Advancing Tau-PET quantification in Alzheimer's disease with machine learning: introducing THETA, a novel tau summary measure.
Robel K GebreAlexis Moscoso RialSheela RaghavanHeather WisteKohl SparrmanFiona HeemanAlejandro Costoya-SánchezChristopher G SchwarzAnthony SpychallaVal LoweJonathan Graff-RadfordDavid S KnopmanRonald C PetersenMichael SchöllClifford R JackPrashanthi VemuriPublished in: Research square (2023)
Alzheimer's disease (AD) exhibits spatially heterogeneous 3R/4R tau pathology distributions across participants, making it a challenge to quantify extent of tau deposition. Utilizing Tau-PET from three independent cohorts, we trained and validated a machine learning model to identify visually positive Tau-PET scans from regional SUVR values and developed a novel summary measure, THETA, that accounts for heterogeneity in tau deposition. The model for identification of tau positivity achieved a balanced test accuracy of 95% and accuracy of ≥87% on the validation datasets. THETA captured heterogeneity of tau deposition, had better association with clinical measures, and corresponded better with visual assessments in comparison with the temporal meta-region-of-interest Tau-PET quantification methods. Our novel approach aids in identification of positive Tau-PET scans and provides a quantitative summary measure, THETA, that effectively captures the heterogeneous tau deposition seen in AD. The application of THETA for quantifying Tau-PET in AD exhibits great potential.
Keyphrases
- cerebrospinal fluid
- computed tomography
- machine learning
- pet ct
- positron emission tomography
- working memory
- transcranial magnetic stimulation
- pet imaging
- single cell
- artificial intelligence
- high resolution
- magnetic resonance
- cognitive decline
- mass spectrometry
- prefrontal cortex
- high frequency
- body composition
- rna seq
- deep learning