Farzane Lal Khakpoor

This study uses domain adaptation methods to reduce cross-ethnic prediction errors in neuroimaging-based cognitive prediction using the ABCD dataset.

This study evaluates MRI modalities' ability to predict cognitive functioning across age, finding that combined MRI markers best predict between-person differences, while longitudinal changes are less captured.

This study develops multimodal MRI markers to predict and track cognitive functioning in ADHD over time, showing they capture cognitive development and symptom relationships.

The paper benchmarks ethnic bias in MRI-based cognitive prediction models and finds that balanced sampling reduces disparities without accuracy loss, with structural MRI showing greater bias than task-based fMRI.

This paper introduces a stacking machine learning approach to improve predictability, test-retest reliability, and generalisability of brain-wide associations for cognitive abilities using multimodal MRI data.