Seeing Beyond the Symptoms: Biomarkers and Brain Regions Linked to Cognitive Decline in Alzheimer’s Disease

Abstract

Objective: Alzheimer's disease (AD) is the most prevalent form of dementia, and its pathological process can only be detected through clinical approaches. Early diagnosis of AD is difficult, as most individuals with AD are not diagnosed in the early stages, and symptoms become more prominent as the disease progresses. Therefore, identifying specific biomarkers and predicting AD in the early stages is crucial. In this study, we aimed to identify effective biomarkers and brain regions that are strongly associated with cognitive decline in AD. Methods: We included a large sample of 1759 individuals, covering a range of cognitive aging, including healthy controls (HC), mild cognitive impairment (MCI), and AD. We extracted nine different biomarkers based on three neuroimaging modalities: structural magnetic resonance imaging (sMRI), positron emission tomography (PET), and diffusion tensor imaging (DTI) to predict three neuropsychological scores: Mini-Mental State Examination (MMSE), Clinical Dementia Rating Scale Sum of Boxes (CDRSB), and Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS). Rather than integrating biomarkers, we monitored and explored the complex interrelated mechanisms underlying the development of AD separately for each biomarker. For prediction tasks, we used the ensemble regression tree by implementing bagging and random forest techniques in four different combination groups consisting of different subsets of subject categories (i.e., HC, MCI, and AD). Results: Our results demonstrated that different biomarkers could predict all three cognitive scores, and we identified the most associated features with the cognitive scores, including (a) the right transverse temporal based on Amyloid-β (Aβ) deposition, (b) the left and right entorhinal cortex, left inferior temporal gyrus, and left middle temporal gyrus based on average cortical thickness (ATH), and (c) the left uncinate fasciculus based on mean diffusivity (MD). Conclusions: The results of this study emphasize the significance of taking an interdisciplinary approach in comprehending the underlying mechanisms of AD. Additionally, these findings shed light on the diversity of the disease and have the potential to contribute to the development of more efficient treatments.