MRes students work on their research project throughout the year.  You can apply for one of the projects listed below, or contact your preferred supervisor to discuss a different project.

Applications will be considered in three rounds; to ensure your preferred project is available, we encourage you to apply by the Round 1 deadline of 15th December.  If you are applying in a later round, some of the projects listed may have already been allocated so please consider including a second or third choice project in your application.

Please visit our "How do I apply?" page for full details of the application process and deadlines.

Projects available for 2022-23

If none of these projects is suitable for you, you can contact your preferred supervisor to discuss an alternative project. Supervisors are based in Bioengineering except where indicated.

Supervisor(s) Project title Project type Project description Pre-requisite skills/background
Kambiz Alavian (Brain Sciences), Andrew Shevchuk (Metabolism, Digestion & Reproduction) Understanding the role of mitochondrial metabolic efficiency in synaptic transmission and plasticity Lab based An ageing-related decline in mitochondrial function is associated with physiological imbalance and neurodegenerative conditions. Deficiency in mitochondrial genetic integrity, ATP production and redox balance is either directly associated with or can be ameliorated by regulation of mitochondrial metabolic efficiency. Particularly, mitochondrial dysfunction and decline in efficiency of ATP production seems to be connected with the impairment of synaptic plasticity and long-term potentiation (LTP), i.e. the long-lasting enhancement of synaptic activity as a result of high-frequency stimulation followed by biochemical and structural changes and metabolic growth of the pre- and post-synaptic neurons. The overall goal of this project is to characterise the synaptic metabolic changes associated with high-frequency stimulation and synaptic plasticity. We will utilise an electrochemical method using platinum-coated nano-electrodes (Shevchuk lab) and primary hippocampal neuron cultures, as well as imaging and biochemical assays (Allavian lab) to achieve this goal. The results of this study will, for the first time, elucidate the role of mitochondrial metabolic efficiency in synaptic plasticity, potentially providing an explanation for the dysfunction associated with normal aging and degenerative conditions such as Alzheimer’s disease.  
Anil Bharath Neural Architectures for Predicting the Behaviour of Dynamical Systems Lab based Fuelled by artificial neural architectures and backpropagation, data-driven approaches now dominate the engineering of systems for pattern recognition. However, the predictive modelling of the behaviour of complex dynamical systems - such as those governed by systems of coupled differential equations - remains challenging in two key ways: (i) long term prediction and (ii) out-of-distribution prediction. Recent progress in disentangled representations (Fotiadis et al, 2021) has nudged the field forward, but it is now time to return to the underlying neural architectures, seeking those that are better suited to the intrinsic dynamics implicit in a system of equations. We seek to explore different approaches to this problem, including Siamese network structures, progressive network growth or, perhaps, neurons which incorporate some form of plasticity.  
Anil Bharath Uncovering the neural code of DRL agents Lab based Neuroscience has evolved exquisite tools to probe the behaviour of neurons in biology.  Yet, very few of these tools are applied to decipher the encoding of deep neural networks. This particularly true in the field of deep reinforcement learning (DRL), where layered artificial neural networks learn mappings from observations to policies, or control signals. In this project, we build on prior work in agents trained to perform visuo-motor control, such as guiding a robot arm towards a target. The aim is to answer specific questions on parameter distributions and activation statistics, comparing these as training algorithms are altered, or environments perturbed.  
Martyn Boutelle, Emmanuel Drakakis

Wearable sensors for detection of ALS

Lab based 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.
 
 Martyn Boutelle Microfabricated multimodality probes for minimally invasive monitoring the brain Lab based The MGB 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.  
 Martyn Boutelle Tracking neuronal activity in the human brain Lab based This project custom designed project comes from a long term collaboration with Prof Anthony Strong and 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 Strongs team. In particular Sharon Jewel, a PhD student from the MGB 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.  
Mazdak Ghajari (Design Engineering), David Sharp (Brain Sciences) Computational prediction of vascular injuries after traumatic brain injury Desk based Head exposure to large mechanical forces in sporting collisions, road traffic collisions and falls can damage different tissues, such as vessels. Vascular injury is a key injury, which determines the direction of care in the acute phase and is a biomarker of mild traumatic brain injury. The capability to accurately predict vascular injuries will provide new opportunities for improving clinical care and prevention 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 and validated for a few cases. This project will focus on two aspects: a) predicting vascular injuries in more cases to improve our understanding of its biomechanics and b) using deep learning to develop a surrogate model that predicts vascular injury in real-time. The outcome of the project will be a new tool that will allow us to test and improve the prevention effects of mitigation systems. mathematics, machine learning, mechanics, finite element modelling
Rylie Green A bioelectronic implant for cancer treatment Lab based This project revolves around aiding in the development of a device for the selective delivery of chemotherapy directly to the site of non-operable brain tumors (glioblastoma multiforme). This device consists of a conductive polymer-based material that can used as an electrically controlled drug delivery system. The goal of this project is to evaluate the drug release profiles for multiple different molecules that are analogs to those commonly used in chemotherapy. Parameters such as molecule size, charge, and stability will be investigated. Characterization of the drug release profiles will be accomplished through chemical, electrochemical, and spectroscopic techniques.   
Rylie Green Biofunctionalising electrodes through conductive hydrogel coatings Lab based Peripheral nerves cuffs can have there biointegration improved through the use of conductive hydrogel coatings. One of our long-term goals is to improve the long term performance of these electrodes. This projects will investigate reducing inflammation at the site of implantation by the incorporation and release of anti-inflammatory mediators from the hydrogel coatings. Different mediators will be investigated along with different release methods and how these impact the performance of the cuff electrodes.   
Rylie Green Injectable brain machine interfaces Desk based Deep brain stimulation (DBS) therapy has seen increasing clinical relevance in the last decade. DBS and similar therapeutic techniques require the use of a chronically implanted neural electrodes.   This project is focused on the development and characterisation of a minimally invasive, organic, neural electrode system. This electrode system is based on an injectable hydrogel functionalized to facilitate the in-situ electrochemical deposition of conducting polymer directly inside the brain to enable recording and stimulation of neural activity. Initial work will focus on the mechanical, electrochemical and biological characterization of the polymeric electrode system before progressing to investigating in-situ depositions in ex-vivo brain tissue.  
Rylie Green Living Bionics: Stimulation to drive neural network development Lab based Electrical stimulation has been demonstrated to induce directional neurite growth in various cell types, both human and non-human using biphasic stimulation. This research project aims to evaluate a range of sinusoid stimulation frequencies to drive activity, growth and release of neurotransmitters of developing neurons using a cell stimulation rig made in house.   
Rylie Green Living electrodes: Adhering cells through biotinylation Lab based Previous work has demonstrated that biotinylation is possible with neural cell types. This project aims to expand this to produce electrodes which can be coated with neural cells to allow for better biointegration at the implant site. The work will involve evaluating different cells types will be evaluated as well as the production of suitable electrode coatings. This builds on our concept of a “living electrode”.  
Rylie Green Neonatal EEG Electrode Cap Lab based The use of electroencephalogram (EEG) electrode caps in neonatal care presents unique challenges surrounding electrode placement and fixation. This project will develop a neonatal EEG cap using soft, flexible conducting polymer composites called conductive elastomers.  The scope of this project covers the design and fabrication of an EEG electrode array cap, development of an interface to monitor 10-20 electrode channels, and validation of biological signals from human subjects.   
Rylie Green Printing of flexible polymer bioelectronics Lab based The overall goal of this project is to investigate the feasibility of fabricating well defined patterns of conducting polymer-based bioelectronics through printing (inkjet or melt electrospin writing). This technique takes advantage of the viscous liquid phase dispersion of the conductive polymer in solvent to enable printing through a small diameter nozzle. Use of thermal processes will be investigated as methods to control viscosity and printing tolerances. Students with robotics interests will have an opportunity to build a bespoke printer which can be controlled through CAD file geometries and used to create 3D implants from the extruded material.   
Rylie Green Spinal cord bridge Lab based Nerve regeneration in an injured spinal cord is often restricted. One possible reason may be the lack of topographical signals from the material constructs to provide contact guidance to invading cells or re-growing axons. This research project aims to evaluate randomly oriented or aligned collagen fibers coated on cuff electrodes to study device topographical effects on astrocyte behavior and neurite outgrowth respectively, using electrical regimes.   
Rylie Green Living electrode-on-a-chip: development of microphysiological models to study bioelectronic interfaces in vitro Lab based The use of metallic electrodes in implantable bioelectronics has been associated with poor tissue integration and the induction of noxious foreign body responses. Biomaterials-based strategies, such as the “Living Electrode” (LE) technology being developed by our lab, could be used to engineer biomimetic interfaces that promote biointegration and enable synaptic neuromodulation of the target tissue. Currently, animal models remain the gold standard to study tissue-level phenomena, since conventional cell culture systems are unable to reproduce the composition, geometry, and spatial organization of bioelectronic interfaces. However, microphysiological “organs-on-chips” are being increasingly used to supplement traditional preclinical models owing to their ability to reproduce the complexity of physiological tissues. This project will focus on the development of microphysiological models that mimic the properties and functionality of bioelectronic interfaces in vitro. A cut and assemble method will be used to fabricate thermoplastic, reconfigurable, and optically clear microphysiological systems for real-time monitoring. Different biomaterials and cell types will be then incorporated into the devices to emulate the multi-layered structure of the LE and the bioelectronic interface in vivo. The student will lead the design, fabrication, and in vitro characterization of LE-on-a-chip devices through the integration of computer-assisted design, microfabrication, biomaterials, and tissue engineering techniques. Expertise in computer assisted design, laser cutting, photocrosslinkable hydrogels and neural cell culture is ideal, but not required.
Rylie Green, Bogachan Tahirbegi Conducting polymer nanowire and graphene based transistors on paper and PDMS for ultradense ECoG (Electrocorticography) arrays Lab based Developing affordable and easy fabrication methods of bioelectronics circuits without the need of clean room facilities are an active field of research. Patterning of the nanocomponents into functional devices is the remaining challenge in the field. In our laboratory, we developed laser based fabrication methods of flexible networks of patterned conducting polymer nanowires for fully polymeric bioelectronics. Shortly, we are developing laser sintering and filter based processing methods for direct pattern transfer of components such as conducting polymer PEDOT nanowires and silver nanowires into paper and PDMS. The resulting films of patterned nanowires are found to possess high conductivity as well as improved wet electrochemical properties in comparison to platinum. Fabricated thin and flexible arrays of PEDOT nanowire films are tested successfully as an Electromyography (EMG) device for muscle contractions. Recently, we discovered that we could fabricate different materials layer by layer using this method. Therefore, we would like to improve our technique and fabricate 3d structures of bioelectronic circuits. MRES student will work as a part of multidisciplinary NISNEM (Non-invasive single neuron electrical monitoring) project to fabricate fully functional transistor based ultradense ECoG (Electrocorticography) arrays for brain research using the methods described above. Once, the device is fabricated and characterized, it will be tested first on neuron cultures and after that on brain.

Prof. Green’s research has been focused on developing bioactive conducting polymers for application to medical electronics. Prof. Green has developed hybrids of conducting polymers and hydrogels to reduce strain mismatch with neural tissue and improve long-term cell interactions at the neural interface.

Dr. Tahirbegi’s research has been focused on the novel electrode materials and new fabrication approaches to enable the fabrication of the super high-density electrode arrays for Electromyography (EMG), electroencephalography (EEG) and micro-electrocorticography (μECoG) to create a disruptive technology to non-invasively detect the activity of large populations of single neurons in the brain and the spinal cord.
Nano/microfabrication, Electronic circuit design, Characterization of neuron cultures on the ultradense ECoG (Electrocorticography) arrays, Calcium imaging
Rylie Green, Bogachan Tahirbegi Fabrication of hybrid electrodes and hydrogels from conductive polymer nanowires, graphene and carbon nanotubes on PDMS  for non-invasive neuroimaging Lab based Current non-invasive neuroimaging techniques, such as electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI), have transformed healthcare over the past decades. Existing metal based electrode technologies used for EEG are not capable of miniaturization due to high noise and low spatial selectivity. The ideal electrode material should be biocompatible and miniaturizable. We will develop novel electrode materials and new fabrication approaches of hybrid soft, flexible and electrically conductive materials (Conducting polymers, graphene and carbon nanotubes) designed to replace the metal electrodes routinely used for non-invasive measurements of neural activity. A range of alternative electrode coating materials have been investigated by Prof Rylie Green and others, including conductive hydrogels (CHs) and conductive elastomers (CEs). These promising approaches use hybrids of conductive polymers (CPs) to provide synergy between low impedance charge transfer and conformability. However, no material technology is available that can address the challenge of fabricating the miniaturized super high-density electrode arrays necessary for the NISNEM (Non-invasive single neuron electrical monitoring) project. In our laboratory, we developed laser based fabrication methods of flexible networks of patterned conducting polymer nanowires for fully polymeric bioelectronics. Shortly, we are developing laser sintering and filter based processing methods for direct pattern transfer of components such as conducting polymer PEDOT nanowires and silver nanowires into paper and PDMS. The resulting films of patterned nanowires are found to possess high conductivity as well as improved wet electrochemical properties in comparison to platinum. Fabricated thin and flexible arrays of PEDOT nanowire films are tested successfully as an EMG device for muscle contractions. MRES student will work as a part of multidisciplinary NISNEM project to fabricate hybrid electrode and hydrogel arrays from conductive polymer nanowires, graphene and carbon nanotubes on PDMS for non-invasive neuroimaging. The fabricated materials will be characterized using microscopy (SEM, TEM, optical) and electrochemical methods. An ultra-dense electrode array will be fabricated from these hybrid conductive materials and will be tested as an EMG device for muscle contractions and as an EEG device for non-invasive neuroimaging.

Prof. Green’s research has been focused on developing bioactive conducting polymers for application to medical electronics. Prof. Green has developed hybrids of conducting polymers and hydrogels to reduce strain mismatch with neural tissue and improve long-term cell interactions at the neural interface.

Dr. Tahirbegi’s research has been focused on the novel electrode materials and new fabrication approaches to enable the fabrication of the super high-density electrode arrays for Electromyography (EMG), electroencephalography (EEG) and micro-electrocorticography (μECoG) to create a disruptive technology to non-invasively detect the activity of large populations of single neurons in the brain and the spinal cord.
Materials, Nano/microfabrication, electronic circuit design, EEG and EMG recordings on brains and muscles
Shlomi Haar (Brain Sciences) Movement and neural variability in real-world motor tasks Lab based Neural activity and movement kinematics are remarkably variable. While repeating the same movement, for example reaching to the coffee cup for taking a sip, each repetition would be slightly different. In a simple reaching task, individual subjects exhibit different magnitudes of kinematic variability, which are consistent (within individual) across movements (Haar et al. 2017 JNeurosci). The same subjects also exhibited different magnitudes of neural variability which partially explained their movement variability. Hence, neural and kinematic variability are reliable and interrelated individual characteristics that may predispose individual subjects to exhibit distinct motor capabilities (Haar et al. 2017 JNeurosci). In this project, we will try to generalise those funding to the real world and with patient groups. The project will run in the Living Lab of the UK DRI Care Research and Technology Centre. This is a domestic studio flat with passive movement sensors, providing a bridge to research ‘in the wild’. We will record body movement and EEG brain activity of healthy subjects and neurological patients as they perform repeated trials of daily tasks (e.g. making tea) and analyse the neural activity and movement kinematics variability patterns and their relation to neurological conditions. Technical background in programming and data science, and interest in neuroscience.
Shlomi Haar (Brain Sciences), Aldo Faisal Real-World Motor Learning in Embodied Virtual Reality Lab based A key challenge in neuroscience, neurology and neurorehabilitation is to measure and train motor control and learning in free behaving real-life tasks. We recently demonstrated the feasibility of studying real-world neuroscience using wearable technologies and data-driven approaches to uncover neural mechanisms of learning. We also developed an embodied virtual-reality (EVR) setup, which allows us to study motor control and learning in a controlled-real-world learning environment. In this project, you will use our EVR setup to induce perturbations aimed to manipulate motor learning mechanisms. You will record subjects’ movement (with body sensor networks) and brain activity (with mobile EEG) while performing a motor learning task with visual perturbations in the VR. This will force subjects to use different learning mechanisms and in your analysis, you will work to map the behavioural changes induced by the perturbations and changes in the brain activity. Strong technical background in programming and data science, and interest in neuroscience
Shlomi Haar (Brain Sciences), Yen Tai (Brain Sciences) Neurobehavioural biomarkers for Deep Brain Stimulation (DBS) Lab based Deep brain stimulation (DBS) is a routine treatment for patients with Parkinson's disease (PD) and Essential Tremor (ET) which improves their motor symptoms and as a result their function and quality of life. While DBS is an effective therapy, it is still not clear how and why it works and therefore there are many open questions as to how it can work better. This research project is part of a program that aims for a better understanding of the effects of DBS on the system level (in addition to improving symptoms) and a search for biomarkers to improve DBS delivery. In this research project, you will help to collect behavioural (movement sensor) and neural (EEG) recordings from PD patients with implanted DBS electrodes. You will analyze the data to study the effects of the DBS parameters on patients' body movement and brain activity, in an attempt to develop digital biomarkers to improve DBS delivery. Those can be used initially for improving DBS programming in the clinic, and later for closed-loop adaptive DBS, where the parameters and continually adapted by an AI, based on biomarkers. Strong technical background in programming and data science, and interest in neuroscience
Johanna Jackson (Brain Sciences) Samuel Barnes (Brain Sciences) Utilisation of SV2a as a biomarker in Alzhimer's Disease Lab based Synapse loss and dysfunction are key features of AD and, to enable the progression of drug discovery in this area, a reliable biomarker is required to track cognitive impairment to determine drug efficacy.  SV2a, a protein present at synaptic vesicles, is currently under investigation as the target of a PET tracer to track synaptopathy in AD.  Here we will characterise the SV2a protein both preclinically and in human tissue by mapping its cellular and synaptic distribution and its relationship to AD pathology.  To do this, we will test the vailidity of expansion microscopy to determine whether the expansion of tissue enhances the visualisation of synaptic puncta by advanced imaging techniques (i.e. imaging mass cytometry, two photon imaging and/or super resolution microscopy).   We will then use expansion mcroscopy and advanced imaging techniques to map the cell-type and synapse-type distribution in mouse and human tissue of the SV2a protein.  Although not essential, it would be beneficial to have some basic image analysis experience.  
Adrien Rapeaux (Electrical & Electronic Engineering) TBC Lab based Please contact to discuss potential projects: https://www.imperial.ac.uk/people/adrien.rapeaux13
https://www.imperial.ac.uk/bio-inspired-technology/people/
 
Christopher Rowlands A New Head Mounted Display Concept: Virtual Reality in a Pair of Sunglasses Lab based In order to experience immersive virtual reality, a display must have a large field of view and a high resolution, otherwise the user will feel like they are 'looking at the world through a toilet roll'. Commercially available head-mounted displays like the Occulus Rift, HTC Vive and Playstation VR solve this problem by placing the screen in front of the eyes, but this is clearly an inelegant solution as it involves basically strapping a brick to your face. More recent designs such as the Microsoft Hololens and Magic Leap One use holographic gratings to project light into the eye, but these have a smaller field of view, leading to the 'toilet roll' problem described above.The Rowlands lab is currently developing a new type of holographic display, which can achieve a large field of view along with high resolution, by making the hologram itself active, rather than passive. Instead of projecting the whole image at once, the display scans a beam across the eye at high speeds, producing the illusion of high resolution but without the compromises needed for the Hololens or Magic Leap One.The student on this project will conduct theoretical and experimental studies into the feasibility of this design. They will be using finite-difference time-domain modelling and fabricating electro-optically active waveguides in an attempt to demonstrate a proof of principle, with the goal of producing a device that can project simple patterns into a stationary eye. The ideal student will have a good background in computer modelling, an interest in microfabrication and photolithography, and possibly some electrical engineering expertise. Any necessary skills can be taught however.  
Christopher Rowlands Analyzing hyperspectral oncological images using cutting-edge data processing Lab based Deep learning techniques have found considerable use in pattern recognition for image analysis, but in medical imaging there are often additional data dimensions which can be exploited for improved diagnosis. This project will involve one such dataset - hyperspectral Raman images taken from tumour resection margins. In this case, the goal is to identify whether any tumor remains in the image, and if so, where it is located. Neural networks and other deep learning techniques will be used to perform this analysis, incorporating spatial and spectral information to make an accurate diagnosis.  
Christopher Rowlands Building a next-generation scanning microscope Lab based Scanning optical microscopy is a workhorse tool for modern biology - it can see things deeper into tissue, with 3D resolution, and observe fast dynamic events. Recently, Drs Rowlands and Pantazis have been interested in developing a technology called Primed Conversion (https://www.nature.com/articles/nmeth.3405) in order to make it easier to use for researchers around the world. Primed conversion involves optically tagging cells as they develop, allowing us to trace the development of an organism from a single cell all the way up to a complete animal and seeing which cells are destined to form which parts.The missing piece for the widespread use of Primed Conversion is the integration of the system into microscope systems. The student on this project will build an add-on to a microscope which can perform Primed Conversion, aligning two lasers and scanning them in parallel through the sample. The skills required involve programming, electronic engineering, some mechanical design and some optical engineering, but any skills that the student doesn't possess can be taught. The most important thing is an aptitude for learning quickly and hard work.  
Christopher Rowlands Developing algorithms to sculpt light in 3D Lab based Photolithography (literally 'light stone-writing') is widely used in the semiconductor industry for patterning microchips, but using light to trigger a chemical or physical change has many uses in biology as well. 3D bioprinting, photodynamic therapy, image recording, and optogenetic control of neurons all employ light to induce a change in a biological system. One important limitation of conventional projection-based optics is that the change is induced by a single photon. This has a subtle problem in 3D applications, because if one wishes to confine the photo-response to a particular plane, the regions above and below the plane are also illuminated.This is a particular problem in optogenetics, a cutting-edge technique in which light is used to excite neurons in the brain. Here, it is desirable to excite one neuron but not the ones above or below, yet this is impossible with conventional optical projection. Fortunately, there is a potential solution - one can use high-speed projectors to make holograms that change very rapidly. None of the projected images are intense enough to trigger a neuron on their own, but the sum of many of them is. One can therefore find a series of patterns that trigger the desired cell, but cause the light above and below the desired cell to 'miss' the important regions, thus having no effect.The student for this project will work on developing an algorithm that can, for a given distribution of neurons, find a sequence of holograms that trigger a single cell but don't affect the surrounding cells. They will develop software to control a projector in order to make these patterns, and if everything goes according to plan, it may be possible to test the algorithms in a laboratory setting.The student for this project should have a moderate to strong mathematical background, and some experience in Matlab or another similar programming environment. If necessary they should have, or be able to develop the lab skills necessary to test their software in real life.  
Christopher Rowlands World's Fastest Video Camera Lab based High-speed imaging requires specialized cameras to capture fleeting events like explosions, hypersonic flow, or even the passage of light. In this project, we are interested in the oscillations of an ultrasound bubble, which occurs at frequencies of a few megahertz. As such, we will need to build a camera that can image at around one hundred million frames per second, for a duration of around one second. These requirements are far beyond even the fastest cameras available today, necessitating a new development program.The student on this project will be building part of the camera, specifically a small piece of the sensor. Using newly-available silicon photomultiplier arrays, we will be constructing a small-scale prototype with the sensitivity and speed necessary to capture data at these incredible speeds. The ideal candidate will have a good background in electrical engineering, and will be designing and testing readout circuitry for the camera. Once this is complete, they will begin testing a small-scale prototype by building the large-scale optical system required to magnify the bubbles enough to be seen by the sensor. This project will also involve a certain amount of programming, in order to reconstruct the data after the experiment is complete.  
Barry Seemungal (Brain Sciences), Simon Schultz Assessing the effect of Dopamine on mutual information of Perceptuo-Motor Coupling in humans via transcranial magnetic stimulation Lab based The human brain is an information processing machine. Our brain can accurately decode external and internal events, like when our finger moves, this could be from our voluntary command or passively from an external force. Transcranial magnetic stimulation (TMS) to the human motor cortex causes an involuntary hand muscle contraction which uncouples muscular contraction from volitional control. If we apply low intensity TMS then we can objectively measure muscular contraction (via Myogenic Evoked Potentials) sometimes without conscious awareness of the contraction. This setup allows us to assess the efficiency of sensory processing by measuring the mutual information between MEP responses and perceived contraction (contraction versus no contraction). Mutual information is the difference between two entropies, a “noise + signal” and a “noise” entropy, and enables an optimal measure of changes in cortical sensory processing. We will assess how a Dopamine agonist modulates sensory processing measured by changes in mutual information. Dopamine is an important neurotransmitter and its loss mediates several features of Parkinson’s Disease (PD). We hypothesise that dopaminergic activation enhances the mutual information of perceiving a TMS-evoked MEPs in healthy subjects. Future studies will involve PD patients. See here for output from a similar Masters project: https://www.jneurosci.org/content/36/36/9303 Programming and data analytical skills. Students will receive training specific to the laboratory setup.
Barry Seemungal (Brain Sciences), Tim Constandinou (Electrical & Electronic Engineering) Use of electrophysiological and structural markers of inter-hemispheric connectivity to model the beneficial effect of noisy galvanic vestibular stimulation upon postural control Lab based Introduction:
Electrical stimulation of the vestibular system – obtained by applying a current to the mastoid processes, and is called Galvanic vestibular stimulation (GVS) - enhances postural control, and vestibular perception, in healthy subjects and patients with neuro-degeneration. Our recent data in traumatic brain injury show impaired postural control with disrupted white matter tracts linking the frontal cortices. We hypothesise that the GVS effect upon postural control is mediated by enhanced bi-frontal connectivity.

Objectives: 1) Use noisy GVS to modulate vestibular-mediated postural control. 2) Assess changes in bi-hemispheric connectivity with GVS using: (a) neuro-navigated motor cortex TMS (transcranial magnetic stimulation); and (b) interhemispheric coherence changes in EEG. 3) Model the link between intervention (GVS) and function (postural control) using neurophysiological (EEG, TMS) and structural parameters (diffusion tensor imaging) of inter-hemispheric connectivity.

This project requires a minimum of 2 students working together (but can accommodate a 3rd student). This project is health themed focusing on brain circuits in health and disease and how this relates to brain disease and diagnosis. You would be working with engineers, scientists and clinicians and both healthy and patient cohorts developing the stimulation devices and analysing the data through appropriate tools. "
 
Molly Stevens Developing innovative biomaterials for regenerative medicine, advanced therapeutics and biosensing Lab based We have a range of projects available focused on the design of novel material-based strategies applied to disease diagnostics, regenerative medicine, advanced therapeutics and drug delivery. Our portfolio includes fascinating research from understanding the fundamental mechanisms of the cell-material interactions to developing highly translatable technologies such as ultrasensitive point-of-care diagnostics, bioinspired tissue engineering scaffolds and  pioneering bionanomaterial characterisation equipment. Our research group provides a welcoming, inclusive and stimulating environment to develop creative and collaborative early career researchers. We are an award-winning, highly multidisciplinary team looking for the very best students in chemistry, engineering, cell biology, physics, materials science and medicine. Diversity in all its forms is important to us, and we welcome students from all over the world. Please contact Akemi Nogiwa (a.nogiwa-valdez@imperial.ac.uk) for more information. www.stevensgroup.org.  
Reiko Tanaka Automatic quantification of fungal burdens in histology images using deep neural networks Desk based Invasive aspergillosis (IA) is a critical lung disease that is characterised by uncontrolled fungal growth of Aspergillus fumigatus in the lungs. IA occurs in immunocompromised patients, such as patients undergoing chemotherapy, and is treated with antifungal drugs. Due to the increase of antifungal resistance, there is a real need to locate novel treatments. Experiments investigating new IA treatments usually evaluate treatment responses by reported fungal burden. However, the fungal enumeration methods in the community are not standardised, resulting in different quantifications of pulmonary fungal burden, making quantitative evaluation of disease progression often challenging. Quantifying fungal burden in murine models of infection remains a difficult task. The most accurate and sensitive qPCR-based methods cannot capture fungal viability. As a result, the community relies on less sensitive metrics such as conidial forming units (CFUs), which require comparison with histology images. Histological images of murine lung biopsies can give us an idea of disease progression caused by viable fungal burden. However, enumeration of fungal burden, extent of tissue invasion and occlusion of the airspaces is still done manually. Recently, we proposed a fully automated image analysis pipeline to identify the fungal regions on histological images. The histology images are first transformed into different colour space channels that preserve fungi-related colour information. After applying image enhancement methods to accentuate the fungal regions’ pixel intensity, the pixels are clustered by their intensity values to identify the fungal region in the image. The results broadly show good agreement with the reference annotations, achieving an average Dice score of 65% across 5-fold cross-validation with an 80/20 train/test split of a dataset of 33 images.This project aims to further improve this pipeline by utilising deep neural networks to automatically detect and classify fungal lesions into “spores” and “hyphaes” and subsequently enumerate them (number of spores and hyphael length). The student is expected to review, implement, and validate the different stages of the image analysis pipeline using off-the-shelf deep-learning and image analysis software packages for segmentation, classification and quantification of “spores” and “hyphae” regions.  
Reiko Tanaka Development of computational tools to predict the occurrence of eczema using machine learning methods Desk based One of the main difficulties experienced by people with atopic dermatitis (AD, or eczema) is the day to day unpredictability of AD symptoms. Patients and their families are often perplexed by flares appearing out of the blue for no obvious reason. It is difficult to predict when AD flare-ups occur, whether the flare-ups persist or whether they are going to be mild and transient and thus do not require step-up treatments. The aim of this project is to develop computational models that can predict patient-specific dynamics of AD severity, like weather forecasting, in order to develop interventions that may offer AD patients novel ways of better disease control by a personalised medicine approach. Using the existing data from two large published clinical trials in which participants recorded AD severity scores daily or weekly, we will extract patient-specific intrinsic dynamic patterns in fluctuations of the AD severity, by applying model-based machine learning.   
Samuel Barnes (Brain Sciences) Investigating the Effect of Temporal Interference Stimulation to induce brain wide plasticity Lab based
Overview: The brain processes the dynamic sensory environment in the face of unpredictable ‘noisy’ neural activity1,2. To meet this challenge, homeostatic plasticity mechanisms have evolved to regulate fluctuating neuronal activity and prevent extreme levels1,3,4. One feature of Alzheimer’s Disease (AD) is the destabilisation of spontaneous neural activity levels5, suggesting that homeostatic plasticity may be failing in early AD6,7. Destabilised activity is predicted to increase neural noise5,8 and trigger aberrant molecular cascades6,7 which can ultimately lead to synapse loss6,7. Recent research suggests that the emergence of destabilized spontaneous activity in mouse models of early AD is concomitant with a decline in the power of brain-wide neuronal network oscillations important for organizing neuronal activity across multiple spatial scales9,10,11,12. Interestingly, reinstating these brain rhythms restores spontaneous activity to physiological levels10,12 and alleviates amyloid load10,13, a central player in AD, thus highlighting the therapeutic potential of neuromodulation for treating AD. This project will characterize changes in neuronal activity following the application of a novel deep brain stimulation (DBS) technique, Temporal Interference14 (TI), in mouse models capturing key features of AD, including amyloidosis. Understanding the mechanisms through which TI can modify neuronal circuitry offers the opportunity to both tune TI stimulation parameters to target specific cell types affected in AD and reveal intrinsic deficits in homeostatic plasticity processes in the amyloidogenic brain.  To do this the Barnes lab uses a combination of rodent models, in vivo widefield calcium imaging, immunofluorescence, TI stimulation and behavioural assessment of cognition. The successful student will employ these techniques and analysis in MatLab to test the hypothesis that patterned TI stimulation modulates neuronal activity through the induction of Hebbian or homeostatic modifications to synaptic connectivity.
Matlab skills are preferred but not essential 
Simon Schultz Mapping amyloid plaques in whole brains using serial section two photon tomography Lab based Alzheimer’s Disease (AD) is the most common type of dementia – accounting for about 70% of the nearly 50 million dementia cases in the world. It is characterised by neuronal degeneration caused by the presence of extracellular amyloid plaques and neurofibrillary tangles in the brain. Genetically modified rodent models have helped advance our understanding of the underlying mechanisms of this disease. One of these models, called 5xFAD, recapitulates many AD-related phenotypes and has a relatively early and aggressive presentation. Amyloid plaques are seen in mice as young as two months of age. However, the degree to which the amyloid plaques affect behavioural performance in these models is still not well known. In this study, high throughput serial two-photon whole brain imaging will be performed in order to map the spatial distribution of amyloid plaques across age in 5xFAD mice, labelled with Methoxy-X04, using the TissueCyte imaging platform. Together with the region-specific progression of plaque densities in critically affected brain structures, these models present an invaluable tool for early intervention and improved pre-clinical assessment of potential therapeutic approaches for AD. This project will involve wet lab work as well as development of python or MATLAB based image analysis code.  
Simon Schultz, Ann Go Multiphoton imaging of the hippocampus in mouse models of neurodegenerative disease Lab based The project will involve two photon imaging of calcium signals in populations of neurons in hippocampal subfield CA1, as mice perform a basic spatial memory exploration task. A specific question examined will  be the extent of aberrant excitability, as well as hyper-synchrony, in treated and untreated 5xFAD mice – something that has previously been examined in cortex but not in the hippocampus during behaviour. Training
 will be provided by the Schultz laboratory in stereotactic viral injection, recovery surgery for window implantation, behavioural training and multiphoton calcium imaging, and data analysis for multiphoton imaging. The Foust laboratory will provide advice and training on optical microscopy and biophotonics."
 

Projects available for 2022-23

Examples of past projects FOR INFORMATION ONLY

Some of the MRes projects from previous years are shown here to give an idea of the topics covered by our students.  These projects are not available for this year. However, if you are interested in working on a similar project, please contact the relevant supervisor to discuss similar opportunities.

Previous projects

A robotic platform to study the neural control of high-dimensional behaviour in mice

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

Auditory blast biomarkers

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.

Cerebral organoid models for optical investigation of neural circuit dynamics in neurodegenerative diseases

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.

Classification of EEG responses to multi-dimensional transcranial electrical stimulation

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)?

scott project diagram

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

Computational modelling of neurovascular injury

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.

Continuous Brain Estrogen Sensors based on DNA Binding Receptors

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.

Deep brain stimulation using bioharmonophores

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.

Drug delivery across the blood-brain barrier using therapeutic acoustic wavelets

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.

Dynamic Modulation of Circuits by Serotonin

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.

Effect of cancer immunotherapy on mental health

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.

Engineering a new miniaturised platform for patch-clamp electrophysiology

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.

Information theoretic data mining of large-scale neuropixels electrophysiological recordings from the mouse visual system

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 one11(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 one11(1).

Mapping the geometry of the “neural manifold” across the primate sensorimotor system

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.

Microfabricated multimodality probes for minimally invasive monitoring the brain

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.

Model-based estimation of upper limb joint kinematics and kinetics using surface electromyography

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.

Neural manifold analysis of social cognition in rats

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.

Tracking neuronal activity in the human brain

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.

Using Cutting-Edge Microscopy Techniques to Advance Neurobiology

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.

Wearable sensors for detection of ALS

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.

What wavelengths can mice see?

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.