Dementia and Cognitive Impairment Research

The paper identifies transcriptomic changes in normal aging brains that resemble Alzheimer's disease in specific subgroups, suggesting early molecular links between aging and LOAD.

This paper introduces FMFS, a framework for selecting hippocampal morphometry features linked to Aβ/tau pathology, showing it outperforms existing methods and identifies key subregions for biomarker development.

This study uses 1H-MRS to examine excitatory/inhibitory balance in the posteromedial cortex of young APOE E4 carriers and finds no evidence of altered neurotransmitter levels.

This study finds that adult brain age is more influenced by early life factors like birth weight and polygenic scores than by longitudinal brain decline.

This paper develops a multi-modal neural network for Alzheimer's disease diagnosis using MRI and SNPs, achieving 93.5% AUC and 96.1% AP on the ADNI dataset.

The paper introduces disease-specific neuroimaging templates for frontotemporal dementia (FTD) to improve registration accuracy and reduce biases in analyses of affected populations.

This study links reduced alpha peak frequency to episodic memory impairment in aging, showing correlations with encoding efficiency and hippocampal function.

This paper develops a method to predict brain amyloid burden using MRI-based morphometry statistics and sparse coding, achieving high accuracy in ADNI and OASIS cohorts.

The paper evaluates an AI-based cognitive assessment tool for detecting cognitive impairment in MCI and AD patients, showing good validity and generalisability across populations.

The paper examines the independent associations of PET, CSF, and plasma tau biomarkers with Alzheimer's disease markers, finding that CSF and plasma p-tau181/p-tau217 are more linked to early AD markers, while tau-PET is strongly associated with cognitive and neurodegenerative progression.

This study uses deep learning to identify hippocampal subfields contributing to MCI progression, finding that certain subfields are more critical than others.

This paper shows that spatial inhibition of return is impaired in mild cognitive impairment and mild Alzheimer's disease, and may detect early pathological changes.

This study examines the effect of APOE genotypes and polygenic risk scores on dementia and cognitive decline in a longitudinal cohort of healthy older individuals, finding increased risks for APOE ε4 carriers and high PRS tertiles.

The paper uses multivariate pattern analysis to show that language network connectivity distinguishes prodromal Alzheimer's disease from healthy controls and increases at the prodromal stage.

This study uses a balanced random forest model to predict Alzheimer's disease conversion from early mild cognitive impairment patients using clinical features, achieving 93.6% accuracy.

This study finds that worse self-reported sleep relates to faster hippocampal microstructural decline in β-amyloid positive adults, independent of genetic risk.

The paper examines how age-related changes in brain network segregation correlate with Alzheimer's pathology and memory decline using fMRI and PET.

This study uses Mendelian Randomization to investigate causal relationships between blood metabolites and Alzheimer's Disease, identifying several metabolites as potential causal factors.

This study investigates memory fidelity in APOE ε4 carriers and finds no deficit in object-location binding despite spatial navigation impairments.

The paper uses machine learning and Bayesian modeling to analyze lesion patterns in stroke patients, showing how left and right hemisphere damage relates to specific cognitive impairments.

The paper shows that white matter hyperintensities (WMHs) cause systematic errors in grey matter (GM) volume estimates, affecting clinical associations like age and Alzheimer's disease.

This study uses structural equation modeling on UK Biobank data to show obesity leads to cognitive impairment via metabolic syndrome and cerebrovascular disease.

This paper develops a 3D-CNN model using transfer learning to predict conversion from MCI to DAT, achieving 82.4% accuracy and identifying key brain regions associated with disease progression.

The paper investigates the association between VKORC1 genetic variation and dementia risk, finding a link with vascular dementia but not general or Alzheimer dementia, and no effect of warfarin use on risk.

This paper uses Mendelian randomization to examine causal relationships between glycaemia, cognitive function, brain structure, and dementia in UK Biobank data, finding little evidence of causal associations.

This study finds that higher white matter network efficiency is associated with greater resilience to cognitive decline in elderly individuals, independent of amyloid and tau pathology.

This paper predicts Alzheimer's disease progression using multi-modal data and predictive learning models, showing better performance than single-modal approaches.

The paper identifies seven metabolites associated with executive function decline in early Alzheimer’s disease using longitudinal plasma samples from the WRAP cohort.

This study develops a deep neural network to subtype Primary Progressive Aphasia patients into three variants using speech and language data, achieving 80% accuracy.

This study identifies three dimensions of cortical ageing in the human brain and links the most general dimension to cognitive decline across multiple domains in older adults.