In this chapter, we delve into clinical applications of functional connectivity (FC) analyses using the example of schizophrenia. Schizophrenia is not only one of the most common psychiatric disorders but also one of the most debilitating ones. Further, its diverse clinical symptoms and neurodevelopmental aspects suggest involvement of various brain areas and networks, which renders it as a distinguished brain disorder to apply FC analyses to better understand the underlying disease pathophysiology.After presenting an overview on schizophrenia itself, we summarize the most commonly implemented FC approaches applied in schizophrenia research: graph theory, seed-based, and independent component analysis (ICA) approaches. We discuss findings from these approaches and highlight possible future directions of schizophrenia research. Despite the evident mathematical differences between these approaches, some commonalities are noticed: an anatomical overlap across studies, distinct patterns of dysconnectivity, and less flexible brain connectivity in patients with schizophrenia.