- Peng H, Gong W, Beckmann CF, Vedaldi A, Smith SM. Accurate brain age prediction with lightweight deep neural networks. Med Image Anal. 2021. [Paper](https://doi.org/10.1016/j.media.2020.101871), [Code](https://github.com/ha-ha-ha-han/UKBiobank_deep_pretrain)
- Jonsson, B.A., Bjornsdottir, G., Thorgeirsson, T.E. et al. Brain age prediction using deep learning uncovers associated sequence variants. Nat Commun 10, 5409 (2019). [Paper](https://www.nature.com/articles/s41467-019-13163-9), Code on request.
- Baecker L, Garcia-Dias R, Vieira S, Scarpazza C, Mechelli A. Machine learning for brain age prediction: Introduction to methods and clinical applications. EBioMedicine. 2021 [Paper](https://pubmed.ncbi.nlm.nih.gov/34614461/)
- Leonardsen EH, Peng H, ... Wang Y. Deep neural networks learn general and clinically relevant representations of the ageing brain. Neuroimage. 2022. [Paper](https://pubmed.ncbi.nlm.nih.gov/35462035/)
Datasets
- UKBB
- ADNI
- PPMI
UKB data wrangling
- Copy files from squashfs on Beluga
- Organize them in psudo-bids
for i in `ls | grep sub- | grep -v json`; do
mkdir -p ../`echo $i | cut -d "_" -f1`/ses-2/anat;
mv `echo $i | cut -d "_" -f1`* ../`echo $i | cut -d "_" -f1`/ses-2/anat/;
done
- input_visit --> output_visit
1. Baseline --> Baseline
2. Baseline + Followup --> Baseline
3. Baseline + Followup --> Baseline + Followup
- features + models
1. DKT (Ridge, RF)
2. T1w normalized to the MNI template(s) (SFCN, LSN)
- nulls
1. Median (+2) age prediction
- perf metrics
1. mean abs error
2. pearson's r
3. temporal consistency
Experiments: model biases on control cohorts
- age vs brainage_error bias (effectiveness of linear correction)
- short vs long visit_delta: UKB longterm cohort (FU - BL > 3yr)
- study+scanner variation: ADNI, PPMI control cohorts
Experiments: brainage gap
- Single number: Disease stages vs study-specific controls vs long_visit UKBB vs short_visit ukbb
- Note even with two visits only BL brainage value is likely to be useful.
- DeepNet representations: Richer constellation / clusterring of subjects from model embeddings