CIND Funded Projects 2023-2024
The Centre for Integrative Neuroscience Discovery has sponsored six research projects run by ECRs and PIs based at the University of Cambridge, which center on the intersections of neurocognition, neurocomputation and neurotechnology. The projects are highly multidisciplinary and will contribute to the professional development of ECRs, and help PIs developing further insights from which draw upon significant research funding projects. Please find out more about our awardees and their research below.
Title: Pinging hidden brain states using simultaneous TMS and fMRI
Abstract
Imagine hosting a dinner party. Your mind is filled with tasks like buying ingredients, cooking, and awaiting guests' arrivals. Yet you only pay attention to one task at a time— be it boiling pasta or answering the doorbell. This ability to “actively” hold and manipulate limited information against a broader “hidden” cognitive background is known as working memory (WM). WM is pivotal in human cognition. Impairments in WM feature prominently in a variety of neuropsychiatric conditions like frontal lobe disorders. Insights from WM research influence various fields from education to artificial intelligence.
Active WM has been studied extensively, and is often considered the core of WM. Despite its role in computational models, hidden WM is invisible to traditional imaging methods and is poorly understood. In this project, I use the innovative and technically demanding combination of transcranial magnetic stimulation (TMS) with precision brain imaging to visualize hidden states. I plan to use TMS in a similar fashion to how a ship emits echo pulses to map the ocean floor.
Typical current TMS-fMRI setups have limited spatial resolution for fMRI because the need to accommodate the TMS coil has limited the number of radio-frequency (RF) MR channels to 14. With support from technical and MRI specialists, I have developed a novel setup incorporating two flexible RF coils that wrap around both the head and the TMS coil, resulting in a 22-channel configuration. This is a significant advance as it makes high quality MRI sequences, and therefore high-precision imaging, possible.
Leveraging this setup, we have embarked on stimulating participants engaged in cognitive tasks involving specific visual stimuli—such as faces and houses. Crucially, TMS pulses are administered not while the tasks are actively being performed, but during intervals of cognitive rest. Preliminary results suggest the effect of TMS propagates to the brain circuit that was associated with each task, even though this association was not actively being used. This is proof of concept that TMS-fMRI can be used to probe hidden connections between task and stimuli, expanding on previous work suggesting that TMS can momentarily reactivate hidden WM states.
Title: Development of an implantable nerve neurotechnology for the study and treatment of neuropathic pain
Abstract
Neuropathic pain is a condition affecting 7-10% of the population which significantly reduces quality of life. A major limitation in the development of new treatments are the challenges in translating findings from animal models to humans. Traditional models rely on behavioural models applied over large cohorts of animals, hindering deeper assessment and more accurate assessment of treatments. Implantable neurotechnologies offer a solution by recording nerve activity in freely-moving animals, providing more realistic data. However, existing devices struggle with long-term recording and signal classification. Implantable neurotechnologies also show promise for directly treating neuropathic pain but lack specificity, inhibiting normal nerve function.
Recent advancements have led to the development of a nerve implant capable of stable long-term recording and signal velocity determination, addressing previous limitations. This technology promises improved insights into neuropathic pain models and more accurate treatment assessments. Additionally, it may pave the way for closed-loop therapeutic neuroprosthetics, optimizing pain relief while preserving normal nerve function. This project is developing an implantable neurotechnology for long-term recording of neuropathic pain signals and a computational methodology for their selective identification based on signal velocity. The developed technology is validated in animal models of neuropathic pain research, offering potential breakthroughs in the research and treatment of this condition.
Title: Personalizing Cochlear Implant Healthcare: translating the Panoramic ECAP Method from laboratory to clinic
Abstract
Cochlear implants (CIs) are neuro-prosthetic devices that restore auditory perception to severe-to-profoundly deaf individuals by directly stimulating the auditory nerve. While these devices are largely successful at providing auditory sensations, there is a lot of variability between patients, both in their hearing pathologies and their ability to understand speech with their devices. This highlights a need for patient-specific diagnostic tools enabling hearing healthcare professionals to identify patients’ individual patterns of hearing loss. With these types of tools it will be possible to optimise the CI technology and help patients maximize their hearing potential with their devices.
The Panoramic ECAP Method (PECAP) leverages objective measurements of neural responses to a cochlear implant and provides detailed estimates of variation in neural-activation patterns along the length of the cochlea for individual patients (Garcia, et al., 2021). This method has been streamlined such that the data collection only takes 8 minutes per participant, rendering it practical and possible to apply in a clinical environment (Garcia, et al., 2023). However, the research-grade software both for analysing and collecting this data is not appropriate for clinical use, and needs to be developed in order to consider translation of this test into clinical environments.
Thus far, the funding for this project from CIND has enabled development of a website application that would allow users both to collect the necessary data from their cochlear implant patients using already available clinical software, and to upload that data for analysis to extract patient-specific neural-activation patterns. This effectively enables a clinician to run the test themselves within a clinical appointment. The website is currently in a beta stage and is being assessed for usability and security. It is our hope that by developing this test and translating it to a clinical environment, it will help improve speech perception for cochlear-implant patients that may otherwise remain more auditorily isolated from their family and friends.
References
Garcia, C., Goehring, T., Cosentino, S. et al. The Panoramic ECAP Method: Estimating Patient-Specific Patterns of Current Spread and Neural Health in Cochlear Implant Users. JARO 22, 567–589 (2021). https://doi.org/10.1007/s10162-021-00795-2
Garcia, C., Deeks, J.M., Goehring, T. et al. SpeedCAP: An Efficient Method for Estimating Neural Activation Patterns Using Electrically Evoked Compound Action-Potentials in Cochlear Implant Users. Ear Hear 44, 627–640 (2023). https://doi.org/10.1097/AUD.0000000000001305
Title: SleepWell, a digital therapy application for sleep powered by AI and EEG
Abstract
The SleepWell project provides a personalised solution for insomnia, a common sleeping disorder that affects 1 and 3 adults here in the UK on a weekly basis. The underlying causes for insomnia vary widely case to case including mental health disorders such as anxiety and depression, physical illnesses such as migraine or cancer, medications, hormonal changes occurring in menstrual cycle, pregnancy and menopause and neurological problems etc. Thus personalisation is essential in insomnia treatments. By integrating digital therapy, AI and EEG, the project generates adaptive treatments based on music therapy, cognitive behavioural therapy, and hypnotherapy, depending on the patient's brain function. This is the first project of its kind to propose adaptive personalised therapy generation driven by objective validation from a patient's brain response. As a therapy-based treatment it’s safer than the conventional drug based treatments. Moreover, it has the ability to provide faster relief compared to time-consuming in-person therapy sessions or visits to GP. The SleepWell core model is based on a digital twin of the patient's brain, adapted to the state of the patient, evolving with age. This project will revolutionise healthcare and wellbeing by providing digital treatments that are accessible to patients globally, reducing the burden on healthcare.
Title: Developing neuroinformatic phenotypes for human cerebral organoid models of neurodevelopmental disorders
Abstract
Human cerebral organoids are generated from stem cells, forming a brain-like tissue in the laboratory dish. When grown from patient-specific induced pluripotent stem cells (iPSC), they offer an in vitro model for mechanistic and therapeutic studies of neurodevelopmental disorders. Prior work has shown that human cortical organoids follow the developmental milestones of the foetal brain and recapitulate human cortical cell type diversity, layering and functional neuronal networks. In addition, organoids can offer personalised screening of different drugs. However, such an approach depends critically on the availability of in vitro cellular-scale phenotypes of cognitive processing, which are currently lacking.
Here we propose a new class of neuroinformatic phenotypes which directly measure the computational properties of human cerebral organoids, thereby promising to unlock their therapeutic potential. In particular, we will pilot this approach in a human organoid model of Rett Syndrome - a severe neurodevelopmental disease caused by loss-of-function mutations in the MECP2 gene and marked by cognitive decline in the first year of life. To do so, we assembled a multidisciplinary team with a track-record in organoid biology, neurodevelopmental disorders, cognitive and computational neuroscience.
Title: Building reproducible open workflows for network analyses
Abstract
The central aim of our project is to provide reproducible methodology for network analysis in neuroscience. We wish to build an accessible toolset fully integrated with existing workflows and provides more intuitive use possibilities for conducting graph analyses on biological data. Existing workflows are built around NiPype (https://nipype.readthedocs.io <https://nipype.readthedocs.io>), a community driven python API for processing and handling neuroimaging data. The present project will translate graph theoretical quantification and statistical evaluation into an open and reproducible workflow for network analyses of biological systems. Tutorials will be developed so that these methods are not only reliable and robust, but that other users can easily work with them and interpret their outputs. While the proof-of-concept development will in first instance be benchmarked against existing outcomes from human brain imaging data, from the outset this workflow aims to be generalisable across biological data domains and even beyond biology However, to ensure that the methods are readily understandable, all our examples will use neuroscience datasets in the first instance.
Beyond the immediate goal of providing a reliable toolbox for network analysis, this is a new collaboration between PIs that work mostly on different types of data (neuroimaging and neurophysiology) yet have common interests in open and reproducible neuroinformatics research. Our collaboration aims to develop a case study of how neuroinformatics could be supported in a distributed manner at Cambridge. Our goal is to translate success in supporting specifically network analysis in Cambridge into a “neuroinformatics core” capable of helping researchers handle broader questions of handling and processing of neural data