Tau Protein Accumulation Trajectory-Based Brain Age Prediction in the Alzheimer's Disease Continuum.
Min WangMin WeiLuyao WangJun SongAxel RomingerKuangyu ShiJie-Hui Jiangnull nullPublished in: Brain sciences (2024)
Clinical cognitive advancement within the Alzheimer's disease (AD) continuum is intimately connected with sustained accumulation of tau protein pathology. The biological brain age and its gap show great potential for pathological risk and disease severity. In the present study, we applied multivariable linear support vector regression to train a normative brain age prediction model using tau brain images. We further assessed the predicted biological brain age and its gap for patients within the AD continuum. In the AD continuum, evaluated pathologic tau binding was found in the inferior temporal, parietal-temporal junction, precuneus/posterior cingulate, dorsal frontal, occipital, and inferior-medial temporal cortices. The biological brain age gaps of patients within the AD continuum were notably higher than those of the normal controls ( p < 0.0001). Significant positive correlations were observed between the brain age gap and global tau protein accumulation levels for mild cognitive impairment ( r = 0.726, p < 0.001), AD ( r = 0.845, p < 0.001), and AD continuum ( r = 0.797, p < 0.001). The pathologic tau-based age gap was significantly linked to neuropsychological scores. The proposed pathologic tau-based biological brain age model could track the tau protein accumulation trajectory of cognitive impairment and further provide a comprehensive quantification index for the tau accumulation risk.
Keyphrases
- resting state
- white matter
- cerebrospinal fluid
- functional connectivity
- mild cognitive impairment
- end stage renal disease
- cognitive decline
- cerebral ischemia
- newly diagnosed
- chronic kidney disease
- ejection fraction
- cognitive impairment
- neoadjuvant chemotherapy
- amino acid
- multiple sclerosis
- prognostic factors
- working memory
- binding protein
- blood brain barrier
- peritoneal dialysis
- mass spectrometry
- brain injury
- deep learning
- lymph node
- high speed
- locally advanced
- subarachnoid hemorrhage
- machine learning
- convolutional neural network
- climate change
- rectal cancer