Abnormal Organization of White Matter Network in Patients with No Dementia after Ischemic Stroke

Structural changes after ischemic stroke could affect information communication extensively in the brain network. It is likely that the defects in the white matter (WM) network play a key role in information interchange. In this study, we used graph theoretical analysis to examine potential organization alteration in the WM network architecture derived from diffusion tensor images from subjects with no dementia and experienced stroke in the past 5.4–14.8 months (N = 47, Mini-Mental Screening Examination, MMSE range 18–30), compared with a normal control group with 44 age and gender-matched healthy volunteers (MMSE range 26–30). Region-wise connectivity was derived from fiber connection density of 90 different cortical and subcortical parcellations across the whole brain. Both normal controls and patients with chronic stroke exhibited efficient small-world properties in their WM structural networks. Compared with normal controls, topological efficiency was basically unaltered in the patients with chronic stroke, as reflected by unchanged local and global clustering coefficient, characteristic path length, and regional efficiency. No significant difference in hub distribution was found between normal control and patient groups. Patients with chronic stroke, however, were found to have reduced betweenness centrality and predominantly located in the orbitofrontal cortex, whereas increased betweenness centrality and vulnerability were observed in parietal-occipital cortex. The National Institutes of Health Stroke Scale (NIHSS) score of patient is correlated with the betweenness centrality of right pallidum and local clustering coefficient of left superior occipital gyrus. Our findings suggest that patients with chronic stroke still exhibit efficient small-world organization and unaltered topological efficiency, with altered topology at orbitofrontal cortex and parietal-occipital cortex in the overall structural network. Findings from this study could help in understanding the mechanism of cognitive impairment and functional compensation occurred in patients with chronic stroke.


Introduction
Ischemic stroke is one of the leading causes of adult disability, resulting from cessation of blood supply due to an occlusion of a cerebral artery. Patients suffered from ischemic stroke are at increased risk of developing vascular cognitive impairment [1,2], which ranges in severity from mild and/or isolated cognitive impairment to vascular dementia. Vascular cognitive impairment with no dementia is a prodromal, mild stage of dementia, lying on a continuum between normal cognition and dementia [3]. Among survivors of ischemic stroke, the prevalence of post-stroke dementia is about 30%, and the risk of dementia is doubled as compared with subjects who have not had stroke [4]. Hence, identification of factors that cause cognitive impairment would benefit interventions to prevent the progression to dementia.
Multiple magnetic resonance imaging (MRI) techniques have been used to investigate the structural and functional changes in the brains of post-stroke patients. Structural MRI analyses included white matter (WM) lesion distribution [5], gray matter atrophy [6], and frontal lobe atrophy [7] were used to evaluate the cognitive impairment after ischemic stroke. Furthermore, restingstate functional MRI (rs-fMRI) was also used to identify the poststroke connectivity changes [8][9][10]. Since diffusion tensor imaging is sensitive to detect the microstructural changes in WM [11], recent studies have investigated the post-stroke abnormalities of diffusion tensor measurements, such as mean diffusivity, fractional anisotropy (FA), and apparent diffusion coefficient [12][13][14]. In addition, complex network graphs that quantify interactions between brain regions have recently given new insights into the spontaneous reorganization of functional and structural brain networks after stroke [15][16][17]. Although WM damages have been found to be potentially associated with the development of cognitive impairment after ischemic stroke [14,18], it remains unknown whether the overall WM organization of brain are affected in patients with no dementia after ischemic stroke.
Since diffusion MRI tractography is advantageous for investigating WM pathways and visualizing brain structural connectivity in vivo [19][20][21], graph theoretical approaches have been proposed to characterize the organization and architecture of the structural networks of the human brain [22][23][24]. Indeed, the alteration in topological organization of brain WM network have been demonstrated useful to understand the cognitive functions [25] and to find the characteristic mechanisms of psychiatric and neurological diseases [26][27][28][29][30][31]. These studies indicate that it is feasible to employ DTI tractography to investigate structural networks in patients with chronic stroke. Our goal in this study is to apply WM tractography and graph theoretic analysis to investigate abnormalities in the organization and architecture of the structural connectivity pattern at the macroscopic scale in patients after ischemic stroke, based on the following hypotheses: (i) Patients with chronic stroke could exhibit efficient small-world topology, as has been successfully established in several disease models including stroke model in rats [32]. On this basis we would like to further identify the possible alterations of overall topological properties compared with normal controls. Because either decreased small-world metrics related to impaired neural circuits directly caused by infarct lesions, or increased measurements reflecting excessive neuronal clustering and wiring after stroke [32] or as a compensation mechanism as described previously in schizophrenia [33], possibly occurs in chronic stroke patients; (ii) Regions previously considered to be responsible for a certain type of cognitive impairment (e.g., post-stroke depression [34], executive dysfunction [35]) could be identified with altered nodal characteristics, thus help to give an anatomical & topological manifestation of post-stroke functional abnormality; (iii) Correlations between Mini-Mental Screening Examination (MMSE) or National Institutes of Health Stroke Scale (NIHSS) scores and network characteristics can be possibly established. Since there are evidences suggest that functional or structural connectivity changes are correlated with MMSE score in amnestic mild cognitive impairment (aMCI) and Alzheimer's disease [36], and reflect the post-stroke motor network alterations [37] and severity of stroke in rat model [32].

Subjects
All the subjects were right-handed and were all informed and signed with a verbal and written consent form. Ethical approval was obtained from the Ethics Committee in the Chinese University of Hong Kong. Forty-seven patients with no dementia after ischemic stroke (29 males and 18 females; 68.769.6 years old) and 44 age-and gender-matched normal controls (NC) (

MRI Acquisition
All subjects were scanned using a clinical 3T MRI scanner with an 8-channel Sense head coil (Achieva, Philips Medical Systems) at the Prince of Wales Hospital in Hong Kong. For each subject, T1weighted (T1W) images, fluid attenuation inversion recovery (FLAIR) images, and diffusion tensor images (DTI) covering the whole brain were acquired in the axial orientation. T1W images were obtained using 3D fast field echo imaging sequence with the following parameters:

Delineation and Mapping of Infarcts
The FLAIR image of each subject with chronic stroke was first co-registered to T1W images using FMRIB's Linear Registration Tool (FLIRT) [40]. The infarcts of each patient were manually delineated on FLAIR images with T1W image loaded as a reference by an experienced neuroradiologist (YYX). Then T1W image was co-registered to the ICBM152 template in Montreal Neurological Institute (MNI) space using FMRIB's Non-linear Image Registration Tool (FNIRT) [41], and the delineated infarcts was transformed to MNI space. The infarcts were masked out during the registration by setting their voxel values in the weighting volume as zero. Then the infarcts on gray matter and white matter were mapped to automated anatomical labeling (AAL) [42] and ICBM DTI-81 white matter labels atlas [43] respectively. The location of infarcts for each subject was determined by AAL and DTI-81 atlas with visual quality control, and the spatial distribution of infarcts of whole patient group was obtained (Table 1 and Figure 1).

Connectivity Network Construction
Image Preprocessing. The whole schematic flowchart of WM connectivity network construction is shown in Figure 2. Artifacts induced by head motion and eddy current in DTI data were processed by affinely registering diffusion-weighted volumes to the non-diffusion-weighted b0 volume using the FMRIB's Diffusion Toolbox of FSL [44]. Skull and non-brain tissue were removed in the masked and corrected diffusion data by applying the FSL Brain Extraction Tool [45] to b0 image. The T1W image of each individual subject was co-registered to their b0 image and transformed to native DTI space using FLIRT.
White Matter Tractography. For each individual subject, diffusion tensor model was first estimated using the linear leastsquares fitting method [46]. White matter tracts of the whole brain were subsequently reconstructed using the fiber assignment by continuous tracking (FACT) algorithm [47] with the FA threshold at 0.15 and tracking turning angular threshold at 35u between two connections. Afterwards, a spline filtering was applied to smooth the streamline tractography. Diffusion Toolkit 0.6 (http://www. nitrc.org/projects/trackvis/) was used for DTI data processing and tractography in this work.
Brain Parcellation. The brain was automatically partitioned into 90 non-cerebellar regions (45 cortical and subcortical regions in each hemisphere, Table 2) using the AAL parcellation atlas. For each subject, individual T1W image in the native DTI space was mapped to the ICBM152 template in MNI space using FNIRT, and the AAL atlas was inversely warped to the native DTI space by applying nearest-neighbor interpolation. The regions with detected infarcts were masked out before performing image registration, so that image registration was based on reliable information from image of normal tissues.
Structural Connectivity Mapping. In the native DTI space, two AAL regions were considered to be connected if at least one fiber was presented between them [19]. The fiber connection density between two AAL regions was defined as the normalized number of fibers per unit volume [23,48,49]. The number and length of fibers connecting each pair of AAL regions were used to calculate the fiber connection density, where V i and V j denote the volume of AAL regions i and j, respectively, F e denotes the set of fibers connecting regions i and j, and l f denotes the length of fiber f along its trajectory. Taking fiber connection density between two AAL regions as its connection weight, a weighted 90690 structural connectivity matrix for each subject was generated. Each node of the constructed network corresponded to one AAL region.

Network Graph Analysis
The topological organization of the weighted structural connectivity network was characterized at the nodal and global levels using the Brain Connectivity Toolbox [50] based on graph theory. The nodal behavior was quantified in terms of connection strength, local clustering coefficient, betweenness centrality, regional efficiency and vulnerability. The global network architecture was described in terms of the global connection strength, global clustering coefficient, characteristic path length, global betweenness centrality, global vulnerability, normalized clustering coefficient, normalized path length, and small-worldness. Each metric provided a different viewpoint to describe major features of the large-scale architecture.
Regional Nodal Characteristics. The connection strength S w i of each node i in the weighted network was computed as the sum of the weights of all its connections, which provided information on the total level of connectivity [30,50], where w ij is the weight between nodes i and j in the network, and N is the set of all nodes in the network. The local clustering coefficient C w i of each node i in the weighted network was defined as the likelihood that its neighbors were interconnected to each other, which described the strength of one node and its neighbors were clustered [51,52], where k i is the node degree defined as the number of connections to node i. The local clustering coefficient of the nodes with less than two connections was set as zero.
The betweenness centrality B w i of each node i in the weighted network was defined as the fraction of all shortest paths in the network that pass through it, which was essentially a measurement of the influence of a node over the information flow between itself and other nodes [53,54], where r hj i ð Þ is the number of shortest path between nodes h and j that passed through node i, n is the total number of nodes. Figure 2. Whole schematic flowchart of WM network construction, (1) brain extraction, (2) DTI preprocessing and diffusion tensor model estimation, (3) white matter fiber tracking, (4) co-registration T1W image with non-diffusion-weighted b0 image, (5) mapping T1W image to ICBM152 template and warping the AAL atlas to native DTI space, (6) structural connectivity mapping and connectivity matrix generation, (7) weighted WM network construction. doi:10.1371/journal.pone.0081388.g002 The regional efficiency E w i of each node i in the weighted network was defined as the inverse of harmonic mean of the shortest path length between itself and all other nodes, which was used to quantify the importance of a node in the communication within the network [55], where d w ij is the length of the shortest weighted path between nodes i and j in the weighted network, where g w i<j indicates the shortest weighted path between nodes i and j.
The vulnerability V w i of each node i in the weighted network is defined as the drop in global efficiency when this node and its connections are removed from the network graph, which was used to identify the most indispensable node to network efficiency [24,56], where E glob is the global efficiency of the network, E i glob i ð Þ is the global efficiency of the network after removing node i. The global efficiency is defined as the inverse of the harmonic mean of the shortest path length between every two nodes in a network, Overall Graph characteristics. The global strength S w G of the weighted network was computed as the mean connection strength across all nodes in the network, The global clustering coefficient C w G was defined as the average of the local clustering coefficient C w i over all nodes in the network [52], The weighted characteristic path length L w G of the entire network was measured by the harmonic mean of the shortest path between every two nodes and was equivalent to the inverse of the global efficiency, which expressed how well the overall network was connected and represents the capacity to exchange information [57,58], The global betweenness centrality B w G was calculated as the average of the values of the nodal betweenness B w i over all nodes, The global vulnerability V w G of the entire network was defined as the maximum vulnerability for all of its nodes [56], The measure of small-worldness s w G was defined as ratio between the normalized clustering coefficient c w G and the normalized characteristic path length l w G , which denoted the small-world organization of the weighted network [59].
where the C w random and L w random are the average of global clustering coefficient and characteristic path length of 100 matched random networks that preserved the same number of nodes, edges, and degree distribution as the real network.
Hub Identification. The putative hubs were usually the nodes with high connection strength, high betweenness centrality, high regional efficiency, low clustering coefficient, and/or high vulnerability, which played a crucial role in fast information transferring and efficient integration of information in whole network communication [22,30,[60][61][62]. The hubs of the structural brain network were identified using the hub score, a rank-based analysis of the multiple mean nodal metrics. The hub identification criteria included (1)   V w i . For each node, a hub score between 0 to 5 was assigned. Nodes with a hub score of 2 or higher were considered as hubs. [22,61].

Statistical Analysis
Overall Graph Characteristics. Differences in overall graph characteristics (S w G , C w G , L w G , B w G , V w G , c w G , l w G , and s w G ) between the patient and NC groups were examined using permutation testing [30,31,60]. First, the observed values of the test were calculated as the differences in the measured overall graph characteristics between the two groups. Then the subjects in both groups were pooled and randomly assigned to either one of two groups consisting of the same size as the original patient and NC groups. The differences in overall graph characteristics between the two random groups were computed. This procedure was repeated for 5,000 times. The one-tailed p-value was then calculated as the proportion of sampled permutations where differences that were greater than (or smaller than) the observed values. A significance threshold of p = 0.05 (uncorrected) was used for testing the overall graph characteristics.
Regional Nodal Characteristics. The statistical analysis for the regional nodal characteristics was similar to those for global network characteristics. The regional characteristics (S w i , C w i , B w i , E w i , and V w i ) of each node in the network were compared between the patient and NC groups for all nodes using the above-mentioned permutation test. The one-tailed p-value was calculated and the false discovery rate (FDR) [63] with q = 0.05 was used to correct the multiple comparisons. The FDR was the expected proportion of false positives among significant results.
Correlation with the MMSE and NIHSS score. The Pearson partial correlation between the MMSE score and the network properties, and between the NIHSS score and the properties at both the global and nodal levels were evaluated in patient group while controlling for age and gender. The significance level for testing the overall graph characteristics was set at p,0.001 (uncorrected). A statistical significance level of p,0.001 (uncorrected) was used for testing the regional nodal characteristics.

Overall Topological Properties
Both normal controls and patients with chronic stroke exhibited efficient small-world topology (s w G w1) in structural networks (NC: s w G~4 :25+0:27, patients: s w G~4 :37+0:29). In addition, a significant increase of small-worldness (p = 0.0226) and a decrease of normalized characteristic path length (p = 0.0026) in patient group were observed ( Figure 3). Furthermore, as compared with the overall graph characteristics of patients, no significant differences in global strength S w G global clustering coefficient C w G , characteristic path length L w G , and global vulnerability V w G (all p.0.05) were found (Figure 3). The global betweenness centrality B w G (p = 0.0104) was significantly decreased in patient group (Figure 3).

Distribution of Hub Region
In both NC and patient groups, 25 hubs were identified by multiple nodal metrics (Table 3, Figure 4). In particular, 23

Nodal Characteristics Alteration
The nodal characteristics (S w i , C w i , B w i , E w i , and V w i ) of each cortical and subcortical region between the two groups were further compared. The S w i , B w i , and V w i of a number of regions were altered (Table 4, Figures 5, 6, and 7). No significant alteration of the local clustering coefficient C w i and regional efficiency E w i (FDR critical p-value = 0) were found between the two groups. The S w i , B w i , V w i of left superior occipital gyrus [SOG.L] were significantly increased.

Correlation with MMSE and NIHSS scores
No significant correlations were found between MMSE score and the network properties in patient group at both the global and nodal levels. The NIHSS score of patient is correlated with the betweenness centrality of right pallidum [PAL.R] (p = 0.0000, r = 0.644) and the local clustering coefficient of left superior occipital gyrus [SOG.L] (p = 0.0002, r = 0.5523). No significant correlation was found between NIHSSS and overall graph characteristics.

Small-world Organization
Both normal controls and patients with chronic stroke were found to demonstrate small-world architectures. In particular, patients with chronic stroke displayed small-worldness close to the values of the structural networks in previous DTI-based studies with similar age groups [25][26][27]. Although WM networks of patients with chronic stroke showed prominent small-world attributes, several small-world measurements were found to be significantly altered. The patients showed increased small-worldness and decreased normalized characteristic path length, suggesting a more regular organization of WM network in patients with chronic stroke. Since there are no difference in characteristic path length (global level) and regional efficiency (nodal level), the decreased normalized characteristic path length is not necessarily an advantage in a complex network. The effective balance between local specialization and global integration arising from the small-world nature of anatomical connectivity possibly conserved in patients with chronic stroke. The increased smallworldness has also been observed in schizophrenia [33], during recovery from traumatic brain injury [64], and during recovery from stroke [32]. Furthermore, as patients with chronic stroke have not shown strong changes in local and global clustering coefficient, characteristic path length, and regional efficiency, our findings suggest the topological efficiency is not unaltered between patients and NCs.

Alteration of Hub Distribution
Compared with the hub criteria based on the level of single nodal metrics in previous DTI-based studies, such as the betweenness centrality [65,66] and regional efficiency [26,28], the rank-based analysis of multiple nodal characteristics are more Table 4. Nodal characteristics comparison between post-stroke and NC.    (p = 0.301) or course of stroke (p = 0.864) was found between the patients with left and right lateralized infarct lesions.

Altered Nodal Characteristics
Group differences in betweenness centrality reflect the effects of the disease on the global roles of every node region. Reduced betweenness centrality is mainly observed in orbitofrontal cortex. The finding of decreased centrality in frontal lobe is in line with the pattern of frontal lobe atrophy in ischemic stroke patients [7,68]. The orbitofrontal cortex is a prefrontal cortex region in the frontal lobes which is involved in the cognitive processing of decision-making. Five regions with decreased centrality were found in left orbitofrontal cortex of patients after ischemic stroke, supported by the fact that patients with subcortical ischemic vascular disease often have executive dysfunction [35]. Furthermore, cortical thinning in orbitofrontal gyrus has shown in vascular cognitive impairment associated with small vessel ischemic disease [69]. In this study, 20/47 patients exhibited cognitive impairment which support the findings of the orbitofrontal abnormalities. Decreased centrality is observed in four orbitofrontal hub regions, suggesting a decreased hub role of orbitofrontal regions in the brain network of patients. Interestingly, increased betweenness centrality is observed in parietal-occipital cortex. Increased centrality may indicate a shift to more parietaloccipital brain regions as a possible compensatory mechanism for decreased centrality of orbitofrontal regions. As the total infarct number in the left hemisphere is greater than that in the right hemisphere, the locations with altered centrality between the left and right hemispheres have showed the most asymmetry.
Apart from betweenness centrality, vulnerability is also used to characterize the influence of nodes in a network. As betweenness and vulnerability quantify the node in different aspects, the nodes with higher betweenness are not necessarily more vulnerable [19]. From the results in this study, the increased vulnerability is consistent with the increased betweenness in parietal-occipital cortex. Nevertheless, a decreased betweenness is found in the prefrontal cortex but no reduced vulnerability is observed. The occipital regions with increased vulnerability include cuneus [CUN] and superior occipital gyrus [SOG] in bilateral occipital lobe, which are important for visual processing. These changes suggest that these regions have increased influence in the brain communication compared with NCs. This could be a potential evidence for understanding of the visual system recovery in patients with chronic stroke. Particularly, we have observed reduction in both centrality and vulnerability in left rolandic operculum [ROL.L], which is a key component for speech processing [70]. This finding is supported by the finding reported in the reduced white matter integrity and damaged tracts related to speech processing in post-stroke patients [71]. The structural abnormality of this area was also reported to be associated with stuttering [72].
The connection strength is the most fundamental network measure, and provides information of the total degree of connectivity. Decreased connection strength is found in the left caudate [CAU.L] and left inferior temporal gyrus [ITG.L]. The altered connection strength in CAU.L is consistent with the pattern of altered communicability [16]. The region of ITG.L is known to be included in the default mode network [73]. The rs-fMRI has revealed the association between altered functional connectivity of left middle temporal cortex in the DMN and poststroke depression severity [34]. The decreased structural connectivity may give an anatomical comprehension of post-stroke depression. Imaging evidence also reveals the connectivity alteration of this region in post-stroke aphasia recovery [74,75].
Specifically, the left superior occipital gyrus [SOG.L] presents significant increase in connection strength, centrality, and vulnerability, which is supported by reported gray matter atrophy in patients with ischemic stroke [6]. Recent research has associated degraded oral reading performance with acute left hemispheric stroke [76]. The betweenness of SOG.L is also larger than its counterpart in the right hemisphere in normal subjects [19].

Correlation with the MMSE and NIHSS scores
Significant correlations were failed to be established between the MMSE score and the global or nodal characteristics. On one hand, the subjects studied are patients with no dementia, which means only mild cognitive impairment could be identified. On the other hand, MMSE provides only a general evaluation of cognitive function and suggests a compromised sensitivity in detecting mild cognitive dysfunction [77]. Furthermore, the severity of sensorimotor dysfunction, which is the major deficit after ischemic stroke, can not be reflected by MMSE. By contrast, NIHSS with more emphasis put on the aspects of sensorimotor functions was found to be correlated with the betweenness centrality of right pallidum [PAL.R] and the local clustering coefficient of SOG.L. Pallidum is mainly involved in the regulation of voluntary movement, and its change is supported by previous structural studies which revealed increased grey matter volume and cortical thickness in a series of motor-related areas during recovery from stroke [78,79]. Besides, the correlation between clustering coefficient of SOG.L and NIHSS further confirmed the increased connection strength, centrality, and vulnerability observed in this region, suggesting its increasingly critical role in the reorganized network after stroke.

Methodological Issues
Some methodological issues should be taken into account when interpreting our results. First, we used AAL atlas and FNIRT in FSL to perform brain parcellation. Since there is no widely accepted standard to construct cortical and subcortical regions in the brain, network nodes defined using atlas mapping are more valuable than random parcellation. Using different atlases, i.e., AAL atlas (90 cortical and subcortical regions), the Harvard-Oxford atlas (110 cortical and subcortical regions), Destrieux cortical atlas (66 cortical regions) [80], and LONI Probabilistic Brain Atlas (54 cortical regions) [81], different node sets would be generated and thus different properties of network would be produced. Although a research has suggested that the properties of WM network are highly conserved over multiple atlases and spatial resolution [82], different parcellation strategies affect WM connectivity and may result in different topological properties [83]. Moreover, voxel level (small area level) partition of cerebral cortex may be finer than atlas level partition if the parcellation methods are precise.
Second, we used fiber connection density as the inter-regional WM connectivity in the construction of the graphs. Previous researches have suggested different connectivity matrices for the WM network construction, i.e., fiber connection density [23], fiber number (FN) [28], mean FA [25], normalized fiber number [82,84], and multiplication between FN and mean FA [27]). FNbased connectivity matrices are used to describe the interconnection of fiber tracts among regions of interest and should always be normalized, as the number of fibers detected varies among individuals. Mean FA is computed by averaging FA values along the tract and is used to describe the organization of the underlying white matter. Although the physiological meaning of these measures is poorly understood, both FN and FA based connectivity matrices are commonly used to characterize white matter integrity, and similar results have been found from FN-weighted and FA-weighted network [27,31]. Compared with FN and FA, fiber connection density involved geometric measures including the volume of the cortical regions and the mean path length of fibers connecting each pair of regions is more stable.
Third, we used rank-based analysis of multiple nodal characteristics to identify the hub regions, and used hub score to describe the central role of hubs. Since there is no gold standard to define anatomical hubs, single or multiple nodal measures were usually used in other DTI-based studies [28,30,31,49]. Compared with the level of single nodal metric, the rank-based criterion based on multiple nodal characteristics is more comprehensive to define a hub, and the hub score gives a quantitative description of its central role in the network.

Conclusions
We have applied DTI tractography, WM connection mapping, and graph theoretic analysis to demonstrate altered organization of WM network in patients with chronic stroke. In this study, we comprehensively investigated different characteristics of WM connection in patients with chronic stroke and our findings suggest that patients have reduced betweenness centrality at orbitofrontal cortex, and increased betweenness centrality and vulnerability at parietal-occipital cortex, and exhibit efficient small-world organization and unaltered topological efficiencies. Compared to the NC, patients with chronic stroke exhibit significant increase of small-worldness, and connection strength S w i , betweenness centrality B w i , and vulnerability V w i at left superior occipital gyrus [SOG.L], but significant decrease of normalized characteristic path length L w G and global betweenness centrality B w G . Our results also demonstrated the MMSE score of patients with chronic stroke is not significantly correlated with network properties.
The abnormities at orbitofrontal cortex and parietal-occipital cortex may affect the information exchange, but need further verification by fMRI studies to understand the functional difference between groups. The results from this study provide extra information for understanding of white matter connectivity changes in patients with chronic stroke in addition to the diffusion parameters obtained in the previous studies [85][86][87].