Morphological brain network assessed using graph theory and network filtration in deaf adults

Hearing Research 315 (2014) 88e98

Contents lists available at ScienceDirect

Hearing Research journal homepage: www.elsevier.com/locate/heares

Research paper

Morphological brain network assessed using graph theory and network filtration in deaf adults Eunkyung Kim a, b, c, 1, Hyejin Kang a, d, 1, Hyekyoung Lee a, b, Hyo-Jeong Lee e, Myung-Whan Suh g, Jae-Jin Song h, Seung-Ha Oh f, g, **, Dong Soo Lee a, b, c, i, * a

Department of Nuclear Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea Institute of Radiation Medicine, Medical Research Center, Seoul National University, Seoul, Republic of Korea Interdisciplinary Program in Cognitive Science, Seoul National University, Seoul, Republic of Korea d Data Science for Knowledge Creation Research Center, Seoul National University, Seoul, Republic of Korea e Department of Otorhinolaryngology-Head and Neck Surgery, Hallym University College of Medicine, Chuncheon, Republic of Korea f Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University College of Medicine, Seoul, Republic of Korea g Sensory Organ Research Institute, Seoul National University Medical Research Center, Seoul, Republic of Korea h Department of Otorhinolaryngology-Head and Neck Surgery, Seoul National University Bundang Hospital, Seongnam, Republic of Korea i Department of Molecular Medicine and Biopharmaceutical Sciences, Graduate School of Convergence Science and Technology, and College of Medicine or College of Pharmacy, Seoul National University, Seoul, Republic of Korea b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 10 March 2014 Received in revised form 13 June 2014 Accepted 24 June 2014 Available online 10 July 2014

Prolonged deprivation of auditory input can change brain networks in pre- and postlingual deaf adults by brain-wide reorganization. To investigate morphological changes in these brains voxel-based morphometry, voxel-wise correlation with the primary auditory cortex, and whole brain network analyses using morphological covariance were performed in eight prelingual deaf, eleven postlingual deaf, and eleven hearing adults. Network characteristics based on graph theory and network filtration based on persistent homology were examined. Gray matter density in the primary auditor cortex was preserved in prelingual deafness, while it tended to decrease in postlingual deafness. Unlike postlingual, prelingual deafness showed increased bilateral temporal connectivity of the primary auditory cortex compared to the hearing adults. Of the graph theory-based characteristics, clustering coefficient, betweenness centrality, and nodal efficiency all increased in prelingual deafness, while all the parameters of postlingual deafness were similar to the hearing adults. Patterns of connected components changing during network filtration were different between prelingual deafness and hearing adults according to the barcode, dendrogram, and single linkage matrix representations, while these were the same in postlingual deafness. Nodes in frontolimbic and left temporal components were closely coupled, and nodes in the temporo-parietal component were loosely coupled, in prelingual deafness. Patterns of connected components changing in postlingual deafness were the same as hearing adults. We propose that the preserved density of auditory cortex associated with increased connectivity in prelingual deafness, and closer coupling between certain brain areas, represent distinctive reorganization of auditory and related cortices compared with hearing or postlingual deaf adults. The differential network reorganization in the prelingual deaf adults could be related to the absence of auditory speech experience. © 2014 Elsevier B.V. All rights reserved.

Abbreviations: pre-LD, prelingual deaf; post-LD, postlingual deaf; A1, primary auditory cortex; VOIs, volumes of interest * Corresponding author. Department of Nuclear Medicine, Seoul National University College of Medicine, 28 Yeongeon-dong, Jongno-gu, Seoul, 110-744, Republic of Korea. Tel.: þ82 2 2072 2501; fax: þ82 2 745 7690. ** Corresponding author. Department of Otorhinolaryngology, Seoul National University College of Medicine, 28 Yeongeon-dong, Jongno-gu, Seoul, 110-744, Republic of Korea. Tel.: þ82 2 2072 2442; fax: þ82 2 831 2826. E-mail addresses: [email protected] (S.-H. Oh), [email protected] (D.S. Lee). 1 Contributed equally to this work. http://dx.doi.org/10.1016/j.heares.2014.06.007 0378-5955/© 2014 Elsevier B.V. All rights reserved.

1. Introduction Both auditory and visual deprivations have major effects on the brain (Rauschecker, 1999, 2001; Sadato et al., 2002). In deaf children, auditory and adjacent cortices showed hypo-metabolism (Lee et al., 2001), but in prelingual deaf (pre-LD) adults these areas showed hyper-metabolism in long-term follow-up (CatalanAhumada et al., 1993). In pre-LD adults, gray matter volume was

E. Kim et al. / Hearing Research 315 (2014) 88e98

also found to be preserved in the auditory cortex on morphometric analyses (Emmorey et al., 2003; Penhune et al., 2003; Shibata, 2007). The increased metabolism and preserved morphological volume represent “brain reorganization by neural plasticity” in preLD adults. In postlingual deaf (post-LD) adults, metabolism in the auditory cortices decreased shortly after the onset of deafness, but had recovered upon long-term follow-up (Lee et al., 2003). However, very little has been reported about the morphological changes of auditory cortices in post-LD adults, especially with long-term follow-up. These functional or morphological changes, however, could not entirely explain the pattern of brain reorganization in pre-LD and post-LD adults, especially considering the network characteristics of the brain. The characteristics of brain network can be measured using graph theoretical analysis (Sporns, 2011; Sporns et al., 2004) or network filtration of persistent homological framework (Lee et al., 2011, 2012). In this study, we investigated the morphological reorganization of deaf adults using both graph theoretical analysis and network filtration methods based on the morphological covariance of the unmodulated concentrations of gray matter tissue (i.e. gray matter density) of the brain regions. Several papers have substantiated that the individual's variability in morphometric or morphological features such as gray matter density, volume, or cortical thickness can imply structural association/interaction between brain regions like functional connectivity or association (Alexander-Bloch et al., 2013). For instance, the significant covariance of bilateral homologue regions was observed by using gray matter volume (Mechelli et al., 2005), which is similar to metabolic covariance (Lee et al., 2008). The morphological covariance of the default mode network was associated with general cognitive status, and as expected declined in Alzheimer's disease (Spreng and Turner, 2013). Using the morphological feature of cortical thickness as obtained by structural T1 images, significant covariance between Broca's and Wernicke's areas was observed overlapping with the arcuate fasciculus (Lerch et al., 2006). More recently, an intrinsic similarity of information traffic patterns was observed between the covariance of cortical thickness and the diffusion connections of the brain (Gong et al., 2012). Morphological covariance is related to the fibre tracts or functional interactions in the brain. Thus, if crosssubject covariance in tissue density maps is correctly analyzed and understood its correlates, it could be a companion to understanding the active reorganization of brain networks (Evans, 2013; Evans et al., 2008), including those in deaf adults. Using a graph theoretical approach and network filtration methods on data acquired from a correlation analysis of MRI density of specified volumes of interest (VOIs), we described characteristics of morphological covariance network quantitatively in deaf adults. The complex network patterns of human brains have been analyzed by graph theoretical measures that quantify the topological properties of the network (Sporns, 2011; Sporns et al., 2004). To understand the network characteristics of deaf adults' brains, we measured segregation, centrality, and efficiency. Network segregation can be quantified by the clustering coefficient (Bassett et al., 2008; He et al., 2008), which measures the connections between sets of nodes that belong to the neighbors of a specific node and normalized by all the possible connections of these neighbor nodes. Betweenness centrality quantifies the number of shortest paths between two arbitrary brain nodes passing through a specific node in relation to all the numbers of shortest paths connecting these two brain nodes (Gong et al., 2009; Iturria-Medina et al., 2008). Network efficiency is measured using nodal efficiency, which shows how efficient the fault-tolerance of a connection is in a specific node by calculating the sum of the inverse length of the shortest paths from that node to other nodes (Latora and Marchiori, 2001). Intuitively, the clustering coefficient of a node indicates the

89

strength of connections between its neighbor nodes. Betweenness centrality indicates how many network shortcuts pass through the node. Nodal efficiency indicates how efficiently a node is connected to all other nodes in the brain. Network filtration using a persistent homological framework (Lee et al., 2011, 2012) was applied to characterize the topological features of networks using data acquired from interregional correlations of density on MRIs. Thresholding of the brain network was found to affect network topology (Rubinov and Sporns, 2010). In terms of network edge, the sparsest network with a giant all-nodeconnected component could be chosen in each group, but the number of edges could not be the same between groups. Optimal sparsity, selecting the giant connected components with the same edge numbers between groups, was also arbitrary in that it used specific thresholds for each group. Threshold choice led to disregarding weaker correlations, which could be a sensitive indicator to differentiate and characterize the brain networks of different groups (Bassett et al., 2012). Instead of choosing one arbitrary threshold, we applied network filtration using varying distance thresholds, which allowed us to look into network changes with varying thresholds. Counting the connected components in the thresholded network was based upon the topological interpretation of networks on a persistent homological framework, which was once successfully applied to differentiation of psychiatric patients using correlations acquired from PET images (Lee et al., 2012). In this study, we investigated whether the gray matter density of the primary auditory cortex (A1) was preserved in pre-LD or post-LD adults. Reduction of gray matter density was considered to represent the loss of neurons and/or glial cells (Rusch et al., 2003), and thus the decrease of the local amount of the gray matter (Chen et al., 2006). We assumed that the preserved density of the gray matter reflected recovery in the affected brain regions following long-term adaptation to the sensory deficit in deaf adults. If pre-LD adults demonstrated morphological recovery while post-LD adults did not, it would confirm that pre-LD adults have network properties different from those of post-LD adults or the hearing adults. We assumed that this morphological recovery would lead to preserved tissue MRI density, but with a different organization expressed as different interregional correlations. Regarding the persistent homological characteristics of the brain networks, we expected that network filtration would disclose differential characteristics between pre-LD or post-LD adults and normal hearing adults. We explored how the networks in deaf adults were reorganized compared with hearing adults after prolonged auditory deprivation had influenced and changed the topological characteristics of the networks. 2. Materials and methods 2.1. Participants Three groups of subjects participated in this study: eight pre-LD (5M/3F; mean age, 50.4 ± 6.1 years; duration of deafness, 45.8 ± 6.5 years; onset age of deafness, 4.6 ± 1.4 years), eleven post-LD (4M/ 7F; mean age, 50.9 ± 12.2 years; duration of deafness, 15.3 ± 14.0 years; onset age of deafness, 35.6 ± 18.2 years), and eleven agematched normal hearing adults (6M/5F; mean age, 49.5 ± 8.9 years). All participants were right-handed and were given at least 9 years of formal education. All deaf participants had a mean threetone unaided threshold of >70 dB hearing level (HL), the International Organization for Standardization (ISO) criterion for severe to profound hearing loss. The average hearing threshold of the hearing adults was 13 dB HL. In this study, the pre-LD adults became deaf before or around the usual language acquisition period, which did not exceed 7 years of age. The pre-LD adults used sign language as their primary

90

E. Kim et al. / Hearing Research 315 (2014) 88e98

communication tool, and were not aided by hearing aids or cochlear implants. In contrast, the post-LD became deaf after language acquisition, and used lip-reading skills to communicate with others and spoke fluently with hearing aids. Participants had normal/corrected-normal visual acuity and no previous history of neurological or psychiatric disorders. This study was approved by the institutional review board (IRB) of the Seoul National University College of Medicine. All participants gave informed consent as provided by the Seoul National University Hospital. 2.2. MR image acquisition Structural MR images were acquired using GE Signa 3.0 T EXCITE systems (General Electric Healthcare, Milwaukee, WI). T1 images of deaf groups were acquired using a three-dimensional (3D) spoiled gradient-recall (SPGR) inversion recovery (IR) acquisition protocol with the following parameters: axial acquisition with a 320 mm  192 mm; TR ¼ 5.9 ms; TE ¼ 1.5 ms; FOV 200 mm, except for one pre-LD adult whose TR ¼ 6 ms. T1 images of the hearing adults were acquired using the same parameters (eight subjects) as the deaf groups, except for 3 subjects who had different parameters: TR ¼ 6 ms, 5.8 ms, 4.6 ms; TE ¼ 1.5 ms, 1.4 ms, 1.2 ms, respectively. All scans had 104 or 106 continuous slices, and a 512 x 512 image matrix with a 0.39  0.39  1.5 mm voxel size, except one of the hearing adults whose voxel size was 0.47  0.47  1.5 mm. The thickness was 1.5 mm and the flip angle was 20 . 2.3. Image pre-processing The unmodulated concentration of gray matter tissue - a density map of the gray matter - was generated using voxel-based morphometry (VBM) with the DARTEL (Diffeomorphic Anatomical Registration Through Exponentiated Lie algebra) tool, implemented in Statistical Parametric Mapping (SPM 8, www.fil.ion.ucl. ac.uk/spm) to acquire better registration results (Ashburner, 2007). First, each 3D T1 image was segmented after being aligned to the anterior and posterior commissures, and an average shaped brain template and flow field files of rigidly aligned gray matter images were created. The flow fields were used to normalize the gray matter images to Montreal Neurological Institute (MNI) space by non-affine warping. For VBM analysis, an 8 mm FWHM was used for spatial smoothing. To construct the morphological covariance network, correlation coefficients between VOIs were calculated from gray matter density maps without spatial smoothing, to avoid the effects of spatial overlapping between VOIs (Van den Heuvel et al., 2008). Network nodes were defined as 90 regions chosen from the Automated Anatomical Labeling (AAL) template (http://www.cyceron.fr/web/ aal__anatomical_automatic_labeling.html). 2.4. Voxel-based morphometric analysis After removing non-brain voxels by masking the brain, ANCOVA with three conditions (groups) was performed with the age and global covariates as nuisance variables. In this analysis, the significance level was set at P < 0.005 within a cluster of an extent threshold of k > 50 voxels. We also investigated the effect of duration and onset age of deafness on gray matter density of bilateral A1 using Spearman's correlation coefficients in the pre-LD and post-LD groups. 2.5. Mapping correlation with A1 region seed density After removing non-brain voxels, the effects of age and total gray matter density were controlled using ANCOVA to discount the

global variation in the gray matter caused by age and head size. Correlation analyses within groups were performed with the density values of A1 regions as seed regions in each hemisphere. The density value of A1 was extracted from the AAL. Correlation r maps were transformed to z maps by Fisher's z transformation and compared between the deaf groups and hearing adults to yield group differences (P < 0.005 within a cluster of an extent threshold of k > 50). 2.6. Mapping the whole brain covariance network over the VOIs as nodes 2.6.1. Network parametric characterization based on graph theory To construct the whole brain covariance network, gray matter density of 90 VOIs was extracted from individual maps. In each group, a positive correlation matrix was constructed across the 90 VOIs with age as a nuisance variable. If we choose a threshold arbitrarily for the hearing adults, pre-LD, and post-LD, the networks can be biased because the groups have a different number of edges (He et al., 2008). To avoid these biases, we chose the threshold while considering the sparsity of the matrix (Achard and Bullmore, 2007; Bassett et al., 2008; He et al., 2007, 2008; Wang et al., 2010), where sparsity was estimated and chosen for each group to create the same number of edges for each group. In addition, the sizes of connected components were made the largest (and the same) for all groups. Thus, the number of connected components is equal to one. Sparsity follows the equation S ¼ K/(N (N1)/2), where K represents the total number of edges, and N represents the number of nodes. We chose the threshold as a 12.3%, at which point the cost of the network was minimized, while the fully connected components in each group were maintained. From these sparsityoptimized, weighted network matrices, network properties such as the clustering coefficient, betweenness centrality, and nodal efficiency were calculated using the Brain Connectivity Toolbox (http://www.brain-connectivity-toolbox.net/). Each measure characterized the nodal properties. The clustering coefficient reflects local segregation of a given specific node in a network (Watts and Strogatz, 1998). It represents local connectivity by calculating connections between neighbors of a given node among all possible connections of these neighbor nodes. It ranges from zero to one, where a value close to zero indicates weaker connections between neighbors of a given node, while the value close to one indicates that more neighbors of a given node are connected. Betweenness centrality represents the centrality of information flow through a given specific node in a network (Rubinov and Sporns, 2010). It is measured per node by counting the fraction of all the shortest paths in the network which pass through a specific node in the correlation network (Rubinov and Sporns, 2010). The shortest path stands for a path between two nodes in a network which pass from node i to node j as a shortcut. The node having low value indicates that the specific node rarely has the shortest path passing through itself, while that having high value indicates the specific node has many paths passing through itself. Nodal efficiency represents the nodal faulttolerance of information flow when the specific node is removed (Latora and Marchiori, 2001). It is measured per node by the sum of the inverse of the shortest path lengths of a specific node yielding paths to the other nodes. The node having low value indicates that the specific node is inefficiently connected to the other nodes in a network, while that having high value indicates that the specific node is efficiently connected to the other nodes in a network. After obtaining each measure per nodes in each group, we drew the frequency distribution of the clustering coefficient, betweenness centrality, and nodal efficiency over the nodes, and compared the distributions of the groups (hearing adults, pre-LD, and post-

E. Kim et al. / Hearing Research 315 (2014) 88e98

LD) using a two-sample KolmogoroveSmirnov (KS) test based on the general form of a bootstrap procedure. We created a null distribution of the parameter of the KS test, statistic D, as repeated 10,000 times, and set the statistical significance from the null distribution. We also investigated which node(s) had significantly different network properties compared with the other groups by generating a null distribution of network properties. More specifically, the gray matter density of 90 VOIs extracted from individual maps was concatenated into one single matrix. The size of the matrix was 30 (number of subjects)-by-90 (number of VOIs). We randomly selected data comprised of pseudo-groups. Partial correlation matrices were generated in each pseudo-group using the corresponding age. Using this partial correlation matrix, we estimated the sparsity by selecting the smallest number of edges across groups, while maintaining the fully connected components, and then obtained each network property per node in each pseudogroup. To compare network properties between groups, the differences of the network properties were computed. This was done 5000 times to generate a null distribution of the difference value of network properties per node. Significance was set at P < 0.005 (two-tailed) for group comparison. 2.6.2. Network filtration to yield all-threshold correlation of density based on a persistent homological framework To construct brain networks of MRI density across all thresholds, where r was the correlation coefficient between the gray matter density in VOIs with age as a nuisance variable, the distance matrix (1-r) was used to generate a barcode, single linkage dendrogram, and single linkage matrix for each group (hearing adults, pre-LD, and post-LD) based on persistent homological perspectives (Lee et al., 2012). When the network was filtered, two nodes were considered to be connected only if they had a shorter distance than the threshold. By increasing the threshold distances, more nodes were allowed to be connected to each other (Lee et al., 2011). The barcode represented changes in the number of connected components of the filtered network when the threshold was varied during network filtration. In the barcode, each connected component is represented by a bar which starts and ends at the filtration values when the corresponding connected component appears and disappears. The number of connected components is monotonically decreasing function for all filtration values. Its maximum value is equal to the number of nodes (number of VOIs) at the filtration value zero, and its minimum value is one when all nodes are connected. If bars in the barcode are rearranged according to the location of the VOIs, it becomes a dendrogram which represents the hierarchical clustering of brain regions. A single linkage matrix is the matrix representation of a single linkage dendrogram, and was used to disclose the statistical differences between the hearing adults, pre-LD, and post-LD groups. The single linkage matrix shows the local change of connected structure of brain network during filtration. Since its element represents the filtration value when two brain regions belong to the same connected component for the first time, we can find which areas are connected earlier than the other regions. A nonparametric, unpaired permutation test was used to obtain the probability that the observed difference between two groups occurred by chance (the null hypothesis) by generating the pseudo-matrices of the single linkage matrix. To yield two pseudo-group data (pseudohearing adults and pseudo-pre-LD), individuals were randomly chosen from data of mixed population, and single linkage distances between nodes i and j (dij) were calculated for each pseudo-group, with partialling out corresponding age. For each pseudo-group's data, single linkage distances were converted to z-transformed metrics following the equation, zij ¼ 1/2 log [(2  dij)/(dij)]. These z-

91

values were then compared between groups by z statistics using Zdiffij ¼ (zij1  zij2)/√(1/(n13)þ1/(n23)), where n1 and n2 represent the number of subjects in each group. This procedure was repeated 5000 times to generate the null distribution of Zdiffij, which represents a group difference between single linkage matrices. Significance was set at P < 0.001 (two-tailed) for group comparison. 3. Results 3.1. Gray matter density in pre-LD and post-LD compared with hearing adults After correcting for multiple comparisons via Bonferroni correction and the False Discovery Rate (FDR) correction (Benjamini and Hochberg, 1995), there was no significant difference between each deaf group and hearing adults. However, when analyzed using the uncorrected P < 0.005 (k > 50), the gray matter density of bilateral A1 was preserved in the pre-LD group (Fig. 1A), but decreased in the post-LD group (Fig. 1B). There was no significant correlation between the density of bilateral A1 and duration or onset of deafness in both groups. 3.2. Correlation of bilateral superior temporal regions with A1 density in pre-LD and post-LD compared with hearing adults The left and right A1s were correlated positively with bilateral superior temporal regions, including A1, in all three groups (Supplementary Fig. 1). Group comparisons between the pre- or post-LD groups and the hearing adults showed a differential correlation using the uncorrected P < 0.005 (k > 50). Compared with the hearing adults, the pre-LD group showed increased correlation coefficients of left A1 with the bilateral superior temporal gyri, superior temporal pole, middle temporal gyri, insulae, and left inferior frontal operculum (Fig. 2A, upper row). Also, the pre-LD group showed similarly increased correlation coefficients of right A1 with the bilateral superior temporal gyri, right superior temporal pole, bilateral middle temporal gyri, and insulae as compared with the hearing adults (Fig. 2B, upper row). In contrast, there were no significant differences between the post-LD group and the hearing adults with regard to the correlation between the bilateral A1s and other brain regions (Fig. 2A and B, lower rows). 3.3. Network characteristics of pre-LD, post-LD, and hearing adults using graph theory The distribution of the nodal properties of the network characteristics based on graph theoretical analysis yielded unique patterns in the hearing adults (Fig. 3, left column). In terms of the clustering coefficient (Fig. 3A), betweenness centrality (Fig. 3B), and nodal efficiency (Fig. 3C), the pre-LD group showed a different distribution from the hearing adults (Fig. 3, middle column). In contrast, the post-LD group showed a similar distribution of nodal properties to the hearing adults (Fig. 3, right column). In the pre-LD group, the nodal distribution of clustering coefficients was shifted to a higher value than that of hearing adults (P < 0.001), indicating there were more nodes with greater clustering coefficients in the pre-LD group. In addition, more nodes with higher betweenness centrality were found in the pre-LD group than in the hearing adults (P < 0.05). The skewness of betweenness centrality of the pre-LD group was also higher than that of the post-LD or the hearing adults (pre-LD; 1.43, post-LD; 0.68, hearing adults; 0.62). The nodal efficiency of the pre-LD group spread into the upper and lower extremes of the

92

E. Kim et al. / Hearing Research 315 (2014) 88e98

Fig. 1. Results of voxel-based morphometry. Compared with the hearing adults, (A) the density of bilateral A1 was preserved in the pre-LD group, while (B) the density of bilateral A1 was decreased in the post-LD group. Blue represents the areas where density was lower in the deaf groups than the hearing adults, while red represents the areas where density was higher in the deaf groups than the hearing adults. The A1 region is overlaid in purple. The statistical threshold was set as P value <0.005 and cluster level was 50. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

distribution than the hearing adults, and was significantly higher than that of the hearing adults (P < 0.05) as well. However, the distribution of all three measures was not different between the post-LD and hearing adults. To localize the nodes with different nodal properties between the pre-LD and the hearing adult groups, observed nodal parameter values were compared with the null distribution of the subtracted values of parameters made from the pseudo-data matrices of the permutated pre-LD and hearing adult groups (P < 0.005). The cortical region with higher nodal efficiency in the pre-LD group compared with the hearing adults was the left cuneus. The

clustering coefficient, betweenness centrality, and nodal efficiency were not different between the post-LD and hearing adult groups. 3.4. Network properties based on network filtration using a persistent homological framework The changes in the number of connected components of the brain network when varying the thresholds as filtration values were displayed by barcode for the hearing adults, pre-LD, and postLD groups (Fig. 4A). Increasing the distance during filtration, the barcode of the pre-LD group showed steeper decrease in the

Fig. 2. Differences in correlations between the pre-LD and hearing adults (upper rows), and between the post-LD and hearing adults (lower rows). To compare the correlation between the deaf groups with the hearing adults, positive interregional correlations of the entire cortex (voxels) were searched for using (A) the left A1 or (B) the right A1 as seed regions. Voxels showing increased correlation are colored (P < 0.005 and cluster level of 50). The pre-LD group showed increased bilateral correlation with either hemisphere's A1 as seed region compared to the hearing adults, while the post-LD group did not reveal any difference with the hearing adults.

E. Kim et al. / Hearing Research 315 (2014) 88e98

93

Fig. 3. Network properties based on graph theory. (A) Clustering coefficient, (B) betweenness centrality, and (C) nodal efficiency of 90 VOIs were measured and displayed to represent the number of nodes in the hearing adults (left column), pre-LD (middle column), and post-LD adults (right column).

number of connected components than that of the hearing adults, whereas the barcode of the post-LD group showed a pattern similar to the hearing adults. Thus, the number of connected components of the pre-LD group approached one (fully connected) faster than the post-LD and hearing adults. The dendrogram arranged the VOIs of the brain along the axis of the ordinate (see Supplementary Table 1), and showed a pattern of merging nodes during network filtration according to the varying thresholds (Fig. 4B). It was also displayed in a matrix format, d, where dij means the filtration value connecting nodes i and j according to the production principle (Lee et al., 2012) of a single linkage matrix (Fig. 4C). The changes in connected components in each group during network filtration were arrayed in sequence on the brain templates (Fig. 5). During the early filtration periods, in the hearing and post-LD adults, nodes were connected between homologous regions of each hemisphere. In the hearing adults, for instance, homologous regions of medial orbital frontal gyrus, dorsolateral superior frontal gyrus, supplementary motor area, caudate, and thalamus were connected when the filtration value was 0.1 (Fig. 5A). In the pre-LD group, however, in a very early filtration period (filtration value ¼ 0.05), nodes were already connected in the fronto-temporo-limbic areas, including the right dorsolateral superior frontal gyrus, bilateral supplementary motor areas, right inferior temporal gyrus, right median cingulate, and left amygdala (Fig. 5B). At the same time, nodes were also connected between the bilateral superior temporal gyri, as well as in visuo-parietal areas including the left cuneus, bilateral precuneus, and right superior parietal gyrus (Fig. 5B). In the post-LD group, the right middle frontal gyrus and postcentral gyrus merged into a connected component in the very early filtration period (filtration value ¼ 0.05, Fig. 5C). Connections of homologous nodes were observed between the median cingulate and caudate areas on each hemisphere with the filtration value 0.1 (Fig. 5C).

To examine the significance of the observed differences in the morphological covariance networks, a single linkage matrix was compared between groups by permutation testing. In the pre-LD group, the merging of VOIs into one connected component occurred earlier between the frontal and limbic VOIs, and between VOIs of the left temporal areas, indicating that in terms of interregional density correlation, these regions were coupled more closely in the pre-LD group than in the hearing adults. We suggest that this comparatively earlier coupling during filtration represents ‘stronger connectivity’. In the pre-LD group, connectivity was stronger between the right dorsolateral superior frontal gyrus and the right median cingulate or left amygdala, between the left amygdala and bilateral supplementary motor areas, and between the left superior temporal pole and left inferior temporal gyrus (P < 0.001, Fig. 6A, Table 1), than in the hearing adults. In contrast, the connectivity between the right superior temporal pole and left inferior parietal gyrus, and between the right parahippocampal and left middle temporal gyrus, was weaker in the pre-LD group compared to the hearing adults (P < 0.001, Fig. 6A, Table 1). The post-LD group showed no significantly different pattern of VOI merging during network filtration compared with the hearing adults (Fig. 6B, Table 1). 4. Discussion Brain tissue density measures in long-term deaf adults using MRI revealed distinctive and differential characteristics in the morphological changes in the brains using voxel-based morphometry, voxel-wise correlation with the A1, and whole brain network analyses using morphological covariance. The density of the A1 was preserved in the pre-LD adults, but tended to decrease in the post-LD adults. The correlation of auditory cortices with related brain regions was increased in the pre-LD adults compared with the hearing adults, which in combination with preserved density suggested that reorganization of auditory

94

E. Kim et al. / Hearing Research 315 (2014) 88e98

Fig. 4. Network properties based on graph filtration using a persistent homological framework. (A) Barcode, (B) dendrogram, and (C) single linkage matrix of the hearing adults (left column), pre-LD (middle column), and post-LD adults (right column). In the barcode, the X-axis represents filtration values of the network, and the Y-axis represents the number of connected components. When the filtration values increase, the number of connected components decrease toward one, which represents the fully connected component in a network. In the dendrogram, the X-axis represents the filtration value of the network and the Y-axis represents VOIs. The single linkage matrix is the matrix representation of these dendrograms, which effectively shows the merging pattern of the network during filtration when using distance thresholds. F: Frontal, L: Limbic, P: Parietal, T: Temporal, B: Basal ganglia, O: Occipital.

cortices and related brain areas would have generated this distinctive characteristic in the pre-LD adults. Network characteristics of nodal segregation, centrality, and efficiency on the morphological covariance network acquired via interregional correlation of tissue density of 90 VOI nodes in MRI based on graph theory revealed nodal characteristics of the pre-LD adults distinctive from the hearing adults or post-LD adults. Barcodes or single

linkage dendrograms/matrices also revealed notably earlier coupling between the nodes in the pre-LD adults, differentiating them from the hearing and post-LD adults. Differing from the hearing and post-LD adults, earlier coupling during network filtration (and thus stronger connectivity) was found in the frontolimbic regions, while later coupling (and weaker connectivity) was found in between the nodes of the left middle temporal and right

Fig. 5. The sequence of filtered networks and corresponding node connections depicted in the brains of the (A) hearing adults, (B) pre-LD, and (C) post-LD adults. In the pre-LD group during network filtration while varying the distance (1-correlation coefficient) between nodes, several nodes merged earlier, indicating they were more closely coupled than in the hearing and post-LD adults. When the filtration value was increased, the merging pattern became complex and could not reveal intergroup differences easily.

E. Kim et al. / Hearing Research 315 (2014) 88e98

95

Fig. 6. Differences in the morphological covariance network between the (A) pre-LD and hearing adults, and the (B) post-LD and hearing adults. Using single linkage matrices and pseudo-matrices constructed by mixing the matrices of both groups, significantly different (P < 0.001) connections were identified. Red edges indicate that the connection showed earlier merging during filtration (and thus closer coupling), and blue edges indicate that the connection showed later merging (and thus looser coupling), in the deaf groups than the hearing adults. While connectivity of the post-LD adults were the same as the hearing adults, the pre-LD group showed closer or looser coupling between certain nodes than the hearing adults, thus implying different cortical reorganization. AMYG: Amygdala, IPG: Inferior parietal gyrus, ITG: Inferior temporal gyrus, MCC: Median cingulate and paracingulate gyri, MTG: Middle temporal gyrus, PHG: Parahippocampal gyrus, SFG: Superior frontal gyrus, dorsolateral, SMA: Supplementary motor area, STGp: Temporal pole of superior temporal gyrus. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

Table 1 Significantly different connectivity in a single linkage matrix between the pre-LD and hearing adults, and between the post-LD and hearing adults, using a permutation test (P < 0.001). Density

Region1

Region2

Zdiffa value

Correlation coefficient Pre-LD

Pre-LD vs. Hearing adults Increase L. STGp R. SFG R. SFG R. SMA L. AMYG Decrease R. STGp R. PHIP

L. ITG R. MCC L. AMYG L. AMYG L. SMA

Density

Region1

Hearing adults

2.06 1.93 1.80 1.80 1.80

0.96 0.91 0.85 0.91 0.95

0.19 0.39 0.33 0.21 0.46

0.83 0.70

0.08 0.42

0.89 0.84

Region2

Zdiff value

Correlation coefficient Post-LD

Hearing adults

Post-LD vs. Hearing adults Increase None

Decrease L. IPG L. MTG

None

AMYG ¼ Amygdala; IPG ¼ Inferior parietal gyrus; ITG ¼ Inferior temporal gyrus; MCC ¼ Median cingulate and paracingulate gyri; MTG ¼ Middle temporal gyrus; PHIP ¼ Parahippocampal gyrus; SFG ¼ Superior frontal gyrus, dorsolateral; SMA ¼ Supplementary motor area; STGp ¼ Temporal pole of superior temporal gyrus; L ¼ Left hemisphere; R ¼ Right hemisphere. a Zdiff is derived from the equation as follows: Zg ¼ (1/2)*log((1 þ single linkage distanceg)./(1-single linkage distanceg)), Zdiff ¼ (Zg1  Zg2)/(sqrt((1/n1  3) þ (1/n2  3))), where n1 and n2 indicate the number of individuals in each group.

parahippocampal gyri, and between the right superior temporal pole and left inferior parietal gyrus in the pre-LD adults. 4.1. Preserved density and the reorganized morphological covariance network of auditory cortex in pre-LD adults We investigated gray matter density in the pre-LD and post-LD adults, and found that it was preserved in the pre-LD adults, but tended to decrease in the post-LD adults. This finding is in accord with previous reports showing preserved gray matter volume of the A1 in pre-LD adults (Emmorey et al., 2003; Penhune et al., 2003). If brain plasticity reorganized the affected brain areas, earlier onset and longer duration of the auditory deprivation would have allowed time to recover the tissue density or volume of the auditory cortices. Additionally, it might be related to the effective handling of visual sign language or other visual stimuli by the auditory cortices in pre-LD adults (Finney et al., 2001; Petitto et al., 2000). In this regard, preserved density combined with more extensive connectivity of the A1 in the pre-LD adults may suggest a

reorganization of the brain (Fig. 2A and 2B, upper rows). In contrast, in the post-LD adults, the density of the A1 decreased, and the correlation of A1was not different from the hearing adults. Followup duration was the critical factor in revealing the metabolic changes, in that glucose metabolism of the A1 decreased shortly after the onset of deafness, but recovered after long-term follow-up (Lee et al., 2003). In pre-LD adults, deafness duration influenced the metabolic activity of the auditory and adjacent cortices (Lee et al., 2001). In terms of structural changes, in this study we found no correlation of auditory tissue density with deafness duration or onset age of deafness. 4.2. Distinctively different features of network characteristics based on graph theory using MRI density of pre-LD adults versus hearing and post-LD adults Since auditory information flows beyond the auditory cortex and is processed over the entire brain (Hackett, 2011), global network properties of the whole brain are important for

96

E. Kim et al. / Hearing Research 315 (2014) 88e98

understanding the reorganization in response to the long-term deficit of auditory sensory input in deaf adults. In the pre-LD adults, nodal network properties were significantly different from those of the hearing adults. Their clustering coefficient and betweenness centrality were larger than those of the hearing adults, which implied that localized coupling was tighter in the preLD adults. Furthermore, average nodal centrality tended to be greater in the pre-LD adults than in the normal hearing adults. In the pre-LD adults, the frequency distribution of nodal efficiency (Fig. 3C) tended to show a tri-modal distribution, indicating that some nodes had higher efficiency and some had lower efficiency, defined as the degree of fault-tolerance in a global range (Latora and Marchiori, 2001). The endeavor to find which nodes were different in their segregation and centrality did not yield definitive answers, partly because of the small number of subjects. However, the left cuneus showed higher efficiency in the pre-LD adults compared to the hearing adults. We interpreted this finding according to the concept of neuroplasticity and its consequential reorganization, which could even be demonstrated by interregional correlation of VOI density on MRIs. To adapt to deficient auditory input, the visual area of the pre-LD adults could be more efficiently connected to other brain regions. By increasing the efficiency of the visual areas, pre-LD adults would have paid a larger cost to maintain network cohesion while compensating for the auditory deficit. In contrast, the nodal network properties of the post-LD adults were all similar to those of the hearing adults in their global distribution and local differences. The above differential and distinctive network characteristics may connote compensatory reorganization in pre-LD and post-LD adults, which will subsequently affect the reorganized morphological covariance network of the brain. 4.3. Single linkage network characteristics of MRI density in pre-LD adults differed from hearing and post-LD adults During network filtration, quantitatively changing connected component can be visualized in barcodes (Edelsbrunner and Harer, 2008; Ghrist, 2008; Zomorodian and Carlsson, 2005). The barcodes of the hearing and post-LD adults looked grossly similar, but the barcodes of the pre-LD adults showed a steeper slope of the boundary, indicative of earlier merging of the VOI nodes in their correlation between the tissue densities of the VOIs. These barcodes were derived from single linkage transformations of the VOI nodes' correlation matrix, and are thus equivalent to the single linkage dendrograms of each group (Lee et al., 2011). When we arrayed the 90 VOIs in a certain order (Supplementary Table 1), the single linkage dendrogram of the pre-LD adults was also unique, in that nodes merged earlier to make the connected components absorb the following nodes with larger distances. Note that a lower threshold means a stricter threshold, thus allowing only the VOI nodes having very high correlations (and thus smaller distances than the threshold) to merge into connected components. To circumvent the complexity of display hampering easy recognition of the merging pattern, single linkage matrices were used (Lee et al., 2011, 2012). The lower rows of Fig. 4 are the results for the hearing adults, pre-LD, and post-LD adults. All three displays showed distinctively earlier coupling between the nodes in the pre-LD adults, differentiating them from the hearing and post-LD adults. In the pre-LD adults compared with the hearing adults, the earlier coupling during network filtration (and thus stronger connectivity) was observed between the right frontal (dorsolateral superior frontal gyrus) and both limbic (right median cingulate and left amygdala) regions, between the bilateral supplementary motor areas and left amygdala, and between the left temporal regions (superior temporal gyral pole and inferior

temporal gyrus) (Fig. 6A). These findings suggest that neuroplastic changes likely reorganized the coupling between these VOI nodes in the pre-LD adults. Stronger connectivity of the fronto-limbic and left temporal regions can represent the reorganized brain network for communication needs specific for pre-LD adults. Since the pre-LD adults used sign language as their primary communication tool, the strengthened fronto-limbic network relative to the hearing adults could help them communicate via sign language and other non-verbal cues. The amygdala and superior frontal region are known to be connected on resting state fMRI studies (Robinson et al., 2010). In addition, the supplementary motor areas play roles in sign language processing (Petitto et al., 2000) and listening to and initiating speech (Wilson et al., 2004), and their connection to the median cingulate is considered to be important for the speech-motor components (Alexander et al., 1989; Faw, 2003). In order to process non-verbal cues, the amygdala plays a role in emotional processing and cognitive regulation of emotion (Salzman and Fusi, 2010), and its strengthened fronto-limbic connectivity might explain the cognitive-emotional top-down regulation in pre-LD adults required to understand others' language and emotional expressions for communication (Carr et al., 2003; Lee et al., 2004). Weaker connectivity between the left middle temporal and right parahippocampal gyri, as well as between the left inferior parietal gyrus and right superior temporal pole, in the pre-LD adults might indicate the deficit of auditory spatial processing. The inferior parietal lobule plays a role in auditory spatial working memory (Alain et al., 2008), and a lesion in the inferior parietal gyrus or temporal pole causes deficits in sound localization or auditory recognition, respectively (Clarke et al., 2002). In contrast, the merging pattern of the VOI nodes during network filtration in the post-LD adults was not significantly different from the hearing adults (Fig. 6B). This was interesting in that a similar small number of subjects and the same statistical analysis were used for both comparisons. The hearing and post-LD adults showed bilateral connections first, in accord with a recent study showing prominent fiber connections between homologous areas (Pinotsis et al., 2013), and aberrant homologous connections, if any, should have been found in patients (He et al., 2008). The similarity between the post-LD and hearing adults with regard to the merging pattern during network filtration of interregional density correlation signifies a morphologically unwavering language network in post-LD adults, despite deficient auditory input. 4.4. Differences between pre-LD and post-LD adults in brain reorganization as revealed by network interpretation According to these findings, we propose that neuroplastic changes occurring in pre-LD adults are more extensive than those in post-LD adults, due to their deprivation of auditory experience before language acquisition. In animal studies, early sensory experience modulated the development of sensory systems (for review, see Grubb and Thompson, 2004) and functional recovery (Chang and Merzenich, 2003). These results may be applicable to humans in that prelingual auditory deprivation would affect the neuroplastic capability of brain networks, because auditory deprivation results in a higher likelihood of changes of regional auditory related areas. The brains of pre-LD adults could also have been more susceptible to plastic changes than that of post-LD adults. In post-LD adults with auditory brain areas adapted to language processing, deprivation of auditory input causes gray matter density of the A1 to tend to decrease. Considering that synaptic changes or plasticity occur less actively after the sensitive period of

E. Kim et al. / Hearing Research 315 (2014) 88e98

neurodevelopment (Kral, 2007), the brains of post-LD adults would have been relatively stable and tend towards being conserved (Lee et al., 2007). This was supported by fewer observations of abnormal connectivity in the post-LD adults. Sensitive period interpretation regarding visual systems (Desai et al., 2002) also supports this interpretation.

97

Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.heares.2014.06.007.

References 4.5. Limitations Despite the small sample size, we could find differential and distinctive characteristics of the morphological brain network of preLD and post-LD adults. The comparison was made possible by permutation tests on pseudo-data matrices. However, the detected effects found in this study could be considered only trends due to the small sample size and use of uncorrected statistical significance, and need to be confirmed in large sample data. In addition, for the KS test, the data comprising each distribution were dependent on each other. In these correlated measurements, the probability of Type I errors in the testing is increased in the KS test, and a higher probability of incorrectly rejecting the null hypothesis occurs. Therefore, the different nodal distributions between the pre-LD and hearing adults cannot be generalized to the population level as it stands, and needs confirmation using proper statistics. To overcome this issue, Olea and Pawlowsky-Glahn (2009) applied a bootstrapping procedure to the KS test for spatially correlated data. Bootstrapping is performed from the empirical sample with replacement assuming independence. We created a null distribution of a key parameter of the KS test (statistic D), using a general form of bootstrapping, but could not implement their methods as the exact form. Nevertheless, although the dependency of the data can strongly affect the significance of the results, we acquired statistical significance from the null distribution. The elucidation of regional nodes also still suffers from the lack of sensitivity of the methods. In this study, we used the unmodulated concentration of gray matter to understand the interregional correlation between VOI nodes, so the findings would have been overly conservative. Functional evaluation of brain networks using the same analytical methods coupled with 18F-fluorodeoxyglucose PET or other functional measurements are warranted. Finally, brain parcellation can be done in various ways, and our choice of 90 VOIs using the AAL template was purely arbitrary. Author contributions E.Kim and H.Kang designed experiments, analyzed data, and wrote the manuscript. H.Lee developed new methods for data analysis. H-J.Lee, M-W.Suh and J-J.Song designed and performed experiments and wrote the manuscript. S-H.Oh planned this study and interpreted the data. D.S.Lee planned this study, interpreted the data, and wrote the manuscript. Conflict of interest disclosure We wish to confirm that there are no known conflicts of interest associated with this publication and there has been no significant financial support for this work that could have influenced its outcome. Acknowledgments This work was supported by the National Research Foundation (NRF) funded by the Korean government (MSIP) (No. 20062005090)]. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2011-0030815).

Achard, S., Bullmore, E., 2007. Efficiency and cost of economical brain functional networks. PLoS Comput. Biol. 3, e17. Alain, C., He, Y., Grady, C., 2008. The contribution of the inferior parietal lobe to auditory spatial working memory. J. Cogn. Neurosci. 20, 285e295. Alexander, M.P., Benson, D.F., Stuss, D.T., 1989. Frontal lobes and language. Brain Lang. 37, 656e691. Alexander-Bloch, A., Giedd, J.N., Bullmore, E., 2013. Imaging structural co-variance between human brain regions. Nat. Rev. Neurosci. 14, 322e336. Ashburner, J., 2007. A fast diffeomorphic image registration algorithm. Neuroimage 38, 95e113. Bassett, D.S., Bullmore, E., Verchinski, B.A., Mattay, V.S., Weinberger, D.R., MeyerLindenberg, A., 2008. Hierarchical organization of human cortical networks in health and schizophrenia. J. Neurosci. 28, 9239e9248. Bassett, D.S., Nelson, B.G., Mueller, B.A., Camchong, J., Lim, K.O., 2012. Altered resting state complexity in schizophrenia. Neuroimage 59, 2196e2207. Benjamini, Y., Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. 57, 289e300. Carr, L., Iacoboni, M., Dubeau, M.C., Mazziotta, J.C., Lenzi, G.L., 2003. Neural mechanisms of empathy in humans: a relay from neural systems for imitation to limbic areas. Proc. Natl. Acad. Sci. U.S.A. 100, 5497e5502. Catalan-Ahumada, M., Deggouj, N., De Volder, A., Melin, J., Michel, C., Veraart, C., 1993. High metabolic activity demonstrated by positron emission tomography in human auditory cortex in case of deafness of early onset. Brain Res. 623, 287e292. Chang, E.F., Merzenich, M.M., 2003. Environmental noise retards auditory cortical development. Science 300, 498e502. Chen, S., Xia, W., Li, L., Liu, J., He, Z., Zhang, Z., Yan, L., Zhang, J., Hu, D., 2006. Gray matter density reduction in the insula in fire survivors with posttraumatic stress disorder: a voxel-based morphometric study. Psychiatry Res. 146, 65e72. Clarke, S., Thiran, A.B., Maeder, P., Adriani, M., Vernet, O., Regli, L., Cuisenaire, O., Thiran, J.P., 2002. What and where in human audition: selective deficits following focal hemispheric lesions. Exp. Brain Res. 147, 8e15. Desai, N.S., Cudmore, R.H., Nelson, S.B., Turrigiano, G.G., 2002. Critical periods for experience-dependent synaptic scaling in visual cortex. Nat. Neurosci. 5, 783e789. Edelsbrunner, H., Harer, J., 2008. Persistent homology - a survey. Contemp. Math. 453, 257e282. Emmorey, K., Allen, J.S., Bruss, J., Schenker, N., Damasio, H., 2003. A morphometric analysis of auditory brain regions in congenitally deaf adults. Proc. Natl. Acad. Sci. U.S.A. 100, 10049e10054. Evans, A.C., 2013. Networks of anatomical covariance. Neuroimage 80, 489e504. Evans, A.C., Lee, J.M., Kim, S.I., Fukuda, H., Kawashima, R., He, Y., Jiang, T., Kim, J.S., Chen, Z., Im, K., Lyttelton, O., Lerch, J., Singh, V., Sato, K., Taki, Y., Goto, R., Kinomura, S., Mok, K., Lee, J.K., U., Y., 2008. Human cortical anatomical networks assessed by structural MRI. Brain Imaging Behav. 2, 289e299. Faw, B., 2003. Pre-frontal executive committee for perception, working memory, attention, long-term memory, motor control, and thinking: a tutorial review. Conscious. Cogn. 12, 83e139. Finney, E.M., Fine, I., Dobkins, K.R., 2001. Visual stimuli activate auditory cortex in the deaf. Nat. Neurosci. 4, 1171e1173. Ghrist, R., 2008. Barcodes: the persistent topology of data. B Am. Math. Soc. 45, 61e75. Gong, G., He, Y., Chen, Z.J., Evans, A.C., 2012. Convergence and divergence of thickness correlations with diffusion connections across the human cerebral cortex. Neuroimage 59, 1239e1248. Gong, G., He, Y., Concha, L., Lebel, C., Gross, D.W., Evans, A.C., Beaulieu, C., 2009. Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cereb. Cortex 19, 524e536. Grubb, M.S., Thompson, I.D., 2004. The influence of early experience on the development of sensory systems. Curr. Opin. Neurobiol. 14, 503e512. Hackett, T.A., 2011. Information flow in the auditory cortical network. Hear Res. 271, 133e146. He, Y., Chen, Z.J., Evans, A.C., 2007. Small-world anatomical networks in the human brain revealed by cortical thickness from MRI. Cereb. Cortex 17, 2407e2419. He, Y., Chen, Z.J., Evans, A.C., 2008. Structural insights into aberrant topological patterns of large-scale cortical networks in Alzheimer's disease. J. Neurosci. 28, 4756e4766. Iturria-Medina, Y., Sotero, R.C., Canales-Rodriguez, E.J., Aleman-Gomez, Y., MelieGarcia, L., 2008. Studying the human brain anatomical network via diffusionweighted MRI and Graph Theory. Neuroimage 40, 1064e1076. Kral, A., 2007. Unimodal and cross-modal plasticity in the 'deaf' auditory cortex. Int. J. Audiol. 46, 479e493. Latora, V., Marchiori, M., 2001. Efficient behavior of small-world networks. Phys. Rev. Lett. 87, 198701. Lee, D.S., Kang, H., Kim, H., Park, H., Oh, J.S., Lee, J.S., Lee, M.C., 2008. Metabolic connectivity by interregional correlation analysis using statistical parametric

98

E. Kim et al. / Hearing Research 315 (2014) 88e98

mapping (SPM) and FDG brain PET; methodological development and patterns of metabolic connectivity in adults. Eur. J. Nucl. Med. Mol. Imag. 35, 1681e1691. Lee, D.S., Lee, J.S., Oh, S.H., Kim, S.K., Kim, J.W., Chung, J.K., Lee, M.C., Kim, C.S., 2001. Cross-modal plasticity and cochlear implants. Nature 409, 149e150. Lee, G.P., Meador, K.J., Loring, D.W., Allison, J.D., Brown, W.S., Paul, L.K., Pillai, J.J., Lavin, T.B., 2004. Neural substrates of emotion as revealed by functional magnetic resonance imaging. Cogn. Behav. Neurol. 17, 9e17. Lee, H., Chung, M.K., Kang, H., Kim, B.N., Lee, D.S., 2011. Computing the shape of brain networks using graph filtration and Gromov-Hausdorff metric. Med. Image Comput Comput Assist. Interv. 14, 302e309. Lee, H., Kang, H., Chung, M.K., Kim, B.N., Lee, D.S., 2012. Persistent brain network homology from the perspective of dendrogram. IEEE Trans. Med. Imag. 31, 2267e2277. Lee, H.J., Truy, E., Mamou, G., Sappey-Marinier, D., Giraud, A.L., 2007. Visual speech circuits in profound acquired deafness: a possible role for latent multimodal connectivity. Brain 130, 2929e2941. Lee, J.S., Lee, D.S., Oh, S.H., Kim, C.S., Kim, J.W., Hwang, C.H., Koo, J., Kang, E., Chung, J.K., Lee, M.C., 2003. PET evidence of neuroplasticity in adult auditory cortex of postlingual deafness. J. Nucl. Med. 44, 1435e1439. Lerch, J.P., Worsley, K., Shaw, W.P., Greenstein, D.K., Lenroot, R.K., Giedd, J., Evans, A.C., 2006. Mapping anatomical correlations across cerebral cortex (MACACC) using cortical thickness from MRI. Neuroimage 31, 993e1003. Mechelli, A., Friston, K.J., Frackowiak, R.S., Price, C.J., 2005. Structural covariance in the human cortex. J. Neurosci. 25, 8303e8310. Olea, R.A., Pawlowsky-Glahn, V., 2009. KolmogoroveSmirnov test for spatially correlated data. Stoch. Env. Res. Risk A 23, 749e757. Penhune, V.B., Cismaru, R., Dorsaint-Pierre, R., Petitto, L.A., Zatorre, R.J., 2003. The morphometry of auditory cortex in the congenitally deaf measured using MRI. Neuroimage 20, 1215e1225. Petitto, L.A., Zatorre, R.J., Gauna, K., Nikelski, E.J., Dostie, D., Evans, A.C., 2000. Speech-like cerebral activity in profoundly deaf people processing signed languages: implications for the neural basis of human language. Proc. Natl. Acad. Sci. U.S.A. 97, 13961e13966. Pinotsis, D.A., Hansen, E., Friston, K.J., Jirsa, V.K., 2013. Anatomical connectivity and the resting state activity of large cortical networks. Neuroimage 65, 127e138. Rauschecker, J.P., 1999. Auditory cortical plasticity: a comparison with other sensory systems. Trends Neurosci. 22, 74e80.

Rauschecker, J.P., 2001. Cortical plasticity and music. Ann. N. Y. Acad. Sci. 930, 330e336. Robinson, J.L., Laird, A.R., Glahn, D.C., Lovallo, W.R., Fox, P.T., 2010. Metaanalytic connectivity modeling: delineating the functional connectivity of the human amygdala. Hum. Brain Mapp. 31, 173e184. Rubinov, M., Sporns, O., 2010. Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52, 1059e1069. Rusch, N., van Elst, L.T., Ludaescher, P., Wilke, M., Huppertz, H.J., Thiel, T., Schmahl, C., Bohus, M., Lieb, K., Hesslinger, B., Hennig, J., Ebert, D., 2003. A voxel-based morphometric MRI study in female patients with borderline personality disorder. Neuroimage 20, 385e392. Sadato, N., Okada, T., Honda, M., Yonekura, Y., 2002. Critical period for crossmodal plasticity in blind humans: a functional MRI study. Neuroimage 16, 389e400. Salzman, C.D., Fusi, S., 2010. Emotion, cognition, and mental state representation in amygdala and prefrontal cortex. Annu Rev. Neurosci. 33, 173e202. Shibata, D.K., 2007. Differences in brain structure in deaf persons on MR imaging studied with voxel-based morphometry. AJNR Am. J. Neuroradiol. 28, 243e249. Spreng, R.N., Turner, G.R., 2013. Structural covariance of the default network in healthy and pathological aging. J. Neurosci. 33, 15226e15234. Sporns, O., 2011. The human connectome: a complex network. Ann. N. Y. Acad. Sci. 1224, 109e125. Sporns, O., Chialvo, D.R., Kaiser, M., Hilgetag, C.C., 2004. Organization, development and function of complex brain networks. Trends Cogn. Sci. 8, 418e425. Van den Heuvel, M.P., Stam, C.J., Boersma, M., Hulshoff Pol, H.E., 2008. Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain. Neuroimage 43, 528e539. Wang, L., Li, Y., Metzak, P., He, Y., Woodward, T.S., 2010. Age-related changes in topological patterns of large-scale brain functional networks during memory encoding and recognition. Neuroimage 50, 862e872. Watts, D.J., Strogatz, S.H., 1998. Collective dynamics of ‘small-world’ networks. Nature 393, 440e442. Wilson, S.M., Saygin, A.P., Sereno, M.I., Iacoboni, M., 2004. Listening to speech activates motor areas involved in speech production. Nat. Neurosci. 7, 701e702. Zomorodian, A., Carlsson, G., 2005. Computing persistent homology. Discrete Comput Geom. 33, 249e274.