Alzheimer's disease (AD) is currently diagnosed using a mixture of psychological tests and clinical observations. However, these diagnoses are not perfect, and additional diagnostic tools (e.g., MRI) can help improve our understanding of AD as well as our ability to detect the disease. Accordingly, a large amount of research has been invested into innovative diagnostic methods for AD. Functional MRI (fMRI) is a form of neuroimaging technology that has been used to diagnose AD; however, fMRI is incredibly noisy, complex, and thus lacks clinical use. Nonetheless, recent innovations in deep learning technology could enable the simplified and streamlined analysis of fMRI. Deep learning is a form of artificial intelligence that uses computer algorithms based on human neural networks to solve complex problems. For example, in fMRI research, deep learning models can automatically denoise images and classify AD by detecting patterns in participants' brain scans. In this systematic review, we investigate how fMRI (specifically resting-state fMRI) and deep learning methods are used to diagnose AD. In turn, we outline the common deep neural network, preprocessing, and classification methods used in the literature. We also discuss the accuracy, strengths, limitations, and future direction of fMRI deep learning methods. In turn, we aim to summarize the current field for new researchers, suggest specific areas for future research, and highlight the potential of fMRI to aid AD diagnoses.
While there is a multitude of studies on mild cognitive impairment (MCI; more than 80,000 articles), subjective memory complaints (SMC) have received less attention as a prodromal stage of Alzheimer’s disease (AD; less than 2000 articles). In this perspective review article, we argue that SMC should also be considered as another risk factor for the development of AD, and perhaps a pre-MCI condition. This recognition of SMC could help clinicians to identify individuals at risk of developing dementia and could provide protective treatment for them. Accordingly, in this perspective article, we review key studies that outline the nature of SMC, discuss how SMC is measured, explore SMC in MCI, introduce some approaches to SMC treatment, and we discuss future directions for SMC research. Overall, we argue that, like MCI, there should be more research on SMC as a risk factor for developing AD. Consequentially, we aim to highlight the need for further research on SMC and the condition’s role as a potential neuroprotector against AD (e.g., early-stage marker).
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