experiences (difficulty with household management, managing child mood and behaviour, and pandemic-related positive experiences) and (3) community (macro) level factors (residential instability, ethnic concentration, material deprivation and dependency, an indicator of age and labour force) with harsh parenting practices and inter-partner conflict during the early lockdown of the COVID-19 pandemic in Ontario, Canada.

Design: A cross-sectional analysis of data from the Ontario Parent Survey.

Setting: A convenience sample of 7451 caregivers living in Ontario, Canada, at the time of baseline data collection (May-June 2020).

Feedforward and feedback interactions between visual cortical areas use different population activity patterns.

Brain function relies on the coordination of activity across multiple, recurrently connected brain areas. For instance, sensory information encoded in early sensory areas is relayed to, and further processed by, higher cortical areas and then fed back. However, the way in which feedforward and feedback signaling interact with one another is incompletely understood.

Modelling the neural code in large populations of correlated neurons.

Neurons respond selectively to stimuli, and thereby define a code that associates stimuli with population response patterns. Certain correlations within population responses (noise correlations) significantly impact the information content of the code, especially in large populations. Understanding the neural code thus necessitates response models that quantify the coding properties of modelled populations, while fitting large-scale neural recordings and capturing noise correlations.

Neuronal variability reflects probabilistic inference tuned to natural image statistics.

Neuronal activity in sensory cortex fluctuates over time and across repetitions of the same input. This variability is often considered detrimental to neural coding. The theory of neural sampling proposes instead that variability encodes the uncertainty of perceptual inferences.

Temporal Contingencies Determine Whether Adaptation Strengthens or Weakens Normalization.

A fundamental and nearly ubiquitous feature of sensory encoding is that neuronal responses are strongly influenced by recent experience, or adaptation. Theoretical and computational studies have proposed that many adaptation effects may result in part from changes in the strength of normalization signals. Normalization is a "canonical" computation in which a neuron's response is modulated (normalized) by the pooled activity of other neurons.