Yaakov Stern

The paper examines how resting-state brain signal variability relates to age, cognition, and cognitive reserve using neuroimaging data from 470 participants.

This study examines how amyloid-beta burden affects task-modulated brain connectivity during working memory retrieval and links these changes to task performance and cognitive resilience.

This study uses machine learning to show that integrating brain connectome data with amyloid-beta PET imaging improves prediction of cognitive decline in older adults.

This study uses radiomic features from thalamic nuclei to identify associations with cognitive performance in multiple sclerosis patients, finding radiomic features outperform volumetric measures.

This study uses Network Control Theory to analyze differences in brain energy landscapes between MCI patients and healthy controls, finding lower transition energy in the limbic network and reduced global entropy in MCI.

The paper examines cf-mtDNA and GDF15 as potential biomarkers of mitochondrial health in aging, finding GDF15 correlates with age and diabetes while cf-mtDNA does not.

The paper links cognitive reserve to brain activity during memory formation, showing it moderates the impact of Alzheimer's pathology on cognitive performance and predicts slower decline.

The paper examines immunosenescence trajectories in diverse populations, finding associations between immunological age and brain atrophy, while highlighting CMV status and genetic ancestry as key factors.

This paper evaluates the polygenic risk score for Alzheimer's disease in Japanese individuals, showing its utility despite genetic differences from European populations.

The paper develops and validates a measure of cognitive reserve using task-based fMRI data and applies it to resting-state data, finding theoretical validity in some contexts but not others.

The paper examines brain hemodynamic responses and structural differences in extremely preterm and full-term individuals solving fraction subtraction using different strategies.

This study uses persistent homology-based functional connectivity to analyze resting-state brain networks and finds it better explains cognitive ability variance than traditional measures.

This paper develops a deep learning model to quantify cognitive reserve using structural MRI data from healthy cohorts and validates its generalizability across different populations and scanners using transfer learning.

This study examines how dynamic neural flexibility during rest and task-based scans relates to age and cognitive performance in adults, finding task-based flexibility is more strongly linked to cognitive outcomes.

The paper investigates whether diurnal rhythm affects cognitive performance in healthy adults and finds no significant effect of testing time on cognitive test results.

This study uses an epidemic spreading model to show that tau spreads through neuronal connections in Alzheimer's disease, with beta-amyloid accelerating this process in some brain regions.

This study uses machine learning to predict Alzheimer's disease incidence from administrative health data, achieving moderate accuracy in 1-4 year predictions.

The paper examines the relationship between brain-predicted age difference scores and cognitive functions using multi-site MRI data, finding significant negative correlations in several cognitive domains.

The paper identifies a task-invariant cognitive reserve network by analyzing functional connectivity changes during cognitive tasks and their correlation with IQ.

This paper develops a blood-based machine learning model to predict cerebral spinal fluid Aβ1-42 levels, showing high accuracy in identifying Alzheimer's disease risk.

The paper investigates how low white matter microstructure in young adults correlates with white matter hyperintensities in older adults, finding that normatively low fractional anisotropy regions are more prone to damage.

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 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.