Merle C. Hoenig

Sep 3, 2024 | biorxiv.org | Merle C. Hoenig |Verena Dzialas |Elena Doering |Gérard N Bischof

AbstractObjective: To examine interactive effects of modifiable factors, genetic determinants and load-dependent pathology effects on tau pathology progression. Methods: Data of 162 amyloid-positive individuals were included, for whom longitudinal [18F]AV-1451-PET scans, baseline information on global amyloid load, ApoE4 status, body-mass-index (BMI), hypertension, education, neuropsychiatric symptom severity and demographic information were available in ADNI.

May 1, 2024 | flipboard.com | Verena Dzialas |Merle C. Hoenig |Stéphane Prange |Gérard N Bischof |Alexander Drzezga |Thilo van Eimeren

Structural underpinnings and long-term effects of resilience in Parkinson’s diseaseResilience in neuroscience generally refers to an individual’s capacity to counteract the adverse effects of a neuropathological condition. While …

Jan 16, 2024 | biorxiv.org | Merle C. Hoenig |Elena Doering |Gérard N Bischof |Alexander Drzezga

AbstractIntroduction: Consistent with the amyloid-cascade-hypothesis, we tested whether regional amyloid burden is associated with tau pathology increases in spatially independent brain regions and whether functional connectivity serves as a mediator bridging the observed spatial gap between these pathologies. Methods: Data of 98 amyloid-positive and 35 amyloid-negative subjects with baseline amyloid (18F-AV45) and longitudinal tau (18F-AV1451) PET were selected from ADNI.

Jan 2, 2024 | jnm.snmjournals.org | Elena Doering |Georgios A. Antonopoulos |Merle C. Hoenig |Thilo van Eimeren

machine learningcognitive impairmentneuroimagingBrain aging entails changes in cognitive performance, brain function, and structural parameters of brain integrity. Brain age can be modeled using machine learning algorithms by estimating a person’s chronologic age from their neuroimaging data. Higher brain age than chronologic age, that is, a positive brain age gap (BAG), is associated with neurodegenerative diseases such as Alzheimer disease (AD).

Nov 30, 2023 | jnm.snmjournals.org | Georgios A. Antonopoulos |Merle C. Hoenig |Thilo van Eimeren |Elena Doering

machine learningcognitive impairmentneuroimagingBrain aging entails changes in cognitive performance, brain function, and structural parameters of brain integrity. Brain age can be modeled using machine learning algorithms by estimating a person’s chronologic age from their neuroimaging data. Higher brain age than chronologic age, that is, a positive brain age gap (BAG), is associated with neurodegenerative diseases such as Alzheimer disease (AD).