Dementia

A novel neuroimaging based early detection framework for alzheimer disease using deep learning.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that significantly impacts cognitive function, posing a major global health challenge. Despite its rising prevalence, particularly in low and middle-income countries, early diagnosis remains inadequate, with projections estimating over 55 million affected individuals by 2022, expected to triple by 2050. Accurate early detection i...

Developments in MRI radiomics research for vascular cognitive impairment.

Vascular cognitive impairment (VCI) is an umbrella term for diseases associated with cognitive decline induced by substantive brain damage following pathological changes in the cerebrovascular system. The primary clinical manifestations include behavioral abnormalities and diminished learning and memory cognitive functions. If the location and extent of brain injury are not identified early and th...

Machine learning model for predicting Amyloid-β positivity and cognitive status using early-phase F-Florbetaben PET and clinical features.

This study developed machine learning models to predict Aβ positivity in Alzheimer's disease by integrating early-phase F-Florbetaben PET and clinical...

Potential role of TNFRSF12A in linking glioblastoma and alzheimer's disease via shared tumour suppressor pathways.

Tumor suppressor genes (TSGs) are critical regulators of cellular homeostasis and are extensively studied in cancer biology. However, their roles in n...

Enrichment of extracellular vesicles using Mag-Net for the analysis of the plasma proteome.

Extracellular vesicles (EVs) in plasma are composed of exosomes, microvesicles, and apoptotic bodies. We report a plasma EV enrichment strategy using ...

Attention-driven hybrid deep learning and SVM model for early Alzheimer's diagnosis using neuroimaging fusion.

Alzheimer's Disease (AD) poses a significant global health challenge, necessitating early and accurate diagnosis to enable timely interventions. AD is...

Artificial Intelligence in Palliative Care: A Scoping Review of Current Applications, Challenges, and Future Directions.

BackgroundArtificial Intelligence (AI) is increasingly integrated into healthcare systems, presenting opportunities to improve clinical outcomes. In t...

An intelligent screener for mild cognitive impairment via integrated eye-tracking and the digital clock drawing test.

BackgroundMild cognitive impairment (MCI) is a risk factor for dementia, and early screening is crucial for patient prognosis.ObjectiveTo construct an...

Electroencephalogram features support the retrogenesis hypothesis of Alzheimer's disease: Exploratory comparison of brain changes in aging and childhood.

BackgroundThe retrogenesis hypothesis (RH) suggests that the functional and cognitive decline observed in Alzheimer's disease dementia mirrors in reve...

Cognition-Eye-Brain Connection in Alzheimer's Disease Spectrum Revealed by Multimodal Imaging.

BACKGROUND: The connection between cognition, eye, and brain remains inconclusive in Alzheimer's disease (AD) spectrum disorders.

Utility of machine learning algorithms in classification of progressive cognitive impairment in Alzheimer's disease: A retrospective cohort based on China.

BackgroundDistinct risk factors influence Alzheimer's disease (AD) stage stratification, yet effective tools for early diagnosis and prognosis remain ...

Difference between eyes-open and eyes-closed resting state quantitative electroencephalography (qEEG) for predicting cognitive impairment using deep learning.

OBJECTIVE: Electroencephalography (EEG) is a method that offers detailed observations of electrical activities occurring in the brain's cerebral corte...

Plasma lipidomic fingerprinting enables high-accuracy biomarker discovery for Alzheimer's disease: a targeted LC-MRM/MS approach.

Dysregulation of lipid metabolism is increasingly recognized as a key factor in the pathogenesis of Alzheimer's disease (AD). Unfortunately, an accura...

γ neuromodulations: unraveling biomarkers for neurological and psychiatric disorders.

γ neuromodulation has emerged as a promising strategy for addressing neurological and psychiatric disorders, particularly in regulating executive and ...

Development and validation of a combined clinical and MRI-based biomarker model to differentiate mild cognitive impairment from mild Alzheimer's disease.

BACKGROUND: Two of the most common complaints seen in neurology clinics are Alzheimer's disease (AD) and mild cognitive impairment (MCI), characterize...

Association of peripheral immune markers with brain age and dementia risk estimated using deep learning methods.

BACKGROUND: The peripheral immune system is essential for maintaining central nervous system homeostasis. This study investigates the effects of perip...

Novel insights from comprehensive analysis: The role of cuproptosis and peripheral immune infiltration in Alzheimer's disease.

BACKGROUND: Cuproptosis is increasingly recognized as an essential factor in the pathological process of Alzheimer's disease (AD). However, the specif...

Exploring Factors Related to Social Isolation Among Older Adults in the Predementia Stage Using Ecological Momentary Assessments and Actigraphy: Machine Learning Approach.

BACKGROUND: As the global population ages, the economic burden of dementia continues to rise. Social isolation-which includes limited social interacti...

Machine learning-based prediction of amyloid positivity using early-phase F-18 flutemetamol PET.

BackgroundPrevious studies have suggested that early-phase imaging of amyloid positron emission tomography (PET) may offer information for predicting ...

Uncovering injury-specific proteomic signatures and neurodegenerative risks in single and repetitive traumatic brain injury.

Traumatic brain injury (TBI) is a major public health concern associated with an increased risk of neurodegenerative diseases including Alzheimer's di...