Chapter 13 discusses the analysis processes that transform raw brain imaging data into meaningful neuroscientific insights. It explains the methodical progression from preprocessing to advanced analytical techniques, emphasizing that analysis is not merely a technical afterthought but a fundamental component of neuroimaging research. The chapter begins by addressing preprocessing steps – quality control, artifact correction, normalization, and smoothing – that prepare data for subsequent analysis while preserving signal integrity. It then explores single-subject processing approaches that aggregate experimental conditions and trials to establish individual response patterns before proceeding to group-level analyses that enable population-level inferences. Statistical considerations receive particular attention, with the chapter explaining how techniques like statistical parametric mapping function as the interpretive lens through which brain activity becomes visible. The problematic issue of multiple comparisons is thoroughly examined, illustrating how whole-brain analyses necessitate statistical correction to prevent false positives in the tens of thousands of simultaneous tests typical in neuroimaging. The chapter extends beyond traditional univariate approaches to cover network analysis methodologies that reveal functional connectivity patterns between brain regions. It concludes by addressing emerging analytical frontiers: real-time analysis for brain–computer interfaces, closed-loop brain stimulation paradigms, and the methodological limitations that necessitate careful interpretation of neuroimaging results. Throughout, the chapter emphasizes that analytical expertise is as essential as technical proficiency with imaging hardware, and that understanding analytical limitations is crucial for responsible interpretation of the neural basis of cognition and behavior.