Machine Learning Identifies Large-Scale Reward-Related Activity Modulated by Dopaminergic Enhancement in Major Depression

Keyphrases

• Major Depressive Disorder 100%
• Machine Learning 100%
• Placebo 80%
• Whole Brain 40%
• Control Subjects 30%
• Healthy Controls 30%
• Striatal 20%
• Functional Magnetic Resonance Imaging 20%
• System Level 20%
• Area under the Curve 20%
• Dopaminergic Signaling 20%
• Machine Learning Techniques 10%
• Pathophysiology 10%
• Antidepressants 10%
• Placebo-controlled 10%
• Targeted Intervention 10%
• Reward Processing 10%
• Experimental Therapeutics 10%
• Brain Response 10%
• Therapeutic Approaches 10%
• Model Selection 10%
• Reward Anticipation 10%
• Brain Data 10%
• Pharmacological Challenge 10%
• Amisulpride 10%
• Unmedicated 10%
• Data-driven Approach 10%
• Rigorous Evaluation 10%
• Brain Features 10%
• Pathophysiological Mechanisms 10%
• Predictive Features 10%
• Pharmacological Mechanism 10%
• Predictive Classification Model 10%
• Truncated Model 10%

Pharmacology, Toxicology and Pharmaceutical Science

• Dopamine Receptor Stimulating Agent 100%
• Major Depression 100%
• Placebo 90%
• Pathophysiology 10%
• Antidepressant 10%
• Amisulpride 10%
• Experimental Therapeutic 10%
• Model Selection 10%

Neuroscience

• Major Depressive Disorder 100%
• Dopaminergic 100%
• Placebo 90%
• Functional Magnetic Resonance Imaging 20%
• Antidepressant 10%
• Amisulpride 10%