Dementia and Cognitive Impairment Research

Modeling Temporal Dependencies and Feature Interactions Reveal Novel Clinical and Molecular Insights into Alzheimer's Disease Progression

The paper introduces a framework to model feature interactions and temporal dynamics in Alzheimer's disease progression using ADNI data, identifying key clinical and molecular markers.

ConvNeXt-Driven Detection of Alzheimers Disease: A Benchmark Study on Expert-Annotated AlzaSet MRI Dataset Across Anatomical Planes

This study benchmarks ConvNeXt models for Alzheimer's disease classification using MRI scans, showing ConvNeXt-Base achieves 98.37% accuracy on coronal slices.

Neuroimaging-derived brain endophenotypes link molecular mechanisms to Alzheimer's disease and aging

This paper links molecular mechanisms to Alzheimer's disease and aging by integrating neuroimaging endophenotypes with molecular quantitative trait loci.

Evaluation of Deep Learning Algorithms to Predict Multiple Dementia-Related Neuropathologies from Brain MRI, Clinical and Genetic Data

This study evaluates deep learning models that predict multiple dementia-related neuropathologies using MRI, clinical, and genetic data, showing the potential of multimodal AI for non-invasive profiling.

Longitudinal functional connectivity during rest and task is differentially related to Alzheimer's pathology and episodic memory in older adults

The paper examines how changes in functional connectivity during rest and task relate to Alzheimer's pathology and episodic memory in older adults.

Comparative Analysis of Diffusion Models for Enhancing Alzheimer's Disease Classification

This study compares two diffusion models for generating synthetic MRI images to improve Alzheimer's disease classification, achieving higher precision and recall than baseline models.

The Role of Genomic-Informed Risk Assessments in Predicting Dementia Outcomes

This paper constructs a genomic-informed dementia risk score combining genetic and clinical factors and finds that most memory clinic patients have at least one high-risk indicator, with higher burden linked to increased dementia risk.

Neuro-Metabolic and Vascular Dysfunction as an Early Diagnostic for Alzheimer's Disease and Related Dementias.

This study identifies neuro-metabolic and vascular dysfunction (MVD) as an early indicator of Alzheimer's disease by analyzing clinical data from ADNI.

Myo-inositol and total NAA in the hippocampus are linked to CSF tau pathology in cognitively normal older adults

The paper links hippocampal myo-inositol and total NAA levels to CSF tau pathology in cognitively normal older adults using 1H-MRS.

Brain-age models with lower age prediction accuracy have higher sensitivity for disease detection

This study shows simpler brain-age models with lower age prediction accuracy are more sensitive to disease-related changes, suggesting they may be better biomarkers for detecting neurological and psychiatric disorders.

Associations between volumes of grey matter regions and white matter hyperintensities with cognitive empathy in oldest-old adults

The paper examines associations between grey matter volumes and white matter hyperintensities with cognitive empathy measures in oldest-old adults, finding significant links between specific brain regions and different empathy assessment methods.

Hippocampal grey matter changes across scales in Alzheimer's Disease

This study examines hippocampal macrostructure and microstructure changes in Alzheimer's Disease and MCI compared to healthy controls using ADNI data.

An Explainable Web-Based Diagnostic System for Alzheimer's Disease Using XRAI and Deep Learning on Brain MRI

This paper develops an explainable AI system for Alzheimer's disease severity classification using brain MRI and deep learning, achieving high accuracy and interpretability through XRAI and a web-based interface.

Sex and insulin resistance biomarker modelling in a new large-scale Alzheimer's disease transcriptomic resource

The paper develops a large-scale blood transcriptomic resource for Alzheimer's disease, identifies sex-specific differences, and models insulin resistance biomarkers using machine learning.

Evaluating Fairness and Generalizability of Alzheimers Disease Diagnosis Models Trained on Racially Imbalanced Datasets

This study evaluates how Alzheimer's disease diagnosis models perform across racial groups and finds that models trained on one group underperform on others, but fairness techniques can reduce bias.

Neuropathological changes in the nucleus basalis of Meynert in people with type 1 or type 2 diabetes mellitus.

The paper examines neuropathological changes in the nucleus basalis of Meynert in diabetes patients, finding reduced ChAT expression in T1DM and increased hyperphosphorylated-Tau in T2DM.

Age-related constraints on the spatial geometry of the brain

This study quantifies age-related changes in brain spatial geometry using structural MRIs and links these changes to cognitive impairments.

Multimodal Imaging and Logistic Weighted Cognitive Scores for Classification of MCI, AD, and FTD Subtypes

The paper develops a multimodal classification approach combining neuroimaging and cognitive scores to differentiate between MCI, AD, and FTD subtypes, achieving varying levels of accuracy.

The neurophysiology of healthy and pathological aging: A comprehensive systematic review

This review evaluates neurophysiological measures in healthy aging, MCI, and dementia using EEG, MEG, and combined methods, highlighting their potential as diagnostic tools and the need for standardization.

Familial Risk for Dementia, Cognitive Performance, and the Human Cerebello-Hippocampal Circuit: A Study in the European Prevention of Alzheimers Disease cohort

This study examines the cerebello-hippocampal circuit's role in aging, dementia risk, and sex differences using MRI and cognitive assessments in a large cohort of older adults.

Domains of Frailty as Early Risk Factors for Alzheimer's Disease -- Genetic and Causal Evidence

This study identifies distinct frailty domains with varying causal relationships to Alzheimer's disease using genetic methods.

The Association Between SLIT2 in Human Vitreous Humor and Plasma and Neurocognitive Test Scores

This study finds higher SLIT2 levels in vitreous humor compared to plasma and links lower vitreous SLIT2 to worse cognitive scores in middle-aged individuals.

Differential Speed and Accuracy Trade-off in Working Memory Retrieval and Bilateral Precuneus between Older Men and Women

This study finds sex differences in the relationship between precuneus volume and working memory performance, with women showing stronger correlations between left precuneus volume and memory accuracy.

Differences in LC integrity and fMRI activations in healthy aging and MCI associated with successful memory encoding

The paper examines how locus coeruleus integrity and fMRI activations relate to memory encoding in healthy aging and MCI.

Predicting Alzheimer's Disease Progression from Sparse Multimodal Data by NeuralODE Models

This paper predicts Alzheimer's disease progression from sparse multimodal clinical data using NeuralODE models, showing improved accuracy over existing methods.

Toward Robust Neuroanatomical Normative Models: Influence of Sample Size and Covariates Distributions

This study evaluates how sample size and covariate distributions affect neuroanatomical normative models in Alzheimer's disease, showing that demographic alignment is crucial for reliable results.

Early Metabolic Alterations in Cerebrospinal Fluid Fatty Acid Profiles Linked to Cognitive Decline and All-Cause Dementia

This study identifies CSF fatty acid profiles as better predictors of cognitive decline than traditional biomarkers like Aβ42 and tau, suggesting early metabolic changes in dementia.

Identifiability-Guided Assessment of Digital Twins in Alzheimer's Disease Clinical Research and Care

This study assesses identifiability in digital twins for Alzheimer's Disease to evaluate model validity despite data sparsity and variability.

Mind the Gap: Does Brain Age Improve Alzheimer's Disease Prediction?

This study compares brain age models and direct models for predicting Alzheimer's Disease outcomes, finding that direct models perform better despite brain age models having larger training data.

Cortical thickness changes precede high levels of amyloid by at least seven years

The paper shows cortical thickness changes occur years before high amyloid levels in Alzheimer's disease, challenging the view that amyloid is the first imaging marker.