Artificial intelligence and major depression: Toward mechanistic and clinically actionable models

Figure 1 From etiological factors to artificial intelligence-driven precision psychiatry: A mechanistic framework for depression. AI: Artificial intelligence; PRS: Polygenic risk scores; GWAS: Genome-wide association studies; ACEs: Adverse childhood experiences; CVD: Cardiovascular disease; BDNF: Brain-derived neurotrophic factor; VEGF: Vascular endothelial growth factor; IGF-1: Insulin-like growth factor-1; NGF: Nerve growth factor; IL: Interleukin; CRH/ACTH: Corticotrophin-releasing hormone/adrenocorticotropic hormone; DMN: Default mode network; DLPFC: Dorsolateral prefrontal cortex; ACC: Anterior cingulate cortex; SCM: Structural causal models; DAG: Directed acyclic graphs; NLP: Natural language processing; MFCC: Acoustic feature extraction; CRP: C-reactive protein; SNPs: Single nucleotide polymorphisms; EEG: Electroencephalography; ERPs: Event-related potentials; TMS: Transcranial magnetic stimulation; REM: Rapid eye movement; fMRI: Functional magnetic resonance imaging; sMRI: Structural magnetic resonance imaging; DTI: Diffusion tensor imaging; PET/SPECT: Positron emission tomography/single-photon emission computed tomography; MDD: Major depressive disorder; PTSD: Post-traumatic stress disorder; ECT: Electroconvulsive therapy; GCN: Graph Convolution Network; GAT: Graph Attention Network; TRD: Treatment-resistant depression.