Fellows & Graduate Students

The specific aims of this project funded by a Singapore Translational Research (STAR) Investigator Award are : 1)To study, in a cohort of 700 subjects with up to 5 years longitudinal follow up, the independent and joint associations of MRI, retinal imaging, blood and neurobehavioural markers with risk of cognitive decline & vascular events. We hypothesise that a) Longitudinal MRI, retinal as well as blood-based and neurobehavioural markers are associated with poorer cognitive performance and incidence of dementia and vascular events. B) Both baseline and serial change in, one or more of these biomarkers, will add additional predictive information on progression of cognitive decline and incident events beyond the utility of currently used predictors. 2)To examine how Mild Behavioural Impairment (MBI) is influenced by the independent and interactive effects of MRI, retinal and blood biomarkers. We hypothesise that : a) Severity of CeVD and neurodegeneration, structural and functional disruptions and reduced perfusion on MRI are associated with MBI; b) Retinal markers are associated with MBI; c) Altered levels of bloodbased markers are associated with MBI; d) Interaction between the above mentioned biomarkers influence MBI and NPS.

The specific aims of this project funded by a Singapore Translational Research (STAR) Investigator Award are : 1) To examine longitudinal brain network and microstructural changes using multimodal MR imaging and evaluate their interactions with AD & CeVD and cognitive and behavioural decline in patients with NCI, MCI and dementia. The hypotheses are : a) Plasma amyloid-β and p-tau are related to specific brain functional and structural network breakdown leading to cognitive decline and behavioral problems; b) The effect of network changes and atrophy on cognitive performance and 23ehavior is network-specific and disease stage-dependent and modulated by CeVD markers c) Individuals with both CeVD and AD would have an accelerated trajectory of neurodegeneration and cognitive decline. 2) To build a large international longitudinal database comprising local and international imaging and neurobehavioural data to develop AI algorithms for predicting dementia risk and progression. We hypothesize (a) that deep learning can be used to harmonize imaging and neurobehavioural data across multiple sites; b) By pooling harmonized data across multiple sites, the larger sample size will dramatically improve prediction of future cognitive decline and clinical outcomes.

The specific aims of this project funded by a Singapore Translational Research (STAR) Investigator Award are : 1) To determine the relationship of existing (retinal vasculature, Optical Coherence Tomography (OCT), OCTAngiography) and novel (Doppler OCT, 24ehavior24ize, hyperspectral OCT) retinal imaging measures over time to progression and development of vascular cognitive impairment and other imaging markers of dementia, with a goal to determine the potential diagnostic and prognostic value of retinal imaging. We hypothesise that as multiple cross-sectional studies have consistently demonstrated the association of retinal imaging biomarkers and dementia, retinal imaging biomarkers will be predictive of the incidence and progression of dementia. 2) The creation of an international big data consortium, AIDA (Artificial Intelligence for Dementia Assessment) to evaluate retinal imaging markers of dementia. Using datasets from multiple institutions around the world and through the integration of deep learning, we hypothesize that we will be able to further improve the sensitivity and specificity of retinal imaging biomarkers to detect dementia.

The specific aims of this project funded by a Singapore Translational Research (STAR) Investigator Award are : 1) To develop novel blood-based biomarkers of oxidative stress, vascular disease and neurodegeneration, and to examine their potential diagnostic and prognostic value for cognitive impairment and dementia. 2) To examine novel blood-based biomarkers of neurodegeneration and oxidative stress using a state-of-theart immunoassay platform and assess their relationships with brain integrity and cognition, 3) To compare plasma vs. neural- or endothelial-specific extracellular vesicle measurements to assess the diagnostic and prognostic utility of these biomarkers 4) To develop a combination of multiple biomarkers with high accuracy in predicting longitudinal disease development. We hypothesise that markers involved in the disease pathophysiology, can identify individuals with high disease risk. Specifically, that a) blood-based biomarkers of neurodegeneration and oxidative stress are predictive of incident CeVD in parallel with cognitive decline; b) neural- or endothelial-specific extracellular vesicle biomarkers are more sensitive than neat plasma biomarkers in diagnosing and prognosis for cognitive impairment and CeVD; c) A combination of multiple biomarkers adds value to the diagnostic and prognostic performance of single blood-based biomarkers.

The specific aims of this project funded by a Large Collaborative Grant are : 1) To investigate novel interventions for vascular cognitive impairment (VCI). We propose to conduct a large communitybased innovative trial as part of the World Wide FINGERS interdisciplinary network for the prevention of cognitive impairment or dementia The trial will recruit 1200 subjects at high risk to develop cognitive impairment and dementia. The main goal is to determine the efficacy and safety of multimodal lifestyle interventions together with intensive blood pressure lowering. We hypothesize that these interventions will reduce cognitive decline and severity of cerebrovascular disease (CeVD) and other VCI biomarkers. 2) To examine how CeVD, tau, and amyloid impact longitudinal brain integrity and cognitive decline in elderly atrisk for cognitive decline or dementia. We hypothesize that CeVD and AD have distinct influence on brain, retina and blood markers of neurodegeneration and cerebrovascular burden. Further, we hypothesize that these multimodal measures at baseline coupled with polygenic scores could identify high risk individuals and predict future disease progression or response to intervention.

Students from National University of Singapore:
FYP: Chu Tei Kar, Casado Rodrige Daniel

NSWS: Ang Zhi Qin, Swislar Tan, Ezali, Su, Mah Hoew Yang, Lorraine Yap, Lee Ying Xin, Joy Goh, Wei Jie, Amanda Lee, Macalister Tan

Students from Singapore Institute of Technology:
Amanda Lee, Macalister Tan

Students from Temasek Polytechnic:
Nathan Lim, Ranveer, Ayrton Teo

Students from NUS High School:
Jayden Tan, Kyusik Kim