Brain entropy mapping in Alzheimer's Disease

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ABSTRACT. Alzheimer’s Disease (AD) has affected tens of millions of people but remains incurable. The major risk factor for AD is aging, which entails high brain entropy due to the progressive brain damages. Notably, the brain constantly consumes a large amount of energy to maintain its functional integrity, likely creating a big “reserve” to counteract the high entropy. Malfunctions of this reserve may indicate a critical point of disease conversion and progression. Reserve malfunction can be measured by the compensation outcome: functional brain entropy (fBEN), which may reveal critical information for bridging the knowledge gap between AD pathology and clinical symptoms: AD pathology begins long before AD symptoms and may not lead to dementia, and may provide a brain marker for early disease detection. This project is proposed to characterize fBEN in normal aging and the AD continuum and test an inverse U-shape fBEN model: fBEN increases with age and AD pathology in normal aging but decreases in the AD continuum. We will test the model using large existing resting state fMRI (rsfMRI) data. Our group started rsfMRI-based fBEN mapping in 2010 and released the first open-source fBEN mapping tool. The tool has been widely used in many neuroscientific and translational studies. To be scalable for large data, we will develop a further accelerated version in this project. In Aim 1, we will calculate fBEN using data from 2000+ young and old healthy individuals from public databases and examine the associations of fBEN to age, education, and cognitive function. We hypothesize that the brain has a reserve-related network whose fBEN decreases with years of education and is negatively correlated with brain function, suggesting low fBEN in this network as an indicator of brain reserve. Aim 2 will characterize fBEN in AD and assess its associations to AD pathology and clinical symptoms. We hypothesize that there is a strong pathology vs disease interaction on fBEN: fBEN increases with age and pathology in normal aging and lower fBEN correlates with better cognition; those associations will be reversed in the AD continuum so that fBEN will decrease with pathology level and lower fBEN will correlate to worse cognition. Aim 3 will evaluate the feasibility of baseline fBEN in regions elucidated in Aims 1 and 2 for early disease detection. Because fBEN may only reflect some part of the functional abnormalities in AD, we will combine fBEN with other imaging biomarkers in the model in order to achieve higher prediction accuracy. We will also build a model to use fBEN to predict AD pathology in normal aging. The clinical impact of this project includes: 1) testing the hypothetical fBEN model will help delineate the pathology vs clinical discrepancy in AD; 2) finding the disease related fBEN patterns may provide a potential intervention target; 3) testing the prediction model will aid early disease detection; 4) AD pathology prediction is of great clinical value for normal aging and AD management as current AD pathology measurement is either painful or too expensive. The feasibility of this novel project and the many new hypotheses have been initially demonstrated by our extensive preliminary data.