Dementia and Cognitive Impairment Research

This study compares hippocampal volume and SNIPE score to predict dementia onset in cognitively normal individuals, finding SNIPE score has a larger effect size in distinguishing progressors.

The paper develops a 5-minute computerized cognitive assessment tool (ICA) that reliably measures cognitive performance through visual processing speed and accuracy, showing strong correlation with standard tests.

This study finds higher plasma cortisol levels are associated with reduced brain glucose metabolism and gray matter volume in Alzheimer's disease and mild cognitive impairment patients.

This study finds that white matter hyperintensities (WMHs) are common in midlife and are associated with lower IQ and greater cognitive decline from childhood to midlife.

The paper derives age-related normative percentiles for hippocampal and grey matter volume using UK Biobank data to aid in clinical and research assessments of brain health.

This paper uses multimodal MRI and machine learning to classify Alzheimer's disease and mild cognitive impairment, showing high accuracy in diagnosis and progression prediction.

This study uses deformation-based morphometry to quantify cerebral atrophy in bvFTD patients, showing ventricular expansion as a sensitive marker for disease progression and providing sample size estimates for clinical trials.

The paper uses information-theoretic model-averaging to analyze cognitive decline factors in mid-life, finding sex, literacy, and APOE ɛ4 effects with varying impacts.

The paper identifies genetic loci associated with cognitively-defined subgroups of late-onset Alzheimer’s disease using SNP data from multiple studies.

The paper develops longitudinal standards for mid-life cognitive performance using quantile regression to identify abnormal within-person changes and their associations with clinical outcomes.

This study applies 3D CNNs to MRI data to classify Alzheimer’s disease, showing high accuracy and identifying the hippocampal formation as a key predictive region.

This study links Alzheimer's disease genetic risk loci to incident Mild Behavioral Impairment using a genetic risk score and logistic regression analysis.

This study uses CVBOLD to detect altered physiological pulsations in Alzheimer’s disease and frontotemporal dementia, finding disease-specific patterns in brain regions.

The 100-plus Study characterizes Dutch centenarians with preserved cognitive health, finding associations between APOE alleles and mortality, and higher education and socio-economic status.

This study uses data-driven clustering to identify spatial patterns of tau-PET distribution in Alzheimer’s disease and compares them to pathology-driven ROIs, finding two clusters with strong cognitive associations.

The paper identifies altered bile acid profiles in Alzheimer's disease, linking them to cognitive impairment and genetic variants through a large study.

The paper identifies associations between serum bile acid metabolites and AD biomarkers in the A/T/N framework, suggesting a potential role for bile acid pathways in AD pathophysiology.

The paper examines spatial relationships between white matter degeneration, amyloid load, and cortical volume in amnestic mild cognitive impairment and finds that WM degeneration correlates more strongly with cortical volume than amyloid.

The paper examines MRI patterns and functional connectivity in Alzheimer's Disease patients and identifies a functional signature in c-MCI that may indicate conversion to AD.

This paper introduces SuStaIn, a machine learning method that identifies disease subtypes and their temporal progression from cross-sectional data, improving prediction of diagnostic conversion in neurodegenerative diseases.

This paper uses machine learning on brain morphometry and connectomes from MRI to predict Alzheimer's disease risk and progression, achieving high accuracy in both single-site and multi-site datasets.

The paper introduces an ANTs-based longitudinal cortical thickness pipeline and demonstrates its effectiveness in analyzing ADNI-1 data for detecting structural brain changes over time.

This study shows that iron-sensitive MRI can detect AD pathology distribution patterns in post-mortem brain tissue, with EOAD showing more severe MRI changes.

The paper detects pre-symptomatic shape changes in the thalamus using spatiotemporal analysis of MRI scans from the GENFI study, finding differences five years before symptoms appear.

This study examines neuropathological hallmarks in centenarians' brains and their association with cognitive performance, finding that some pathologies correlate with lower cognitive test results.

The paper predicts age-related brain metabolic changes using a variational autoencoder and shows APOE4 status significantly affects metabolic degeneration in specific brain regions.

This study compares MRI-based feature representations for predicting MCI-to-AD conversion, finding regional features perform best but without significant statistical differences.

The paper develops a verbal similarity task to differentiate frontal lobe patients from others, finding differentiation errors are specific to frontal patients.

This study examines the association between polygenic risk scores for neurodegenerative disorders and cognitive performance, finding lower cognitive performance with higher risk scores for AD, ALS, and FTD.