Dementia

Machine-learning prediction and risk stratification of 12-month cognitive decline in Alzheimer's disease using routine clinical and MRI data.

Early identification of patients with Alzheimer's disease (AD) who will experience near-term cognitive decline can support trial enrichment and risk-stratified follow-up. Using the Alzheimer's Disease Neuroimaging Initiative (ADNI), we developed two prognostic models for 12-month Mini-Mental State Examination (MMSE) decrease (≥ 3 points): (i) a clinical logistic-regression model and (ii) a random-...

Early detection of Alzheimer's disease based on leveraging multimodal features of the clock drawing test.

BackgroundAlthough multi-task handwriting analysis has the potential to improve early detection of Alzheimer's disease (AD), the educational bias inherent in its text-based tasks poses a significant obstacle to its widespread adoption across different regions.ObjectiveUsing the clock drawing test, we aim to design a deep neural network to extract features from static images and process signals to ...

Detecting multimorbidity patterns in Alzheimer's disease using unsupervised machine learning: A nationwide emergency department study (2007-2022).

BackgroundAlzheimer's disease (AD) patients frequently present to emergency departments (EDs) with complex comorbidities that complicate triage and ma...

Exploratory analysis of the associations of the brain age gap with cognitive function and amyloid-β accumulation: participants selection based on metabolic and physiological blood markers.

The brain age gap (BAG) is defined as the difference between brain age estimated from MRI using artificial intelligence and chronological age, and has...

Establishing a relationship between iron-based blood measures and structural brain changes using neural networks in UK Biobank.

BACKGROUND: Disturbance of iron homeostasis in both the brain and blood is linked to cognitive impairment and neurodegenerative diseases. Investigatio...

Dual modal data-driven virtual reality-based mild cognitive Impairment assessment using MCIformer.

BACKGROUND: Mild Cognitive Impairment (MCI) assessment is critical for identifying cognitive decline and enabling early intervention to reduce the ris...

HIF3A-mediated aberrant activation of TXNIP promotes Alzheimer's disease progression.

Oxidative stress (OS) is a hallmark of Alzheimer's disease (AD), yet the cell type-specific mechanisms remain unclear. We analyzed a single-cell RNA s...

Contralateral language network integration predicts and protects against naming decline after temporal lobe resection.

OBJECTIVE: Anterior temporal lobe resection (ATLR) is an effective treatment for drug-resistant temporal lobe epilepsy (TLE) but carries a substantial...

Clinical and pathologic correlations of machine learning quantification of Aβ deposits across 3 brain regions of decedents with Alzheimer disease.

Machine learning enables scalable quantification of neuropathology, offering deeper phenotyping of Alzheimer's disease (AD). In this validation study,...

EENet-RLA: An Explainable Prediction Learning Framework for Alzheimer's Disease Classification from EEG Signals.

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting millions worldwide. Electroencephalography (EEG), a non-invasive, cost-ef...

Resource-stratified machine learning framework for cognitive status classification and mild cognitive impairment to dementia progression prediction.

Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.

Artificial intelligence (AI) is transforming biomarker discovery in neurology by overcoming key limitations of conventional approaches that are often ...

2.5D HAU-Net with gated spatial attention for automatic hippocampus segmentation in MRI.

BACKGROUND: The hippocampus is a key brain region and biomarker for Alzheimer's disease (AD). Accurate automated hippocampal segmentation is essential...

Modeling inter-slice dependencies with temporal graph learning for Alzheimer's disease.

Accurate and early diagnosis of Alzheimer's disease (AD) remains a major clinical challenge, particularly in distinguishing mild cognitive impairment ...

Machine Learning-Integrated Pharmacophore, DFT Analysis, and molecular dynamics of Diosmetin as a potent ache inhibitor with neuroprotective activity in a Scopolamine-Induced alzheimer's zebrafish model.

Alzheimer's Disease (AD) is a degenerative disorder of the brain that causes a gradual loss of cognitive function. The cholinergic hypothesis suggests...

Exploring feature importance in machine learning for neuroimaging traits in Alzheimer's disease across a multiethnic cohort.

BackgroundAlzheimer's disease (AD) affects 55 million people worldwide, projected to reach 139 million by 2050; yet, most machine learning (ML)-based ...

AI-Driven Mental Health Support for Caregivers of Individuals With Alzheimer Disease: Systematic Literature Review and Development of a Conceptual Framework.

BACKGROUND: Caregivers supporting individuals with Alzheimer disease and related dementias (AD/ADRD) frequently encounter prolonged emotional strain, ...

Leveraging Naturalistic Driving Digital Biomarkers for Early Mild Cognitive Impairment Detection: Deep Learning Strategies.

BACKGROUND: Alzheimer disease and related dementias are increasing worldwide, with early detection during the mild cognitive impairment (MCI) stage cr...

Digital support systems to improve child development in Peru: A cluster-randomized controlled open-label trial.

Digital technologies have the potential to transform early childhood development (ECD) interventions by delivering personalized support at scale. We c...

MRI In Vivo Detection of Amyloid-β Protein Deposition in Different Brain Regions of Patients with AD and MCI.

To investigate a non-invasive magnetic resonance imaging (MRI)-based method for detecting amyloid-β (Aβ) protein deposition in different brain regions...