Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network

Bayesian optimization of large-scale biophysical networks

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

A biophysical model of dynamic balancing of excitation and inhibition in fast oscillatory large-scale networks

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

Examining the generalizability of research findings from archival data

This initiative examined systematically the extent to which a large set of archival research findings generalizes across contexts. We repeated the key analyses for 29 original strategic management effects in the same context (direct reproduction) as well as in 52 novel time periods and geographies; 45% of the reproductions returned results matching the original reports together with 55% of tests in different spans of years and 40% of tests in novel geographies. Some original findings were associated with multiple new tests. Reproducibility was the best predictor of generalizability—for the findings that proved directly reproducible, 84% emerged in other available time periods and 57% emerged in other geographies. Overall, only limited empirical evidence emerged for context sensitivity. In a forecasting survey, independent scientists were able to anticipate which effects would find support in tests in new samples

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

Simulating brain resting-state activity: what matters?

In the field of computational neuroscience, large-scale biophysical modelling is a bottom-up approach to study the interaction between brain structure and function. In this thesis, we propose two models of varying biophysical plausibility as a mechanistic explanations for spontaneous brain activity, as measured with magnetoencephalography (MEG). Mathematically, these models take the form of large systems of non-linear coupled delay-differential equations, and we implement software to numerically solve such systems efficiently. After analysing the empirical data and extracting key features of interest (relating to the temporal dynamics of measured signals), we use Bayesian optimisation to fit our models with two different parameterisation of increasing complexity: first assuming a spatially uniform brain in which the pattern of connections between cortical regions is the only source of temporal structure in the simulations; and second allowing smooth variations of intrinsic parameters across the cortical surface. Our results outperform those published in the scientific literature to date. We contribute an original derivation of a conductance-based model, and an in-depth analysis of the effects of intrinsic model parameters; software to build and simulate large models of delay-networks efficiently; a new approach to the exploration of high-dimensional spaces in the context of Bayesian optimisation (using space-partitioning); an original parameterisation allowing smooth spatial variations of intrinsic parameters across the cortical surface (using spherical harmonics); a novel analysis of structural brain data (from tractography), and several original methods to analyse MEG data (e.g. exploiting the Hilbert phase and extending Riemannian metrics).</p

Modulations of local synchrony over time lead to resting-state functional connectivity in a parsimonious large-scale brain model

Biophysical models of large-scale brain activity are a fundamental tool for understanding the mechanisms underlying the patterns observed with neuroimaging. These models combine a macroscopic description of the within- and between-ensemble dynamics of neurons within a single architecture. A challenge for these models is accounting for modulations of within-ensemble synchrony over time. Such modulations in local synchrony are fundamental for modeling behavioral tasks and resting-state activity. Another challenge comes from the difficulty in parametrizing large scale brain models which hinders researching principles related with between-ensembles differences. Here we derive a parsimonious large scale brain model that can describe fluctuations of local synchrony. Crucially, we do not reduce within-ensemble dynamics to macroscopic variables first, instead we consider within and between-ensemble interactions similarly while preserving their physiological differences. The dynamics of within-ensemble synchrony can be tuned with a parameter which manipulates local connectivity strength. We simulated resting-state static and time-resolved functional connectivity of alpha band envelopes in models with identical and dissimilar local connectivities. We show that functional connectivity emerges when there are high fluctuations of local and global synchrony simultaneously (i.e. metastable dynamics). We also show that for most ensembles, leaning towards local asynchrony or synchrony correlates with the functional connectivity with other ensembles, with the exception of some regions belonging to the default-mode network

Short-term memory advantage for brief durations in human APOE ε4 carriers

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 fifty-nine 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

Overview of analysis pipeline for simulated neural activity and MEG data.

<p>After computing activity timecourses for each region, both simulated and real signals are processed through the same pipeline.</p

Alpha band functional connectivity profiles in data and model.

<p>Functional connectivity in (a,b) AEC, (c,d) PLV,and (e,f) PLI, shown in data, and in the model for the parameters marked by the red dot in <b>Figs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006007#pcbi.1006007.g004" target="_blank">4</a>, <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006007#pcbi.1006007.g006" target="_blank">6</a></b> and <b><a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006007#pcbi.1006007.g007" target="_blank">7</a></b>.</p