What is this Study about?

Early detection of brain changes is crucial to prevent cognitive decline and dementia. This study uses electroencephalography (EEG) to record the brain's electrical signals during simple tasks, with the hope of validating a measure that can improve early detection of Alzheimer’s disease.

We are recruiting 150 individuals with mild memory issues and mild dementia at Baycrest in Toronto and Bruyère in Ottawa. In a separate study we are also recruiting older adults with no memory problems. The results of both studies together will help us determine if our new EEG measure can detect abnormal changes in brain health early on and if it can help predict future cognitive changes. We hope that this new test will be used in the clinic to identify individuals who may benefit the most from early treatment and interventions.

What is this Study about?

CAN-PROTECT is a Canada-wide online study recruiting participants and caregivers of persons with dementia to explore how lifestyle, background, and caretaking factors affect our health, quality of life, cognition, behaviour, and function as we age. CAN-PROTECT data will be analyzed and jointly published by the University of Calgary in partnership with the University of Exeter.

What is this Study about?

Our study titled "Investigating the Role of Quantitative Susceptibility Mapping and Oxygen Extraction Fraction as Advanced Neuroimaging Biomarkers in Alzheimer's Disease" aims to develop non-invasive diagnostic biomarkers for Alzheimer's Disease (AD). We focus on evaluating the potential of QSM and OEF to differentiate between AD, Mild Cognitive Impairment (MCI), and Cognitively Unimpaired populations and to predict amyloid and tau pathology, using a Systems Biology approach. Leveraging the McGill University's TRIAD cohort, the study utilizes advanced imaging and machine learning to predict amyloid and tau presence, which could transform AD diagnostics and treatment. Methodologically, it involves preprocessing neuroimaging data and employing machine learning classifiers to analyze these features. The anticipated outcomes include validating QSM and OEF as non-invasive biomarkers for AD, establishing their correlation with amyloid and tau proteins, and developing predictive models for clinical decision-making, potentially resulting in significant advancements in AD diagnostics.