Since the advent of the resting state fMRI connectivity paradigm and advance diffusion weighted imaging techniques for white matter tracts, studies on connectivity abnormalities in major mood disorders – major depression and bipolar disorders have increased exponentially. Along with the development of imaging methods for data acquisition imaging analysis has also advanced significantly. From simple region of interest analysis, these methods have currently evolved into advanced techniques such as multi-modality parallel independent component analysis, graph theoretic analysis, dynamic causal modeling and artificial intelligence algorithms. These advanced methods have been used for understanding brain mood regulation circuitry, diagnostic classification, treatment biomarkers as well as how to integrate imaging, genomic and behavioral measures. As circuit level and large scale brain network paradigms have been developed, it has been increasingly recognized that noise in the data as well as the problems of multiple comparisons need to be addressed using advanced image acquisition and statistical methods. Furthermore, most findings have been reported at a group level and methods which can be used for interpreting at a single subject level need be developed to provide measures which can be used clinically. In this Research Topic, we welcome the latest reports on these exciting themes and thereby synthesize from the current literature to identify the most robust findings as well as chart out a course for future directions.
Since the advent of the resting state fMRI connectivity paradigm and advance diffusion weighted imaging techniques for white matter tracts, studies on connectivity abnormalities in major mood disorders – major depression and bipolar disorders have increased exponentially. Along with the development of imaging methods for data acquisition imaging analysis has also advanced significantly. From simple region of interest analysis, these methods have currently evolved into advanced techniques such as multi-modality parallel independent component analysis, graph theoretic analysis, dynamic causal modeling and artificial intelligence algorithms. These advanced methods have been used for understanding brain mood regulation circuitry, diagnostic classification, treatment biomarkers as well as how to integrate imaging, genomic and behavioral measures. As circuit level and large scale brain network paradigms have been developed, it has been increasingly recognized that noise in the data as well as the problems of multiple comparisons need to be addressed using advanced image acquisition and statistical methods. Furthermore, most findings have been reported at a group level and methods which can be used for interpreting at a single subject level need be developed to provide measures which can be used clinically. In this Research Topic, we welcome the latest reports on these exciting themes and thereby synthesize from the current literature to identify the most robust findings as well as chart out a course for future directions.