Title: BRAIN AI: Biomarker Research to Assess Inflammation in Neurodegeneration using Artificial Intelligence
Funder: Health Education England

Abstract: BackgroundEarly diagnosis of dementia allows for prompt medical management which helps preserve the patient's cognition, daily function and quality of life for longer. Dementia diagnosis often requires multiple investigations, including cognitive assessments, blood and/or cerebrospinal fluid (CSF) tests and neuroimaging. Despite this wide-range of techniques currently at our disposal, diagnosis remains a highly subjective and lengthy process. There is strong demand from the clinical community for objective tools to enable early dementia diagnosis.Neuroimaging studies show that reduced brain perfusion is an early functional marker of dementia. In addition, new evidence implicates inflammation as a driving force for neurodegeneration. Blood and CSF biomarkers of inflammation, some of which were pioneered by researchers in Southampton, are associated with accelerated progression of dementia. The quantitative combination of these diagnostic techniques could support earlier diagnosis of dementia.AimTo evaluate the role of routine clinical and targeted biomarkers of inflammation in dementia progression and identify neuroimaging signatures of the impact of inflammation on brain perfusion. To exploit the added value of using inflammation markers for dementia prognosis by combining them with neuroimaging and patient demographics using a quantitative diagnosis support tool that will facilitate objective diagnosis of dementia. MethodsThis study proposes secondary analysis of data from a cohort of 446 patients at University Hospital Southampton collected during the NIHR-funded Brain Imaging in Dementia (BRAIID) study, which focused on optimising the use of imaging data for diagnosis and prognosis. BRAIID includes data on neuroimaging, patient demographics and blood/CSF results, presenting a unique opportunity for multiparametric analysis. Targeted biomarkers including cytokines and vascular markers will be measured in BRAIID CSF samples using assay kits. Secondary data analysis of routine biomarkers from previously assayed blood and CSF samples will also be performed. Functional brain perfusion imaging data will be analysed using statistical parametric mapping to identify neuroimaging signatures of inflammation in patients with high vs low burden of inflammation.To harvest the complementary nature of inflammation markers, neuroimaging and patient demographics, these data will be quantitatively combined into a diagnosis support tool using Artificial Intelligence techniques. The tool will be trained and validated on clinical data from BRAIID, to support future integration into dementia care pathways, with input from patients and clinicians to direct its development most appropriately. Impact, timelines for delivery and dissemination This research will provide: Neuroimaging signatures of routine (publication by Mar. 2022) and targeted (publication by Nov. 2023) biomarkers of inflammation. These signatures will provide new insights into the impact of inflammation on brain function, highlighting the areas of the brain most affected and expanding our understanding of dementia pathophysiology and progression in the presence of inflammation A quantitative diagnosis support tool for objective assessment of dementia (Complete evaluation using BRAIID cohort data and publish outcomes by Aug 2024).Dissemination will be achieved through: Open-access publishing in reputable scientific journals Presentation of results in scientific conferences Participation in Patient and Public Involvement engagement events Visiting other NHS Trusts to share knowledge and methods, and to cross-validate the developed techniques.

Lay abstract: BackgroundOver 850,000 people live with dementia in the UK, and it is estimated that 1 in 3 people born in 2020 will develop dementia. Dementia affects patients' memory, language and problem-solving abilities and as the disease progresses it impacts the patients' ability to carry out their normal daily activities. Currently there is no cure for dementia, but there are drugs that can improve symptoms and, in some cases, delay progression. 1 in 5 people over the age of 65 show symptoms of dementia and out of those, 1 in 3 people progress to develop dementia. However, it is difficult to predict which patients will progress so more accurate early diagnosis is crucial to help with this prediction and to provide effective treatment options and support for patients and their carers. In addition, as it is thought that infection in the body and the inflammation that results can lead to faster progression of the disease, it would be valuable to measure this inflammation to help predict how quickly symptoms might progress. Brain scans, blood tests and questionnaires are some of the tools used to assess memory and brain function, yet the accuracy of diagnosis using these tools depends on the doctor's experience and ability to combine this information. Teaching computers to combine the results of the various tests would help provide a more accurate diagnosis of dementia. Aim(s)Measure inflammation in patients and investigate its relationship with changes in patients' brain images. Design computer programs to combine measures of inflammation with patient images to assist doctors to more accurately diagnose dementia.Design and methods used In my Fellowship, I will use data and samples already collected at University Hospital Southampton during the Brain Imaging In Dementia (BRAIID) study. The BRAIID study has collected information from 446 patients with dementia including brain scans, cerebrospinal fluid (CSF) samples and questionnaires. In my study I will carry out a new analysis of CSF to measure inflammation. I will compare the brain scans of patients with a high level of inflammation in their CSF to those with a low level and also to people without dementia. This is find patterns showing exactly where inflammation affects the brain on the brain scan. These patterns will assist doctors to predict which patients will progress and enable personalised care. Understanding the impact of inflammation is vital in our understanding of dementia and how to defeat it.Artificial Intelligence techniques will combine information from measures of inflammation and brain scans and will be used to teach the computer how to calculate diagnostic scores to accurately assess dementia. This information will empower doctors to be able to make early objective and more accurate diagnoses of dementia. Patient and Public Involvement I will ask patients and their carers for their input as to what information and tools are important to them to help with the prediction and management of the disease, at various stages of the diagnostic journey. This will be through face-to-face meetings and an online survey designed in accordance to the dementia digital design guidelines to improve accessibility. This information will guide me to design the computer tool in a way that helps doctors better address the issues that are most important to patients and their carers.DisseminationI will share my results by: Open Access publishing in reputable scientific journals and presenting in international scientific meetings Presenting to- and training healthcare professionals within Wessex and other regions and NHS Trusts with neurology expertise such as University College London Hospitals Engaging with patients and the public through outreach activities including hospital and university Open Days

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES
Health categories: NEUROLOGICAL