Biology subjects

Vidaurre, D.

Publications and source records attributed to Vidaurre, D..

9 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

Transient spectral events in resting state MEG predict individual time-frequency task responses

Even in response to apparently simple tasks such as hand moving, human brain activity shows remarkable inter-subject variability. Presumably, this variability reflects genuine behavioural or functional variability. Recently, spatial variability of resting-state features in fMRI - specifically connectivity - has been shown to explain (spatial) task-response variability. Such a link, however, is still missing for M/EEG data and its spectrally rich structure. At the same time, it has recently been shown that task responses in M/EEG can be well represented using transient spectral events bursting at fast time scales. Here, we show that individual differences in the spatio-spectral structure of M/EEG task responses, can, to a reasonable degree, be predicted from individual differences in transient spectral events identified at rest. In a MEG dataset of diverse task conditions (including motor responses, working memory and language comprehension tasks) and resting-state sessions for each subject (n = 89), we used Hidden-Markov-Modelling to identify transient spectral events as a feature set to learn the mapping of space-time-frequency content from rest to task. Resulting trial-averaged, subject-specific task-response predictions were then compared with the actual task responses in left-out subjects. All task conditions were predicted significantly above chance. Furthermore, we observed a systematic relationship between genetic similarity (e.g. unrelated subjects vs. twins) and predictability. These findings support the idea that subject-specific transient spectral events in resting-state neural activity are linked to, and predictive of, subject-specific trial-averaged task responses in a wide range of experimental conditions.

neuroscience↗

bioRxiv2018

Spontaneous network activity accounts for variability in stimulus-induced gamma responses

Gamma range activity in human visual cortex is believed to play a major role in cognitive functions, such as selective attention. Although recent studies have revealed substantial variability in gamma activity, its origins are still unclear.\n\nWe investigated whether variability in stimulus-induced gamma activity is related to the spontaneous dynamics of resting-state networks using Hidden Markov Modelling. The magnetoencephalogram (MEG) of 15 healthy participants was recorded at rest and while they were performing a task involving a visual stimulus inducing strong, narrow-band gamma activity. Brain states were inferred from the tasks baseline periods and from resting-state recordings, respectively.\n\nOur results show how network states are related to the amplitude of stimulus-induced gamma responses. Across trials, we found an association between the amplitude of gamma responses and the brain state occurring immediately prior to stimulus presentation. Strong gamma responses followed a state characterized by prominent delta/theta oscillations in parieto-occipital regions and comparably weak alpha activity. Across subjects, the overall probability of visiting this state in the baseline period, i.e. the individual preference for this state, correlated positively with the amplitude of the trial-averaged gamma response. Remarkably, this relationship persisted when states were inferred from resting-state recordings rather than the tasks baseline.\n\nIn summary, both within- and across-subject variability in stimulus-induced gamma activity can in part be explained by the ongoing dynamics of whole-brain network states. Fast, pre-stimulus modulations of brain states account for differences between trials while stable, individual state preferences account for differences between subjects.

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

The network properties of the brain at the time of normal birth support the acquisition of language processing

Language acquisition appears to rely at least in part on recruiting pre-existing brain structures. We hypothesized that the neural substrate for language can be characterized by distinct, non-trivial network properties of the brain, that modulate language acquisition early in development. We tested whether these brain network properties present at the normal age of birth predicted later language abilities, and whether these were robust against perturbation by studying infants exposed to the extreme environmental stress of preterm birth.\n\nWe found that brain network controllability and integration predicted respectively phonological, bottom-up and syntactical, top-down language skills at 20 months, and that syntactical but not phonological functions were modulated by premature extrauterine life. These data show that the neural substrate for language acquisition is a network property present at term corrected age. These distinct developmental trajectories may be relevant to the emergence of social interaction after birth.

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

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

Image Processing and Quality Control for the first 10,000 Brain Imaging Datasets from UK Biobank

UK Biobank is a large-scale prospective epidemiological study with all data accessible to researchers worldwide. It is currently in the process of bringing back 100,000 of the original participants for brain, heart and body MRI, carotid ultrasound and low-dose bone/fat x-ray. The brain imaging component covers 6 modalities (T1, T2 FLAIR, susceptibility weighted MRI, Resting fMRI, Task fMRI and Diffusion MRI). Raw and processed data from the first 10,000 imaged subjects has recently been released for general research access. To help convert this data into useful summary information we have developed an automated processing and QC (Quality Control) pipeline that is available for use by other researchers. In this paper we describe the pipeline in detail, following a brief overview of UK Biobank brain imaging and the acquisition protocol. We also describe several quantitative investigations carried out as part of the development of both the imaging protocol and the processing pipeline.

neuroscience↗