Dementia and Cognitive Impairment Research

This study investigates the association between Klotho gene variants and cognitive function in 335,074 UK Biobank participants, finding no significant effect of KL-VS haplotype on cognitive function or decline.

The paper analyzes spoken language data from healthy elderly people to find that variations in scaling laws correlate with cognitive functions, suggesting language scaling as a potential biomarker for cognitive decline.

The paper develops a polygenic risk score integrating AD biomarkers and shows it better predicts cognitive decline than a clinical AD-based PRS.

This study identifies metabolites and pathways in serum and brain tissue associated with cognitive impairment and develops models to predict antecedent cognitive decline in older adults.

This paper uses machine learning to build and analyze MRI-based survival models predicting conversion from mild cognitive impairment to Alzheimer's disease.

This paper predicts MMSE scores from finger-tapping measurements using a machine learning pipeline, discovering associations between motor attributes and cognitive ability.

This study finds a genetic link between schizophrenia polygenic risk and psychotic symptoms, particularly delusions, in Alzheimer's disease through a meta-analysis of 11 cohort studies.

This paper develops a deep learning model using MRI to classify Alzheimer’s disease and finds it outperforms traditional biomarkers in detecting prodromal disease and predicting progression.

The paper investigates the association between dementia severity and global neuropsychiatric disturbances, finding that dementia-related factors are strongly linked to general psychopathology and specific behavioral symptoms.

This paper introduces a medical image processing technology for diagnosing and monitoring Alzheimer’s disease using MRI and cognitive assessments, achieving high accuracy in both diagnosis and prognosis.

This study investigates the association between sleep-disordered breathing and gray matter volume or brain aging in Alzheimer's disease and mild cognitive impairment patients, finding no significant links.

This study compares whole hippocampus and subfield biomarkers for Alzheimer’s disease detection and prediction using multimodal MRI data, finding subiculum biomarkers improve prediction accuracy.

The paper investigates how mitochondrial DNA haplogroups and a polygenic risk score interact to influence dementia risk and age of onset.

The paper shows that cognitive function predicts progression to Aβ-positivity, even when controlling for baseline biomarker levels, suggesting cognitive measures may identify early AD risk.

The paper shows that higher Alzheimer’s disease polygenic risk scores are linked to greater pupillary dilation during cognitive tasks in midlife, suggesting a genetic biomarker for early AD risk.

The paper examines Contralateral Delay Activity (CDA) as a marker for Mild Cognitive Impairment risk in older adults, finding no group differences in CDA amplitudes despite behavioral performance differences.

The paper assesses tau pathology in cognitively normal older adults using PET imaging and links it to age, sex, race, amyloid status, and cognitive decline.

The paper assesses memorability of photographs in healthy controls, subjective cognitive decline, and mild cognitive impairment, identifying images with diagnostic potential.

The paper evaluates the agreement between 18F-Florbetapir PET imaging and CSF biomarkers measured on the Lumipulse G System, finding good diagnostic agreement for Aβ1-42, tTau, pTau, and their ratios with Aβ1-42.

This paper develops a model combining polygenic risk scores and quantitative MRI to predict dementia onset up to eight years earlier than traditional methods.

This study develops a unified neurocognitive model of anterior temporal lobe contributions to semantics, language, social behavior, and face recognition using data from semantic dementia patients.

The paper maps cognitive performance to brain structure using voxel-based morphometry and subcortical shape analysis in a large population sample.

The paper develops a machine learning algorithm to predict MCI conversion to AD using clinical data, achieving high AUROC and sensitivity.

This study shows that adding VGF peptide levels to conventional biomarkers improves prediction of conversion from mild cognitive impairment to Alzheimer's Disease.

BLAzER is a semi-automated workflow for regional brain PET quantification validated on ADNI data, showing strong agreement with ADNI and minimal discrepancies.

The paper uses machine learning on a large administrative claims dataset to predict incident dementia 4-5 years in advance, achieving an AUC of 0.693.

This study identifies neurology-related proteins in plasma associated with cognitive ability and brain volume in older adults, with partial mediation by brain volume.

This study uses Mendelian randomization to assess if antihypertensive drugs reduce Alzheimer’s disease risk by lowering systolic blood pressure, finding limited evidence of an effect.

The paper examines neural correlates of exceptional memory in SuperAgers using PET and MRI, finding increased FDG SUVr in the left subgenual ACC and reduced connectivity between sACC and posterior cingulate.

This paper identifies three atrophy-cognitive factors in Alzheimer’s disease using a hierarchical Bayesian model, showing they are stable across cohorts and associated with distinct Tau deposition patterns.