Supervisors: Juan Alvaro Gallego (Bioengineering), Etienne Burdet (Bioengineering) 

Animals, including humans, perform an extremely varied repertoire of movements. Motor control requires the coordinated participation of a large number of sensorimotor structures in the brain. How these structures interact during behaviour remains elusive, to a great extent due to technological and theoretical limitations that we can now start to overcome.The goal of this project is to design and build a robotic platform to study high-dimensional behaviour in head-fixed mice. The student will start from an existing three-dimensional treadmill setup that will be available in the lab [1]; this setup consists of a polystyrene ball that levitates over a series of air plugs; head-fixed mice can run freely because the ball has negligible friction. The student will design, build, and test an electromechanical actuation system to apply rapid, brief mechanical perturbations to the ball that perturb the mouse movements. By placing around ten actuators in different directions, we will be able to randomly combine their actions (simultaneously or in sequences) and thus elicit a high-dimensional set of corrective movements. In a second phase, the student will integrate this robotic perturbation system with the lab’s deep-learning based movement tracking system [2] in order to deliver perturbations during specific phases of the behaviour.To validate this new experimental paradigm, we will study and model the complexity of the motor responses elicited during a series of experiments in mice performed with lab members.

[1] http://www.sphericaltreadmill.com/

[2] https://www.mousemotorlab.org/deeplabcut

Supervisors: Andrei Kozlov (Bioengineering), Tobias Reichenbach (Bioengineering)

The project will focus on using EEG data recorded from healthy and blast-TBI rats to discover electrical signatures of auditory cortex damage. Furthermore, extracellular multielectrode array recordings will be used simultaneously with EEG, and the student will attempt to develop an algorithm to predict multiunit activity from EEG. The ultimate goal of this work is to develop a set of electrophysiological markers for auditory processing disorder, a common debilitating condition in people exposed to blast (such as war veterans). The student will work in collaboration with other members of the Kozlov lab working in this area. Dr Reichenbach and I have an on-going collaboration on this topic, and this project will be a useful addition to our development of the data analysis pipeline. As for the interdisciplinary aspect of the project, Dr Kozlov’s lab will bring expertise in auditory cortex neuroscience in animal models, whereas Dr Reichenbach will contribute invaluable knowledge of EEG analysis methods used in humans. Our common goal is to bridge the gap between the animal (invasive) and human (non-invasive) studies of auditory processing disorders.

Supervisors: Simon Schultz (Bioengineering), Ann Go (Bioengineering)

Alzheimer’s Disease (AD) is the most common form of dementia, and is having an increasing impact on healthcare in aging populations worldwide. It is characterised at the functional level by slowly progressing memory and cognitive deficits, and at the cellular level by a pathogenic cascade involving abnormal accumulation of amyloid-b peptide, and downstream of this, the generation of hyperphosphorylated tau protein aggregates within affected cells. To develop treatments it is important that we understand how these cellular and sub-cellular pathologies give rise to deficits in information processing at the neural network level, and ultimately to the memory and cognitive deficits that afflict patients. Currently, studying such network-level phenomena largely requires the use of transgenic mouse lines that express human genes resulting in the formation of amyloid plaques or neurofibrillary tau tangles. As well as requiring the use of large numbers of animals, the effect of inter-species differences presents a major confound, with e.g. in the case of tau, substantial differences between mouse and human tau, and over-expression of human tau in mice resulting in deficits not seen in AD. The development of cerebral organoid models based on patient-derived human induced pluripotent stem cells promises to offer substantial prospect of progress, with the potential to increase throughput on analysis of therapeutics for different patient sub-groups. Critical to this, however, is the ability to recognise changes in network information processing properties similar to those observed using in vivo multiphoton calcium imaging in mouse models of AD. In this project we therefore propose to demonstrate proof of principle for observing changes in neural network dynamics due to tau pathology, as an example of a neurodegenerative disease state.

Supervisors: Gregory Scott (Brain Sciences), Ines Violante (second supervisor tbc)

Background
A major shortcoming of medical practice is the lack of an objective measure of conscious level. Impairment of consciousness is common, e.g. following brain injury and seizures. Brain injury can also interfere with sensory processing and volitional responses, which confounds the behavioural assessment of conscious level. Any objective measure of consciousness would therefore ideally bypass sensory processing. One such example is the perturbational complexity index (PCI) [1], which quantifies the average complexity of brain activity to multiple pulses of transcranial magnetic stimulation to provide a uni-dimensional measure of conscious level.

Proposal
We are interested in developing a novel measure of brain state derived from the set of brain responses evoked by a multi-dimensional (i.e. potentially varying in electrode space and time) ‘program’ of transcranial electrical stimulation (TES). 

We have recently acquired the GTEN neuromodulation system, state-of-the-art hardware which allows TES and EEG to be recorded through the same high-density (256 electrode) cap (Figure A, simplified). This system allows stimulation through “any” combination of electrodes whilst simultaneously recording EEG through non-stimulation electrodes, or with all electrodes used for recording immediately after stimulation (Figure B).

We will use the GTEN system to execute a “program” of brain stimulations (or “pings”), covering a spatially-distributed set of electrode configurations, and record the EEG responses following each stimulation (Figure C). We propose that clinically relevant information about brain state can be inferred from the properties of the set of responses evoked by the multi-dimensional program of stimulation. This approach would allow conscious states to be placed on a multi-dimensional landscape [2], rather than reduced to a uni-dimensional measure of conscious level [1].

A simple analogy is active sonar technology which, in its basic form, emits a well-defined pulse of sound, often called a "ping", listens for reflections (echoes) of the pulse, and draws inferences about nearby objects based on the properties of the reflected pulses. Our approach extends this analogy by systematically varying the spatiotemporal form of the stimulations (“pings”) in order to derive more information than would be possible from repetitions of a single form of stimulation. That is, a diverse program of stimulation reveals more facets of a system than that afforded by analysing responses to a single stimulation.

Question
This pilot project will investigate the question of whether EEG responses to diverse TES montages can even be distinguished in healthy resting awake participants, i.e. whether a machine learning classification algorithm can reliably classify EEG responses according to the stimulation applied. For example, given a set of 40 EEG recordings obtained from 10 repeats of 4 stimulation montages a-d, can the 10 EEG recordings corresponding to each of the montages a-d be correctly labelled a,b, etc. (Figure D)?

Methods
We expect the student will carry out data collection in a small group of healthy participants using the GTEN system and analyse data using e.g. Matlab, signal processing, statistics and machine learning to answer the research question. The scope of the data acquisition and analyses experiments will depend on time, progress made and other experimental constraints.

Additional information

• Existing source code will be available for some of the required tasks.
• We expect students to take an active role in data curation and organisation.
• We ask all students interested in the project to come and discuss the project in advance, having read any appropriate references.

Relevant references
Casali AG et al. A theoretically based index of consciousness independent of sensory processing and behavior. Science translational medicine. 2013;5(198):198ra
Bayne T, Hohwy J, Owen AM. Are there levels of consciousness? Trends Cogn Sci 2016;20:405–13

Supervisors: Mazdak Ghajari (Design Engineering), David Sharp (Brain Sciences)

Mechanical forces produced in the brain during road traffic and sporting collisions, falls and assaults can damage the vessels in the brain. This leads to acute bleeding or blood brain barrier damage. Understanding how these forces cause injury is key to the design of prevention strategies or smart detection systems. We have developed a high fidelity computational model of traumatic brain injury (TBI), which allowed us to predict the location of pathology seen in post-mortem cases and MRI data from live patients. The model has been improved by incorporating detailed anatomy of vessels. This project will focus on using this model to simulate a large number of cases to predict the distribution of maximal forces in the vascular network. Neuroimages (SWI) from a large cohort of TBI patients will also be analysed to determine the patterns of vascular abnormalities. We will test whether there is a relationship between distribution of mechanical forces and injury patterns. The outcome of this study will be the validation of our computational model. This model will be a new tool that will allow us to test and improve the prevention effects of helmets.

Supervisors: Parry Hashemi (Bioengineering), Tom Ellis (Bioengineering) 

Estrogen is an important hormone in the body where it has been studied extensively for its roles in body development and function. Estrogen is also present in the brain where it has two distinct receptor systems, the ER alpha (ERalpha) and ER beta (ERbeta) receptors. While estrogen’s roles in the body are well established, less is known about the roles and function of this hormone in the brain. This shortcoming arises primarily because of the difficulties in measuring estrogen in a physiologically relevant manner (i.e. in vivo, in real-time and continuously). While electrochemical methods have facilitated fast measurements of electroactive modulators such as dopamine and serotonin, these fast methods have not been applied to non-electroactive analytes, such as estrogen. In this project, carbon fiber microelectrodes will be modified with DNA binding receptors. These proteins that will act as recognition components to enable a continuous signal from estrogen. The study will involve modifying, optimizing and characterizing electrodes. The successful completion of this project will enable the first continuous estrogen sensor application in vivo to study brain estrogen.

Supervisors: Periklis (Laki) Pantazis (Bioengineering), Amanda Foust (Bioengineering)

Noninvasive deep brain stimulation is an important goal in neuroscience and neuroengineering. The application of optogenetics to thick and large tissues such as human brains has been hampered because the stimulating light (wavelength: ~430–610 nm) used to activate light-sensitive proteins is heavily scattered and absorbed by tissues. Recent studies have shown that injectable upconverting nanoparticles (UPCN), which emit visible light in response to tissue-penetrating near-infrared (NIR) light irradiation, can be used for minimally invasive actuation of neurons deep in the brain. Yet, the low upconversion yields of UPCN demand high-energy NIR illumination which can cause abrupt tissue heating and photodamage. Further, their chemical composition can raise long-term health concerns. To overcome these limitations, you will assess whether bioharmonophores, biodegradable nonlinear optical nanoparticles that can convert pulsed infrared light into local signal of blue/red light, can be employed as a flexible and robust minimally invasive nanotechnology-assisted approach for the optical control of neuronal activity. Specifically, you will establish optical conditions to assess whether molecularly tailored bioharmonophores can activate excitatory and inhibitory channelrhodopsin expressed in dopaminergic neurons. Devising an illumination regimen of bioharmonophores for compatibility with light-activated channels will introduce a clinically-relevant approach to deep brain stimulation and neurological disorder therapies.

Supervisors: James Choi(Bioengineering), Magdalena Sastre (Brain Sciences)

Neurological diseases are among the most difficult diseases to treat, because drugs cannot enter the brain, because cerebral capillaries are lined by a blood-brain barrier (BBB).

We are developing a non-invasive and localised method of delivering drugs across the BBB. Using ultrasound and bubbles, we have been able to non-invasively and locally open the BBB for a short duration (less than 10 minutes) (Morse SM, et al., Radiology 2019), which allows drugs into the brain tissue.

The purpose of this project is to evaluate the effectiveness and safety of our acoustic wavelet drug delivery technology. For example, the student may explore the delivery of new and exciting drugs, such as nanobodies, antibodies, nanoparticles, etc; or may study the safety of the procedure, such as whether microglial cells and astrocytes have been activated.

This is a multidisciplinary project that spans physical acoustics, bubble physics, BBB structure and function, and neuroscience. Dr. James Choi will guide the use and development of the acoustic technology and the evaluation of drug delivery while Dr. Magdalena Sastre will guide the analysis of safety.

Supervisors: Parastoo Hashemi (Bioengineering), Simon Schultz (Bioengineering)

Background and Rationale for Project
Serotonin has long thought to be neuromodulator, however the roles of this messenger in the modulation of brain activity remain poorly defined. The significance here is that serotonin is the primary target of most antidepressant compounds, that aim to increase extracellular serotonin levels. The rationale behind these agents is that extracellular serotonin levels are lower during depression (there remains, to date, no tangible evidence for this) and that these lower concentrations functionally change modulation of activity and lead to depression phenotypes. Because serotonin’s roles as a modulator are poorly defined and the chemical hypothesis of depression is yet to be verified, it is not surprising that antidepressants are not clinically efficacious for the vast majority of patients.

A fundamental and critical question, for better understanding how serotonin exerts its roles in the brain during normal function and disease, is how does serotonin modulate circuit activity? And do changes in this modulation alter circuit function?  There are very challenging questions to answer. Until recently, in vivo, it has been difficult to a) chemically measure serotonin and b) observe local waves of activity corresponding to volume transmission (the mode via which serotonin is thought to signal). The two PIs on this proposal are leaders in their respective fields in precisely these two types of measurements. Hashemi pioneered the first in vivo fast electrochemical methods to measure real time chemical changes in serotonin. Schultz has been one of the leaders of the development of multiphoton optical measurements of brain activity, and in the development of novel techniques for analysing optically and electrically recorded neurophysiological data.

In this interdisciplinary project, an MRes student will be tasked with combining these two niche methods in vivo to provide the first platform for studying serotonin’s roles in modulating circuit activity in the context of behavioural phenotypes.

Project Overview
The project is to marry fast scan cyclic voltammetry (FSCV)  at carbon fiber microelectrodes (CFMs) for serotonin measurements to in vivo multiphoton imaging of deep brain structures such as the hippocampus.

There are two specific objectives, outlined below:

Flexible, Implantable CFMs
Currently, FSCV experiments are performed with CFMs fabricated in glass pipettes. While these electrodes are successful for work in anesthetize animals, for our longer-term vision of studying neuromodulation during behaviour, these electrodes are not suitable because they are very fragile. The first phase of this project, thus, is to engineer a flexible material to insulate the carbon electrodes that can be utilized as an alternative to glass. The new devices, house in this new insulation material will be characterized rigorously for parity with their glass counterparts.

Simultaneous Measurements of Serotonin and Circuit Activity
A key part of the project will involve making measurements of serotonin in vivo while performing multiphoton imaging. This will involve (i) viral transduction of hippocampal CA1 neurons with a genetically encoded calcium indicator, using established techniques, (ii) optimising the surgery to allow both optical access to the hippocampus as well as insertion of a stimulation electrode as well as the carbon electrode and guidance to a location where it can be visualised the multiphoton imaging window, and (iii) performing two photon calcium imaging in the above experimental setup.

Expected Project and Mentoring Outcomes
At the completion of this project we expect to have a functional platform to test how serotonin modulates activity during behaviour with a view on how this modulation affects circuit function during depression, which is the basis of a PhD project.

The MRes student will receive unique mentoring and training in chemical measurements at ultra microelectrodes (Hashemi) and high resolution in vivo microscopic methods (Schultz) with the opportunity to utilize materials engineering and instrument optimization. There will be training in both fundamental and behavioural neuroscience.

Supervisors: Jun Ishihara (Bioengineering), Parry Hashemi (Bioengineering) 

Recent research revealed that excess immune system activation causes mental illness. Cancer immunotherapy is a revolutionary invention to fight cancer through activating the immune system. Together, it is suggested that immunotherapy may cause mental illness, however the molecular mechanism between immunotherapy and mental illness remain unclear. Serotonin and histidine are crucial molecules for regulating mental condition. Parry Hashemi lab has developed a biosensor that can monitor the serotonin/histamine level in the brain in vivo. Jun Ishihara is an immuno-engineer, who can produce immunotherapy drugs in his laboratory. Jun can make immunotherapeutics both unmodified versions (which rapidly distribute to the whole body after injection; thus systemic therapy) and engineered versions (which stick to ECM proteins at the injection site; thus localised therapy). Our central hypothesis is that cancer immunotherapy causes mental illness through serotonin/histidine concentration changes and cytokine release, and this could be solved by a drug delivery system approach.

1. Systemic cancer immunotherapy may cause mental health issues.
2. Engineered cancer immunotherapy that can localise at the cancer would reduce the mental health issue.

This research will shed a light on connection between immunotherapy and mental illness, and can propose a solution for immunotherapy-induced mental illness. Students can learn various aspects of immunotherapy and neurotechnology related techniques.

Supervisors: Mark Friddin (Dyson School of Design Engineering), Connor Myant (Dyson School of Design Engineering) 

The patch-clamp is a well-established electrical technique that has been used for decades to characterise ion channels, to probe excitable cells and to study the bioelectronic profiles of biological membranes. While the standard setup involves the use of a glass micropipette to electrically isolate a small membrane patch of a biological cell, there is also a cell-free alternative that uses synthetic lipid bilayers that are reconstituted by the user. This approach has become increasingly popular in recent years as it does not require the same amount of equipment, nor the need to sustain living cells, however a key drawback is that the membranes formed this way tend to be small, making it difficult to insert proteins. The aim of this project is to develop a new method that allows much larger lipid bilayers to be assembled on a 3D printed platform, and to showcase the fundamental electrical properties of these membranes using a miniature state-of-the-art patch-clamp device. The project sits at the interface of neuroscience and engineering and has potential applications in the study of brain disorders, for developing biosensors and in drug discovery. Students will develop knowledge and practical skills of assembling lipid membranes, will learn how to take low-noise electrical measurements of these bilayers and will gain experience of developing a novel platform technology. The project will be directly supervised by Dr Mark Friddin, an expert in constructing synthetic lipid bilayers and performing ion channel electrophysiology, and Dr Connor Myant who will support the design and additive manufacturing of the platform.

Supervisors: Simon Schultz (Bioengineering), Pier Luigi Dragotti (Electrical and Electronic Engineering)

The recent development of Neuropixels probes (comprising 960 electrode sites from which up to 384 selected sites can be read out) has substantially increased our ability to acquire large-scale, densely sampled neurons from the nervous system. With implantation of multiple Neuropixels arrays, simultaneous recordings can be made from many thousands of neurons. While the experimental techniques have advanced, the progress of data analysis techniques capable of taking advantage of the scaling of these recordings has lagged. The Allen Institute have recently made available a dataset comprising large scale Neuropixels recordings from the mouse visual system (see https://portal.brain-map.org/explore/circuits/visual-coding-neuropixels). This includes 6 Neuropixels probes, implanted such that they cover the dorsal lateral geniculate nucleus (dLGN), the lateral pulvinar nucleus (LP), and primary and secondary visual cortices. A battery of standard stimuli were applied. One disadvantage of the Allen dataset is the relatively crude receptive field (RF) mapping performed, in comparison to more specific dLGN studies such as Tang et al (2016). However, other stimuli such as natural scene images and movies were presented, which may present an opportunity to rectify this computationally. In this project we will firstly use a recently developed information-theoretic receptive field mapping approach which allows the recovery of RFs from natural movies, in order to recover detailed structure of dLGN RFs. Then, beginning with ON or OFF RFs in the dLGN, we will “mine” the dataset for overlapping RFs with the aim of reconstructing individual information processing operations performed throughout the circuit.

Prof Schultz will bring to this project expertise in systems neuroscience and information theoretic analysis of neurophysiological data. Prof Dragotti will bring expertise in sparse signal processing. Prof Nikolic developed the RF mapping algorithm we will use in the project, and will provide advice and assistance in its use, and further development. The project will provide multidisciplinary training in engineering approaches to the analysis of large-scale neuroscience datasets. It would suit a student with good python programming skills and a strong mathematical foundation (from electrical or biomedical engineering, physics or mathematics) and a strong desire to learn neuroscience.

Katz, M. L., Viney, T. J., & Nikolic, K. (2016). Receptive field vectors of genetically-identified retinal ganglion cells reveal cell-type-dependent visual functions. PloS one, 11(2).
Tang, J., Jimenez, S. C. A., Chakraborty, S., & Schultz, S. R. (2016). Visual receptive field properties of neurons in the mouse lateral geniculate nucleus. PloS one, 11(1).

Supervisors: Juan Álvaro Gallego (Bioengineering), Claudia Clopath (Bioengineering)

Recent studies of neural population activity during behaviour have consistently uncovered that it is dominated by a handful of —around ten— neural covariation patterns (Gallego et al., 2017; Shenoy et al., 2013). These patterns span a neural manifold, a low-dimensional surface in the high-dimensional space defined by the activity of all recorded neurons. We refer to the neural population activity within the manifold as latent dynamics.

Remarkably, studying the neural manifold and its associated latent dynamics has helped advance our understanding of how the brain controls behaviour, providing insight into questions that had remained elusive when studying single neuron activity. The “manifold approach” has also helped made brain-computer interfaces (BCI) more robust (Gallego, Perich et al., 2020; Pandarinath et al., 2018).

Yet, there are many open questions regarding the geometrical properties of the neural manifold, with important implications both in basic neuroscience and BCIs. The goal of this project is to perform the first systematic comparison of manifold properties across the main areas of the sensorimotor system: premotor cortex, primary motor cortex, and primary sensory cortex. Such comparison will be carried out in a dataset with simultaneous recordings of around a hundred neurons in each of these areas, obtained as monkeys performed the same reaching task —the dataset from (Gallego, Perich et al., 2020). To assess the geometry of the neural manifold, we will fit the neural data with a broad range of manifold models that incorporate different assumptions about the data: from principal component analysis, and factor analysis, to isomap, and autoencoder neural networks. We will then explore the ability to predict the monkey’s behaviour from each of these different manifolds as we vary their assumed dimensionality. We expect this comparative study to inform about the difference in computations performed across these areas during a given behaviour, and on how to improve BCI design.

Dr. Gallego will bring to this project expertise in systems neuroscience, and neural data analysis, in particular focusing on the application of neural manifold approaches. Prof. Clopath is a leading expert in computational neuroscience and the application of mathematical methods and modelling tools to understand neural computations. This project would suit a student with good Python programming skills and a strong mathematical foundation (from electrical or biomedical engineering, physics or mathematics) and a strong desire to learn neuroscience.

References
J.A. Gallego, M.G. Perich, L.E. Miller, S.A. Solla. Neural manifolds for the control of movement. Neuron 94(5):978–84, 2017
J.A. Gallego^*,M.G.Perich^*, R.H. Chowdhury, S.A. Solla, L.E. Miller. Long-term stability of cortical population dynamics underlying consistent behaviour. Nature Neuroscience 23:260-276, 2020.
C. Pandarinath, K.C. Ames, A.A. Russo, A. Farshchian, L.E. Miller, E.L. Dyer, J.C. Kao. Latent factors and dynamics in motor cortex and their application to brain-machine interfaces. Journal of Neuroscience 38(44):9390-9401, 2018.
K.V. Shenoy, M.T. Kaufman, M. Sahani, M.M. Churchland. A dynamical systems view of motor preparation: implications for neural prosthetic system design. Prog Brain Res. 192 33-58, 2011.

Supervisors: Martyn Boutelle (Bioengineering), second supervisor to be confirmed 

The Boutelle Group has a long history of developing novel devices to monitor the acutely injured human brain. This project comes from an on-going research collaboration with Professor George Malliaras in Cambridge. We are using micro fabrication techniques to make highly flexible probes that combine both the measurement of local field potentials in the brain with chemical measurements, The probes are designed to lie on the surface of the cortex of the human brain and flexible enough to move with the brain. We have made prototype devices and the project would involve evaluating the performance of these for picking up neurochemicals in vitro, and then using this information to help design new improved devices. The project will be supported by CDT Neurotechnology PhD student De-Shaine Murray.

Supervisors: Dario Farina (Bioengineering), Angela Kedgley (Bioengineering)

Electromyography (EMG) is one of the few methods that provide a window on muscle activity and hence muscle force production during functional movements. As one of its applications, research in myoelectric control has produced active prosthetic devices with multiple degrees-of-freedom (DOFs), paralleled by EMG-based control strategies.

The aim of this project is to evaluate contrasting approaches for estimating upper limb joint kinematics and kinetics using surface EMG. Two such approaches are mathematical modelling (e.g. neural networks) and multibody dynamic musculoskeletal modelling.

From Dr. Farina students will gain the expertise to design and perform experiments to measure and process surface EMG signals. They will also learn how to build mathematical models linking measured EMG and kinematics.

From Dr. Kedgley students will learn to design and perform experiments to measure and process human body kinematics using motion capture systems. Furthermore, they will acquire skills in multibody dynamics modelling and simulation of the human upper limb.

The candidates are required to have strength programming in at least one of Python or MATLAB. Some knowledge of machine learning and biomechanical analysis is desirable.

Supervisors: Simon Schultz (Bioengineering), second supervisor to be confirmed

This project will involve collaboration with the group of Richard Morris in Edinburgh, on analysing data from experiments in which microendoscopic probes are used to image calcium signals in populations of prefrontal cortical neurons in rats performing a social interaction task. The main focus of the project will be applying nonlinear dimensionality reduction methods to visualise the population code for various facets of social cognition.

Supervisors: Martyn Boutelle (Bioengineering), second supervisor to be confirmed 

This custom designed project comes from a long term collaboration with Prof Anthony Strong and his team at King's College Hospital NHS Trust. In the past we have demonstrated the importance of Spreading Depolarisations (SDs) in the development of secondary brain injury in patients who have had a severe traumatic brain injury. This project involves working with the field potential data streams we obtain from out patients. We are looking to examine the role of electrical events in our clinical data sets. In particular we would like to know how such events change the stage of the brain tissue. The project will involve visits to the Hospital to see the clinical team together with working with project members in MGB group and Prof Strong's team. In particular Sharon Jewel, a PhD student from the Boutelle group and expert in neurophysiology. The aim will be to work with Sharon and a state of the art human neurophysiological instrument, and to program this instrument to monitor electrophysiological events and see how these parameters are changed by interaction with the events. The work will be supported by Boutelle group PhD student Sharon Jewel.

Supervisors: Christopher Rowlands (Bioengineering), Stephen Brickley (Life Sciences)

Structured Illumination Microscopy is a modern microscopy technique for improving resolution past the so-called diffraction limit. This makes it suitable for imaging small features like synaptic terminals, lipid vesicles, viruses and other biologically-relevant sub-diffraction features. Nevertheless, the way the data is captured (with a number of camera frames which are processed to yield one super-resolution frame) can lead to significant spatiotemporal artefacts. The Rowlands lab is in the process of developing a new instrument with very high temporal resolution to overcome this limitation.

The student on this project will be putting the finishing touches to the new microscope and using it to image test samples. Once characterization is complete, the system will be used to image biologically-relevant phenomena, such as calcium activity in dendritic spines.

Supervisors: Martyn Boutelle (Bioengineering), Emmanuel Drakakis (Bioengineering) 

This project follows on from an EPSRC funded project to detect the progression of ALS (motor neurone disease) in patients. It is a Collaboration between the Boutelle group, the Drakakis Group and Prof Chris Shaw (Maurice Wahl Clinical Research Centre, King's College Hospital). ALS is a devastating disease that is characterised by rapid deterioration in motor function leading typically to death within a few years of diagnosis. Development of therapies is hampered by the lack of a reliable method of determining the progression of the disease. Typically clinical function assessments are used, but the scale is too corse and too subjective to allow evaluation of drug therapies that might for example slow the rate of disease progression.

We have been following a different approach, where we use multiple skin contact to record EMG's from pairs of major muscle groups in the arms or legs of a patient. We are looking for complex fasciculation that seem to be characteristic of ALS.

We have moved to the stage of evaluating instrumentation that can be used by patients at home. The project is to help set-up this new system and to further develop pattern recognition algorithms to group ALS fasciculation potentials to allow processing of large volumes of data. It will be supported by James Blashford (KCH) as well as the MGB and Drakakis groups.

Supervisors: Simon Schultz (Bioengineering), Amanda Foust (Bioengineering)

It is commonly known that mice and other rodents don't have the rich colour vision that trichromats like humans do. Less well known is the fact that mice have ultraviolet-sensitive cones in their retina allowing them to see into the UV part of the spectrum - i.e. see things that humans would find invisible. At the same time, it is commonly assumed that mice can't see "red" - but there is a recent report (yet to be confirmed) of behavioural sensitivity in the red end of the spectrum. In fact, the spectral sensitivity of the mouse visual system has not been rigorously characterised (from an optics perspective). In this project we will build a device for mapping the spectral sensitivity of mouse behaviour, using the optomotor response to drifting gratings as a readout. Narrowband illumination will be scanned from the UV to the IR, and mouse head movements (measured via DeepLabCut software) used to generate psychometric functions. This project will involve hardware development, optics, python programming and wet lab work, and would suit someone with good engineering skills and a strong interest in neuroscience.