Brains are among the most complex evolved objects. In recent years we have seen an explosion in the development of artificial cognitive systems constructed in silico (i.e. digital brains). In fact, we are now capable of creating digital brains whose operation is so complex that they are effectively black boxes (Castelvecchi, 2016; Gunning, 2017). Previous work (Marstaller et al., 2013; Hintze et al., 2018; Kirkpatrick and Hintze, 2019) has identified and expanded upon various information-theoretic measures that can shed light on the internal processes of digital brains. Here we introduce a new information-theoretic measure called Fragmentation (F) which can measure how fragmented information is in an a digital brain. To provide a example of the application of F we look at the evolutionary emergence of complexity. Questions regarding the evolution of complexity have been of interest for as long as evolution has been a theory (Gregory, 1935). Nature is responsible for the development of a massive array of complex organisms, each comprised of various organs and regulatory systems that are themselves complex (McShea and Brandon, 2010). It has been observed that complexity can evolve even when complexity itself is being selected against (Beslon et al., 2021). We conclude by using F to show a case of evolved complexity that results in coincidental encryption.