Motivated behaviour relies on the brain’s capacity to evaluate effort and re- ward. Dysregulation within these processes contributes to a spectrum of conditions, from hyperactivity in attention-deficit/hyperactivity disorder (ADHD) to dimin- ished goal-directed behaviour in apathy – characterised by disrupted effort sensi- tivity and/or reward sensitivity. This thesis investigates the neural mechanisms underlying ADHD using electroencephalography (EEG) and examines individual differences in effort and reward sensitivity using neuroimaging, applying machine learning approaches through three main studies. In Study 1, task-based and resting-state EEG were employed with machine learn- ing models to classify adult individuals with ADHD and healthy controls. Machine learning classifiers trained on task-based EEG during a stop signal task outper- formed those trained on resting-state EEG data, with the strongest predictive fea- tures arising from gamma-band spectral power over fronto-central and parietal re- gions. These results demonstrate that ADHD-related neural alterations become most evident when inhibitory control is actively engaged. This also aligns with evidence of reduced gamma band activity in ADHD, potentially reflecting disrupted dopamine-mediated reward circuits. In Study 2, to investigate the structural basis of motivated behaviour, diffusion MRI and whole-brain data-driven permutation-based analyses were used to identify the association between white matter integrity and two computationally modelled parameters reflecting effort and reward sensitivity. Effort sensitivity related to SMA- , dACC- and OFC-connected white matter clusters, and reward sensitivity addition- ally involved fronto-parietal and sensorimotor clusters, with tracts connected to the SMA emerging as a central hub for both of these parameters. Using machine learn- ing approaches, individual differences in these sensitivity parameters were reliably predicted by microstructural integrity at identified clusters. Finally in Study 3, grey matter morphometry, specifically with regional grey matter volumes extracted from structural T1-weighted MRI, was used to examine the grey matter correlates of individual differences in effort sensitivity, reward sensitivity, and subclinical apathy. Effort sensitivity was related to volumes in the dorsal insula, primary motor cortex, and superior parietal cortex, whereas reward sensitivity was associated with volumes in the ventromedial putamen and posterior parietal cortex. Reduced grey matter volume in the medial prefrontal cortex, supplementary motor areas, and ventral temporal regions was also associated with higher apathy scores. Machine learning analyses confirmed that grey matter volumes allowed robust decoding of reward sensitivity and apathy levels. Machine learning to study the neural mechanism of motivated behaviour. Through these three studies, the thesis advances understanding of the neural mechanisms underlying motivated behaviour by identifying neural predictors of ADHD and individual differences in effort and reward sensitivity. Across three studies, common neural circuits emerged, most notably the fronto-parietal circuits, indicating its central role in effort valuation and reward processing to support motivated behaviour. The findings in this thesis may potentially serve as clinically neural biomarkers not only for improving diagnostic accuracy in conditions such as ADHD and motivational impairments, but also for guiding the development of personalised and targeted neurotechnological interventions for individuals with neuropsychiatric disorders.