Latest AI and machine learning research in alzheimer's disease for healthcare professionals.
Background: Identifying early brain-based markers of cognitive decline is critical for preventive strategies in Alzheimer's disease. Individuals with a familial risk may exhibit subtle functional brain changes years before clinical symptoms emerge. This exploratory study examined whether baseline functional brain network topology differentiates high-risk cognitively normal older adults who later p...
Aging and genetic risk shape the molecular programs that confer cellular vulnerability in Alzheimer's disease (AD), but whether these programs differ between clinical symptoms and neuropathological burden remains unclear. Using single nucleus RNA sequencing (snRNA seq) from the dorsolateral prefrontal cortex, we applied TriSCOPE, integrating multivariate predictive modeling, differential expressio...
A limitation of social contingency research with infants is that scientists can only instruct the caregivers to modulate their interactive behaviour w...
BACKGROUND AND OBJECTIVES: Effective education on Alzheimer's disease (AD) requires methods fostering empathy, confidence, and knowledge. Artificial i...
Timely and accurate diagnosis of dementia remains a critical yet challenging task. Although machine learning (ML) techniques have shown considerable p...
Background: Amyotrophic lateral sclerosis (ALS) is clinically heterogeneous, and genetic modifiers may drive molecular endophenotypes without obvious ...
Early detection of dementia enables timely intervention and better care planning. Electroencephalography, being accessible and noninvasive, offers a p...
Genetic-based risk prediction is becoming increasingly available for a wide range of common diseases thanks to the growth of large-scale biobanks and ...
Accurate histopathologic interpretation is key for clinical decision-making; however, current deep learning models for digital pathology are often ove...
Early and accurate diagnosis of Alzheimer's disease (AD) remains a critical challenge in neuroimaging-based clinical decision support systems. In this...
Online cancer peer-support communities generate large volumes of patient-authored and caregiver-authored text that may reflect distress, coping, and i...
Background: Digital health technologies, including artificial intelligence (AI)-powered tools and virtual reality (VR) interventions, are increasingly...
Discrete flow models (DFMs) have been proposed to learn the data distribution on a finite state space, offering a flexible framework as an alternative...
Genome-wide association studies of physical activity traits have mapped numerous loci, yet the molecular mechanisms through which exercise influences ...
Alzheimer's disease (AD) is a complex neurodegenerative disorder characterized by widespread dysregulation of gene expression and regulatory pathways....
High-dimensional structural MRI (sMRI) images are widely used for Alzheimer's Disease (AD) diagnosis. Most existing methods for sMRI representation le...
Background Distinguishing individuals with cognitive decline (CD), including early Alzheimers disease, from cognitively normal (CN) individuals is ess...
High-dimensional neuroimaging data presents challenges for assessing neurodegenerative diseases due to complex non-linear relationships. Variational A...
A central objective in neuroscience is to elucidate how the brain generates complex dynamic activity through the interactions of brain areas. In this ...
Synthetic neuroimaging data can mitigate critical limitations of real-world datasets, including the scarcity of rare phenotypes, domain shifts across ...