We readdress the diffusion tractography problem in a global and probabilistic manner. Instead of tracking through local orientations, we parameterise the connexions between brain regions at a global ...level, and then infer on global and local parameters simultaneously in a Bayesian framework. This approach offers a number of important benefits. The global nature of the tractography reduces sensitivity to local noise and modelling errors. By constraining tractography to ensure a connexion is found, and then inferring on the exact location of the connexion, we increase the robustness of connectivity-based parcellations, allowing parcellations of connexions that were previously invisible to tractography. The Bayesian framework allows a direct comparison of the evidence for connecting and non-connecting models, to test whether the connexion is supported by the data. Crucially, by explicit parameterisation of the connexion between brain regions, we infer on a parameter that is shared with models of functional connectivity. This model is a first step toward the joint inference on functional and anatomical connectivity.
Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to ...use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.
•Hidden Markov Modelling of resting-state EEG and fMRI.•Model conveys connectivity information within and transition rules between networks.•fMRI correlates of HMM-EEG networks resemble resting state network (RSN) maps.•Subsecond EEG network activity underlies slow fMRI RSN fluctuations.•Similar dynamic structure of HMM-EEG and HMM-fMRI networks suggests scale-free behaviour.
Variational Bayes (VB) has been proposed as a method to facilitate calculations of the posterior distributions for linear models, by providing a fast method for Bayesian inference by estimating the ...parameters of a factorized approximation to the posterior distribution. Here a VB method for nonlinear forward models with Gaussian additive noise is presented. In the case of noninformative priors the parameter estimates obtained from this VB approach are identical to those found via nonlinear least squares. However, the advantage of the VB method lies in its Bayesian formulation, which permits prior information to be included in a hierarchical structure and measures of uncertainty for all parameter estimates to be obtained via the posterior distribution. Unlike other Bayesian methods VB is only approximate in comparison with the sampling method of MCMC. However, the VB method is found to be comparable and the assumptions made about the form of the posterior distribution reasonable. Practically, the VB approach is substantially faster than MCMC as fewer calculations are required. Some of the advantages of the fully Bayesian nature of the method are demonstrated through the extension of the noise model and the inclusion of automatic relevance determination (ARD) within the VB algorithm.
We propose a hierarchical infinite mixture model approach to address two issues in connectivity-based parcellations: (i) choosing the number of clusters, and (ii) combining data from different ...subjects. In a Bayesian setting, we model voxel-wise anatomical connectivity profiles as an infinite mixture of multivariate Gaussian distributions, with a Dirichlet process prior on the cluster parameters. This type of prior allows us to conveniently model the number of clusters and estimate its posterior distribution directly from the data. An important benefit of using Bayesian modelling is the extension to multiple subjects clustering via a hierarchical mixture of Dirichlet processes. Data from different subjects are used to infer on class parameters and the number of classes at individual and group level. Such a method accounts for inter-subject variability, while still benefiting from combining different subjects data to yield more robust estimates of the individual clusterings.
We present a fully Bayesian approach to modeling in functional magnetic resonance imaging (FMRI), incorporating spatio-temporal noise modeling and haemodynamic response function (HRF) modeling. A ...fully Bayesian approach allows for the uncertainties in the noise and signal modeling to be incorporated together to provide full posterior distributions of the HRF parameters. The noise modeling is achieved via a nonseparable space-time vector autoregressive process. Previous FMRI noise models have either been purely temporal, separable or modeling deterministic trends. The specific form of the noise process is determined using model selection techniques. Notably, this results in the need for a spatially nonstationary and temporally stationary spatial component. Within the same full model, we also investigate the variation of the HRF in different areas of the activation, and for different experimental stimuli. We propose a novel HRF model made up of half-cosines, which allows distinct combinations of parameters to represent characteristics of interest. In addition, to adaptively avoid over-fitting we propose the use of automatic relevance determination priors to force certain parameters in the model to zero with high precision if there is no evidence to support them in the data. We apply the model to three datasets and observe matter-type dependence of the spatial and temporal noise, and a negative correlation between activation height and HRF time to main peak (although we suggest that this apparent correlation may be due to a number of different effects).
Even in response to simple tasks such as hand movement, human brain activity shows remarkable inter-subject variability. Recently, it has been shown that individual spatial variability in fMRI task ...responses can be predicted from measurements collected at rest; suggesting that the spatial variability is a stable feature, inherent to the individual’s brain. However, it is not clear if this is also true for individual variability in the spatio-spectral content of oscillatory brain activity. Here, we show using MEG (N = 89) that we can predict the spatial and spectral content of an individual’s task response using features estimated from the individual’s resting MEG data. This works by learning when transient spectral ‘bursts’ or events in the resting state tend to reoccur in the task responses. We applied our method to motor, working memory and language comprehension tasks. All task conditions were predicted significantly above chance. Finally, we found a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. Our approach can predict individual differences in brain activity and suggests a link between transient spectral events in task and rest that can be captured at the level of individuals.
•Using AR-Hidden-Markov-Modeling, we predict diverse task responses from MEG resting state only.•On a group-level, events identified from resting state only can reconstruct group task responses.•Learning the mapping of these states onto task data enables prediction of single unseen subjects.•Genetically closer subjects show better predictability to each other than unrelated subjects.
In recent years, one of the most important findings in systems neuroscience has been the identification of large scale distributed brain networks. These networks support healthy brain function and ...are perturbed in a number of neurological disorders (e.g. schizophrenia). Their study is therefore an important and evolving focus for neuroscience research. The majority of network studies are conducted using functional magnetic resonance imaging (fMRI) which relies on changes in blood oxygenation induced by neural activity. However recently, a small number of studies have begun to elucidate the electrical origin of fMRI networks by searching for correlations between neural oscillatory signals from spatially separate brain areas in magnetoencephalography (MEG) data. Here we advance this research area. We introduce two methodological extensions to previous independent component analysis (ICA) approaches to MEG network characterisation: 1) we show how to derive pan-spectral networks that combine independent components computed within individual frequency bands. 2) We show how to measure the temporal evolution of each network with millisecond temporal resolution. We apply our approach to ~10h of MEG data recorded in 28 experimental sessions during 3 separate cognitive tasks showing that a number of networks could be identified and were robust across time, task, subject and recording session. Further, we show that neural oscillations in those networks are modulated by memory load, and task relevance. This study furthers recent findings on electrodynamic brain networks and paves the way for future clinical studies in patients in which abnormal connectivity is thought to underlie core symptoms.
► Non-invasive characterisation of task positive electrical brain networks using MEG ► Novel extension to ICA allowing network characterisation across frequency bands ► Time–frequency characterisation of task induced change in network oscillations ► Results show modulation of network oscillations with memory load. ► Modulation of network oscillations with stimulus relevance also shown
A hallmark symptom after psychological trauma is the presence of intrusive memories. It is unclear why only some moments of trauma become intrusive, and how these memories involuntarily return to ...mind. Understanding the neural mechanisms involved in the encoding and involuntary recall of intrusive memories may elucidate these questions.
Participants (n = 35) underwent functional magnetic resonance imaging (fMRI) while being exposed to traumatic film footage. After film viewing, participants indicated within the scanner, while undergoing fMRI, if they experienced an intrusive memory of the film. Further intrusive memories in daily life were recorded for 7 days. After 7 days, participants completed a recognition memory test. Intrusive memory encoding was captured by comparing activity at the time of viewing 'Intrusive scenes' (scenes recalled involuntarily), 'Control scenes' (scenes never recalled involuntarily) and 'Potential scenes' (scenes recalled involuntarily by others but not that individual). Signal change associated with intrusive memory involuntary recall was modelled using finite impulse response basis functions.
We found a widespread pattern of increased activation for Intrusive v. both Potential and Control scenes at encoding. The left inferior frontal gyrus and middle temporal gyrus showed increased activity in Intrusive scenes compared with Potential scenes, but not in Intrusive scenes compared with Control scenes. This pattern of activation persisted when taking recognition memory performance into account. Intrusive memory involuntary recall was characterized by activity in frontal regions, notably the left inferior frontal gyrus.
The left inferior frontal gyrus may be implicated in both the encoding and involuntary recall of intrusive memories.
Mixture models are often used in the statistical segmentation of medical images. For example, they can be used for the segmentation of structural images into different matter types or of functional ...statistical parametric maps (SPMs) into activations and nonactivations. Nonspatial mixture models segment using models of just the histogram of intensity values. Spatial mixture models have also been developed which augment this histogram information with spatial regularization using Markov random fields. However, these techniques have control parameters, such as the strength of spatial regularization, which need to be tuned heuristically to particular datasets. We present a novel spatial mixture model within a fully Bayesian framework with the ability to perform fully adaptive spatial regularization using Markov random fields. This means that the amount of spatial regularization does not have to be tuned heuristically but is adaptively determined from the data. We examine the behavior of this model when applied to artificial data with different spatial characteristics, and to functional magnetic resonance imaging SPMs.
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.
•Biophysical models provide insight into the brain's structure-function interaction.•Assessing their ability to generate dynamics of interest is a difficult problem.•We propose and demonstrate the use of Bayesian optimisation for this purpose.
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