In Alzheimer's disease (AD) assessment, traditional deep learning approaches have often
employed separate methodologies to handle the diverse modalities of input data …

Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron
emission tomography (PET), provides complementary information about the brain that can …

Brain connectivity analysis plays a crucial role in unraveling the complex network dynamics
of the human brain, providing insights into cognitive functions, behaviors, and neurological …

Purpose This paper is a systematic literature review of the use of artificial intelligence
techniques to detect early dementia. It focuses on multi-modal feature analysis in …

Background Growth hormone deficiency (GHD) and idiopathic short stature (ISS) are the
major etiologies of short stature in children. For the diagnosis of GHD and ISS, meticulous …

Essential tremor with resting tremor (rET) and tremor-dominant Parkinson's disease (tPD)
share many similar clinical symptoms, leading to frequent misdiagnoses. Functional …

Abstract Background and Objective Alzheimer's disease (AD) is a dreaded degenerative
disease that results in a profound decline in human cognition and memory. Due to its …

Alzheimer's disease (AD), the most prevalent degenerative brain disease associated with
dementia, requires early diagnosis to alleviate worsening of symptoms through appropriate …

Abstract Amyloid-$\beta $(A $\beta $) plaques in conjunction with hyperphosphorylated tau
proteins in the form of neurofibrillary tangles are the two neuropathological hallmarks of …

Multimodal brain network analysis enables a comprehensive understanding of neurological
disorders by integrating information from multiple neuroimaging modalities. However …