UCL Queen Square Institute of Neurology


Research Themes

Computational Neurostimulation

We have introduced computational neurostimulation as a framework to interrogate the behavioural and physiological effects of non-invasive brain stimulation in health and disease. We use biophysically grounded neural network models to predict the behavioural consequences of noninvasive brain stimulation, and utilise simulations of current flow to optimize existing approaches for the delivery of brain stimulation in patient populations (Evans et al., 2020).

High-precision neurophysiology of human movement control

One of our long-standing ambitions is to understand the fine-grained neurophysiological processes of movement control, disorders of movement, and the processes underpinning recovery. To achieve this, we develop novel approaches for high-precision magnetoencephalography, together with Prof Gareth Barnes’ group at the Wellcome Centre for Human Neuroimaging. This now enables laminar-resolved assessment of cortical responses (Bonaiuto et al., 2018), and the trial-wise measurement of motor cortical transient (burst) signals (Little et al., 2019). We now extend these approaches to wearable (OPM) MEG to study the physiological basis of natural movements and patient groups characterized by abnormal movement control, such as stroke.

The neuroscience of upper limb rehabilitation

Stroke is the most common cause of long-term neurological disability worldwide, with a large proportion of patients left with impairments of limb movement. Recovery of movement is of high importance for stroke survivors but the behavioural principles for therapy are not well understood, nor is the physiological basis of recovery. Bringing together our progress in high-precision neurophysiology during movement with wearable MEG technology, and cutting-edge brain stimulation approaches, we study the basis of recovery after stroke in patients undergoing intensive upper limb therapy. This work leverages the unique clinical service set-up by Prof Nick Ward.