In an established environment of neuro-intensive care, large quantities of data can be captured from bedside monitors. This data contains a wealth of information about the pathophysiology of the critically ill patient, however the extraction of this information often requires sophisticated signal analysis. Such analysis is usually performed using universal data analysis packages like Matlab, but requires engineering/mathematics expertise and is only available in off-line mode, and as such in not applicable in a clinical setting. Our own software ICM+ (http://www.neurosurg.cam.ac.uk/icmplus), which is by now used in many clinical centres worldwide, attempts to bridge the gap between laboratory and clinical application. It collects data from bedside monitors, calculates in real time ‘secondary’ parameters defined using highly configurable signal processing formulae, and produces continuously-updated charts. This way, complex information coming off of the bedside monitors can be summarised in a concise fashion and presented to medical staff in a simple way that alerts them to the development of various pathological processes.
The projects offered will include the following stages:

1. Development of new methodologies of analysis of physiological signals acquired in  Neuro-Intensive Care, including time and frequency domain, stationary, and non-stationary methods, linear, and non-linear approaches.
2. Validation of the new methodologies against retrospective and prospective data from the Neuro-Intensive Care unit.
3. Implementation of the novel algorithms into ICM+ plugins, packaged as Windows dll libraries, so that they can be used in real time by the bedside.

These projects would suit graduates in Biomedical Engineering, but also potentially Physics, Mathematics, and Computer Science.

Traumatic brain injury is an extremely complex pathology with a highly dynamic time profile developing over the course of the very first days in a critical care unit. Secondary pathological processes occur resulting from the initial trauma, with severe and often fatal consequences. The key aspects of the patient treatment/management during this period are: prevention (if possible), detection, and subsequent alleviation of those insults.

Monitoring of various metrics, like pressures, flows and electrical activities from the body and the brain provides some indications for the onset and severity of those processes. However, interpretation of those measurements as presented by the patient monitors, as well as the electronic record systems, is still rather simplistic and largely based on trivial metrics, like hourly mean values of individual measurements. In addition, critical values for those individual measurements are population based, and thus do not reflect inter-individual differences and are not adjusted over the course of the patient stay in ICU. The aim of the project is to develop sensitive and robust metrics based on multi-parameter, continuous neuro-monitoring that would be better suited for guiding therapeutic interventions in traumatic brain injury patients in ICU. The project will involve application of cutting-edge time series analysis methods to a large number (1300+) of data sets (at full, waveform level, temporal resolution) collected by the group from neuro ICU in Cambridge over the past decades as well as the new, prospective, data set to be collected in the neuro intensive care unit. As part of the project, methods for detecting early onsets of adverse events will be developed and algorithms for real time calculation of dynamic management targets proposed and implemented in software, building on top of the flagship software developed by the group ICM+ (https://icmplus.neurosurg.cam.ac.uk) and extending its functions with Python plugins. Furthermore, development and implementation of methods for automated data curation/pre-processing based on machine learning approaches will also form part of this project to ensure high quality of the input data for the decision-making support algorithms.

Dynamic, pathological, biochemical processes triggered by a severe traumatic brain injury lead to brain swelling, often with devastating, persistent consequences to the brain tissue, frequently culminating in the patient’s death. Various physical properties of the brain vasculature and cerebral tissue controlling the cerebral blood flow naturally reflect those processes but they are impossible to monitor directly using currently available technology.

Instead, one must rely on surrogate measurements, like pressures and flows in the brain, and analysis of patterns carried by the temporal changes in those measurements at various time scales. Many metrics have been proposed, with some more successful then others, each of them carrying certain assumptions and gross simplifications. The purpose of this project is to use mathematical (system) modelling as well as statistical learning approaches to build on previous discoveries but ultimately aiming to provide a simplified, robust, and readily interpretable set of complemental metrics reflecting the physical properties of the cerebral vasculature along with the accuracy indicators. The project will take advantage of a large number (1300+) of data sets (at full, waveform level, temporal resolution) collected by the group from the neuro ICU in Cambridge over the last decades. The new metrics will be ultimately implemented for real time use at the bedside using tools included in the brain monitoring software written by the group ICM+ (https://icmplus.neurosurg.ca m.ac.uk), and by extending its battery via Python plugins. Appropriate visualisation methods for presentation of those metrics to the clinician at the bed-side will also be developed.

*Note: These projects would suit graduates in Biomedical Engineering, Physics, Mathematics, Signal Processing, Electrical Engineering, and Computer Science.

Dynamical properties of cerebral blood flow (CBF) and cerebrospinal fluid circulation (CSF) can be modelled by a structure of nonlinear differential equations (1). Models describe such phenomena as autoregulation of blood flow, brain venous blood outflow, compensatory role of CSF circulation, etc (2). They are relevant to understanding of pathophysiological mechanisms after traumatic brain injury, subarachnoid haemorrhage, stroke, in hydrocephalus and in idiopathic intracranial hypertension. A database of recorded clinical signals is available for verification or identification of successful modelling structures. The project will be focused on further refinement of the existing models, including their application for non-invasive assessment of intracranial pressure (3), asymmetry of CBF and phenomenon related to collapse of cerebral venous sinuses. The project would suit neuroscientists with strong computer skills (including writing own codes, advanced Matlab, etc).

Further reading (4). 1. Czosnyka et al. J Neurol Neurosurg Psychiatry. 1997 Dec;63(6):721-31. 2. Piechnik et al. J Cereb Blood Flow Metab. 2001 Feb;21(2):182-92. 3 Kashif FM et al. Sci Transl Med. 2012;4(129):129ra44. 4.http://www.neurosurg.cam.ac.uk/pages/brainphys/index.php

Various methods exist for assessment of autoregulation of Cerebral Blood Flow (CBF). They incorporate different modalities: arterial blood pressure, intracranial pressure, blood flow velocity, cerebral perfusion pressure, and brain tissue oxygenation (1). Dynamical tests of cerebral autoregulation include time series analysis, analysis of models based on transfer function, wavelet decomposition, non-linear decomposition, etc. The aim of the project is to compare various methodologies, various modalities and compare them from the point of view of clinical utility in a group of head injured patients. Strong emphasis will be put on feasibility studies of new and existing methodologies at the bedside: such as a concept of ‘optimal’ Cerebral Perfusion Pressure or individualized threshold of intracranial pressure. Also links between brain physics modalities and electrophysiology and biochemistry will be of interest. The project is ideal for a person having a medical or biological background with strong practical computer skills.
Further reading (4). 1. Czosnyka M et al. Neurocrit Care. 2009;10(3):373-86. 2. Aries MJ et al. Crit Care Med. 2012; 40(8):2456-63. 3. Lazaridis C et al. J Neurosurg. 2014 Apr;120(4):893-900. 4. http://www.neurosurg.cam.ac.uk/pages/brainphys/index.php

In various pathologies, reasons for intracranial hypertension may be different. In hydrocephalus: disturbed outflow of Cerebrospinal Fluid (CSF); in idiopathic intracranial hypertension (IIH): obstruction of venous blood outflow; in head injury and stroke: brain edema, increased vasogenic component of ICP, failing regulation of cerebral blood volume or all three factors together, etc. (1) This is an interdisciplinary project requiring a good background in clinical neurosciences, brain physics and computational methods (time series analysis, dynamic modelling). Clinical applications are envisaged (but not limited to) mainly in the area of hydrocephalus (2) and IIH . A vast database of recorded signals and clinical material (over 5000 cases) can be used for mastering new methodologies of processing and modelling (3). Strong knowledge of brain imaging techniques will be essential. The project is ideal for a person having a medical or biological background with strong practical computer skills.
Further reading (4). 1. Czosnyka M, Pickard JD. J Neurol Neurosurg Psychiatry. 2004;75(6):813-21. 2. Weerakkody RA et al. Acta Neurol Scand. 2011;124(2):85-98. 3. Varsos GV et al.