Machine learning on task-based EEG outperforms resting-state for ADHD classification, while diffusion and structural MRI link white-matter integrity and grey-matter volume in fronto-parietal regions to effort-reward parameters and subclinical apathy.
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Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity
Machine learning on task-based EEG outperforms resting-state for ADHD classification, while diffusion and structural MRI link white-matter integrity and grey-matter volume in fronto-parietal regions to effort-reward parameters and subclinical apathy.