Friday, May 9, 2025
5/9/2025
Causal and Event Based Modeling of Brain Alterations in ADRD - ABSTRACT - More than 1 in 9 seniors in the United States is living with Alzheimer’s disease (AD), and the number of people affected is expected to double over the next two decades, rising to 13 million in 2050 (Alzheimer’s Association, 2022). The recent shift to creating a biological definition of Alzheimer’s disease for research purposes- advocated by the NIA and the U.S. Alzheimer’s Association task force, has led to great interest in neuroimaging and other biomarkers of brain health from MRI and PET. While emerging treatments may resist cognitive decline by 25-37%, there are risks for severe side effects. Efforts to identify precise markers of disease progression in the living brain as early as possible are essential. Furthermore, knowing if, how, and when the early deviations can be adjusted through modification of lifestyle factors can be essential in preventing decline. Unfortunately, many efforts to track AD reduce the rich biomarker information available in neuroimaging to a single metric - hippocampal volume or amyloid positivity. The vast richness of brain wide data is often overlooked and the temporal sequence of biomarker changes is often ignored when evaluating each risk or resistive factor. We lack causal models of the biomarker changes, to identify modifiable factors in our lifestyle or genome that could resist or delay the onset of the disease, or guide treatment selection, even in the presence of confounding factors. Here we address these gaps in the AD research field with a multi-arm project that seeks to use sophisticated high-dimensional deep learning, event based modeling to elucidate the spatial rich causal sequence of brain biomarker changes that lead to AD and causal factors that influence them. We have 3 Specific Aims: Aim 1) We will fit novel adaptations of multishell dMRI models to more widely available single-shell clinical dMRI data from multiple independent public datasets to predict CSF and PET-derived Aβ load in symptomatic vs pre-symptomatic individuals. Aim 2) Use our novel AI models to generate individualized whole brain MRIs at future time points based on sex-specific normative models, creating individualized prediction maps like ‘digital twins’ of brain morphology at any age. We expect these to be more sensitive to identifying abnormal aging trajectories than current full brain methods, and more specific to disease pathology and risk factors than “brain age” alone. Aim 3) Use our novel marginal sensitivity model for continuous treatments to identify causal relationships, which are partially robust to confounding, between modifiable lifestyle factors including specific diet scores and physical activity and brain alterations in key dementia related neurodegenerative pathways. We will model causal effects and their dependency on age and sex. We hypothesize that neuroimaging metrics that show associations with earlier pathologic changes, including microstructure, will be more tolerant to confounding in retaining a causal relationship between lifestyle and brain integrity. The resulting set of tools will be more inclusive of the rich biomarker data in AD, offering a means to track its evolution and the causal factors that affect it.
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