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.
You must name at least one potential supervisor in your personal statement when you apply.
Applications will be considered in three rounds. We encourage you to apply in Round 1 or 2. If you are applying in round 3, some projects may have already been allocated so please consider including a second or third choice project in your application.
Visit our How do I apply? page for full details of the application process including deadlines.
Projects available for for 2025-26 entry
- Dr Amy Howard
- Dr Andriy Kozlov
- Professor Anil Bharath
- Professor Claudia Clopath
- Dr Dandan Zhang
- Professor Dario Farina
- Dr David Labonte
- Dr Gregory Scott
- Dr Hayriye Cagnan
- Dr Huai-Ti Lin
- Dr James Choi
- Dr Johanna Jackson
- Dr Juan Gallego
- Dr Marta Varela Anjari
- Professor Mauricio Barahona
- Professor Mengxing Tang
- Dr Nir Grossman
- Professor Rylie Green
- Professor Simon Schultz
- Dr Sophie Morse
- Professor Tim Constandinou
Dr Amy Howard
Contact details: a.howard@imperial.ac.uk
| Project title | Description |
| Mapping brain connectivity via low-cost 3D polarised light imaging | Polarised light imaging (PLI) is a powerful microscopy method for ex vivo investigations of brain connectivity ("the structural connectome"). PLI utilises the optical property of birefringence within brain tissue to estimate axonal orientations within brain tissue with micron-scale resolutions. However, most PLI systems can only reliably inform on axon orientations within the 2D microscopy plane. This is problematic as it limits the utility of 2D PLI for imaging the 3D trajectories of axons linking different brain regions. Extracting 3D orientations typically requires bespoke set-ups which are expensive, limiting wide-spread access. This project aims develop a low-cost 3D polarised light imaging system for accessible, high-resolution connectomics. This will include microscope development, data acqusition using postmortem brain samples, image analysis and connectivity mapping. Investigations could consider whole-brain connectomics, or focus on specific structures such as the hippocampus which has key functions in memory and learning, and is implicated in conditions such as Alzheimer's disease. Comparisons with diffusion MRI acquired in the same tissue can be used to validate a drive methods for estimating brain connectivity in vivo. |
| Can combined MRI-PLI analysis provide reliable myelin estimates? | Myelin, the insulating sheath surrounding axons in the brain, is crucial for the efficient transmission of electrical signals in the nervous system, enabling faster communication between neurons and supporting overall brain and spinal cord function. Reliable myelin imaging is therefore essential to the diagnosis of different demyelinating pathologies such as multiple sclerosis. Polarised light imaging (PLI) - a microscopy method sensitive to myelinated axons in the brain - uses the optical property of tissue birefringence to inform on axonal orientations in ex vivo brain tissue samples. One of the signals from PLI, the tissue retardance, is dependent simultaneously on both the 3D orientation of axons and the amount of myelin in the tissue. Without the use of bespoke set-ups, these two signals are difficult to disentangle, making robust analysis of either property (the 3D orientation or amount of myelin) ill-posed. This project will aim to build on the combined analysis of MRI and PLI data to develop a robust method to simultanously estimate both the axons orientation and its degree of myelination. These data can provide insight on the fundamental understanding of how myelin varies across different pathways in the brain, and the preferential dyemyelination of specific white matter bundles in pathological conditions. Comparisons with myelin-sensitive MRI in the same samples will ellicuidate the extent to which demyelinating pathologies can be detected in vivo. |
Dr Andriy Kozlov
Profile: https://profiles.imperial.ac.uk/a.kozlov
Contact details: a.kozlov@imperial.ac.uk
| Project title | Description |
| Biomimetic neural networks | This is a machine learning project that is a continuation of our published work: https://www.biorxiv.org/content/10.1101/2023.10.26.564127v1 Proficiency with pytorch is required. It is appropriate for students with a computational background experienced with training ANNs and interested in fundamental questions about natural and artificial neural networks. For more information candidates are encouraged to read the above paper and contact the supervisor. |
Professor Anil Bharath
Profile:
Contact details:
| Project title | Description |
| Forget about it: Machine Unlearning of Individuals Data | Motivation: Large Language Models (LLMs) are quickly becoming ubiquitous tools in many domains improving the efficiency of workers performing repetitive text-based tasks, such as writing and summarizing documents, drafting reports, or editing text. This comes at a hidden cost, however, as the LLMs are trained on vast amounts of data, which was contributed by humans, potentially without their consent. Once used for training, the example contributes to the LLMs outputs for the entire lifespan of the LLM, potentially indefinitely. This is at odds with the 'right of data erasure' [1] which gives individuals the right to request the deletion of their data from a collection, such as a dataset or a machine learning model. Even organizations in countries that do not formally enforce such laws might be bound by them when processing data from citizens of other nations (e.g., EU), an increasingly likely scenario in a globalised world. This inhibits the use of state-of-the-art LLMs in domains where compliance with rights pertaining to individuals’ data is paramount, such as finance, law and healthcare. This gives rise to the research area of "Machine Unlearning" [2], a collection of methods to remove the impact of selected training data on trained machine learning models. However, it is infeasible to apply these methods to conventional deep neural network based LLMs, because of their size and complexity and because after training, the effect of a single training sample on LLM outputs is intractable. An obvious choice for an architecture that can be used to enforce data erasure is the K-Nearest-Neighbors (K-NN) LLM, where the contribution of each example to each output is exactly quantifiable, and thus training examples can easily be removed from future computations if necessary. Due to this traceability, however, K-NN LLMs have been shown to suffer from a higher risk of disclosing potentially private training data compared to conventional LLMs [3]. This gives rise to the exciting opportunity to propose a methodology that enforces data erasure by relying on K-NN LLMs, and to quantify and mitigate the associated privacy issues [4]. Objectives & Deliverables: In this project, the student will have the opportunity to gain exposure to state-of-the-art research at the intersection of different areas, including Machine Learning, Privacy and Security and Large Language Models. Specifically, the student is expected to (a) implement a mechanism that allows data providers (e.g., patients) to revoke the use of their data for an existing LLM of a specific architecture (e.g. K-NN LM); (b) systematically evaluate the impact of revocation on a selection of down-stream tasks relevant in healthcare settings, such as classification or summarization; (c) evaluate the potential of the modified architecture to disclose sensitive information and (d) investigate approaches to mitigate such disclosure. Co-supervisors: Dr Viktor Schlegel and/or Dr Zhengzi Xu References: 1: e.g., Right to data Erasure in EU’s General Data Protection Regulation 2: https://arxiv.org/pdf/2306.03558 3: https://iclr.cc/virtual_2020/poster_HklBjCEKvH.html 4: https://aclanthology.org/2023.emnlp-main.921.pdf |
| So Different Yet So Alike: Generating Synthetic Examples to Label Sensitive Data without Violating Privacy Laws | Motivation: Large Language Models (LLMs) are quickly becoming ubiquitous tools in many domains improving the efficiency of workers performing repetitive text-based tasks, such as writing and summarizing documents, drafting reports, or editing text. However, due to their size, it becomes increasingly infeasible for small and/or non-technical organizations to deploy their own solutions, forcing them to rely on "big tech" providers like Microsoft or Google who have the resources to host resource-intensive LLMs. These providers might be located in other countries, which violates the requirement of local (national level) data storage and processing enforced by many governments [1]. To address this issue, one idea under exploration is to generate data that is at the same time both different from each individual example but also representative of a collection of examples [2]. This data can be freely shared and labelled (e.g., whether an example describes a smoking or non-smoking patient), and labels obtained on this generated data can then be mapped back to the collection of private records. This approach allows organizations to leverage the advanced capabilities of LLMs without exposing their confidential data to external providers, thus maintaining compliance with local data processing regulations. However, the examples must be generated by considering the trade-off of being similar enough to warrant the same labels but at the same time not representative of any single patient record. Failing to achieve this would constitute a privacy breach [3,4]. Objectives & Deliverables: In this project, the student will have the opportunity to gain exposure to state-of-the-art research at the intersection of different areas, including Machine Learning, Differential Privacy and Large Language Models. Specifically, the student is expected to (a) adapt a mechanism to generate and label synthetic data representative of a private collection of texts; (b) design and implement an algorithm to transfer labels from public to private data; (c) evaluate the overall performance of the method and the potential of disclosing sensitive information by means of existing evaluation protocols. References: 1: SG Personal Data Protection Act 26(1): https://sso.agc.gov.sg/Act/PDPA2012?ProvIds=pr26- 2: https://arxiv.org/pdf/2210.13918 3: https://www.ieee-security.org/TC/SP2017/papers/313.pdf 4: https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf |
| Uncovering the neural code of DRL agents | Deep neural networks can contain hundreds of thousands to millions of parameters collected in a layered organisation. The sheer number of parameters assigned to a single neuron makes the interpretation of how the network achieves a desired function (such as solving a control task, or a visual task) difficult to understand. Interestingly, experimental neuroscience has developed a series of tools that can be applied to problems of this complexity; even though complete "explainability" of how a neural network works is very difficult, there are nice principles that can be investigated. One of these principles is population codes, by which we mean the manner in which a group of artificial neurons jointly encode a stimulus or state of the world. The same principle can be applied to understanding how an RL agent controls (though a policy) a robot arm, or performs a tracking and detection task. Our aim in this progress is to use response weighted noise averaging, derived from neuroscience, but applicable to explaining some aspects of neural coding. The project builds on strong work from an undergraduate project, and several PhD projects, in order to take a step forward in explanability of neural networks through concepts borrowed from neuroscience. |
Professor Claudia Clopath
Profile: https://profiles.imperial.ac.uk/c.clopath
Contact details: c.clopath@imperial.ac.uk
| Project title | Description |
| Computational modelling of memory transfer | Gaining a better understanding of the brain is an urgent challenge in our society, due to an aging population, which has led to a higher incidence of neurological diseases, such as Alzheimer's and Parkinson's disease. Neuroscience can be studied under different angles, either experimentally, by measuring different aspects of the brain, or theoretically, by constructing models that mimic the brain. Theses two approaches can work hand-in-hand, where experimental findings influence theoretical models, models allow a broader and more concise understanding, predicting new phenomena, in-turn influencing new experiments. Our lab is on the modeling side, working in tight collaboration with experimental labs. We are especially interested in the field of learning and memory, which is thought to happen when connections between neurons change, a process called synaptic plasticity. This research has two main types of applications: medical applications leading to translational research and engineering applications helping for example to design machines that approach human-like learning capabilities. In this projct we are going to model how memories are transfered from your short term memory centre to your long-term memory centre. |
Dr Dandan Zhang
Profile: https://profiles.imperial.ac.uk/d.zhang17
Contact details: d.zhang17@imperial.ac.uk
| Project title | Description |
| Semi-Autonomous Control for Augmented Robotic Hand Using Tactile Feedback | Introduction and Background: Neurotechnology has advanced rapidly, particularly in the development of neuroprosthetics that aim to restore motor and sensory functions for individuals with limb loss or neuromuscular impairments. However, current robotic prosthetics often lack the ability to provide tactile feedback and semi-autonomous control, which are essential for delicate tasks requiring precision and safe interaction with objects. There is a need for prosthetic systems that can autonomously adjust their grip based on sensory input, enhancing both control and user experience. This MSc project will focus on developing semi-autonomous control algorithms for an augmented robotic hand using tactile feedback. The goal is to create a system that not only allows the user to perform high-level tasks such as gripping and releasing objects but also autonomously adjusts the grip force based on real-time sensor input. This would simulate natural reflexes, such as detecting when an object is slipping or being over-gripped. Objectives: 1. Tactile Sensor Integration: Design and integrate tactile sensors into a augmented robotic hand or prosthetic hand to capture sensory data such as pressure, texture, and contact forces. These sensors will provide critical input for detecting grip states like object slipping or over-gripping. 2. Algorithm Development: Develop algorithms to classify sensor data and detect grip states (e.g., slipping, over-gripping) in real time; Implement control logic that adjusts the grip force autonomously based on sensor input, mimicking natural reflexes. This will enable the robotic hand to react to changes in object handling conditions without user intervention. 3. Simulation and Testing: Simulate the system's performance using a robotic hand model and test its functionality in a series of object manipulation tasks, such as gripping objects of different sizes, shapes, and textures. 4. Performance Evaluation: Evaluate the system‚ efficiency in improving grip stability, object handling, and user experience. The focus will be on ensuring that the semi-autonomous control algorithms enhance the user‚ ability to handle objects with precision and safety. This project lies at the intersection of neurotechnology, robotics, and machine learning. The development of semi-autonomous control systems for robotic prosthetics or augmented robotic hand can significantly enhance user experience by improving grip stability and reducing cognitive load. With a focus on tactile feedback, this project will contribute to the creation of more natural, reflexive prosthetic hands or augmented hand that adapt to real-world conditions, helping to restore functionality for individuals with disabilities. |
Professor Dario Farina
Profile: https://profiles.imperial.ac.uk/d.farina
Contact details: d.farina@imperial.ac.uk
| Project title | Description |
| Development of an optimal experimental protocol to characterise the behaviour of motor units innervating the forearm muscles in relation to adaptation of wrist stiffness |
Humans use coactivation of agonist-antagonist muscles to modulate dynamic (e.g., stiffness) properties of the limb in a time- and task-dependent manner, independently from the limb kinematics. In this project, we aim to develop the experimental set-up and protocol needed to investigate the properties of motor units innervating agonist-antagonist muscles of the forearm during tasks involving adaptation of wrist stiffness. - Decomposition and manual editing of motor units from HD-sEMG signals |
| Ultrasound-based SoftHand Control | The goal of this project is to implement ultrasound-based soft hand control, aiming to achieve flexible and reliable grasping using the perceptual ability of ultrasound sensing and the adaptive ability of soft hands. We will build an experimental platform to connect muscle ultrasound signals with hand movements, design advanced machine learning algorithms to infer hand movement intentions from muscle ultrasound signals, and complete pilot experiments to enable flexible grasping of versatile objects and even hand manipulation. |
| Development of a Hill Muscle model-inspired Force Sensor for Rehabilitative and Assistive Applications | We have developed a novel bio-inspired pneumatic device that provides passive assistance or resistance for exoskeletons. Furthermore, with adequate sensorisation, this device could also act as a reliable force sensor. This project aims to enhance the actuator's technology readiness level and redesign it to function as a low-cost alternative to traditional force sensors in exoskeletons. The project objectives include: (1) Improving/Adapting the existing device to realise it for proposed application, (2) Integrating a sensor to measure actuator performance and simultaneously provide reliable force values, (3) Improving on an existing testing rig to characterise the sensorised device and to validate long-term performance, (4) Validating the actuator against existing force sensors using Motion Capture and force sensors, (5) Incorporating the actuators into exoskeleton systems developed in the lab. Students interested in mechanical design, mathematical modelling, circuit design and device validation would be interested in this project. This project requires skills in CAD design (Fusion360/SolidWorks), 3D printing, machining, electronics/circuit design, Arduino, MATLAB, and Python. All skills are not expected and the project can be tuned to the student's expertise and interests in expanding skills. |
| Musculoskeletal modelling-based Controller Design for Soft Exoskeletons | Soft exoskeletons have the potential to become ubiquitous as devices worn as regular clothing for rehabilitation and assistance. However, as these technologies work in parallel with the human body, mass adoption will only be realised when intuitive and natural control of these devices is achieved. As these devices work in parallel with the human musculoskeletal system, it is crucial for a deep understanding of the human-exo system as a combined system. High Density EMG along with other sensing modalities such as kinematic and force sensing would provide us with an innate understanding of the mechanics of the human-exo system, allowing us to develop more robust controllers. The project would involve the student leveraging the groups expertise in MS modelling, HD-EMG signal processing and robotics to develop a modelling platform and then conduct experiments to refine the platform and develop optimised controllers. The project would involve MS modelling in OpenSim or equivalent software, controller development (traditional or RL-based) and a deployment of controller strategies on a soft exo platform. |
Dr David Labonte
Profile: https://profiles.imperial.ac.uk/d.labonte
Contact details: d.labonte@imperial.ac.uk
| Project title | Description |
| How far can you throw a ball (and thus how fast can animals run?)? | Understanding the physical limits to muscle performance is of obvious interest. A classic perspective tells us that each unit of muscle can do a fixed amount of work, and this fixed "work density" determines the speed of animals at different sizes. But perhaps there are other limits to performance that are often overlooked? One such limit stems from the maximum shortening speed of muscle. If this limit is reached before muscle has delivered its maximum work capacity, it is more relevant. What determines which limit is reached first? A simple toy experiment to explore this idea is to test how fast humans can throw objects of different mass: if work matters, the speed should vary in direct proportion to the mass of the object; if shortening speed matters, it should be independent of mass. Clearly, for a large enough mass range speed eventually decreases - but it also seems intuitive that throw a ping pong ball about as fast as a tennis ball. Clearly, a thorough experimental approach is needed and must be complemented by sound theoretical mechanical analysis - both is the task of this project. How far can humans throw balls of different mass? |
Dr Gregory Scott
Profile: https://profiles.imperial.ac.uk/gregory.scott99
Contact details: gregory.scott99@imperial.ac.uk
| Project title | Description |
| Exploring reservoir computing as a way to understand brain function and dysfunction | Reservoir computing (RC) is a computational approach that exploits the properties of complex systems, like artificial recurrent neural networks (RNNs), to perform computation in a way that differs from traditional ‚Turing-style‚ computation. In a typical in silico RC architecture, the ‚reservoir‚ is connected to an input layer and a readout module. In recent neuroscientific applications, because a range of network architectures and dynamics can be implemented in the reservoir, it becomes possible to impose biologically-plausible network architectures that relate to brain network organization. We can then train and test the reservoir on a wide range of simulated ‚ cognitive‚ tasks involving time-varying input data, e.g., working memory, motor learning, natural language processing, etc. Open toolboxes have recently become available to design and test RC architectures in this way. Studies have begun to probe the relationship between brain structure, e.g., as defined by white matter topological connectivity data, and brain function, i.e., the computational ‚capabilities‚ of the reservoir across a range of tasks. The hope is that, by reconceptualising brain function as computation, RC can be used to provide a more mechanistic understanding of structure-function relationships in the brain. In this project, we will use available empirical brain data (e.g., from the Human Connectome Project [HCP] and clinical datasets) and open RC toolboxes to explore the RC paradigm as a way to understand brain function and dysfunction. |
Dr Hayriye Cagnan
Profile: https://profiles.imperial.ac.uk/h.cagnan
Contact details: h.cagnan@imperial.ac.uk
| Project title | Description |
| Dual-site transcranial alternating current stimulation for tremor control | Involuntary shaking is a common symptom of Parkinson Disease and Essential Tremor, affecting around one million people in the UK. This project aims to leverage plasticity ‚brain‚ ability to adapt and change‚ for therapeutic purposes by delivering well-timed electrical inputs to key regions across the tremor network. Based in the Cagnan lab, the focus will be on piloting dual-site stimulation of the motor cortex and cerebellum to achieve longer-lasting therapeutic benefits for tremor patients. Your role will include (1) modelling the volume of tissue activated during dual-site stimulation, (2) developing and testing closed-loop control algorithms and (3) developing approaches for efficient optimisation of stimulation parameters. We are looking for a student with strong skills in engineering, instrumentation, and programming, along with a background in neuroscience. 1. Schwab BC, Knig P, Engel AK. Spike-timing-dependent plasticity can account for connectivity aftereffects of dual-site transcranial alternating current stimulation. NeuroImage. 2021;237:118179. doi:10.1016/j.neuroimage.2021.118179 2. Schwab BC, Misselhorn J, Engel AK. Modulation of large-scale cortical coupling by transcranial alternating current stimulation. Brain Stimulation. 2019;12(5):1187-1196. doi:10.1016/j.brs.2019.04.013 3. Saturnino GB, Madsen KH, Siebner HR, Thielscher A. How to target inter-regional phase synchronization with dual-site Transcranial Alternating Current Stimulation. NeuroImage. 2017;163:68-80. doi:10.1016/j.neuroimage.2017.09.024 4. Fleming JE, Sanchis IP, Lemmens O, et al. From dawn till dusk: Time-adaptive bayesian optimization for neurostimulation. PLOS Computational Biology. 2023;19(12):e1011674. doi:10.1371/journal.pcbi.1011674 5. Cagnan H, Pedrosa D, Little S, et al. Stimulating at the right time: phase-specific deep brain stimulation. Brain. 2017;140(1):132-145. doi:10.1093/brain/aww286 |
| Modulatory role of transcranial stimulation on cognitive control | Everyday decision-making depends on our ability to adapt and sometimes stop actions unexpectedly. This skill can range from something as simple as resisting a tempting slice of cake to something as critical as hitting the brakes in an emergency. Cognitive control can be compromised in a range of neuropsychiatric disorders and remains difficult to restore using invasive and non-invasive brain stimulation techniques. We previously targeted the medial prefrontal cortex, a key brain region involved in response inhibition, using transcranial electrical stimulation to modulate neural rhythms and associated behaviors. This project, based in the Cagnan lab, will focus on (1) data analysis of electrophysiological and behavioral responses, (2) stimulation artifact removal, and (3) modeling behavioral and electrophysiological data. We are looking for a student with strong signal processing skills and a background in neuroscience. Tuning the brakes ‚ Modulatory role of transcranial random noise stimulation on inhibition Mandali, Alekhya Torrecillos, Flavie ... Cagnan, Hayriye et al. Brain Stimulation: Basic, Translational, and Clinical Research in Neuromodulation, Volume 17, Issue 2, 392 - 394 |
| Phase Transitions in Circadian Tremor Patterns | Involuntary shaking is a common symptom of Parkinson Disease (PD), affecting approximately 150,000 people in the UK. Tremor in PD can be triggered or influenced by various factors throughout the day (e.g., stress or medication intake), making it crucial to identify trends that are key to effective clinical management. The project will take place in the Cagnan Lab and will involve analysing a unique dataset consisting of long-term (2 years) recordings of PD patients collected via wearable sensors in free-living conditions. Tremor events in this dataset have already been identified through machine learning algorithms. With this project, we will explore the circadian dynamics of tremor in PD over several days using recurrence quantification analysis (RQA), a nonlinear data-driven technique that provides objective markers for regularity, trends, and phase transitions in time series data. A particular focus will be on the impact of patients‚ medication schedule changes on these dynamics. We are seeking a motivated student with programming and signal processing skills who is eager to deepen their understanding of the circadian progression in neurodegenerative disorders. |
| Sleep Fragmentation in Parkinsons Disease and its impact on tremor | Sleep fragmentation, characterised by frequent awakenings or disruptions, has a significant impact on daytime functioning, leading to increased fatigue, reduced motor control, cognitive decline, and heightened stress and anxiety. In Parkinson disease (PD), sleep fragmentation can diminish a persons ability to manage and compensate for daily tremors, worsening their symptoms' severity and duration. While recent evidence supports this connection, a systematic and comprehensive study with a representative PD cohort and long-term follow-up is still lacking. This project aims to investigate sleep fragmentation from multiple angles and assess how it affects the severity and duration of daily tremors in PD patients. The research will be conducted in the Cagnan Lab, utilising a unique dataset containing two years of long-term recordings from PD patients in free-living conditions, with tremor events already identified by machine learning algorithms. We are looking for a motivated student with strong programming and signal processing skills who is eager to better understand the relationship between sleep fragmentation and Parkinson disease symptoms. |
Dr Huai-Ti Lin
Profile: https://profiles.imperial.ac.uk/h.lin
Contact details: h.lin@imperial.ac.uk
| Project title | Description |
| Automated individual identification and behavioural monitoring of insects | An automatic behaviour monitoring system for insects will be developed in collaboration with colleagues in Life Sciences. |
Dr James Choi
Profile: https://profiles.imperial.ac.uk/j.choi
Contact details: j.choi@imperial.ac.uk
| Project title | Description |
| Optical hand tracking using machine learning | Purpose. Implement optical hand tracking using machine learning, analyse speed and bottlenecks, and explore ways of improving existing methods. Motivation. Optical hand tracking is a method used in virtual reality, augmented reality, and human-machine interfacing as it allows the user to interact with virtual environments and communicate with robots and machines in a natural way. However, optical hand tracking has not been able to achieve widespread adoption due to limitations in speed and precision, and a constant breaking of the immersive experience. The purpose of this project is to analyse existing optical hand tracking methods and quantify their speed, precision, and failure rates; and explore ways of improving the optical hand tracking performance. Work. The student will setup their own optical hand tracking setup using a camera (e.g., a webcam) and write his/her own optical hand tracking method from scratch using python and PyTorch. The student will then improve the algorithm using the state-of-the-art published algorithms and quantify the speed, precision, and failure rates of all of these methods. The student will evaluate the bottlenecks in each of these categories. For example, what is the physical, hardware, or computational reasons for these limitations. Certainly the speed of light is fast and so is not constraining the speed of calculations. Perhaps it's the two-step process of identifying where the hand is in the image and the subsequent steps of identifying where the hand joints are located? Is the constraint due to the hardware's calculation speeds? We will explore these questions and many others. The students will learn how to approach a common machine learning problem with the deep analytical abilities of an academic researcher. Work. |
| Visualising Sound using Machine Learning and/or Signal Processing Algorithms | Purpose. The purpose of this project is to develop a deep neural network or beam forming algorithms that can reconstruct the location of acoustic sources using multiple microphones. Motivation. In therapeutic ultrasound, a focused ultrasound transducer is used to concentrate energy to a point in the body, allowing us to noninvasively and locally manipulate tissue (tumour ablation, drug release from acoustically-active particles, etc). Our laboratory developed therapeutic ultrasound devices for delivering drugs to the brain (across the blood-brain barrier) for the treatment of brain cancers, neurodegenerative diseases, and other neurological conditions. However, the success or failure of the technique has been difficult to track as clinicians are unable to directly observe what is happening within the body. An emerging way of monitoring this procedure is with the use of microphones located around the focused ultrasound transducer. Sound generated during the procedure are captured by the microphones. We then reconstruct an image of the treated area using passive beamforming algorithms. The reconstruction of a signal source based on multiple sensor signals is broadly known as beamforming. In addition to medical imaging, it is used in underwater acoustics, astronomy, and other disciplines. The problem with many existing passive beamforming algorithms is the poor spatial resolution in the reconstruction of the sound sources. This means we can't precisely locate where the source is coming from. The purpose of this project is to develop a deep neural network and/or signal processing methods that can reconstruct an image of the treated region with better accuracy and spatial resolution. Work description. This work will involve generating training data using computer simulations on a Matlab toolbox known as k-wave. We will then train the deep neural network on PyTorch or develop fundamental signal processing algorithms. We will explore conventional neural networks such as convolutional neural networks, recurrent neural networks, and others; and, potentially, more advanced techniques, such as transformers and physics-inspired neural networks. |
| Ultra-High-Speed Video Camera at 10 Million Frames per Second | Purpose: Develop an ultra-high-speed video camera at 50 million frames per second. Motivation: Certain phenomena, such as ultrasound imaging and therapy with microbubbles (contrast agents) operate in the MHz rate. Such fast dynamics cannot be captured using traditional cameras, which operate at around 60 Hz. Commercially available cameras can reach 1 million frames per second, which is still not enough. And while a 10 million frames per second camera is available on the market for £200k, that camera cannot capture more than 256 frames (25.6µs of data). We propose a new video camera concept that could reach up to 50 million frames per second, capable of capturing nearly unlimited amounts of frames. By creating this device, we would be able to observe phenomena in biological tissue that no one has been able to observe. Outside of the domain of biomedical engineering, this camera could be used to image plasma in fusion reactors, high-speed objects in space, and other high-speed phenomena that requires incredibly high frame rates. Work: This project requires electrical engineering skills. The students would be asked to make circuits that are connected to a unique sensor array. If the analog circuit is successful, we would then require some digital electronics skillsets and optics (physics). |
| Microfluidic Devices for Engineering Advanced Microparticles for Noninvasive Surgery | Purpose: To develop microfluidic devices to engineer advanced microparticles that can be controlled noninvasively with focused ultrasound devices. Motivation. The vision for noninvasive surgery is to manipulate and probe tissue deep in the body without having to cut open the body. Dr. Choi's laboratory develop noninvasive ultrasound devices that emit and receive sound from the patient's surface. We are working with Dr. Au's laboratory to create particles that our devices could manipulate. Here, we ask the student to develop a microfluidic platform to create advanced microparticles that our noninvasive devices could manipulate. In particular, we would like to design microbubbles to address one of the greatest medical challenges of our time - treating brain disorder. Drugs developed to treat brain disorders, such as Alzheimer's disease are untreatable, not because great drugs aren't available, but because those drugs cannot cross the brain's blood vessels, which is lined by a blood-brain barrier. Using engineered microbubbles remotely controlled by ultrasound, we can open the blood-brain barrier, finally allowing drugs to enter the brain. The work. Build microfluidic devices. This includes working in a cleanroom. You also may be exposed to working with ultrasound devices, so strength in engineering and physics would be helpful. |
Dr Johanna Jackson
Profile: https://profiles.imperial.ac.uk/johanna.jackson
Contact details: johanna.jackson@imperial.ac.uk
| Project title | Description |
| Synaptic proteomic profiling in AD | Synapse loss is a key feature of many neurodegenerative diseases, including Alzheimers Disease (AD), and understanding synapse loss and synapse dysfunction alongside other pathology and ‚ changes would enable a greater understanding of the time course of synapse loss. Characterisation of the synaptic proteome will allow associations between neuropathological stage and molecular pathological markers at the genomic level across brain regions to be related to the proteomic composition as an index of functional integrity of the synapse. Synapse proteomic mass spectrometry informs on the protein composition of large populations of synapses extracted from tissue and synaptomics measures the protein and morphological features of individual synapses in tissue sections using confocal microscopy. Together these complementary methods provide depth of proteome composition and detailed mapping of the diversity and pathology of individual synapses. This project will use synaptoneurosome preparations from human brain post mortem tissue samples with and without AD to gain a greater understanding of proteomic changes at the synapse. Here, we will build a novel spectral library for use in the analysis of the human synaptic proteins. Protein changes will be verified by staining and imaging of syanptoneurosomes or synapse markers in human tissue. Analysis of the synapse proteome will provide new knowledge on the synaptic pathology in AD to enable us to develop therapies to directly target the synapse in AD to ultimately lead to improvement in the cognitive impairment associated with the disease. |
Dr Juan Gallego
Profile: https://profiles.imperial.ac.uk/juan-alvaro.gallego
Contact detail: juan-alvaro.gallego@imperial.ac.uk
| Project title | Description |
| Advancing game control by decoding the users intent with surface electromyography | Surface electromyography (sEMG) is a non-invasive neural interfacing technique which can predict the motor intent arising from brain's higher-level processing before the body's physical output. This is achieved by simply detecting electrophysiological activity from muscles at the skin surface. These recordings are then effectively reverse engineered to identify single motor unit discharge patterns, which encode movement information [1][2][3]. One could use this information to control external devices via a non-invasive neuro-muscular interface, to achieve brain-computer interaction or human augmentation. Recently, sEMG for neural interfacing has gained attention from industry, beyond its conventional clinical and research use, whereby neural activity is mapped into real-time myo-control paradigms for augmented experiences with gaming and consumer electronics [4][5]. To explore the complete interfacing process from recording to signal processing to encoding, this multi-faceted project will entail designing and implementing a neuromuscular interface for motion/force control of a simple game such flappy bird, ping-pong, snake etc. There are two branches to the project implementation: 1. Interface design combined with sEMG signal recording and processing, targeting self-selected forearm muscles. 2. Algorithm design for the mapping of neural activity into a myo-control paradigm, and a system to transfer information to the chosen/designed gaming platform. We plan to make use of your resultant set-up for future control experiments in the lab, as well as give you the opportunity to partake in future outreach activities, teaching the public about the exciting and diverse applications of sEMG. References [1] D. Farina and A. Holobar ., Characterization of Human Motor Units from Surface EMG Decomposition, Proceedings of the IEEE, vol. 104, pp. 353373, 2016 [2] A. Holobar and D. Farina ., Non-invasive neural interfacing with wearable muscle sensors: Combining convolutive blind source separation methods and deep learning techniques for neural decoding IEEE Signal Processing Magazine, vol. 38, no. 4, pp. 103-118, July 2021 [3] D. Farina et.al , Decoding the neural drive to muscles from the surface electromyogram, vol. 121, pp. 1616“1623. Clin Neuro- physiol, 2010 [4] E.F. Melcer et.al, CTRL-labs: Hand Activity Estimation and Real-time Control from Neuromuscular Signals, Conf.Hum.Factors Comput.Syst. -Proc.,2018-July pp.1-4, 2018 [5] T. Sharp et.al, Accurate, robust and flexible real time hand tracking, Conf. Hum. Factors Comput. Syst. "Proc., vol. 2015-April, pp. 3633-3642, 2015. |
| A comparison of brain-computer interface performance across cortical regions in humans | Brain computer interfaces (BCIs) map the activity of hundreds of cortical neurons into signals to move a computer cursor or a robot, or even to reanimate the subject paralysed limbs using electrical stimulation of muscles or nerves [1,2]. Yet, despite these tremendous feats, there remain many open questions regarding the best design for BCIs. One crucial open question is where to implant the recording electrodes. Most BCIs are based on recordings from motor cortex, since this region is the main brain to the spinal circuits that drive movement [3]. However, motor cortex may not be ideal because it filters out many higher-order features of behaviour, such as which part of a sequence you are executing [3,4]. An alternative approach is to record neural activity from parietal cortex, a cortical area that is more involved in associative and visuomotor processing. In this project you will compare motor cortical and parietal BCIs by analysing publicly available data from two human participants implanted with intracortical electrodes in these regions [5,6]. Primarily, you will investigate: (1) whether people solve the same task using similar mental strategies based on each of these recordings, and (2) whether the stability of neural population dynamics [7] and BCI control shows different degrees of stability in each of these regions. Answering these questions will inform whether one of these two regions is better for generalisation across individuals and to achieving robust control, thus helping to address two of the main questions in the field. [1] Bensmaia & Miller. Nature Reviews Neuroscience 2014 [2] Pandarinath & Bensmaia. Physiological Reviews 2022 [3] Gallego, McDougle, Makin. Trends in Neurosciences 2022 [4] Russo et al. Neuron 2020 [5] Guan et al MedRxiv 2022. https://www.medrxiv.org/content/10.1101/2022.12.07.22283227v1 [6] Data: https://doi.org/10.48324/dandi.000252/0.230408.2207 |
| Understanding the neural basis for different types of learning | Animals including humans can rapidly adjust their behaviour in the presence of perturbations. These span counteracting mechanical perturbations to the limb [1] (such as when running into water), or distorted feedback about the state of the limb [2] (such as when reaching into a pond). Yet, despite considerable effort, the neural basis for this rapid learning or motor adaptation remains largely elusive. Recent theoretical and computational advances in understanding the coordinated activity of neural populations [3] are helping to cast light into this fascinating problem. For example, our group has shown that, in contrast to learning a new skill through substantial practice, this form of learning may not need plastic changes in the brain [4,5]. Moreover, while motor regions of the brain seem to be key for counteracting mechanical perturbations [4,6], learning to adapt to distorted feedback may be driven by higher brain regions [6]. In this project, you will systematically compare these two forms of adaptation by multi-area neural population recordings from monkeys performing these two classic experiments on different days (data from [3]). These analyses will include understanding changes in neural dynamics as animals plan and execute these movements, as well as a comparison between motor regions of the brain. Your results will shed light in the neural basis for rapid learning and also inform the field of brain-computer interfaces, which necessitates more flexible “decoding algorithms. [1] Shadmehr and Mussa-Ivaldi. J Neurosci 1990 [2] Krakauer et al. Nature Neurosci 1999 [3] Gallego et al. Neuron 2017 [4] Perich, Gallego & Miller. Neuron 2018 [5] Feulner et al. Nature Communications 2022 [6] Sun, OShea et al Nature 2022 |
Dr Marta Varela Anjari
Profile: https://profiles.imperial.ac.uk/marta.varela
Contact details: marta.varela@imperial.ac.uk
| Project title | Description |
| Physics, Brains & AI: Biophysical Modelling of Psychedelic Drug Action using Deep Learning | The use of psychedelic substances (such as lysergic acid diethylamide, LSD) is showing great promise in the treatment of severe mental health problems, such as treatment-resistant depression. Numerous studies are now underway to characterise the activity of the brain of subjects under the effects of these substances, typically using neuroimaging methods such as functional magnetic resonance imaging (fMRI) [Carhart-Harris et al]. However, typical fMRI protocols measure blood-oxygen-level-dependent (or BOLD) activation, which depends on both neuronal and haemodynamic activity. It is difficult to disentangle whether the observed changes in BOLD activation induced by psychedelics are due to neuronal or haemodynamic effects, but it essential to understand the mechanisms of action of psychedelic substances to deploy them as safe and successful treatments in patients. One potential route to disentangling these effects is to build biophysical models of the brain ‚ sets of ordinary differential equations (ODEs) that reproduce relevant features of in vivo brain activity and which can also include the haemodynamic mechanisms underlying the BOLD signal [Deco et al]. These unified neurovascular models [Ionescu et al] open the possibility of fitting BOLD data from psychedelic neuroimaging experiments to gain insights into the drugs‚ effects on the brain. Aims 1.- Implement and validate a unified computational model that combines both neuronal and haemodynamic terms, with the goal of reproducing real fMRI BOLD data from subjects under the effects of psychedelic drugs. 2.- Use physics-informed neural networks (PINNs) to explore different changes and parameterisations of the model that can explain the observed data, with an emphasis in disentangling the neuronal vs haemodynamic origin of previously reported BOLD effects of psychedelic drugs. This project will be co-supervised by Dr Marta Varela (National Heart and Lung Institute) and Dr Pedro Mediano (Computing). References Neural correlates of the LSD experience revealed by multimodal neuroimaging. Carhart-Harris et al, 2016. https://doi.org/10.1073/pnas.1518377113 Whole-Brain Multimodal Neuroimaging Model Using Serotonin Receptor Maps Explains Non-linear Functional Effects of LSD. Deco et al, 2018. https://doi.org/10.1016/j.cub.2018.07.083 Augmenting physical models with deep networks for complex dynamics forecasting. Yin et al, 2021. https://doi.org/10.1088/1742-5468/ac3ae5 Neurovascular Uncoupling: Multimodal Imaging Delineates the Acute Effects of MDMA. Ionescu et al, 2022. https://doi.org/10.2967/jnumed.122.264391 Physics-Informed Machine Learning, Karnidakis et al, 2022, https://doi.org/10.1038/s42254-021-00314-5 Neural ODEs, Chen et al, 2018, https://proceedings.neurips.cc/paper/2018/hash/69386f6bb1dfed68692a24c8686939b9-Abstract.html |
Professor Mauricio Barahona
Profile: https://profiles.imperial.ac.uk/m.barahona
Contact details: m.barahona@imperial.ac.uk
| Project title | Description |
| Understanding Synergistic Neural Activity in Behavioural Control | This project explores how neural synergy‚ where the collective activity of neurons conveys more or different information than individual components‚Äîaffects behaviour. Traditional studies have focused on single brain regions with limited spatial-temporal resolution, leaving a gap in understanding how synergy across brain regions integrates information for complex behaviours. By leveraging advancements in 2-photon imaging, Neuropixels, and large-scale public data, this study investigates intra- and inter-region synergistic interactions in mice during behavioural tasks. We aim to understand how different brain regions and neuronal sub-populations, particularly excitatory and inhibitory neurons, contribute to behaviour. Using higher-order interaction tests, we will analyse data from the Allen Institute, comparing passive and active sessions to determine the role of synergistic interactions in performance metrics. The project will shed light on the spatial distribution of synergy in neural circuits and its critical role in behavioural control. |
| Training a Reservoir Computing Network with Higher-Order Interaction Regulariser for Neuroscience Applications | This project explores the use of reservoir computing‚ recurrent neural network (RNN) framework that leverages a fixed, randomly connected recurrent layer‚Äîenhanced with a higher-order interaction regulariser. Unlike traditional methods like LSTMs, reservoir computing offers efficient processing of temporal data without the need for backpropagation through time, making it well-suited for dynamic, real-time applications. In this project, a higher-order interaction regulariser will be introduced to capture synergistic relationships between neurons, encouraging the network to model complex, non-linear interactions within neural data. This regulariser enables the RNN to reflect the way neural circuits integrate information across regions, aligning it with biological systems' tendency to generate emergent behaviour through inter-neuronal cooperation. For neuroscience applications, this enhanced network will be applied to analyse neural recordings from tasks such as sensory processing, motor control, or decision-making. By improving the network's ability to capture higher-order neural interactions, we aim to provide deeper insights into how the brain integrates information over time, offering potential advancements in understanding neural mechanisms underlying complex behaviors. |
Professor Mengxing Tang
Profile: https://profiles.imperial.ac.uk/mengxing.tang
Contact details: mengxing.tang@imperial.ac.uk
| Project title | Description |
| Sensing and imaging blood flow in the brain using ultrasound | Non-invasive measurement of blood flow velocity is important in a wide range of clinical applications. E.g. neurological patients with a peak cerebral flow velocity of over 2m/s would require intervention. However the current transcranial Doppler ultrasound has limitations as it requires significant operator experiences. This has largely limited the broad application of such technique in clinical settings. In this project, we aim to develop a technique that can measure blood flow velocities within a large volume using ultrasound so that the outcome will be much less dependent on operator experiences. The project involves the understanding of biomedical ultrasound principles, computer simulation, signal processing, and some experimental evaluation in the later phase of the project. Anyone with an interest in experiments, data/signal processing and simulation/modelling can apply. |
| Functional ultrasound brain imaging using ultrasound | Accelerating 3D ultrasound brain imaging with machine learning Background There currently exists no brain imaging modality that provides high-resolution images, is portable, cheap and generally safe. Existing modalities such as MRI and CT are characterised by the use of expensive and non-portable equipment MRI cannot be applied in the presence of ferromagnetic objects and CT uses ionising radiation. Recently, ultrasound-based imaging of the brain has been proposed as a promising alternative, particularly for imaging brain funcational activities through changes to local blood flow. Ultrasound has a much higher sensitivity to subtle blood flow changes than other modalities. Objectives This project intends to explore ways in which 2D/3D functional imaging of the brain can be achieved using non-invasive ultrasound on small animal models first, with the view of extending it to human in the future. |
Dr Nir Grossman
Profile: https://profiles.imperial.ac.uk/nirg
Contact details: nirg@imperial.ac.uk
| Project title | Description |
| Stability analysis of travelling waves and their role in Alzheimer's Disease | Background: Large-scale neural activity in the human brain exhibits rich and complex wave patterns, but the organisation of these waves and their role in behaviour remains unclear. Recent work by our group developed a framework to unify this diversity of wave phenomena observed in electroencephalography (EEG) recordings. We discovered that neural activity can be understood as resulting from parsimonious interactions between fundamental modes of travelling waves — spirals, sources and sinks and planar waves. Travelling waves propagating across the cortical surface interact to produce various fixed points. For instance, a wave that spirals into or emanates from a brain region corresponds to a stable or unstable fixed point, respectively, while the interaction of multiple wavefronts can give rise to saddle points. While previous work has explored the role of individual waves in cognitive function, stability analysis of multiple interacting wavefronts and their role on cognitive function is unknown. Project Description: This project aims to investigate the relationship between fixed points in the spatio-temporal wave patterns of neural activity and cognitive function. Using principles from dynamical systems theory and stability analysis, we will explore whether the stable, unstable, and saddle points generated by interacting neural wavefronts are related to cognitive function in Alzheimer's disease (AD). A secondary aim of this project is to test if brain atrophy, a hallmark of AD, can be measured via the trajectory of cortical wave patterns. We hypothesise that changes in brain geometry and connectivity may manifest as specific patterns of unstable and saddle points. These points may be sensitive to both the extent of cortical degeneration and the degree of cognitive impairment. To test this, we will: 1. Use phase space analysis to map the trajectories of travelling waves in electroencephalogram (EEG) recordings of patients with AD. 2. Apply stability analysis to examine how interactions between travelling waves (e.g., spirals, sources, sinks and planar) give rise to equilibrium points (EPs). 3. Investigate whether EP dynamics correlate with measures of cognitive function and brain atrophy in individuals with Alzheimer's disease. EEG, being a non-invasive, cost-effective, and scalable neuroimaging tool, offers significant potential for uncovering clinical biomarkers. Hence, there is significant motivation to identify EEG biomarkers for early detection and monitoring disease progression. We are looking for a highly motivated student with a strong desire to develop / improve skills in computational neuroscience, mathematics and programming. Skills: Some previous experience in python / Matlab is highly beneficial. Basic knowledge of linear algebra and vector calculus is required. Some knowledge of differential geometry is preferred but not necessary. |
Professor Rylie Green
Profile: https://profiles.imperial.ac.uk/rylie.green
Contact details: rylie.green@imperial.ac.uk
| Project title | Description |
| Living Bionics: Stimulation to drive neural network development | 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. |
| Spinal cord bridge | 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. |
| In-Ear EEG - signal detection | Implementing signal processing and ML for detection of abnormal brain activity in TBI when monitored through in-ear electrodes |
| Injectable electrodes: Colloidal systems for conductive nanoelectronics | This project is about biomaterials development to make injectable systems that are electrically addressable |
Professor Simon Schultz
Profile: https://profiles.imperial.ac.uk/s.schultz
Contact details: s.schultz@imperial.ac.uk
| Project title | Description |
| Understanding the effects of focused ultrasound stimulation (FUS) on human semantic cognition |
Focused ultrasound stimulation (FUS) operates in the frequency range above human hearing. Tissue is affected by acoustic pressure waves that, for lower intensities, can modulate cell membrane features that affect the likelihood of neurons to fire. The ability to, depending on the stimulation protocol, either increase or decrease neural activity and to reach deep-brain structures has several potential clinical applications. The aim of the project is to develop and establish the combination of FUS and state-of-the-art multimodal imaging, towards an ultimate goal of developing cognitive enhancement technology for treating semantic impairments in aging and neurodegenerative disorders. You will learn how to use FUS safely to healthy participants and collect/analyse behavioural and neuroimaging data. Prior to conducting the experiment, you will receive training in the use of FUS by supervisors. This is a collaboration between the Schultz group and the groups of JeYoung Jung and Marcus Kaiser at the University of Nottingham, where the experiments will be performed, under the supervision of Dr Jung.
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| The search for Sharp Wave Ripples (SWRs) in magnetoencephalographic recordings from human subjects |
Sharp wave ripples (SWRs) are brief (~100 ms) periods of high-frequency (110-180 Hz) oscillations, observable as "ripples" in the local field potential, which begin in area CA3 of the hippocampus and propagate out through the cerebral cortex. They are the most synchronous non-pathological activity pattern in the mammalian brain. They are believed to play a key role in the consolidation of episodic and semantic memories from the hippocampus into the neocortex, as well as being involved in working memory. To date, no one has managed to detect SWRs in humans non-invasively - all human work has been in patients surgically implanted with electrodes. In this project we will attempt to overcome this barrier, by collecting some sample data from human subjects taking a nap in an OPM-MEG (optically pumped magnetometer magneto-encephalography) imaging system. The key aim of the project will be to establish proof of principle that this technology can be used to detect SWRs in human subjects.
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| Detecting sharp wave ripples (SWRs) in human epilepsy patients with medial temporal lobe electrode implants |
Sharp wave ripples (SWRs) are brief (~100 ms) periods of high-frequency (110-180 Hz) oscillations, observable as "ripples" in the local field potential, which begin in area CA3 of the hippocampus and propagate out through the cerebral cortex. They are the most synchronous non-pathological activity pattern in the mammalian brain. They are believed to play a key role in the consolidation of episodic and semantic memories from the hippocampus into the neocortex, as well as being involved in working memory. In this project we will collaborate with neurosurgeon Antonio Valentin at the Institute of Psychiatry, KCL, to analyse data from human patients implanted with recording electrodes (for the purpose of treating epilepsy). Our aim will be to detect SWRs, and then to apply neural manifold analysis methods to investigate their functional and dynamical properties. This project will require good programming skills in Python or MATLAB, and a strong interest in neuroscience and its clinical applications.
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| Multi-scale network theoretic analysis of two-photon mesoscopic calcium brain imaging data | We have been performing experiments in which we image wide areas of the cortex (1x5mm field of view) in mice performing cognitive and memory tasks. There is scope in the laboratory for one or more MRes students to work on analysis of these datasets using techniques from graph (network) theory and information theory. |
Dr Sophie Morse
Profile: https://profiles.imperial.ac.uk/sophie.morse11
Contact details: sophie.morse11@imperial.ac.uk
| Project title | Description |
| Non-invasive manipulation and imaging of the brain’s immune system | Our brain has its own dedicated immune system and rapid response team: microglia. These cells actively survey the brain, clearing away toxins and pathogens. The ability to temporarily stimulate microglia has generated much excitement, due to its potential to treat brain diseases. For example, stimulating microglia can help clear away the amyloid-beta plaques that build up in Alzheimer’s disease. Focused ultrasound is a non-invasive and targeted technology that can stimulate microglia in any region of the brain. However, how ultrasound is stimulating these crucially important cells is unknown. This project aims to visualise whether focused ultrasound stimulates PIEZO1 mechanically sensitive ion channels in microglia to better understand the mechanism of this stimulation (expertise in Dr Morse’s group). A genetically-encoded fluorescent reporter based on PIEZO1, GenEPi, developed in Dr Pantazis’s group, will be used to visualise whether ultrasound is stimulating these ion channels, that play multiple roles in the activation of microglia. The student will design a setup to simultaneously image the activity of PIEZO1 with confocal microscopy while performing ultrasound stimulation, which will be tested in a microglial cell line. These results will provide invaluable insight into the mechanism of how focused ultrasound stimulates microglia, allowing ultrasound treatments to be optimised to achieve improved beneficial therapeutic effects for the treatment of neurological disorders, such as Alzheimer’s disease. |
| Can focused ultrasound delay brain ageing? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer's disease mice and patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this same technology can be used to delay age-related cognitive decline, as well as restore cognition in Alzheimer's disease, and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining and fluorescence microscopy. |
| Can focused ultrasound delay Alzheimer's disease? | Focused ultrasound is a technology that has very recently shown to restore cognitive function in Alzheimer's disease patients. This is a non-invasive technology that can be focused onto specific regions of the brain. One theory is that this technology can restore cognition by stimulating the innate immune cells of the brain as well as neuronal function and health. In this project you will explore 1) whether this same technology can be used to delay Alzheimer's as well as restore cognition and 2) delve into exploring the mechanisms behind why these effects are observed. This will involve working with mouse brain tissue, sectioning, imaging, staining and fluorescence microscopy. |
Professor Tim Constandinou
Profile: https://profiles.imperial.ac.uk/t.constandinou
Contact details: t.constandinou@imperial.ac.uk
| Project title | Description |
| Measuring criticality in mesoscopic recordings of cortical brain activity in mouse models | There is evidence in the literature that the brain is a system that maintains itself in a specific operating state called the critical state. This state is defined as being at the boundary between ordered and chaotic behaviour, wherein the burst firing patterns of neurons follow a power-law relationship between the number of neurons involved and the length of the burst, but with no relationship to either time or space i.e. this behaviour is stochastic in nature and has no characteristic scale. It has been proposed that operating in the critical state is optimal for the brain in terms of information processing i.e. cognitive performance and that the maintenance of this state is the result of a deliberate and carefully calibrated process. Conversely in disease states it is expected that the brain deviates from this state towards the chaotic or ordered operating states, and that we could measure this difference. If this is true we could use criticality as a biomarker for cognitive degradation in disease states such as Alzheimer's Disease and Parkinson's Disease. This project aims to analyse existing datasets of brain activity (obtained in collaboration from Barnes Lab at Imperial College) from mouse models obtained using mesoscopic calcium recordings. These recordings capture changes in activity over nearly all of the cortical surface, making it possible to identify power-law relationships in bursts of activity where present. Mouse models from which this data has been captured include both wild-type and Alzheimer's Disease model mice, enabling comparative analysis of any changes in criticality in healthy and diseased brain. Specific Hypotheses to be investigated in the project: Healthy awake rodents show signatures of critical dynamics that can be measured using mesoscopic calcium recordings of brain activity. Test for these relationships in awake activity, identifying any spatial or temporal trends for power-law population-event size distributions and long-range temporal correlations. Deviation from critical dynamics can occur in models of neurodegenerative conditions like Alzheimers Disease, which is associated with suboptimal information processing in the brain (and as can be measured in mouse models from behavioural assays as a control) In mouse models, deviations from critical dynamics are associated with the presence of amyloid plaques Additionally, the amount or severity of deviation is correlated with amyloid plaque load. Recommended reading: 1. Why Brain Criticality Is Clinically Relevant: A Scoping Review. Zimmern V. Front Neural Circuits. 2020 Aug 26;14:54. 2. Long-range temporal correlations in the brain distinguish conscious wakefulness from induced unconsciousness. Thiery, T. et al. Neuroimage 179, 30‚ 39 (2018). 3. Voltage imaging of waking mouse cortex reveals emergence of critical neuronal dynamics. G Scott, ED Fagerholm, et al. Journal of Neuroscience 34 (50), 16611-16620 (2014). 4. Cortical entropy, mutual information and scale-free dynamics in waking mice. ED Fagerholm, G Scott et al. Cerebral Cortex 26 (10), 3945-3952 (2016). 5. Neuronal avalanches imply maximum dynamic range in cortical networks at criticality. Shew, W. L., Yang, H., Petermann, T., Roy, R. & Plenz, D. J Neurosci 29, 15595‚ 15600 (2009). 6. Criticality in the Healthy Brain. Shi, J. et al.. Frontiers in Network Physiology 1, (2021). 7. How critical is brain criticality? O‚ Byrne, J. & Jerbi, K. Trends in Neurosciences vol. 45 (2022). 8. Implantable brain machine interfaces: first-in-human studies, technology challenges and trends. A. Rapeaux and T.G. Constandinou. Current Opinion in Biotechnology, Vo. 72. (2021) 9. Multi-scale network imaging in a mouse model of amyloidosis. Doostdar, N., Airey, J., Radulescu, C.I., Melgosa-Ecenarro, L., Zabouri, N., Pavlidi, P., Kopanitsa, M., Saito, T., Saido, T., and Barnes, S.J.* Cell Calcium (2021) 102365. doi: 10.1016/j.ceca.2021.102365. We are looking for a student with strong analytical and data analysis skills, ability to use the MATLAB language, understanding of computer modelling techniques (especially where relevant to neuroscience), knowledge of and experience using statistical analysis and tools. Experience using neuroscience-focused modelling toolsets such as NEURON (using the HOC language) is an asset. The student will be working with researchers at the South Kensington and White City campuses and be expected to work in both locations during the course of the project. This project will be co-supervised by Dr Adrien Rapeaux. |