Frontal variant Alzheimer's disease: A systematic narrative synthesis

The dopaminergic system and Alzheimer's disease

Alzheimer's disease is a common neurodegenerative disorder in older adults. Despite its prevalence, its pathogenesis remains unclear. In addition to the most widely accepted causes, which include excessive amyloid-beta aggregation, tau hyperphosphorylation, and deficiency of the neurotransmitter acetylcholine, numerous studies have shown that the dopaminergic system is also closely associated with the occurrence and development of this condition. Dopamine is a crucial catecholaminergic neurotransmitter in the human body. Dopamine-associated treatments, such as drugs that target dopamine receptor D and dopamine analogs, can improve cognitive function and alleviate psychiatric symptoms as well as ameliorate other clinical manifestations. However, therapeutics targeting the dopaminergic system are associated with various adverse reactions, such as addiction and exacerbation of cognitive impairment. This review summarizes the role of the dopaminergic system in the pathology of Alzheimer's disease, focusing on currently available dopamine-based therapies for this disorder and the common side effects associated with dopamine-related drugs. The aim of this review is to provide insights into the potential connections between the dopaminergic system and Alzheimer's disease, thus helping to clarify the mechanisms underlying the condition and exploring more effective therapeutic options.

Transformer-based approaches for neuroimaging: an in-depth review of their role in classification and regression tasks

In the ever-evolving landscape of deep learning (DL), the transformer model emerges as a formidable neural network architecture, gaining significant traction in neuroimaging-based classification and regression tasks. This paper presents an extensive examination of transformer's application in neuroimaging, surveying recent literature to elucidate its current status and research advancement. Commencing with an exposition on the fundamental principles and structures of the transformer model and its variants, this review navigates through the methodologies and experimental findings pertaining to their utilization in neuroimage classification and regression tasks. We highlight the transformer model's prowess in neuroimaging, showcasing its exceptional performance in classification endeavors while also showcasing its burgeoning potential in regression tasks. Concluding with an assessment of prevailing challenges and future trajectories, this paper proffers insights into prospective research directions. By elucidating the current landscape and envisaging future trends, this review enhances comprehension of transformer's role in neuroimaging tasks, furnishing valuable guidance for further inquiry.