EEG Stop-Signal Task Classification Pipeline

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Paper title: Machine learning approaches to uncover the neural mechanisms of motivated behaviour: from ADHD to individual differences in effort and reward sensitivity Abstract: Motivated behaviour relies on the brain's capacity to evaluate effort and reward. Dysregulation within these processes contributes to a spectrum of conditions, from hyperactivity in attention-deficit/hyperactivity disorder (ADHD) to diminished goal-directed behaviour in apathy. 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 learning models to classify adult individuals with ADHD and healthy controls. Machine learning classifiers trained on task-based EEG during a stop signal task outperformed those trained on resting-state EEG, with the strongest predictive features arising from gamma-band spectral power over fronto-central and parietal regions. In Study 2, diffusion MRI and whole-brain permutation-based analyses identified associations between white matter integrity and computationally modelled parameters reflecting effort and reward sensitivity, with SMA-connected tracts emerging as a central hub. In Study 3, grey matter volumes from structural T1-weighted MRI were used to examine correlates of effort sensitivity, reward sensitivity, and subclinical apathy, with machine learning confirming robust decoding of reward sensitivity and apathy levels. Across studies, fronto-parietal circuits emerged as centr Paper body excerpt: Report GitHub Issue × Title: Content selection saved. Describe the issue below: Description: Submit without GitHub Submit in GitHub Back to arXiv Why HTML? Report Issue Back to Abstract Download PDF 1 Introduction 1.1 Introduction 1.2 Research questions 1.3 Thesis structure 1.4 Related publications 2 Related work and background 2.1 Attention-deficit hyperactivity disorder 2.1.1 Neural mechanism underlying ADHD Structural correlates of ADHD Functional brain networks and connectivity 2.1.2 Diagnostic Challenges in ADHD Subjectivity and variability in behavioural assessment Heterogeneity and comorbidity Developmental considerations The need for objective biomarkers 2.1.3 EEG markers of ADHD The theta/beta ratio Spectral band abnormalities across frequency bands 2.2 Effort-based decision making and individual differences in effort and reward sensitivity 2.2.1 Neural mechanisms underlying EBDM 2.2.2 Disruption of EBDM and apathy 2.2.3 Neuroimaging and apathy Grey matter correlates of apathy White matter integrity correlates of apathy Effort and reward sensitivity in computational modelling 2.3 Machine learning in neuroscience research 2.3.1 Machine learning approaches for EEG-based prediction 2.3.2 Strutural neuroimaging and behavioural prediction 3 Task-based versus resting-state EEG for machine learning classification of adult ADHD 3.1 Introduction 3.2 Methods 3.2.1 Participants 3.2.2 EEG data acquisition 3.2.3 EEG data processing and feature extraction EEG data cleaning Resting state EEG data Stop signal task EEG data 3.2.4 Machine learning analysis Model training and evaluation method Permutation test Feature importance analysis 3.3 Results 3.3.1 Classification results With resting-state EEG data With stop signal EEG data Statistical analysis on model performance 3.3.2 Feature importance 3.4 Discussion Superior performance of task-based EEG classification in adult ADHD Modest performance of resting-state EEG classification in adult ADHD Reduced gamma band power and motivation-related circuits Methodological considerations and future work 3.5 Conclusion 4 White matter integrity and computational modelling of effort and reward sensitivity 4.1 Introduction 4.2 Methods 4.2.1 Participants 4.2.2 Data acquisition MRI data acquisition Behavioural data acquisition 4.2.3 Data analyses Computational modelling of acceptance rates MRI data analyses 4.2.4 Statistical analyses Whole-brain cluster-based analysis 4.3 Results 4.3.1 Inter-individual variability in effort and