With a life-time prevalence of 12% in Canada, major depression is the third highest cause of disability worldwide. Mid-to-late-life depression (MLD) confers a 2-5 fold increase in dementia risk, including for Alzheimer’s disease. Also, it has become increasingly clear that depression has a strong link to neuroinflammation. Thus we emphasize the importance of grounding the study in the link between a neurological signature of depression and its link with inflammation. Furthermore, there are sex differences in the immune system. Men and women may also differ in the types of inflammatory markers they produce as they age. The premier approach to studying the neurological markers of depression is neuroimaging, predominantly magnetic resonance imaging (MRI). MRI has uncovered structural changes, functional alterations, or connectivity abnormalities in specific brain regions or networks in patients of depression. Thus, this project will focus on a retrospective analysis of the Canadian Biomarker Integration Network in Depression dataset, which provides inflammatory and MRI assessments in MLD patients. We aim to (1) synthesize a multimodal neuroimaging signature of MLD severity; and (2) assess the associations between this signature and systemic inflammation.

Magnetic Resonance Imaging (MRI) has revolutionized the study of brain aging. It provides non-invasive, detailed images of brain structure and function, allowing researchers to observe changes associated with normal aging and neurodegenerative diseases. MRI results have shown promise in predicting longitudinal brain functions in aging through the following: Volumetry – Measures changes in brain volume, particularly in regions like the hippocampus and prefrontal cortex, which are vulnerable to age-related decline; cortical thickness – assesses the thickness of the cerebral cortex, which can thin with age; white matter integrity – diffusion-tensor MRI (DTI) measures the diffusion of water molecules in white matter tracts, revealing changes in microstructure and connectivity. In this project, we will focus on the use of the MRI and cognitive data from the Baltimore Longitudinal Study of Aging (BLSA), and aim to determine a predictive modeling approach for estimating longitudinal changes in cognitive function in older adults. Methods include but are not limited to linear mixed-effects model, support-vector machines, neural networks and deep learning. The outcome of this project will enable more effective use of MRI in early diagnosis.

Arterial dysfunction is a precursor to aging-related cognitive decline and dementia; a clinical biomarker of dysfunction is blood flow velocity. Specifically, the detection of slowed flow in the anterior choroidal arteries is valuable for the early treatment of hippocampal degeneration, the hallmark of Alzheimer’s disease. 3D time-of-flight (TOF) magnetic-resonance angiography data, which can be collected in as little as 4 minutes, is the most promising non-invasive way for arterial imaging. However, 3D TOF does not provide blood-flow velocity information and is thus of limited clinical value. The alternative method, 3D phase-contrast angiography, provides quantitative blood flow velocity but requires 20-30-minute scans and more complex computations, and is not feasible for routine use in patients. In this work, we propose to develop a deep-learning framework for generating quantitative arterial-velocity measurements from 4 minutes of conventional 3D TOF data. To that end, we will first measure blood-flow velocity in the anterior-choroidal arteries using phase-contrast MRA in healthy adults, which we will use to train a network along with 3D TOF data from the same individuals. This project could provide a proof-of-concept that breathes new value into existing 3D TOF data and facilitates the inclusion of TOF in more clinical studies of dementia. The SUDS Scholar will: Familiarize with existing deep-learning tool for quantitative blood-flow velocity measurement from functional MRI signals; Perform biophysical modeling to understand the relationship between TOF signal intensity and blood-flow velocity; and, Identify ways to adapt existing deep-learning tool for estimating blood-flow velocity from TOF signal.