Innovations in data visualization punctuate the landmark advances in human connectome research since its beginnings. From tensor glyphs for diffusion-weighted imaging, to advanced rendering of anatomical tracts, to more recent graph-based representations of functional connectivity data, many of the ways we have come to understand the human connectome are through the intuitive insight these visualizations enable. Nonetheless, several unresolved problems persist. For example, probabilistic tractography lacks the visual appeal of its deterministic equivalent, multimodal representations require extreme levels of data reduction, and rendering the full connectome within an anatomical space makes the contents cluttered and unreadable. In part, these challenges require compromises between several tensions that determine connectome visualization practice, such as prioritizing anatomic or connectomic information, aesthetic appeal or information content, and thoroughness or readability. To illustrate the ongoing negotiation between these priorities, we provide an overview of various visualization methods that have evolved for anatomical and functional connectivity data. We then describe interactive visualization tools currently available for use in research, and we conclude with concerns and developments in the presentation of connectivity results. © 2013 The Authors. Published by Elsevier Inc. When investigation of the human brain was limited by what the eye could see, its structure, albeit elaborate, was within our mental grasp. But the resurgence of interest in connectivity, like that of cytoarchitectonics a century ago, has established a new dimension of information to assimilate. As our data grow in intricacy, the images we create reflect how we bestow them with significance -because implicit (and often explicit) in our visualizations of the human connectome are the categories, metaphors, and abstractions that we use to make it comprehensible. An analogous transition in visual metaphors was underway with the emergence of mass transit systems over a century ago. Much like the complexity of the brain, the unfamiliar transport systems presented a challenge of how to effectively communicate their structure to the public. Early London Underground maps found comfort in familiarity, and wove the train paths unobtrusively into the contours of the existing cityscape (Fig. 1, top) . It was only decades later that the crisp, emblematic form -subsequently mimicked the world overcame into being (Fig. 1, bottom) . In emphasizing the relative connections, rather than the underlying terrain, the resultant image offered the viewer an intuitive mapping of the relevant information. Maps never show us everything about a place or a space; their value is rather in the cartographer's insight to enunciate selected features over others. Every map has a context -and when the content is overflowing with innumerable data dimensions, the task of creating intuitive, informative, and candid images becomes all the more challenging. The mapping of connections in the human brain has been a visual tale of increasing complexity, continuously pulled between various priorities of data presentation. Our illustrations and figures narrate the transition from describing the brain as a three-dimensional object to describing the proximity of areas in terms of the strength of connections. These two basic models of brain space, though there are more, are the basis for the territorial battles for defining space. The result in any connectome image is a content-dependent balance of anatomical clarity versus connectomic complexity. Connectivity mapping has also been forced to struggle with evolving methodologies -analytic tools that in some cases may have overstated their actual information content. Controversies surrounding the veracity of paths derived from diffusion weighted imagingbased tractography (Dyrby et al., 2007; Hubbard and Parker, 2009; Jbabdi and Johansen-Berg, 2011) or functional connectivity derived using controversial analytic approaches (e.g., Saad et al., 2012; Smith et al., 2011) run the risk of visualizations that make the underlying data appear deceptively tangible. The aim of an image, from this perspective, is to accurately show the uncertainty in the data (Allen et al., in press) -be it statistical or methodological. Given the high NeuroImage 80 (2013) [445][446][447][448][449][450][451][452][453][454][455][456][457][458][459][460][461]
An overview of different frameworks for visualizing the human connectome is given by Margulies et al. REF .
Visualizing the human connectome
NeuroImage
NeuroImage
Computer Science Medicine