Diagnosis and prognosis
About this theme:
New technologies, access to joined-up data, and novel computational modelling methods are improving our understanding of diagnosis and prognosis for people with long term neurological conditions. Our research is focussed on three key areas:
Structural and functional Neuroimaging Methods, combined with a variety of related biomarkers, are used to find associations between brain anomalies and potential causal mechanisms, to improve diagnosis and prognosis for people with dementia and other neurological conditions.
Biomarker Research explores the role of genetic and inflammatory biomarkers on the causes of neurodegeneration, underlying pathological processes, progression characteristics, and outcomes.
Data Modelling allows conclusions to be drawn from data and promotes better understanding. Applications include patterns of service use, risk factor analysis, and disease severity progression. Sources include population demographics and clinical assessment data. Methods make use of data visualisation, statistical analysis, prediction, projection, and AI methods, to better inform and support decision-making.
Researchers working under this theme:
Dr Dave Evenden
Visiting Research Fellow
Dave is a visiting research fellow at the University of Southampton. Previously Dave was a Chartered Engineer in contract R&D, working with high-tech communications systems and medical electronics for over 30 years. Embarking on a […]
Dr Sofia Michopoulou
Medical Physics Expert
Sofia is a medical physics expert (MPE) for nuclear medicine and an NIHR clinical lecturer. She supports the nuclear medicine, SPECT/CT and PET/CT services at UHS and leads the introduction […]
Dr Angus Prosser
Senior Research Fellow
Angus is a Senior Research Fellow within the Faculty of Medicine at the University of Southampton and Innovation Project Manager for the Southampton Emerging Therapies and Technologies (SETT) Centre. Angus’s […]
Professor Chris Kipps
Consultant Neurologist
Chris is a Consultant Neurologist with subspecialty interest in behavioural neurology and cognitive disorders, and Professor of Clinical Neurology and Dementia at University Hospital Southampton and the University of Southampton. […]
Theme projects:
BRAIN AI: Biomarker Research Assessing Inflammation in Neurodegeneration using Artificial Inteligence
Clinician attitudes to dementia investigations (CADI)
Progression modelling of cognitive decline and associated imaging and biomarker patterns
Featured theme publications:
Progression modelling of cognitive decline and associated FDG-PET imaging features in Alzheimer’s disease
Prosser A, Evenden D, Holmes R & Kipps C
Background: Prognosis for Alzheimer’s disease is difficult, with rates of disease progression varying widely. Despite the extensive use of clinical dementia severity assessment scales to determine dementia diagnosis and to monitor progression, there is no consensus on which imaging factors may accurately predict future decline trajectories on these measures. Determination of baseline imaging patterns that are related to slower, or faster, decline rates could be used to identify those at highest risk of worse outcomes and inform care planning.
Computer simulation of dementia care demand heterogeneity using hybrid simulation methods: improving population-level modelling with individual patient decline trajectories
Evenden D, Brailsford S, Kipps C, Roderick P & Walsh B
Objectives: The aim of the study was to model dementia prevalence and outcomes within an ageing population using a novel hybrid simulation model that simultaneously takes population-level and patient-level perspectives to better inform dementia care service planning, taking into account severity progression variability.
Hybrid simulation modelling for dementia care services planning
Evenden D, Brailsford SC, Kipps CM, Roderick PJ and Walsh B
Dementia is an increasing problem in today’s ageing society, and meeting future demand for care is a major concern for policy-makers and planners. This paper presents a novel hybrid simulation model that simultaneously takes population-level and patient-level perspectives to calculate the numbers of patients at different stages of disease severity over time, and their associated care costs.
The impact of regional Tc-HMPAO single-photon-emission computed tomography (SPECT) imaging on clinician diagnostic confidence in a mixed cognitive impairment sample
Prosser AMJ, Tossici-Bolt L and Kipps CM
Aim: To assess the clinical impact of regional cerebral blood flow (rCBF) single-photon-emission computed tomography (SPECT) imaging on diagnosis and clinician diagnostic confidence in a cohort of individuals with cognitive impairment.