Elsevier

NeuroImage

Volume 60, Issue 2, 2 April 2012, Pages 1340-1351
NeuroImage

Full Length Articles
Circular representation of human cortical networks for subject and population-level connectomic visualization

https://doi.org/10.1016/j.neuroimage.2012.01.107Get rights and content

Abstract

Cortical network architecture has predominantly been investigated visually using graph theory representations. In the context of human connectomics, such representations are not however always satisfactory because canonical methods for vertex–edge relationship representation do not always offer optimal insight regarding functional and structural neural connectivity. This article introduces an innovative framework for the depiction of human connectomics by employing a circular visualization method which is highly suitable to the exploration of central nervous system architecture. This type of representation, which we name a ‘connectogram’, has the capability of classifying neuroconnectivity relationships intuitively and elegantly. A multimodal protocol for MRI/DTI neuroimaging data acquisition is here combined with automatic image segmentation to (1) extract cortical and non-cortical anatomical structures, (2) calculate associated volumetrics and morphometrics, and (3) determine patient-specific connectivity profiles to generate subject-level and population-level connectograms. The scalability of our approach is demonstrated for a population of 50 adults. Two essential advantages of the connectogram are (1) the enormous potential for mapping and analyzing the human connectome, and (2) the unconstrained ability to expand and extend this analysis framework to the investigation of clinical populations and animal models.

Highlights

► A framework for visualization and exploration of human connectomics is introduced. ► This ‘connectogram’ representation has significant potential for connectomic analysis. ► Connectogram scalability is demonstrated using a population analysis of 50 adults.

Introduction

The ability to collect enormous amounts of structural and functional connectivity data from human populations has grown so impressively that it has by far surpassed the rate at which methods for the analysis, visualization and interpretation of such data are made available. Due to the high dimensionality and high information complexity of data acquired using magnetic resonance imaging (MRI), functional MRI (fMRI) and diffusion tensor imaging (DTI), novel perspectives upon their optimal utilization often require the innovation of ingenuous ways to capture, condense and systematize complex architectural and functional relationships between cortical units. In the past, cortical network architecture as inferred from neuroimaging has preponderantly been visualized using the obvious symbols of graph theory. Although precise and minimalistic, such representations are not always adequate, however, in the context of the emerging field of connectomics because typical approaches to vertex segregation and edge ordering do not always lead to visual representations that are optimally revealing of essential functional and structural relationships in the brain. Thus, although such representations have been variously adapted to the exploration of cortical structure, function or information-theoretic content, cortical networks remain difficult to understand due to the overwhelming number of complex relationships whose overarching implications can easily be lost during their visual exploration. Consequently, interpretation and analysis of connectivity have traditionally required a disappointing amount of simplification and dimensionality reduction, frequently to the detriment of conveying essential aspects of neural architecture. For these very reasons, historically, the introduction of inventive and illuminating methods for complex data visualization has greatly improved the effectiveness of scientific analysis and dissemination, as in Darwin's introduction of directed graphs to depict phylogenetic information (Darwin, 1859) or Sneath's use of heat maps to understand array and expression data (Sneath, 1957).

The term ‘connectome’ was suggested independently and simultaneously in 2005 by Sporns et al. (2005) and by Hagman (2005) to refer to a comprehensive map of neural connections in the brain. The generation and study of connectomes are known as ‘connectomics’. In this paper, a novel methodology for the conceptual mapping and visualization of human connectomics is introduced through the use of an intuitive circular representation which is tailored in a germane fashion to the depiction of brain architecture. It is demonstrated how this simple and elegant conceptual framework is equipped with the ability to organize, inspect and classify brain connections in a visually-insightful and content-rich manner, and with the clear advantage of a high data-to-ink ratio. We name this representation a ‘connectogram’. Using joint MRI/DTI data acquisition and automatic image segmentation, a protocol is illustrated for the extraction of 148 cortical and 17 non-cortical anatomical parcellations using standard nomenclature, followed by the calculation of structural anatomy metrics (volume, area, cortical thickness, curvature) for cortical regions. For some given human subject, these structural data are subsequently combined with the connectivity profile extracted from DTI to generate the connectogram of that subject. To demonstrate the scalability potential of this approach, the generation of a statistically-informed, population-level connectogram is illustrated for a population of 50 adult male subjects. Intrinsic merits of the present approach include significant potential for connectome mapping and analysis across the human species, as well as an essentially unlimited capability of extending this approach to the study, representation, and comparison of diseased populations.

Section snippets

Subjects

50 healthy adult males with ages between 25 and 35 were included in the study. All subjects were screened to exclude cases of pathology known to affect brain structure, a history of significant head injury, a neurological or psychiatric illness, substance abuse or dependence, or a psychiatric disorder in any first-degree relative.

Neuroimaging, segmentation and parcellation

T1-weighted neuroimaging data were selected from the LONI Integrated Data Archive (IDA; http://ida.loni.ucla.edu). Segmentation and regional parcellation were

Results

Fig. 2 illustrates the connectivity profile of a sample subject drawn from our normal population. To illustrate the capabilities of our methodology, it is useful to discuss specific features of this connectivity profile as obviated by the connectogram. One such feature is the thick red link that connects the left and right superior frontal gyri (SupFG) in the connectogram. The fact that this link is colored in red indicates, as explained in the previous section, that the fiber tracts between

Significance and innovation

With the intense interest in the in vivo mapping of human connectomics, as evident in the launching of the Human Connectome Project (HCP, www.humanconnectomeproject.org) by the National Institutes of Mental Health and Aging, considerable effort has arisen into techniques that graphically represent the interconnectedness of neural structures. Along with ongoing efforts to map population level connectomics is an interest for clinical application to the individual subject or patient. Being able to

Conclusion

Circle-based arrangements for connectome visualization are increasingly gaining acceptance in neuroimaging and computational neuroscience circles (Modha and Singh, 2010). While many examples of connectomic and network-level layouts have been proposed, the approach described here for the joint 2D graphical representation of regional geometric attributes and inter-regional connectivity is a novel, informative, and attractive means for depicting whole brain connectivity from neuroimaging data. It

Disclosure statement

None of the authors has a conflict of interest to disclose.

Acknowledgments

We acknowledge the assistance of the staff of the Laboratory of Neuro Imaging at the University of California, Los Angeles. This work was supported by the National Alliance for Medical Image Computing (NA-MIC; www.na-mic.org), under NIH Roadmap Initiative grant 2U54EB005149, sub-award to J. D. V. H.

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