The imaging genetics targets the investigation and modelling of the link between image-derived endophenotypes and genetic determinants at different scales. Information regarding genetic variants, either at SNP or aggregated levels, and imaging descriptors derived from different modalities is jointly investigated relying on statistical/machine/deep learning models equipped with explanation and interpretation tools, opening to the holistic modeling of the brain and beyond.
Dolci, G., Rahaman, M. A., Boscolo Galazzo, I., Cruciani, F., Abrol, A., Chen, J., ... & Menegaz G., and Calhoun, V. D.
“Deep Generative Transfer Learning Predicts Conversion To Alzheimer’S Disease From Neuroimaging Genomics Data”
In 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW) (pp. 1-5). IEEE.
10.1109/ICASSPW59220.2023.10193683
Cruciani F., Altmann A., Lorenzi M., Menegaz G. and Boscolo Galazzo I.
"What PLS can still do for Imaging Genetics in Alzheimer's disease."
2022 IEEE-EMBS International Conference on Biomedical and Health Informatics
https://ieeexplore.ieee.org/abstract/document/9926813
Dolci, G., Rahaman M.A., Chen J., Duan K., Fu Z., Abrol A., Menegaz G., and Calhoun V.D.
“A deep generative multimodal imaging genomics framework for Alzheimer’s disease prediction”
22nd IEEE International Conference on BioInformatics and BioEngineering (2022)