Advancement in neuroimaging has improved our understanding of the human brain by allowing nonintrusive visualization of its structure and function. As imaging technologies progress, the incorporation of computational methods varies from signal processing and machine learning to advanced statistical modeling and has become necessary in extracting meaningful information from complex brain data. This chapter explores neuroimaging from the perspective of computational analysis, highlighting how algorithms enhance image acquisition, noise reduction, segmentation, and interpretation. Further, it covers the role of computational tools in functional biomarker identification, connectivity mapping, and neurological disorder diagnosis. The combination of neuroscience and data science and computational neuroimaging provides deep understanding of brain mechanisms and enables the development of actual and predictive models of brain health.