Predictions of neural control deficits in elders with subjective memory complaints and Alzheimer’s disease

Alzheimer’s disease (AD) is a common disease, affecting over 6 million elders in the U.S in 2024. However, AD remains untreatable due to the absence of an effective biomarker to assess the underlying deficits in cognitive control. Disruptions in the brain's control systems are key factors in the learning and memory impairments that define AD. In this study, we hypothesized that we could predict neural control deficits in elders with subjective memory complaints (SMC) and AD patients using Diffusion Tensor Imaging (DTI) data and brain controllability analysis. DTI is a non-invasive neuroimaging tool that detects how fluid travels along the white matter tracts inside the brain. To test our hypothesis, we used the DTI data of 12 elders with SMC, 12 AD patients, and 12 healthy subjects from the open-source dataset Alzheimer's Disease Neuroimaging Initiative. First, we constructed individual brain connectivity networks. Using graph theory and brain controllability analysis, we assessed node degree and controllability. We then averaged these measures across brain areas to calculate global metrics for each subject. Results illustrated that the node degree could not identify SMC and AD from healthy subjects, while the controllability measure could differentiate SMC and AD from healthy subjects and distinguish between SMC and AD. In conclusion, this study provides a promising biomarker for detecting neural control deficits in elders with SMC and AD patients for future clinical application.