ORIGINAL RESEARCH ARTICLE published: 11 February 2014 HUMAN NEUROSCIENCE doi: 10.3389/fnhum.2014.00051 Systematic network lesioning reveals the core white matter scaffold of the human brain Andrei Irimia and John D. Van Horn* Department of Neurology, Keck School of Medicine, Institute for Neuroimaging and Informatics, University of Southern California, Los Angeles, CA, USA Edited by: Brain connectivity loss due to traumatic brain injury, stroke or multiple sclerosis can have Shuhei Yamaguchi, Shimane serious consequences on life quality and a measurable impact upon neural and cognitive University, Japan function. Though brain network properties are known to be affected disproportionately by Reviewed by: injuries to certain gray matter regions, the manner in which white matter (WM) insults Keiichi Onoda, Shimane University, Japan affect such properties remains poorly understood. Here, network-theoretic analysis allows Boris Bernhardt, Max Planck us to identify the existence of a macroscopic neural connectivity core in the adult human Institute for Human Cognitive and brain which is particularly sensitive to network lesioning. The systematic lesion analysis Brain Sciences, Germany of brain connectivity matrices from diffusion neuroimaging over a large sample (N = 110) *Correspondence: reveals that the global vulnerability of brain networks can be predicated upon the extent John D. Van Horn, Department of Neurology, Keck School of Medicine, to which injuries disrupt this connectivity core, which is found to be quite distinct from the Institute for Neuroimaging and set of connections between rich club nodes in the brain. Thus, in addition to connectivity Informatics, University of Southern within the rich club, the brain as a network also contains a distinct core scaffold of California, 2001 North Soto Street, network edges consisting of WM connections whose damage dramatically lowers the Room 102, MC 9232, Los Angeles, CA 90089-9235, USA integrative properties of brain networks. This pattern of core WM fasciculi whose injury e-mail: [email protected] results in major alterations to overall network integrity presents new avenues for clinical outcome prediction following brain injury by relating lesion locations to connectivity core disruption and implications for recovery. The findings of this study contribute substantially to current understanding of the human WM connectome, its sensitivity to injury, and clarify a long-standing debate regarding the relative prominence of gray vs. WM regions in the context of brain structure and connectomic architecture. Keywords: connectomics, traumatic brain injury, brain network, neurotrauma, neuroimaging, MRI, DTI INTRODUCTION which transcend the boundaries of local network modules so as Brain lesions due to conditions such as traumatic brain injury to enable network-wide integration; on the other hand, segrega- (TBI), stroke and multiple sclerosis (MS) can have focal, region- tion quantifies effective changes in the strength of interactions specific consequences as well as diffuse effects upon cortical as nodes become more topologically remote from each other circuitry (Van Horn et al., 2012). For this reason, the ability (Rubinov and Sporns, 2010). Because network integration and to quantify injury-related connectomic changes in a systematic segregation reflect network vulnerability to insult, these essential manner is critical for the evaluation of injury severity and for the network properties can aid one to understand the effect of injury personalization of treatment after neurotrauma. In both health upon the brain. and disease, network theory can provide essential insight into the Recent advances in connectomic and network theoretic analy- structural properties of brain connectivity (Sporns, 2011), partic- sis have led to an improved understanding of how GM regions are ularly by providing quantitative measures of lesion impact upon organized from the standpoint of their ability to communicate. neural structure and function, with possible relevance to the pre- For example, it has been proposed that the human connectome diction of clinical outcome variables and to the task of designing has a “rich club” organization, where high-degree cortical nodes patient-tailored rehabilitation protocols (Irimia et al., 2012a,b). are more densely connected to each other than to nodes of lower Within the modeling framework of network theory, the gray degree (Van Den Heuvel and Sporns, 2011). In comatose TBI matter (GM) of the brain can be parcellated into distinct regions patients, network node topological properties have been found to which are conceptualized as graph nodes connected by edges reorganize themselves radically as a consequence of injury, with whose properties are specified by white matter (WM) con- theoretical implications for models of consciousness and practi- nections, having complex topological relationships within the cal ones for clinical care (Achard et al., 2012). Damage to brain hierarchy of the network. Investigating network integration and regions important for communication between functional sub- segregation using network theory allows one to quantify how networks has been found to decrease network modularity as com- much information brain regions can exchange as well as the puted based on functional magnetic resonance imaging (fMRI) extent to which such regions remain structurally segregated from recordings (Gratton et al., 2012). Similarly, one study on stroke each other (Sporns, 2011). On the one hand, network integration patients found that peri-lesional circuits have reduced abilities to captures the capacity of a network to engage in global interactions communicate with the rest of the brain (Crofts et al., 2011). Such Frontiers in Human Neuroscience www.frontiersin.org February 2014 | Volume 8 | Article 51 | 1 Irimia and Van Horn Core scaffold of the brain findings reflect appreciable efforts dedicated to understanding the re-purposing, and no linked coding or keys to subject identity properties of brain network cortical nodes, though less emphasis are maintained. For these reasons, in compliance with the U.S. has been placed on determining the properties and differential Health Insurance Portability and Accountability Act (HIPAA; prominence of network WM edges basedontheircontributions http://www.hhs.gov/ocr/privacy), this study does not involve to network integration. human subjects’ materials. Both structural MRI and DTI volumes By studying how brain vulnerability to insult varies as a were acquired at 3 T using a Siemens Magnetom TrioTim MRI function of WM and GM injury location, lesion effects upon scanner. For the MRI volumes, an MP-RAGE sequence was network properties can be assessed. In this study, we investi- used (voxel size: 1 × 1 × 1 mm; TR = 1900 ms; TE = 2.26 ms; gate the effects of both localized and diffuse injury upon the TI = 900 ms; flip angle: 9◦). For DTI, volumes were acquired in network properties of the human connectome using models of 64 gradient directions (voxel size: 2 × 2 × 2 mm; TR = 7000 ms; brain connectivity based on MRI and diffusion tensor imaging TE = 93 ms). (DTI). By further combining MRI and DTI analysis methods with connectomics and network theory, we identify the exis- IMAGE PROCESSING tence of a macroscopic neural connectivity core in the human The LONI Pipeline environment (http://pipeline.loni.usc.edu) brain. This subset of WM pathways has properties which are was employed for all major image processing operations, includ- particularly important to inter-regional connectivity and it is ing bias field correction, skull stripping, image alignment, etc. found that injury to the connectomic core substantially affects This program is a graphical environment for the construction, brain network organization. Importantly, we propose that the execution and validation of neuroimaging data analysis and WM connectivity scaffold of network edges stands in comple- facilitates automated data format conversion while providing ment to the rich club of nodes in brain networks, leading to a a large library of computational tools (Mackenzie-Graham relationship of structural complementarity between important et al., 2008; Dinov et al., 2009, 2010). DTI data were analyzed WM fibers and prominent GM regions, respectively. We justify in native subject space using TrackVis (http://trackvis.org) to this conclusion based on a direct comparison between the rich reconstruct fiber tracts via deterministic tractography, which was club network of the brain and its connectivity scaffold, which are used instead of probabilistic tractography because the latter is found to differ appreciably. The nature of the complementary often perceived to be a more standard, more commonly used relationship between the rich club network and the connectiv- and better-understood methodology. Eddy current correction ity scaffold contributes essential information to the long-standing was applied to the DTI volumes using the FSL FLIRT utility debate regarding the relative prominence of GM vs. WM regions (http://fsl.fmrib.ox.ac.uk/fsl), and the Diffusion Toolkit within within human brain architecture. An important strength of the TrackVis was used for DTI reconstruction, fiber tracking and present study is that it quantifies the connectomic core using a spline filtering. For each subject, the skull was stripped using the population sample of larger size (N = 110) than typically used skull-stripping utility of the AFNI package (Smith, 2002)andbias in previous connectomic studies. Aside