Biology subjects

Woolrich, M. W.

Publications and source records attributed to Woolrich, M. W..

10 recordsLinked to original sources

bioRxiv2018

Neural dynamics at rest associated with patterns of ongoing thought

Conscious experience is dynamic, and its fluidity is particularly marked when attention is not occupied by events in the external world and our minds are free to wander. Our study used measures of neural function, and advanced analyses techniques to examine how unconstrained neural state transitions relate to patterns of ongoing experience. Neural activity was recorded during wakeful rest using functional magnetic resonance imaging and Hidden Markov modelling identified recurrent patterns of brain activity constituting functional dynamic brain states. Individuals making more frequent transitions between states subsequently described experiences highlighting problem solving and lacking unpleasant intrusive features. Frequent switching between states also predicted better health and well-being as assessed by questionnaire. These data provide evidence that the fluidity with which individuals shift through dynamic neural states has an impact on the nature of ongoing thought, and suggest that greater flexibility at rest is an important indicator of a healthy mind.

neuroscience↗

bioRxiv2018

Metastable brain waves

Traveling patterns of neuronal activity - brain waves - have been observed across a breadth of neuronal recordings, states of awareness, and species, but their emergence in the human brain lacks a firm understanding. Here, we analyze the complex nonlinear dynamics that emerge from modeling large-scale spontaneous neural activity on a whole-brain network derived from human tractography. We find a rich array of three-dimensional wave patterns, including traveling waves, spiral waves, sources, and sinks. These patterns are metastable, such that multiple spatiotemporal wave patterns are visited in sequence. Transitions between states correspond to reconfigurations of underlying phase flows, characterized by nonlinear instabilities. These metastable dynamics accord with empirical data from multiple imaging modalities, including electrical waves in cortical tissue, sequential spatiotemporal patterns in resting-state MEG data, and large-scale waves in human electrocorticography. By moving the study of functional networks from a spatially static to an inherently dynamic (wave-like) frame, our work unifies apparently diverse phenomena across functional neuroimaging modalities and makes specific predictions for further experimentation.

neuroscience↗

bioRxiv2018

Motor learning shapes temporal activity in human sensorimotor cortex

Although neuroimaging techniques have provided vital insights into the anatomical regions involved in motor learning, the underlying changes in temporal dynamics are not well understood. Using magnetoencephalography and Hidden Markov Modelling to model the dynamics of neural oscillations on data-adaptive time-scales, we detected specific changes in movement-related sensorimotor {beta}-activity during practice of a self-paced sequential visuo-motor task. The behaviourally-relevant neural signature generalised to another motor task, emphasising the centrality of {beta}-activity in motor plasticity.

neuroscience↗

bioRxiv2018

Hypothesis testing in the presence of noisy experimental replications

Spatial or temporal aspects of neural organisation are known to be important indices of how cognition is organised. However, measurements and estimations are often noisy and many of the algorithms used are probabilistic, which in combination have been argued to limit studies exploring the neural basis of specific aspects of cognition. Focusing on static and dynamic functional connectivity estimations, we propose to leverage this variability to improve statistical efficiency in relating these estimations to behaviour. To achieve this goal, we use a procedure based on permutation testing that provides a way of combining the results from many individual tests that refer to the same hypothesis. This is needed when testing a measure whose value is obtained from a noisy process, which can be repeated multiple times, referred to as replications. Focusing on functional connectivity, this noisy process can be: (i) computational, e.g. when using an approximate inference algorithm for which different runs can produce different results or (ii) observational, if we have the capacity to acquire data multiple times, and the different acquired data sets can be considered noisy examples of some underlying truth. In both cases, we are not interested in the individual replications but on the unobserved process generating each replication. In this note, we show how results can be combined instead of choosing just one of the estimated models. Using both simulations and real data, we show the benefits of this approach in practice.

neuroscience↗

bioRxiv2018

Temporally unconstrained decoding reveals consistent but time-varying stages of stimulus processing

In this paper, we propose a method to track trial-specific neural dynamics of stimulus processing and decision making with high temporal precision. By applying this novel method to a perceptual template-matching task, we tracked representational brain states associated with the cascade of neural processing, from early sensory areas to higher-order areas that are involved in integration and decision-making. We address a major limitation of the traditional decoding approach: that it relies on consistent timing of these processes over trials. Using a temporally unconstrained decoding analysis approach, we found that the timing of the cognitive processes involved in perceptual judgements can vary considerably over trials. This revealed that the sequence of processing states was consistent for all subjects and trials, even when the timing of these states varied. Furthermore, we found that the specific timing of states on each trial was related to the quality of performance over trials.

neuroscience↗

bioRxiv2017

The relationship between spatial configuration and functional connectivity of brain regions

Brain connectivity is often considered in terms of the communication between functionally distinct brain regions. Many studies have investigated the extent to which patterns of coupling strength between multiple neural populations relates to behavior. For example, studies have used \"functional connectivity fingerprints\" to characterise individuals' brain activity. Here, we investigate the extent to which the exact spatial arrangement of cortical regions interacts with measures of brain connectivity. We find that the shape and exact location of brain regions interact strongly with the modelling of brain connectivity, and present evidence that the spatial arrangement of functional regions is strongly predictive of non-imaging measures of behaviour and lifestyle. We believe that, in many cases, cross-subject variations in the spatial configuration of functional brain regions are being interpreted as changes in functional connectivity. Therefore, a better understanding of these effects is important when interpreting the relationship between functional imaging data and cognitive traits.

neuroscience↗

bioRxiv2017

Directed functional connectivity using dynamic graphical models

There are a growing number of neuroimaging methods that model spatio-temporal patterns of brain activity to allow more meaningful characterizations of brain networks. This paper proposes dynamic graphical models (DGMs) for dynamic, directed functional connectivity. DGMs are a multivariate graphical model with time-varying coefficients that describe instantaneous directed relationships between nodes. A further benefit of DGMs is that networks may contain loops and that large networks can be estimated. We use network simulations, human resting-state fMRI (N = 500) to investigate the validity and reliability of the estimated networks. We simulate systematic lags of the hemodynamic response at different brain regions to investigate how these lags potentially bias directionality estimates. In the presence of such lag confounds (0.4-0.8 seconds offset between connected nodes), our method has a sensitivity of 72%-77% to detect the true direction. Stronger lag confounds have reduced sensitivity, but do not increase false positives (i.e., directionality estimates of the opposite direction). In human resting-state fMRI, we find the DMN has consistent influence on the cerebellar, the limbic and the auditory/temporal network, as well a consistent reciprocal relationship between the visual medial and visual lateral network. Finally, we apply the method in a small mouse fMRI sample and discover a highly plausible relationship between areas in the hippocampus feeding into the cingulate cortex. We provide a computationally efficient implementation of DGM as a free software package for R.

neuroscience↗

bioRxiv2017

Bayesian Optimisation 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. 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.

neuroscience↗

bioRxiv2017

Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks

Frequency-specific oscillations and phase-coupling of neuronal populations have been proposed as an essential mechanism for the coordination of activity between brain areas during cognitive tasks. To provide an effective substrate for cognitive function, we reasoned that ongoing functional brain networks should also be able to reorganise and coordinate in a similar manner. To test this hypothesis, we use a novel method for identifying repeating patterns of network dynamics, and show that resting networks in magnetoencephalography are well characterised by visits to short-lived transient brain states, with spatially distinct power and phase-coupling in specific frequency bands. Brain states were identified for sensory, motor networks and higher-order cognitive networks; the latter include a posterior higher-order cognitive network in the alpha range (8-12Hz) and an anterior cognitive network in the delta/theta range (1-7Hz). Both higher-order cognitive networks exhibit especially high power and coherence, and contain brain areas corresponding to posterior and anterior subdivisions of the default mode network. Our results show that large-scale cortical phase-coupling networks operate in very specific frequency bands, possibly reflecting functional specialisation at different intrinsic timescales.

neuroscience↗

bioRxiv2017

Disambiguating brain functional connectivity

Functional connectivity (FC) analyses of correlations of neural activity are used extensively in neuroimaging and electrophysiology to gain insights into neural interactions. However, correlation fails to distinguish sources as different as changes in neural signal amplitudes or noise levels. This ambiguity substantially diminishes the value of FC for inferring system properties and clinical states. Network modelling approaches may avoid ambiguities, but require specific assumptions. We present an enhancement to FC analysis with improved specificity of inferences, minimal assumptions and no reduction in flexibility. The Additive Signal Change (ASC) approach characterises FC changes into certain prevalent classes involving additions of signal. With FMRI data, the approach reveals a rich diversity of signal changes underlying measured changes in FC, bringing into question standard interpretations. The ASC method can also be used to disambiguate other measures of dependency, such as regression and coherence, providing a flexible tool for the analysis of neural data.

neuroscience↗