Susan M. Landau

The paper shows aging and Alzheimer's disease alter distinct functional connectivity axes in the brain, with AD affecting the sensory-association axis and aging affecting the representation-executive axis.

This paper identifies a posterior predominant amyloid PET binding pattern in multiple cohorts, showing its clinical and biological relevance and suggesting the need for more nuanced analysis methods.

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.

The paper investigates whether hemispheric asymmetry in tau pathology in Alzheimer's disease is linked to reduced inter-hemispheric connectivity or asymmetric amyloid-beta distribution, finding a strong association with amyloid-beta laterality.

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.

The paper links reduced coupling between cerebrospinal fluid flow and global brain activity to increased tau deposition and cognitive decline in Alzheimer's disease.

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 machine learning to predict future regional tau accumulation in asymptomatic and early Alzheimer's disease using multimodal data, showing improved accuracy over traditional measures.

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

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