Abstract
The progressive functional decline that involves both cognitive and neuropsychiatric symptoms characteristic to dementia is one of the leading research topics. The risk for dementia is an intertwined mix between aging, genetic risk factors, and environmental influences. APOEε4, which is one of the apolipoprotein E (APOE) alleles, is the major genetic risk factor for late-onset of the most common form of dementia, Alzheimer's. Advances in machine learning have led to the development of artificial intelligence (AI) algorithms to help diagnose dementia by magnetic resonance imaging (MRI) in order to detect it in the preclinical stage. The basis of the determinations starts from the morphometry of cerebral atrophies. The present review focused on MRI techniques which are a leading tool in identifying cortical atrophy, white matter dysfunctionalities, cerebral vessel quality (as a factor for cognitive impairment) and metabolic asymmetries. In addition, a brief overview of Alzheimer's disease was presented and recent neuroimaging in the field of dementia with an emphasis on structural MR imaging and more powerful methods such as diffusion tensor imaging, quantitative susceptibility mapping, and magnetic transfer imaging were explored in order to propose a simple systematic approach for the diagnosis and treatment of dementia.