Recently, several lines of evidence have supported that synaptic dysfunction represents one of the earliest brain changes in Alzheimer’s disease (AD), leading to hyper-excitation in neuronal circuits. However, network changes related to age, sex and other risk factors such as the apolipoprotein E (APOE) ε4 allele tend to overlap with disease neuropathology, increasing the difficulty of separating disease-specific alterations from those related to normal aging trajectories in males and females (women comprise two thirds of all persons diagnosed with AD dementia, while female ε4 allele carriers are four times more likely to develop AD than men). Further compounding the challenges is the potential for psychiatric conditions to influence these relationships. Specifically, late life depression (LLD) has been proposed as a significant contributor to accelerated cognitive decline and progression to dementia. While it remains unclear which neurobiological aspects of LLD represent pathognomonic features, versus co- occurring aspects of AD, determining their impact on functional outcomes is a significant opportunity to disentangle the relationship between depression and neurodegenerative processes in late life. We will use multi-modal connectomics to analyze excitation-inhibition balance (E-I balance) in the well-characterized ADNI and ADNI-D samples to elucidate the relationship between late- life depression and neurodegeneration. Our pipeline will be based on a novel resting-state structural connectomics (rs-SC) approach that yields a hyperexcitation indicator (HI). Previously, in a group of cognitively normal APOE-ε4 carriers and age/gender matched non- carriers we demonstrated a sex-by-age-by-genotype interaction, with significant hyperexcitation with increasing age only observable in women, but not in men. In particular, results supported that hyperexcitation in female carriers began to exhibit at age 50 in the default mode network (DMN). Further, the degree of hyperexcitation was shown to be related to compensatory recruitment of neuronal resources during a spatial learning memory task (virtual Morris water maze task). Motivated by this pilot study, we will examine the links between mood (late-life depression) and subsequent cognitive decline and development of dementia in the context of synaptic dysfunction.

Late life depression (LLD) is characterized by synaptic dysfunction and represents one of the strongest factors associated with accelerated cognitive decline and progression to dementia. In this proposal, we will apply our new multimodal connectomics tools on ADNI and ADNI-D to explore the links between mood (late-life depression) and subsequent cognitive decline and development of dementia in the context of synaptic dysfunction.