Major Depressive Disorder Signatures: A Review of Artificial Intelligence (AI)-Powered Insights Into Gut Dysbiosis

Major Depressive Disorder (MDD) is a highly common neuropsychiatric disorder globally. A variety of factors contribute to the neuropathology of MDD. Microbiome research in neuropsychiatric disorders such as MDD has recently attracted attention. Indeed, the gut-brain axis could influence the course of MDD through metabolites such as Gamma-Aminobutyric Acid (GABA), Quinolinate, and other factors. Such metabolites may modulate the balance of excitatory and inhibitory signals. Moreover, MDD features abundant hyperinflammatory bacteria, whereas anti-inflammatory butyrate-synthesizing genera are decreased.

Despite mounting evidence on the implications for the microbiome in MDD, it is unclear whether a bidirectional or causal relationship is in effect. To overcome this challenge, researchers have utilized AI tools to investigate the complex association between the microbiome and MDD.

Additionally, there is no solid biomarker recognized for diagnosis and prognosis of MDD, while further application of AI using ML protocols, such as random forest, NNs, SVM, and DL models, could offer a rather solid and reliable comprehension of the complicated nature of microbiome-MDD interplay.

The present article reviews microbiome alterations as well as inflammatory and metabolic pathways in MDD with a focus on AI technology including support vector machines (SVM), random forests (RF), deep neural networks (DNNs), and autoencoders, which are used to identify microbial biomarkers, predict treatment results, and support personalized medicine.