Georgios A. Antonopoulos

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).