Over long timescales, neuronal dynamics can be robust to quite large perturbations, such as changes in white matter connectivity and grey matter structure through processes including learning, aging, development and certain disease processes. One possible explanation is that robust dynamics are facilitated by homeostatic mechanisms that can dynamically rebalance brain networks. In this study, we simulate a cortical brain network using the Wilson-Cowan neural mass model with conduction delays and noise, and use inhibitory synaptic plasticity (ISP) to dynamically achieve a spatially local balance between excitation and inhibition. Using MEG data from 55 subjects we find that ISP enables us to simultaneously achieve high correlation with multiple measures of functional connectivity, including amplitude envelope correlation and phase locking. Further, we find that ISP successfully achieves local E/I balance, and can consistently predict the functional connectivity computed from real MEG data, for a much wider range of model parameters than is possible with a model without ISP.

The relationship between structure and function in the human brain is well established, but not yet well characterised. Large-scale biophysical models allow us to investigate this relationship, by leveraging structural information (e.g. derived from diffusion tractography) in order to couple dynamical models of local neuronal activity into networks of interacting regions distributed across the cortex. In practice however, these models are difficult to parametrise, and their simulation is often delicate and computationally expensive. This undermines the experimental aspect of scientific modelling, and stands in the way of comparing different parametrisations, network architectures, or models in general, with confidence. Here, we advocate the use of Bayesian optimisation for assessing the capabilities of biophysical network models, given a set of desired properties (e.g. band-specific functional connectivity); and in turn the use of this assessment as a principled basis for incremental modelling and model comparison. We adapt an optimisation method designed to cope with costly, high-dimensional, non-convex problems, and demonstrate its use and effectiveness. Using five parameters controlling key aspects of our model, we find that this method is able to converge to regions of high functional similarity with real MEG data, with very few samples given the number of parameters, without getting stuck in local extrema, and while building and exploiting a map of uncertainty defined smoothly across the parameter space. We compare the results obtained using different methods of structural connectivity estimation from diffusion tractography, and find that one method leads to better simulations.

The Apolipoprotein-E (APOE) ε4 gene allele, the highest known genetic risk factor for Alzheimer's disease, has paradoxically been well preserved in the human population. One possible explanation offered by evolutionary biology for survival of deleterious genes is antagonistic pleiotropy. This theory proposes that such genetic variants might confer an advantage, even earlier in life when humans are also reproductively fit. The results of some small-cohort studies have raised the possibility of such a pleiotropic effect for the ε4 allele in short-term memory (STM) but the findings have been inconsistent. Here, we tested STM performance in a large cohort of individuals (N = 1277); nine hundred and fiftynine of which included carrier and non-carriers of the APOE ε4 gene, those at highest risk of developing Alzheimer's disease. We first confirm that this task is sensitive to subtle deterioration in memory performance across ageing. Importantly, individuals carrying the APOE ε4 gene actually exhibited a significant memory advantage across all ages, specifically for brief retention periods but crucially not for longer durations. Together, these findings present the strongest evidence to date for a gene having an antagonistic pleiotropy effect on human cognitive function across a wide age range, and hence provide an explanation for the survival of the APOE ε4 allele in the gene pool. The Apolipoprotein-E (APOE) ε4 gene allele is the highest known genetic risk factor for developing Alzheimer's disease (AD) 1. Approximately 45% of carriers of the gene develop AD by the age of 85 years, compared to 10% of non-carriers 1. It is not surprising therefore that much research has focused on seeking to identify early biomarkers related to the development of AD in ε4 carriers 2-13. But why has this genetic allele, which has such obvious detrimental effects in old-age, been preserved in the human population world-wide? One possible explanation is rooted in a concept that has emerged in evolutionary biology 14. The antagonistic pleiotropy hypothesis proposes that some genetic alleles have different effects on the fitness of an organism at different ages. Therefore, a genetic allele, such as APOE ε4, which confers a disadvantage later in life, might instead provide an advantage, even earlier in life, hence ensuring its survival. Because the power of natural selection to disfavour a genetic allele wanes later in reproductive life, when an animal has less likelihood of passing on its genes, disadvantages in older age have minimal consequences on the survival of a genetic variant. Although there has been little investigation of a potential early advantage in human carriers of the APOE ε4 gene, several authors have argued for the existence of such an advantage, specifically when it comes to brain function 15-17. A few studies on small cohorts have presented mixed evidence for a possible cognitive advantage in young and middle-aged APOE ε4 gene carriers. In children and young adults, better performance on neuropsychological measures of att...