Healthcare provision in low- and middle-income countries is deficient. Populations in less developed rural areas, mostly indigenous, are particularly vulnerable, and affected the most by the precarious service provision. Highly specialized medical personnel and medical equipment is beyond the reach of rural clinics. In the case of brain health, there is a need for smart low-cost neuroimaging tools that can accompany prevention, diagnosing and monitoring, and moreover, that can be accessible to otherwise segregated population.
Near infrared spectroscopy (NIRS) neuroimaging is emerging as an all-encompassing non-invasive brain metabovascular monitoring tool  which uses infrared light to probe brain physiology. A full metabovascular NIRS (mvNIRS) system involves the combination of more than one optical submodality . While possible, but research is only in early stages with still many unresolved challenges.
This project intends to develop a feasible combined bNIRS-TR-DCS prototype of mvNIRS device that combines different modalities of NIRS necessary to afford concomitant metabolic and vascular measurements by means of broadband spectroscopic NIRS (bNIRS) and blood flow measurements by means of time resolved diffuse correlation spectroscopy (TR-DCS) related measurements. Further, it intends for the solution to be smart  e.g. AI based, to support constrained medical expertise and low cost so that it is deployable to less developed rural environments.
To realize these goals, contributions in optical device development, algorithmic signal processing, and data analysis are needed in a truly multidisciplinary project requiring knowledge from fields as diverse as optics, mathematics, neurosciences, electronics and computer sciences.
• Months 1-18: Development of the first mvNIRS prototype and implementation of the basic reconstruction and signal processing algorithms.
• Months 12-24: Validation of the mvNIRS prototype and refinement as needed. Validation of the reconstruction algorithms. Development of an integrated GUI and foundational work on AI-driven data analysis.
• Months 19-36: Smarting the mvNIRS and validation of the computational solutions.
• Months 30-38: Thesis writing and submission
• Months 39-42: Addressing potential comments of examiners.
PI: Dr Felipe Orihuela-Espina. Expected starting date: End of September 2022
About the Computational Neuroimaging Lab
Part of the Computational Life Sciences theme at the School of Computer Science, the Computational neuroimaging lab develops models and analysis tools to understand the neural system. This involves multidisciplinary research from computing, mathematics and statistics, and a bit of physics and neuroscience. We have a key research area on optical neuroimaging modalities such as fNIRS and DCS.
Informal enquiries should be directed to Dr Felipe Orihuela-Espina ( [Email Address Removed]).
Applicants should have a strong background in physics and mathematics, and ideally a background in instrumentation and being enthusiastic about computer sciences. You should have a commitment to research in mathematical modelling and computer simulations and develop neuroimaging instrumentation with applications in neuroscience. You must have exceptional programming and communication skills. You will be a team-player capable of independent learning.
We want our PhD student cohorts to reflect our diverse society. UoB is therefore committed to widening the diversity of our PhD student cohorts. UoB studentships are open to all and we particularly welcome applications from under-represented groups, including, but not limited to BAME, disabled and neuro-diverse candidates. We also welcome applications for part-time study.