The overall goal of the proposed research is to define the specific brain networks that are vulnerable or resilient in aging and Alzheimer?s disease (AD), and subsequently derive new accurate, precise, and robust connectomic imaging biomarkers for (especially preclinical) AD, which could improve diagnosis, disease staging, prediction, assessment of progression, and therapeutic efficacy. Information flows in the human brain through a complex set of structural and functional networks. The complete connectivity map among brain areas, i.e. the connectome, can help to better understand the vulnerability and resilience of the brain architecture and function to aging effects and debilitating neurodegenerative diseases, such as AD, and to discover diagnostically and therapeutically important biomarkers. Focusing on brain regions, but not interregional connectivity, may have hindered progress in understanding and treating disorders characterized as ?disconnection syndromes?. Diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI) are used to noninvasively quantify structural and functional brain networks, respectively. Network-based analysis of the brain has proved promising in revealing the basis of cognitive dysfunction in mild cognitive impairment (MCI) and AD, demonstrating changes distinct from those with healthy aging. Development of treatments to prevent or delay the onset of AD would be greatly facilitated by a noninvasive, sensitive, and specific diagnostic biomarker able to discriminate cognitively normal people and MCI patients who will progress to AD from those who will age healthily. Structural connectivity between two brain regions is often defined based on the dMRI tractography-derived streamlines between them. The direct fiber bundle connecting two brain areas is expected to be the major signal carrier between them; however, multi-synaptic neural pathways (those mediated through other regions) also provide connectivity. The investigators propose to develop and validate novel mathematical and algorithmic models for brain connectivity, while accounting for multi-synaptic neural pathways (Aim 1). Furthermore, they propose to include a comprehensive set of brain regions (Aim 2), given that some brain structures that are important in AD, such as locus coeruleus, basal forebrain, and hypothalamus, are not readily included in neuroimaging toolboxes. They also propose to identify compensatory connections contributing to resilience in aging and preclinical AD (Aim 3). The completion of this study will improve our understanding of how brain networks are affected in aging and AD and will help to derive more accurate AD biomarkers. In this connectomic analysis, ten existing heterogeneous dMRI/rs-fMRI databases of healthy elderly, MCI, and AD populations, totaling approximately 6000 subjects, will be combined, which is expected to improve stratification, prediction, and prognosis. The investigators will validate their network-derived biomarkers via disease staging and correlation with clinical and genetic data on cross-sectional datasets, and via prognosis and prediction of conversion of healthy/MCI to AD on longitudinal datasets.
In this project, using magnetic resonance imaging (MRI) techniques, we will define both vulnerable and resilient brain networks involved in aging and Alzheimer?s disease (AD), and uncover new imaging biomarkers to improve AD diagnosis and treatment. Normal aging, as well as occurrence of neurodegenerative diseases such as AD, can be better understood by mapping complex structural and functional brain networks through which information flows, and via the discovery of important biomarkers that shed light on aging and neurodegenerative disease.
