Thesis: Using multimodal neuroimaging to predict individual differences in cognitive abilities

This thesis focuses on developing robust neuroimaging-based biomarkers for cognitive abilities using stacked machine learning from multiple types of brain MRI. It demonstrates that these biomarkers exhibit high predictability, test-retest reliability, and cross-cohort generalisability, outperforming traditional methods.

Research Interests

• Resting state
• Cognition
• MRI (structural, diffusion, functional)
• EEG
• Machine Learning

Publications (selected)

• https://doi.org/10.1101/2024.05.03.589404
• https://doi.org/10.7554/eLife.87297.4
• https://doi.org/10.1016/j.neuroimage.2022.119588
• https://doi.org/10.3389/fnhum.2020.509075
• https://doi.org/10.1016/j.ijpsycho.2020.01.002
• https://doi.org/10.1097/WNR.0000000000001353