Identifying cognitive and neurological markers of prodromal dementia: A novel, targeted
functional connectivity approach
Dementia is an increasingly prevalent threat to the health and wellbeing of aging communities. With no current cure, identifying biomarkers of prodromal dementia is essential for timely diagnosis and treatment. Loss of brain connectivity due to neurodegeneration is found even in the prodromal stages of dementia. Increased data-sharing in neuroscience provides a new opportunity to identify reliable and accurate biomarkers of prodromal dementia through largescale EEG-based functional connectivity analyses. However, EEG data of this population is sparsely distributed across multiple data-sharing platforms in unstandardised formats. Moreover, current functional connectivity approaches are not robust or computationally efficient enough to analyse
large datasets. This project will use a novel, integrative, and targeted approach to address these problems. The first experimental study will use a task-driven functional connectivity approach to pinpoint the neural network underlying cognitive deficits in prodromal dementia. The second study will evaluate whether changes in connectivity of this specific network can reliably discriminate between individuals with dementia syndromes, prodromal dementia, and healthy aging controls in a large-scale analysis of secondary EEG data. A large EEG dataset of these populations will be compiled and standardised from data-sharing platforms and existing repositories. This project has beneficial research applications by producing a large, publicly available EEG dataset for future research and developing a novel, refined functional connectivity approach. This project will increase understanding of cognitive and neurological markers of prodromal dementia, and the relationship between these, with valuable clinical applications to inform early diagnosis and disease monitoring.