Tammie L.S. Benzinger

This study evaluates how biomarker variability affects individualized amyloid time estimation in Alzheimer's disease, finding that inter-individual variability is the main source of error.

This study finds that a history of traumatic brain injury with loss of consciousness (TBI-LOC) increases late-life amyloid PET burden, especially in APOE ε4 carriers with no clinical dementia.

This study uses multimodal normative modeling on ATN biomarkers to analyze Alzheimer Disease heterogeneity, finding greater intra-group variation in more severe stages and a correlation between disease severity index and cognitive decline.

This study develops a framework to analyze CSF flow patterns using low-b dMRI and identifies factors influencing these patterns across different brain regions.

This study shows hub disruption in ADAD follows a targeted attack pattern, detectable pre-clinically and linked to amyloid beta changes and neuronal activity.

The paper quantifies Aβ production from PSEN1 variants and links it to clinical and biomarker progression in ADAD, showing strong associations with disease trajectories.

This study finds that preserved aerobic glycolysis in the brain is linked to resilience against Alzheimer's disease pathology, while white matter hyperintensity burden is associated with reduced youthful AG patterns.

This study examines how brain age estimates from different MRI modalities relate to Alzheimer's disease biomarkers and cognition in early stages.

This study uses an epidemic spreading model to analyze A{beta} deposition patterns in sAD and ADAD, finding similar but distinct spreading patterns in some ADAD patients.

The paper describes MRI and PET protocols used in the DIAN study to investigate preclinical Alzheimer's disease and reports key imaging findings.

The paper develops a model to predict brain aging from resting state fMRI and finds accelerated brain aging in preclinical Alzheimer's disease, independent of amyloid-beta pathology.

This paper develops data-driven models to predict Alzheimer’s disease progression in dominantly-inherited cases, showing improved accuracy over traditional estimates of years until onset.