Disrupted small world networks in patients without overt hepatic encephalopathy: A resting state fMRI study

European Journal of Radiology 83 (2014) 1890–1899

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European Journal of Radiology journal homepage: www.elsevier.com/locate/ejrad

Disrupted small world networks in patients without overt hepatic encephalopathy: A resting state fMRI study Long Jiang Zhang a,∗∗,1 , Gang Zheng a,b,1 , Liping Zhang c , Jianhui Zhong d , Qiang Li c , Tie Zhu Zhao a,b , Guang Ming Lu a,∗ a

Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu 210002, China College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China c College of Natural Science, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu 210016, China d Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China b

a r t i c l e

i n f o

Article history: Received 11 December 2013 Accepted 24 June 2014 Keywords: Hepatic encephalopathy Magnetic resonance imaging Resting state Blood oxygen level dependent Graph theory Small world property Functional connectivity

a b s t r a c t Purpose: To explore changes in functional connectivity and topological organization of brain functional networks in cirrhotic patients with minimal hepatic encephalopathy (MHE) and non hepatic encephalopathy (nonHE) and their relationship with clinical markers. Materials and methods: Resting-state functional MR imaging was acquired in 22 MHE, 29 nonHE patients and 33 healthy controls. Functional connectivity networks were obtained by computing temporal correlations between any pairs of 90 cortical and subcortical regions. Graph analysis measures were quantitatively assessed for each subject. One-way analysis of covariance was applied to identify statistical differences of functional connectivity and network parameters among three groups. Correlations between clinical markers, such as Child–Pugh scores, venous blood ammonia level, and number connection test type A (NCT-A)/digit symbol test (DST) scores, and connectivity/graph metrics were calculated. Results: Thirty functional connectivities represented by edges were found to be abnormal (P < 0.05, FDR corrected) in cirrhotic patients, in which 16 edges (53.3%) were related with sub-cortical regions. MHE patients showed abnormal small-world attributes in the functional connectivity networks. Cirrhotic patients had significantly reduced nodal degree in 8 cortical regions and increased nodal centrality in 3 cortical regions. Twenty edges were correlated with either NCT-A or DST scores, in which 13 edges were related with sub-cortical regions. No correlation was found between Child–Pugh scores and graph theoretical measures in cirrhotic patients. Conclusion: Disturbances of brain functional connectivity and small world property loss are associated with neurocognitive impairment of cirrhotic patients. Reorganization of brain network occurred during disease progression from nonHE to MHE. © 2014 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Hepatic encephalopathy (HE) is a neuropsychiatric syndrome that develops in patients with severe liver disease and/or portalsystemic shunting, which is characterized by a wide spectrum of clinical manifestations, ranging from alterations of psychometric performance to stupor and coma. This disorder has been considered as a continuum of neurocognitive dysfunction, from minimal

∗ Corresponding author. Fax: +86 02580860815. ∗∗ Corresponding author. Tel.: +86 13405833176. E-mail addresses: [email protected] (L.J. Zhang), [email protected] (G.M. Lu). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.ejrad.2014.06.019 0720-048X/© 2014 Elsevier Ireland Ltd. All rights reserved.

HE (MHE) to overt HE [1]. Patients with MHE, the mildest manifestation of HE, have no obvious clinical manifestation of HE but can be identified with neuropsychological examinations, such as the number connection test and the digit symbol test [1]. MHE has raised wide attention in recent years because of its high prevalence, causing impaired driving [2], decreased quality of life [3], and increased fall [4] in these patients with cirrhosis. These cirrhotic patients suffer from some fundamental cognition function impairments, such as attention, working memory, and fine motion. More importantly, MHE has some propensity to the development of overt HE [5]. However, the exact pathophysiological mechanisms of these cognition function changes in MHE patients remain unknown so far. Neuroimaging plays an important role in uncovering the neural substrate of brain functional abnormality in cirrhotic patients.

L.J. Zhang et al. / European Journal of Radiology 83 (2014) 1890–1899

Specially, resting state functional magnetic resonance imaging (rs-fMRI) has gained increasing attention in recent years since it does not require patients to actively participate in a specific task while undergoing imaging. Using rs-fMRI, Zhang et al. [6] first reported the abnormality of brain default-mode network (DMN) in HE with independent component analysis, indicating that this technique can be used to examine brain function changes in HE patients. Also using rs-fMRI, Qi et al. [7] showed that MHE patients had selective impairments of resting state networks, with aberrant functional connectivity in dorsal attention network (DAN), DMN, visual network (VN), auditory network (AN), and spared sensorimotor network (SMN) and self-referential network (SRN). Another rs-fMRI study by Qi et al. [8] showed decreased amplitude of low-frequency fluctuation in the DMN and increased ALFF in the posterior insular cortex and the changes were found to be dependent on the severity of HE, suggesting continuous impairment of the DMN and a compensatory role of the insula during the progression of HE. Other studies [9,10] also supported HErelated abnormal spontaneous neuronal activity at the resting state. Thus, rs-fMRI appears to be a promising modality for detecting the severity and progression of HE, and for providing a better understanding of the neuropathophysiological mechanism of cognitive function changes in MHE. Recently, Zhang et al. [11] found widespread changes in functional connectivity between various cortical and subcortical regions, and these changes correlated with neuropsychologic impairment in patients with minimal hepatic encephalopathy. However, these studies mainly focus on the connectivity among brain regions in patients with cirrhosis, while less attention was paid to changes in topological organization of wholebrain functional networks. The human cerebral cortex is a complex network that is extremely sparse, yet capable of integrated real-time performance [12]. Many studies have demonstrated that human brain anatomical and functional networks have efficient small-world property that quantifies two major organization principles of the human brain, i.e. functional segregation and integration [13,14]. Normally, brain performance usually requires a suitable balance between local specialization and global integration of brain functional connectivity to perform higher cognitive functions. Several researchers have observed small-world network alterations in patients with schizophrenia, Alzheimer’s disease, attention-deficit hyperactivity disorder and epilepsy [13,14]. Recently, Hsu et al. [14] used rs-fMRI and small-world parameters to investigate abnormalities of functional connectivity in 35 cirrhotic patients including 17 cirrhotic patients without HE, 9 with minimal HE, and 9 with overt HE. They found that alterations in the rs-fMRI network topology of the brain were associated with HE grade; and that focal or diffuse lesions disturbed the functional network to further alter the global topology and efficiency of the whole brain network. However, the study of Hsu et al. only focused on the small-world properties based on graph theoretical analysis, they did not clarify the patterns of specific functional connectivity changes associated with the severity of HE and the role of disrupted network topology in the development of MHE. The purpose of this study was to employ rs-fMRI and graphtheoretical approaches to evaluate the patterns of large-scale brain functional network and small-world properties during the development of MHE and their correlations with clinical markers, such as neuropsychological tests and venous blood ammonia level.

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gender-matched healthy controls (24 males, 11 females, mean age 48.8 years ± 12.5 [SD] years). The patients were recruited from our inpatient or outpatient departments. MHE were defined and classified according to the final report of the working party at the 11th World Congresses of Gastroenterology in Vienna in 1998 [1]. The inclusion criteria for recruitment of the patients were as following: the patients without clinical proven HE, who could finish the fMRI exam without any MRI contraindication, artificial tooth or other foreign bodies in the head causing significant artifacts, and had no other diseases affecting brain function, such as drug abuse, psychiatric diseases and trauma. Two typical neuropsychiatric tests, number connection test type A (NCT-A) and digit symbol test (DST) for evaluation minimal HE, were given to all patients and controls before MR studies. A test result was considered abnormal if it was 2 SD above the mean score of healthy subjects in NCT-A and/or 2 SD below the mean score of healthy subjects in DST. The healthy volunteers without psychiatric or neurological history were recruited from local community. All healthy subjects had no other diseases affecting brain functions. Abdominal ultrasound scans revealed no abnormal findings for all healthy subjects. This study was approved by institutional review board of Jinling Hospital, Nanjing, China, and was conducted in compliance with Health Insurance Portability and Accountability Act. All subjects gave written informed consent before fMRI or neuropsychologic evaluation. 2.2. Laboratory examinations Blood biochemistry tests, including prothrombin time, protein metabolism tests (including total protein, globulin, albumin, and the ration of albumin and globulin), bilirubin metabolism tests (including total bilirubin, direct bilirubin, and indirect bilirubin), glutamic pyruvic transaminase, and glutamic oxalacetic transaminase, were performed within 24 h before MRI scanning for all patients. Some of the tests above were used to calculate the Child–Pugh score to assess the severity of cirrhosis. The scoring system considered five variables selected by clinical experience, i.e., ascites, encephalopathy, prothrombin time and serum levels of bilirubin and albumin. A score ranging from 1 to 3 was assigned to each variable. Patients are classified into class A (score 5–6), B (score 7–9) or C (score 10–15). Thirteen out-patient patients did not have the ammonia test. Laboratory tests were not prescribed to the normal subjects. 2.3. Magnetic resonance imaging

2. Materials and methods

All experiments were performed at a clinical 3T whole-body scanner (TIM Trio, Siemens Medical Solutions, Erlangen, Germany) using a standard birdcage head transmit/receive coil. The head coil with pads was positioned carefully to reduce head movement. A total of 250 volumes of EPI images were obtained axially for the rs-fMRI analysis and the parameters were as follows: field-of-view (FOV) = 240 mm × 240 mm, matrix size = 64 × 64, flip angle = 90◦ , TR = 2000 ms, TE = 30 ms, slice thickness = 4 mm, distance factor = 10%, slices = 30. For each subject, a 3D magnetizationprepared, rapid acquisition gradient echo (MPRAGE) sequence was used to obtain high-resolution, T1-weighted anatomical images for spatial normalization (axial, TR/TE = 2300 ms/2.98 ms, flip angle = 9◦ , 191slices, field of view = 256 mm × 256 mm, acquisition matrix = 256 × 256, slice thickness = 1 mm). During MRI scans, all subjects were instructed to rest with their eyes closed and heads still.

2.1. Subjects

2.4. Preprocessing of fMRI data and time-series extraction

This study included 54 adult patients with cirrhosis (16 females, 39 males; mean age 49.5 years ± 10.4 [SD]) and 35 age- and

Resting-state rs-fMRI data were preprocessed with SPM8 (Statistical Parametric Mapping, http://www.fil.ion.ucl.ac.uk/spm/).

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The first ten volumes were discarded to allow for reaching T1 equilibration. Slice timing and realignments were then performed on the remaining 240 measures. The time course of head motion was obtained by estimating the translation in each direction and the rotation in angular motion on each axis for all 240 consecutive volumes. Three patients and two healthy subjects were excluded from further analysis because either translation or rotation exceeded 1 mm or 1◦ . We also evaluated the differences in translation and rotation of head motion between cirrhotic patients and controls according to the following formula [15]:

1  1−L L

 (xi − xi−1 )2 + (yi − yi−1 )2 + (zi − zi−1 )2

⎧ Cnet ⎪ ⎨ Cnormalized = C

random

⎪ ⎩ Lnormalized = Lnet

(2)

Lrandom

To represent the small-world-ness of a give network, a smallworld metric S can be defined by

Head motion per rotation =

To normalize the cluster coefficient and path length, the value of Cnet and Lnet need to be compared with those of random network (Creandom and Lreandom ) [20], and the normalized cluster coefficient and path length can be calculated by

(1)

i=2

where L is the length of the time series (L = 240 in this study), xi , yi and zi are translations/rotations at the ith time point in the x, y and z directions, respectively. The results showed that the two groups had no significant differences (two sample t-test, both P > 0.05). The functional data were spatially normalized to the Montreal Neurological Institute (MNI) template and resampled to 3 mm × 3 mm × 3 mm. After spatial normalization, the BOLD signal was detrended to eliminate linear trend, and then band-filtered (0.01–0.08 Hz) to reduce the effects of low-frequency drift and high-frequency physiological noise. Nuisance covariates including global mean signals, white matter signals, cerebrospinal fluid signals and head motion parameters, were regressed out from the rs-fMRI data. Finally, we obtained mean time series of 90 region of interests (ROIs) defined by Automated Anatomical Labeling (AAL) atlas [16] for each individual by averaging the rs-fMRI time series over all voxels in each ROI. 2.5. Functional connectivity and graph analysis To assess functional connectivity of each subject, the Pearson correlation coefficient between every pair of regional time series is computed to form a 90 × 90 interregional correlation matrix. In each subject, the node amount N is 90 and the total number of func2 = 90 × 89/2 = 4005). A Fisher’s tional connectivity is 4005 (C90 r-to-z transform is used on the correlation matrix Mij (i,j = 1,. . .N) of each subject to improve the normality of the correlation coefficients [17]. Considering the complicated statistical descriptions of continuous weighting between regions to study network properties, we thresholded the correlation matrix Mij of each subject to create a binarized matrix Aij for simplicity. Therefore, the network (graph) G was represented by a binarized matrix Aij with N nodes and K edges, where nodes and edges indicate cortical regions (90 in this study) and undirected links (4005) corresponding to its nonzero elements, respectively. We applied a series of thresholds in a wide range of sparsity, defined as selections between 5% and 34% of the 4005 possible edges in a network of 90 nodes [18]. Such a selection enabled us to compare the small world parameters between groups as a function of sparsity independently of the precise selection of threshold. The clustering coefficient Cnet and characteristic path length Lnet , as the typical graph theoretical measures, were calculated at a given sparsity from 5% to 34% with steps of 2% to characterize the structure of binary network [19]. The clustering coefficient of a network Cnet is the average of the clustering coefficient over all nodes and indicates the extent of the local interconnectivity or cliquishness in a network [19]. The clustering coefficient is zero if the nodes are isolated or have just one connection. The characteristic shortest path length Lnet is the average minimum number of connectivity that links any two nodes of network.

S=

Cnormalized Lnormalized

(3)

which S is typically >1 for a small world network [20]. In this study, we also investigated the degree of each node of the functional connectivity network. which is a test–retest reliable characteristics as compared to other nodal metrics such as regional efficiency and nodal betweenness [21].To address this property, the degree ki of a node i is defined as the number of connections to the node. We calculated the degree of each node in each subject for further nodal statistical analysis.

2.6. Statistical analysis One-way analysis of covariance (ANCOVA) with age and gender as covariates was performed to explore if the functional connectivity was significantly different among three groups. Since ANCOVA was calculated in all 4005 functional connectivities, a correction for multiple comparisons was absolutely necessary. To correct multiple comparisons, the false discovery rate (FDR) approach was performed at a P value of 0.05 based on the Benjamini–Hochberg procedure [22]. Post hoc comparisons (Sidak-corrected) were performed for inter-groups within the significant connectivity detected by ANCOVA. Correlations between the neuropsychiatric test scores, Child–Pugh scores and functional connectivities which were significantly different among three groups were calculated to investigate the relationships between neuropsychiatric tests, liver function and functional connectivities in cirrhotic patients. A FDR-corrected P value less than 0.05 was considered statistically significant. ANCOVA with age and gender as covariances was applied at each sparsity to detect differences among three groups in graph theoretical measures (e.g. Cnormalized , Lnormalized and S) to find how much the functional connectivity network has changed in the different severity of HE. An ANCOVA P value less than 0.05 was regarded as a significant difference. Post hoc analysis (Sidak corrected) was performed if significant differences were present in ANCOVA among three groups. A Sidak-corrected P value less than 0.05 was regarded as significant. To investigate nodal characteristics, ANCOVA with age and gender as covariants was applied at a fixed sparsity to detect differences in the degree of each node among three groups. ANCOVA P values of 90 nodes were corrected by FDR and post hoc comparisons (Sidak-corrected) then applied. Correlations between the neuropsychiatric test scores, Child–Pugh scores, and graph theoretical measures were calculated at each sparsity to investigate the relationships between neuropsychiatric tests, liver functions and topological changes of functional connectivity networks in cirrhotic patients. A FDRcorrected P value less than 0.05 was considered statistically significant.

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Table 1 Demographical and clinical data of the MHE patients, nonHE patients and controls. Variables

MHE patients (n = 22)

nonHE patients (n = 29)

Controls (n = 33)

P value

Age (years) Sex (Male/female) Post-hepatitic cirrhosis (n) Alcoholic cirrhosis (n) Biliary cirrhosis (n) Schistosomal cirrhosis (n) Unknown etiology (n) Child–Pugh scores (score) Child–Pugh scale (A/B/C) Ammonia level (␮mol/L)@ NCT-A (s)* , # DST (score)* , # , $

53.55 ± 8.99 17/5 15 1 3 1 2 6.68 ± 1.89 11/10/1 59.88 ± 30.64 (n = 16) 75.1 ± 27.7 23.5 ± 7.6

46.38 ± 10.45 22/7 22 1 3 0 3 6.31 ± 1.51 19/9/1 51.64 ± 33.11 (n = 22) 41.3 ± 9.6 42.5 ± 10.2

48.58 ± 12.75 22/11 – – – – – – – – 45.1 ± 12.6 49.5 ± 10.2

0.075| 0.616∼ – – – – – 0.44! – 0.44! <0.001& <0.001&

Values are mean ± SD or number of patients; –: unavailable data; MHE = minimal hepatic encephalopathy; nonHE = non he hepatic encephalopathy; NCT-A = number connection test type A; DST = digit symbol test. | Stands for the result of the one way ANOVA. ∼ Stands for the result of the Kruskal–Wallis test. @ Ammonia is obtained in 16 MHE patients and 22 nonHE patients. ! Stands for the results of two sample t-test. & Stands for the results of the ANOCOVA test with age and gander as covariances. * Stands for significant difference between MHE and nonHE patients. (Post hoc P < 0.001, Sidak-corrected.) # Stands for significant difference between MHE patients and healthy controls. (Post hoc P < 0.001, Sidak-corrected.) $ Stands for significant differences between nonHE patients and healthy controls. (Post hoc P < 0.001, Sidak-corrected.)

3. Results 3.1. Functional connectivity After exclusions as stated above, 51 patients (12 females, 39 males, mean age, 49.5 ± 10.4 years) and 33 controls (11 females, 22 males, mean age, 48.6 ± 12.8 years), matched for age (P = 0.73, two-sample t-test) and gender (P = 0.33, Chi-squire test), remained for further analysis. The demographics and clinical data of the 51 patients were summarized in Table 1. Thirty-three healthy subjects (22 males, 11 females) had a NCT-A score of 45.1 ± 12.6 s and a DST score of 49.5 ± 10.2, which were used to identify MHE patients from cirrhotic patients without OHE. Twenty-nine patients (22 males, 7 females) who had normal NCT-A scores (41.3 ± 9.6 s) and DST scores (42.5 ± 10.2) were classified as nonHE patients. Twentytwo patients (17 males, 5 females) with abnormal NCT-A scores (75.1 ± 27.7 s) and DST scores (23.5 ± 7.6) were identified as MHE. There was no significant difference (P = 0.44) in Child–Pugh scores between MHE patients and nonHE patients. No significant difference (P = 0.44) was found in ammonia level between MHE patients (n = 16) and nonHE patients (n = 22). Thirty functional connectivities represented by edges were significantly changed (ANCOVA, all P < 0.05, FDR-corrected) among three groups (Fig. 1, Supplementary Fig. 1 and Table 2). Of the 30 edges, 16 edges (53.3%) were related with sub-cortical regions (bilateral putamens, bilateral pallidums and bilateral thalami); 14 connectivities (46.7%) were significantly different between cortical regions among three groups. Among the 30 connectivities, 11 positive (Fig. 1A, Supplementary Fig. 1A and Table 2) and 11 negative (Fig. 1B, Supplementary Fig. 1B and Table 2) connectivities (73.3%) were significantly weaker in either MHE or nonHE patients compared with those in controls (Post hoc P < 0.05, Sidakcorrected); 3 positive and 2 negative connectivities were stronger in both MHE and nonHE patients compared with healthy controls (Post hoc P < 0.05, Sidak-corrected, Fig. 1C, Supplementary Fig. 1C and D and Table 2); 3 altered connectivities were found stronger in the nonHE group than those in MHE and control groups (Post hoc P < 0.05, Sidak-corrected, Fig. 1D, Supplementary Fig. 1E and Table 2). Among 30 connectivities, 20 edges were correlated with either the NCT-A or the DST scores (P < 0.05, FDR-corrected). Within the 20 connectivities, 7 edges were between cortical regions and pallidum; 1 was between left putamem and left pallidum; 4 edges

were between cortical regions and thalamus; 1 was between left and right thalamus; the remaining 7 were between cortical regions. Supplementary material related to this article can be found, in the online version, at http://dx.doi.org/10.1016/j.ejrad. 2014.06.019. 3.2. Small-world properties Fig. 2 illustrates mean small world property values of the S, Cnormalized and Lnormalized of each group in a wide range of sparsity (5–34%). In healthy controls, both the lower bound (LB) and upper bound (UB) on 95% confidence interval (CI) of the small-world metric S were over 1 at 7% ≤ sparsity ≤ 34%. In nonHE patients, the UB of S was higher than 1 but LB was lower than 1 at sparsity = 7%. Both MHE and nonHE patients had no small-world-ness until the sparsities >7% (both LB and UB > 1). The ANCOVA results showed that there were significant differences in the Cnormalized and S at all selected sparsities (Fig. 2A and 3B), while there was no difference in the Lnormalized at 7% < sparsity < 34% (Fig. 2C). Post hoc results showed that the Cnormalized and S in the MHE patients were marked lower than those in the healthy controls at all sparsities (all P < 0.001, Sidak-corrected); the Cnormalized and S in the MHE patients were significantly lower than those in nonHE patients at 5% ≤ sparsity ≤ 23% and at 5% ≤ sparsity ≤ 19%, respectively (all P < 0.05, Sidak-corrected); the Lnormalized in MHE patients were only marked lower than controls at 5% ≤ sparsity ≤ 7% (P < 0.05, Sidakcorrected); the Cnormalized in nonHE patients was only significantly lower than controls at sparsity = 5%; no difference was found in the Lnormalized and S between the nonHE patients and controls at all sparsities (P > 0.05, Sidak-corrected). Fig. 3 shows that the correlation coefficients between graph analysis measures (S, Cnormalized and Lnormalized ) and neuropsychatric test (NCT-A and DST) scores in 5–34% sparsity. There were significant correlations between graph analysis measures and neuropsychatric test scores in networks with small sparsities. With the increases of the sparsity, the correlations between S, Cnormalized and neuropsychatric test scores tended to decrease (see Fig. 3A–D), while the correlation coefficients between Lnormalized and neuropsychatric test scores were not disturbed (see Fig. 3E and F). No correlation was found between Child–Pugh scores, venous blood ammonia level, and graph theoretical measures at each sparsity (all P > 0.05, FDR-corrected).

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Fig. 1. Marked differences of functional connectivities among MHE, nonHE and control groups. Notes: Green nodes = cortical ROIs; Red nodes = bilateral pallidums and left thalamus; R = right; L = left; MFG = middle frontal gyrus; PoCG = postcentral gyrus; IFGoperc = opercular part of inferior frontal gyrus; ANG = angular gyrus; INS = insula; MOG = middle occipital gyrus; SPG = superior parietal gyrus; ACG = anterior cingulate gyrus; DCG = middel cingulate gyrus; PCG = post cingulate gyrus; PHG = paraHippocampal gyrus; CUN = cuneus; PAL = pallidum; SOG = superior occipital gyrus; IOG = inferior occipital gyrus; PCUN = precuneus; FFG = fusiform gyurs; PCL = paracentral lobule; PAL = pallidum; PUT = putamen; THA = thalamus; (A) significantly decreased positive functional connectivities in either MHE or nonHE patients; (B) significantly decreased negative functional connectivities in either MHE or nonHE patients; (C) significantly increased positive (red) or negative (yellow) functional connectivities in either MHE or nonHE patients; (D) significantly increased functional connectivities in nonHE patients compared with MHE patients or healthy controls.

In this paper, we selected sparsity of 7% to illustrate the smallworld-ness differences between MHE, nonHE and control groups (Fig. 4). Many studies indicated healthy controls maintained smallworld-ness and the small-world metric S was greater than 1 [19–25]. In this setting, the sparsity of the networks should be chosen to be ≥7% in this study (Fig. 2A). On the other hand, the small-world metric S was related to neuropsychiatric test scores in small sparsity in cirrhotic patients (Fig. 3A and B). When the sparsity of functional networks was selected at 7%, the small-world metric and cluster coefficient in MHE patients were significantly smaller than those in nonHE patients and in controls, while the path length in MHE patients was marked longer than controls. No significant difference was found in graph analysis measures between nonHE patients and controls. 3.3. The nodal property At sparsity = 7%, ANCOVA results showed that the degrees were significant different in the right rectus gyrus, bilateral middle cingulate cortex, left post cingulate cortex, bilateral cuneus, bilateral superior occipital gyri, left middle occipital gyrus, left Inferior

occipital gyrus and right supra Marginal gyrus (Table 3, all P < 0.05, FDR-corrected). Eight out of 11 nodes above (72.7%) were of significantly lower degrees in cirrhotic patients (either MHE or nonHE patients) compared with healthy controls (Post hoc P < 0.05, Sidak-corrected). In the contrast, the degrees of the rectus gyrus, post cingulated cortex and right supra Marginal gyrus markedly increased in either MHE or nonHE patients compared with healthy controls. In all 11 nodes, only the degree of left superior occipital gyrus was found to be correlated with DST scores (Table 3, P < 0.05, FDR corrected).

4. Discussion This study found disrupted brain functional connectivity, abnormal small world properties in cirrhotic patients, and reorganization of brain network that occurred during disease progression from nonHE to MHE. Especially, disturbances of basal gangliathalamocortical circuit revealed in the present study can be associated with loss of small world network properties and development of MHE.

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Fig. 2. Changes of graph analysis measures among healthy controls, nonHE and MHE patients. Note: S, Cnormalized , Lnormalized of healthy controls, nonHE and MHE patients were displayed by their means, upper and lower bounds on 95% confidence interval at a sparsity from 5% to 34% with steps of 2%; (A) The ANCOVA results for the small-world metric (S) in different sparsities among three groups and post hoc analysis results between three groups; (B) The ANCOVA results for the cluster coefficient (Cnormalized ) in different sparsities among three groups and post hoc analysis results between three groups; (C) The ANCOVA results for the path length (Lnormalized ) in different sparsities among three groups and post hoc analysis results between three groups.

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Table 2 Significant changes in functional connectivity among MHE patients, nonHE patients and controls and correlations between functional connectivity and the neuropsychiatric test scores. Functional connectivity

Z values in MHE patients

−0.13 −0.09 0.10 0.24 −0.34 −0.01 −0.08 −0.45 −0.07 −0.11 0.08 0.05 0.06 −0.05 −0.03 −0.06 −0.01 −0.01 0.38 0.22 0.21 0.59 −0.03 0.03 0.18 0.26 −0.08 0.63 0.63 0.95

#,$

MFG.L-PoCG.L MFG.L-PAL.R# , $ IFGoperc.R-PoCG.L# , $ IFGoperc.R-PoCG.R# , $ IFGoperc.R-ANG.L# , $ INS.L-MOG.R# , $ INS.L-SPG.R# , $ INS.L-ANG.L# , $ INS.L-THA.L* , # , $ ACG.L-THA.L* , # DCG.R-PAL.R# , $ PCG.R-PAL.L* , $ PHG.L-MOG.L* , # , $ CUN.L-PAL.L* , # , $ CUN.R-PAL.L* , # , $ CUN.R-PAL.R# , $ SOG.L-PAL.L# , $ SOG.R-PAL.R# , $ MOG.L-IOG.L# , $ MOG.L-IOG.R# , $ MOG.L-PCUN.L* , # , $ IOG.L-IOG.R# , $ IOG.L-THA.R* , # , $ FFG.R-PAL.R# , $ FFG.R-THA.R* , # PoCG.R-PCL.L* , # , $ SPG.R-PUT.R* , # , $ PCL.L-PCL.R# , $ PUT.L-PAL.L* , # THA.L-THA.R* , # , $

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

Z values in nonHE patients

−0.20 −0.04 0.06 0.17 −0.34 −0.10 0.03 −0.45 0.14 0.10 0.19 −0.10 0.16 −0.17 −0.16 −0.15 −0.09 −0.10 0.45 0.25 0.32 0.74 −0.19 0.03 −0.09 0.40 0.03 0.70 0.84 1.11

0.20 0.20 0.19 0.26 0.27 0.22 0.29 0.18 0.28 0.24 0.21 0.19 0.20 0.22 0.19 0.20 0.19 0.24 0.27 0.23 0.19 0.29 0.23 0.24 0.33 0.30 0.23 0.32 0.23 0.33

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.23 0.17 0.25 0.23 0.29 0.20 0.22 0.23 0.27 0.20 0.14 0.17 0.17 0.22 0.22 0.19 0.23 0.15 0.28 0.23 0.27 0.30 0.18 0.16 0.26 0.32 0.15 0.27 0.16 0.29

Z values in healthy controls

−0.37 0.14 −0.15 −0.11 −0.05 −0.28 −0.25 −0.27 0.27 0.17 0.31 0.05 −0.10 −0.30 −0.31 −0.28 −0.25 −0.25 0.67 0.54 0.06 0.95 −0.30 −0.17 −0.18 0.60 −0.18 0.96 0.89 1.31

± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ± ±

0.25 0.19 0.23 0.27 0.24 0.24 0.24 0.20 0.22 0.23 0.18 0.17 0.29 0.19 0.21 0.20 0.20 0.18 0.30 0.30 0.22 0.30 0.24 0.21 0.30 0.27 0.20 0.31 0.21 0.34

NCT-A

DST

CC

P value!

CC

P value!

0.206 −0.114 0.029 0.213 0.018 0.079 −0.078 −0.015 −0.398 −0.371 −0.213 0.173 0.086 0.229 0.228 0.121 0.343 0.224 −0.294 −0.278 0.070 −0.192 0.288 0.117 0.382 −0.300 −0.179 −0.177 −0.381 −0.177

0.119 0.395 0.847 0.110 0.890 0.556 0.556 0.890 0.004 0.004 0.110 0.174 0.546 0.102 0.102 0.392 0.008 0.102 0.029 0.035 0.587 0.149 0.030 0.395 0.004 0.028 0.169 0.169 0.004 0.169

−0.238 0.195 −0.120 −0.285 0.066 −0.295 −0.010 0.077 0.424 0.431 0.405 −0.011 −0.223 −0.372 −0.383 −0.276 −0.384 −0.397 0.307 0.265 −0.195 0.370 −0.387 −0.264 −0.409 0.289 −0.034 0.197 0.375 0.277

0.044 0.095 0.333 0.017 0.613 0.015 0.925 0.558 <0.001 <0.001 0.001 0.925 0.059 0.001 0.001 0.019 0.001 0.001 0.011 0.024 0.095 0.001 0.001 0.024 0.001 0.016 0.815 0.095 0.001 0.019

Values are mean ± SD; R = right; L = left; CC = correlation coefficients; MFG = middle frontal gyrus; PoCG = postcentral gyrus; IFGoperc = opercular part of inferior frontal gyrus; ANG = angular gyrus; INS = insula; MOG = middle occipital gyrus; SPG = superior parietal gyrus; ACG = anterior cingulate gyrus; DCG = middle cingulate gyrus; PCG = post cingulate gyrus; PHG = paraHippocampal gyrus; CUN = Cuneus; PAL = pallidum; SOG = superior occipital gyrus; IOG = inferior occipital gyrus; PCUN = precuneus; FFG = fusiform gyurs; PCL = paracentral lobule; PAL = pallidum; PUT = putamen; THA = thalamus. * Significant connectivity differences between MHE and nonHE patients (ANOCOVA P < 0.05, FDR-corrected; Post hoc P < 0.05, Sidak-corrected). # Significant connectivity differences between MHE patients and healthy controls (ANOCOVA P < 0.05, FDR-corrected; Post hoc P < 0.05, Sidak-corrected). $ Significant connectivity differences between nonHE patients and healthy controls (ANOCOVA P < 0.05, FDR-corrected; Post hoc P < 0.05, Sidak-corrected). ! Correlation P values between functional connectivities and neuropsychological test scores were corrected by FDR.

In this study, we found disrupted brain functional connectivity in cirrhotic patients with a trend of gradual decrease of functional connectivity from nonHE to MHE (total connectivity numbers decreased with significant difference among normal controls, nonHE and MHE patients). This finding further supports the

role of functional connectivity impairment in the development of HE [11]. Of 30 functional connectivities, most nodes are located in the basal ganglia-thalamocortical circuit involved in oculomotor, associative, and limbic functions. In this loop, all information coming from the cortex are passing through the striato-pallidal

Table 3 Significant differences in the nodal degrees among MHE patients, nonHE patients and controls at sparsity of 7%. Node

Degrees in MHE patients

$

REC.R DCG.L$ DCG.R# , $ PCG.L# CUN.L# CUN.R# SOG.L# , $ SOG.R# , $ MOG.L# , $ IOG.L# SMG.R$

5.55 3.73 4.32 9.64 5.09 6.14 5.09 6.00 4.50 3.00 9.27

± ± ± ± ± ± ± ± ± ± ±

3.53 4.16 3.29 5.90 2.64 3.98 3.18 4.63 4.19 2.81 5.96

Degrees in nonHE patients

7.90 3.14 4.83 8.62 7.34 7.86 6.17 6.31 5.21 4.66 11.48

± ± ± ± ± ± ± ± ± ± ±

3.52 2.56 2.96 5.49 3.78 3.27 3.84 3.78 3.42 3.56 5.85

Degrees in healthy controls

4.82 6.15 7.48 5.48 8.88 9.06 10.39 9.70 9.21 7.12 7.03

± ± ± ± ± ± ± ± ± ± ±

3.16 4.36 3.89 4.14 4.83 4.07 4.29 3.79 5.13 3.96 3.92

NCT-A

DST !

CC

P value

CC

P value!

−0.049 0.022 −0.060 −0.004 −0.188 −0.073 −0.193 −0.228 −0.226 −0.214 −0.135

0.807 0.926 0.805 0.972 0.191 0.801 0.218 0.412 0.214 0.187 0.407

0.025 0.143 0.241 −0.035 0.209 0.091 0.299 0.244 0.302 0.260 0.106

0.823 0.306 0.060 0.826 0.104 0.500 0.032 0.069 0.058 0.063 0.465

Values are mean ± SD; R = right; L = left; CC = correlation coefficients; REC = rectus gyrus; DCG = middle cingulate gyrus; PCG = post cingulate gyrus; CUN = cuneus; SOG = superior occipital gyrus; MOG = middle occipital gyrus; IOG = inferior occipital gyrus; SMG = supra Marginal gyrus. # Significant differences between MHE patients and healthy controls (ANCOVA P < 0.05, FDR-corrected; post hoc P < 0.05, Sidak-corrected). $ Significant differences between nonHE patients and healthy controls (ANCOVA P < 0.05, FDR-corrected; post hoc P < 0.05, Sidak-corrected). ! Correlation P values between functional connectivities and neuropsychological test scores were corrected by FDR.

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Fig. 3. The correlations between graph analysis measures and neuropsychological tests. (A) The correlations between NCT-A and S; (B) the correlations between DST and S; (C) the correlations between NCT-A and Cnormalized ; (D) the correlations between DST and Cnormalized ; (E) the correlations between NCT-A and Lnormalized ; (F) the correlations between DST and Lnormalized . (All correlation P values were corrected by FDR.) Quality of the figure is poor. What are symbols for red and blue? It is not good to have “. . . is significant. . ..” In labels!.

system indirectly and/or directly back to the thalamus, which might be a filter for sensory inputs [23]. The disturbance of basal ganglia-thalamocortical circuit is regarded as being involved in basal ganglia-related movement disorders, such as Parkinson’s disease [24]. In cirrhotic patients with chronic portosystemic shunts, symmetrical hyperintensities of basal ganglia on T1 weighted images are often observed, which can resolve after successful liver

transplantation and aggreviate following transjugular intrahepatic portosystemic shunt [10]. Histopathologically, Alzheimer type II astrocytes, the characteristic neuropathological findings in cirrhotic patients, are predominantly found in the cortex, putamen, pallidum, caudate nucleus; and the number of these astrocytes within the pallidum is representative of the degree of astrocytic changes in each individual [36]. The multiple relations of the basal

Fig. 4. The decreased small-world metric, cluster coefficient and increased path length in MHE patients at the sparsity 7%. (A) Significantly decreased small-world metric in MHE patients compared with nonHE patients and healthy controls at the sparsity 7% (ANCOVA P < 0.001, Post hoc P < 0.05); (B) significantly decreased cluster coefficient in MHE patients compared with nonHE patients and healthy controls at the sparsity 7% (ANCOVA P < 0.001, Post hoc P < 0.05); (C) significantly increased path length in MHE patients compared with healthy controls at the sparsity 7% (ANCOVA P < 0.05, Post hoc P < 0.05).

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ganglia not only to the motor cortex but also to the limbic system, frontolateral and cuneus may explain why a disturbance of astrocytic functions in the basal ganglia could result in the variety of symptoms seen in HE [24]. For example, ACG is regarded as an important core region to attention control, response inhibition, and error detection [25]. The cuneus is most known for its involvement in basic visual processing; however, it is also involved in attention, working memory, and reward expectation [26]. We also found these 20 abnormal functional connectivities correlated with DST score (P < 0.05, FDR-corrected r > 0.23), a test of psychomotor speed and attention [1]. This indicated that functional connectivity abnormality could reflect neurocognitive deficits in MHE patients. Thus, it appears decreased basal ganglia-thalamocortical circuit plays an important role in the development of MHE. We also found the remaining 3 functional connectivities first increased in nonHE patients, then decrease in MHE patients, all were intra-cortex functional connectivities. These findings indicated even in nonHE cirrhotic patients, disrupted brain functional connectivity appearing abnormally increased functional connectivity was present. Brain function reorganization is regarded as an optimal interpretation for these findings; more increased brain function conncetivities are recruited to maintain normal brain performance in nonHE patients. Thus, fMRI is a sensitive method to detect early brain functional changes even in patients without any cognition or neuropsychiatric abnormality. The potential to detect early changes of the disease has been demonstrated in one study Sheline et al. [27]. They studied subjects with normal cognitive function and negative evidence for cerebral amyloid deposition but at genetic risk for Alzheimer’s disease (apolipoprotein ␧4 allele carriers) and found these subjects had substantial network disruption at rs-fMRI, indicating network dysfunction at rs-fMRI may represent an early manifestation of a genetic effect and that such changes precede even molecular abnormalities in patients with neurodegenerative disease [27]. In current opinion, the brain is a complex network, organized in a functionally specialized manner with some areas segregated for certain specialized functions, such as vision, motor control, or language, with higher functions depending on integration of information from these regions. The fact that the complex brain network has small world property, i.e. short path length and higher clustering coefficient, has been demonstrated by many studies [13,14]. High clustering ensures functionally segregated processing, whereas short paths ensure effective integrity and the rapid transfers of information between distant brain regions that are thought to constitute the basis of cognitive processes [28]. We applied a set of threshold to illustrate the effects of different thresholds on graph analysis. Our results suggested that different thresholds did not affect the statistical analysis of graph analysis measures among three groups, while they could disturb the post hoc comparisons between groups. Further analysis showed that the small-world metric in small sparsities of functional connectivity networks significantly correlated with neuropsychological test (NCT-A or DST) scores, implying topological differences of functional networks among three groups in small sparsity were closely related with brain dysfunctions caused by HE. Hence, we selected the sparsity of 7% to display the changes of graph analysis measures in cirrhotic patients. Small world-ness (S) >1 indicates the network with a small world organization [29]. When the sparsity was 7%, our quantitative brain functional network measurements demonstrated healthy controls were of small world property (small-world metric S > 1) but both nonHE and MHE patients were with disrupted small world property (S ≈ 1, and S < 1, respectively). In our study, the decreasing tendency of normalized clustering coefficient (Cnormalized ) in MHE patients, nonHE patients and healthy controls was found. There was increasing tendency of normalized path length (Lnormalized ) in cirrhotic patients, while only path length of

MHE patients was significantly increased compared with controls. The global efficiencies of the functional connectivity network are spared possible because of compensational mechanism. We can find more functional connectivity numbers and increased intercortex functional connectivity in nonHE patients compared with MHE patients and healthy controls. The normalized cluster coefficient (Cnormalized ) with statistical differences between MHE patients and non-HE patients also supported the global efficiencies were decreased with the disease progression. Clustering coefficient is associated with short range connections between nearby regions which mediate modularized information processing, or fault tolerance of a network. Lower clustering coefficient in MHE patients indicates sparse local connections of brain functional networks, or that some neurons are damaged and inefficient [14]. Path length is a measure of the average connectivity extent, or overall routing efficiency, of the network and have been demonstrated to promote effective interactions between and across different cortical regions [30]. Longer path length may indicate that information interactions between interconnected brain regions are slower and less efficient [13]. The significant difference for path length Lnormalized was found between MHE and controls rather than between MHE and nonHE patients, indicating that the information transformations between brain regions were marked impaired in MHE patients and not in non-HE patients This finding is expected in nonHE patients because some increased functional connectivities were found compared with MHE patients and controls, which can attributed to compensatory mechanism. No correlation was observed between the small world measurements and venous blood ammonia levels, which is contradictory to Hsu et al.’s study [14], in which they found integrated relative efficiency correlated with ammonia levels (r2 = 0.22, P < 0.01). Possible explanation is HE severity (overt HE patient included in Hsu et al.’s study but none of overt HE in our study) in two studies. However, Hsu et al.’s study [14] demonstrated disrupted abnormal small-world properties in HE patients in that the local and global topological efficiencies of the functional connectivity network were significantly disrupted in HE patients. The functional connectivity networks of cirrhotic patients were also found to show both decreased and increased nodal degrees in cortical regions, which verified the compensatory mechanism in the functional connectivity between cortical regions. Take together, cirrhotic patients had disputed small world property and the brain networks of MHE patients lost small-world-ness; the information transformations in MHE patients were marked disrupted; there could be compensatory in functional connectivity networks of nonHE patients which made less impairments in information delivery in nonHE patients compared with MHE patients. Our study has some limitations. First, methodological limitations deserve comments. In the current study, we used a relatively low sampling rate (TR = 2 s) for multislice (30 slices) acquisitions. Under this sampling rate, respiratory and cardiac fluctuations may still pose a problem for fMRI time series, reducing the specificity of low frequency fluctuations to functional connected regions, despite a band-pass filtering in the range 0.01 < f < 0.08 Hz was used to dampen their effects [29]. Second, results from this preliminary study are limited by a small sample size for MHE patients, which can affect validity of the statistical analysis of this study. A large-cohort study is needed. However, since a standard statistical processing pipeline was followed with accepted software and procedures in this study, we believe most findings therefore are reliable based on these analyses. Third, only two neuropsychiatric tests were used in this study to evaluate the cognitive functions, but the two tests have been recommended to diagnose MHE by the working party at the 11th World Congresses of Gastroenterology [1]. Broader spectrum of tests should be included to more fully evaluate the cognition function of cirrhotic patients. Lastly, we did not include the overt HE patients because small sample and variable disease severity can

L.J. Zhang et al. / European Journal of Radiology 83 (2014) 1890–1899

have an effect of our results of fMRI. In future, a large cohort study should be performed to clarify the issue. 5. Conclusions In conclusion, our study found disrupted brain functional connectivity and abnormal small world properties in cirrhotic patients, and reorganization of brain network occurred during disease progression from nonHE to MHE, especially disturbances of basal ganglia-thalamocortical circuit. These abnormal brain functional connectivities can be associated with the loss of small world network properties and the neurocognitive impairment in cirrhotic patients with MHE. Funding Supported by grants from National Natural Science Foundation of China (grants No. 30700194, 81171313, 81230032 and 81322020to L.J.Z., grant No. 81101039 to G. Z.), Natural Science Foundation of Jiangsu Province (grant No. BK2007572 to L.J.Z.), and China Postdoctoral Science Foundation of Jiangsu Province (grant No. 1002083C to G. Z.) Conflicts of interest The authors disclose no conflicts. References [1] Ferenci P, Lockwood A, Mullen K, Tarter R, Weissenborn K, Blei AT. Hepatic encephalopathy – definition, nomenclature, diagnosis, and quantification: final report of the working party at the 11th World Congresses of Gastroenterology, Vienna, 1998. Hepatology 2002;35:716–21. [2] Wein C, Koch H, Popp B, Oehler G, Schauder P. Minimal hepatic encephalopathy impairs fitness to drive. Hepatology 2004;39:739–45. [3] Bajaj JS. Minimal hepatic encephalopathy matters in daily life. World J Gastroenterol 2008;14:3609–15. [4] Román E, Córdoba J, Torrens M, et al. Minimal hepatic encephalopathy is associated with falls. Am J Gastroenterol 2011;106:476–82. [5] Dhiman RK, Saraswat VA, Sharma BK, et al. Minimal hepatic encephalopathy: consensus statement of a working party of the Indian National Association for Study of the Liver. J Gastroenterol Hepatol 2010;25:1029–41. [6] Zhang L, Qi R, Wu S, et al. Brain default-mode network abnormalities in hepatic encephalopathy: A resting-state functional MRI study. Hum Brain Mapp 2012;33:1384–92. [7] Qi R, Zhang LJ, Xu Q, et al. Selective impairments of resting-state networks in minimal hepatic encephalopathy. PLoS One 2012;7:e37400.

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