Approved Research

Predictive modelling of physiological and psychiatric comorbidities and study of brain circuits and genetic mechanisms

Lay summary

The coexistence of a variety of mental and physical diseases is referred to as multimorbidity. In recent years, the link between mental and physical illnesses has garnered widespread attention from various sectors of society. This systematic connection can enhance research and practice in the fields of medicine and psychology. There is abundant evidence and numerous clues supporting this connection in the academic community. Previous studies have only provided evidence for the existence of the "d" factor, but this factor has not been demonstrated at the behavioral, neuroscience, or genetic levels. Many psychiatric disorders are known to have high positive genetic correlations. However, researchers have also found opposing genetic effects in psychiatric disorders, further highlighting the complexity of the biological processes shared by multiple psychiatric disorders. Similarly, the biological processes shared by physical and mental diseases may be more complex. This complexity has led to the need for integrating behavioral data, neuroimaging data, and genetic data to explore the nature of the connections between comorbidities, the brain, behavior, and genes. Machine learning algorithms were utilized to build models for identifying brain structures that are strongly correlated with disease factors. Using standard modeling methods, we investigated whether changes in brain structure in the comorbidity group and healthy controls were associated with comorbidity factors, in order to determine if this process contributes to comorbidity. Second, we conducted genome-wide association studies (GWAS) to identify genes significantly associated with the "d" factor. Finally, a deep learning framework will be established to predict comorbidities based on multi-omics data.

We estimated that the program would last for three to four years. The research will offer evidence on the neuroscience of comorbidities, as well as genomic and proteomic connections to behavior. This will be valuable in aiding clinicians in diagnosing and treating diseases, as well as in disease prevention. It will also facilitate further research on comorbidities.