Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

WIN Wednesday Works In ProgressLow-intensity transcranial focused ultrasound brain stimulation (TUS) in decision making under threat

Presenter: Caroline Harbison

Authors: Caroline Harbison, Pranav Sankhe, Robin Cleveland, Miriam Klein-Flugge, Matthew Rushworth

Abstract: Much of our everyday behaviour is influenced by fear: to thrive, we must identify and respond adaptively to stimuli predictive of positive outcomes (rewards) while ensuring that we are not curtailed by negative outcomes (threats). A recent study using a foraging task found that the tracking of threat and switching from foraging to vigilance are associated with specific but distributed patterns of activity spanning the habenua, dorsal raphe nucleus, anterior cingulate cortex, and anterior insular cortex. In this study, we will use transcranial ultrasound stimulation and 7T fMRI to assess the causal contribution of these regions to decision-making under threat.


WIN Wednesday Works In Progress

Neural Changes Following Occlusion Therapy in Childhood Amblyopia

Presenter: Rebecca Willis 

Team: Rebecca Willis, Betina Ip, Holly Bridge, Ravi Purohit, Tessa Dekker

Abstract: Amblyopia is a neurodevelopmental condition in which vision is much poorer in one eye than the other. It is the most common cause of vision loss in children. Amblyopia is usually treated with Occlusion Therapy which involves covering the stronger eye with a patch to improve vision from the weaker eye. Occlusion therapy is effective in some children if followed before age 8, but not all. The neural mechanism through which Occlusion works in children has not yet been tested. In this study, we will investigate how Amblyopia and Occlusion Therapy affect the developing human visual system. 

Methods: 35 5-8 year old children with amblyopia and 35 age and sex matched controls will come to WIN for two study visits 6 months apart. During both visits, we will use MRS to measure GABAergic inhibition, fMRI to measure activity in resting state networks, and psychophysics to assess visual function. Between study visits, children with amblyopia will have Occlusion Therapy.  

Conclusion: We hope that findings from this study will eventually lead to more effective treatment for Amblyopia, and more broadly, a better understanding of experience dependent plasticity.  


WIN Wednesday Methods Series


Modelling variability in dynamic functional brain networks using embeddings

Presented by: Rukuang Huang

Abstract: Neuroimaging techniques offer unprecedented insights into the dynamic neural processes underlying cognitive functions and Magnetoencephalography (MEG) is a non-invasive neuroimaging modality with high temporal resolution. With recent studies, data driven models like the Hidden Markov Model (HMM) are getting more attention due to their ability to infer fast temporal dynamics in functional networks in an unsupervised manner. However, these dynamic network models are typically trained at the group level. Whilst it is possible to post-hoc estimate the session-specific networks with the so-called dual estimation, this does not allow the model to discover and benefit from subpopulation structure in the group. We propose an extension to the HMM model that incorporates embedding vectors (c.f. word embedding in Natural Language Processing) to explicitly model individual sessions while training on the entire group. This effectively infers a “fingerprint” for each individual session, which can group together those with similar spatio- temporal patterns. With simulated data, we show that the model can recover the underlying subpopulation structure, achieve higher accuracy than dual estimation on session-specific quantities and can make use of increasing number of sessions to benefit the inference of individual sessions. Applying this model to resting-state and task MEG data, we show the learnt embedding vectors capture meaningful sources of variation across a population. This includes subpopulations related to demographics and systematic differences, such as scanner types or measurement sites. The proposed model offers a powerful new technique for modelling individual sessions while leveraging information from an entire group.