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Developing a novel diagnostic tool for non-Alzheimer’s dementia using the fast periodic visual stimulation oddball paradigm (FPVS-oddball).
: (Alternative Format Thesis)

Student thesis: Doctoral ThesisPhD

Abstract

Accurate and timely differential diagnosis of dementia aetiologies remains a major clinical challenge, particularly for Lewy body disease (LBD), which is frequently undetected until post-mortem examination. Visual processing impairments are a core feature of LBD and emerge early in the disease course, yet current behavioural assessments lack the sensitivity for reliable detection. This thesis aimed to develop and validate passive, objective and clinically viable electroencephalography (EEG)-based measures of visual processing capable of distinguishing between LBD and Alzheimer’s Disease (AD). Across five experiments, the fast periodic visual stimulation oddball paradigm (FPVS-oddball) was used to probe different hierarchical levels of visual processing, including line orientation discrimination, global shape perception, object recognition and location discrimination. FPVS-oddball enables the isolation of neural responses indexing implicit stimulus discrimination without requiring overt behavioural responses or task understanding. Proof-of-principle was established for these tasks using laboratory-grade high-density EEG recordings in cognitively healthy younger adults. Subsequent studies demonstrated that these paradigms could be applied in cognitively healthy older adults and clinical populations using a portable low-density recording system. Crucially, LBD patients showed clear impairment across all visuoperceptual measures and could be differentiated from AD patients with high accuracy. Overall, this thesis demonstrated that FPVS-oddball provides a robust, passive and scalable means of assessing visuoperceptual function in dementia, with clear potential to guide differential diagnosis.
Date of Award24 Jun 2026
Original languageEnglish
Awarding Institution
  • University of Bath
SponsorsBRACE Dementia Research
SupervisorGeorge Stothart (Supervisor), Karin Petrini (Supervisor) & Elizabeth Coulthard (Supervisor)

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