Deep learning model built on neuroimaging data identifies “Brain Age Gaps” as markers of Alzheimer’s disease (AD)

Brain Age Gap is a Composite Biomarker for Dementia Pathology or Severity (GEN): Mayo Clinic scientists have developed a computational model that predicts brain age using a large collection of neuroimaging data obtained using FDG-PET (fluorodeoxyglucose positron emission tomography) and structural MRI (magnetic resonance imaging). The deep learning-based model tests the relationship between brain age gaps in various forms of dementia, including mild cognitive impairment (MCI), Alzheimer’s disease (AD), frontotemporal dementia (FTD), and Lewy body dementia (LBD), as well as in normal brains. … “The ability for deep learning to accurately predict age based on brain imaging data has been known for some time. However, looking at brain age gap or the difference between predicted and actual age, has been thought to have the potential to be utilized as a biomarker. Others have argued that such a brain age gap is only able to mark treatment-level biological differences and is unable to track changes in state and therefore should not be interpreted as accelerated brain aging,” (Senior author of the study, Dr. David) Jones said. “The main finding of our study is that we could indeed find evidence that high brain age gap is behaving as an accelerated brain aging biomarker.” The Study: Deep learning-based brain age prediction in normal aging and dementia (Nature Aging). Abstract: Brain aging is accompanied by patterns of functional and structural change. Alzheimer’s diseas...
Source: SharpBrains - Category: Neuroscience Authors: Tags: Brain/ Mental Health Technology & Innovation AD AD biomarker Alzheimer’s Disease brain-aging Cognitive-impairment deep learning frontotemporal dementia Lewy body dementia mild-cognitive-impairment neurodegenerative neurodegenerat Source Type: blogs