Gray Matter Microstructural Alterations and their Correlation with Systemic Biomarkers in Hepatic Encephalopathy: A NODDI Study Using Gray-matter Based Spatial Statistics

Gray Matter Microstructural Alterations and their Correlation with Systemic Biomarkers in Hepatic Encephalopathy: A NODDI Study Using Gray-matter Based Spatial Statistics | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Gray Matter Microstructural Alterations and their Correlation with Systemic Biomarkers in Hepatic Encephalopathy: A NODDI Study Using Gray-matter Based Spatial Statistics Fengli Xie, Xiaohui Wang, Huina Zhang, Juan Wang, Shaofeng Wang, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8537235/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Hepatic encephalopathy (HE) involves complex neurobiological changes that are often difficult to quantify using conventional MRI. This study aims to utilize Neurite Orientation Dispersion and Density Imaging (NODDI) combined with Gray-matter Based Spatial Statistics (GBSS) to characterize microstructural alterations in patients with HE and explore their relationship with clinical biochemical markers, specifically within the globus pallidus (GP). Methods Thirty-three patients with HE and 31 healthy controls underwent 3T MRI including a multi-shell diffusion protocol for NODDI. GBSS was performed to assess differences in the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI). Pearson correlation analyzed relationships between GP NODDI parameters and blood biochemical indices. Results HE patients exhibited significantly decreased NDI across widespread cortical and subcortical regions (frontal, parietal, temporal, cingulate, insula, thalamus) and increased ODI in the posterior cerebellum/vermis. Crucially, the NDI of the right GP showed positive correlations with indirect bilirubin, prothrombin time, and INR (all p < 0.05), while the ODI of the left GP positively correlated with hemoglobin concentration (p = 0.046). Conclusion NODDI reveals extensive neurite loss and cerebellar disorganization in HE. The dissociated correlation patterns of GP NDI and ODI with distinct blood markers suggest a “double-hit” pathophysiological model: toxic metabolite accumulation may drive cellular swelling (increased NDI), while systemic factors like anemia may reduce structural complexity (decreased ODI). These findings highlight NODDI as a sensitive tool for monitoring the progression and metabolic impact of HE. Hepatic Encephalopathy Neurite Orientation Dispersion and Density Imaging (NODDI) Gray-matter Based Spatial Statistics (GBSS) Globus Pallidus Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Hepatic encephalopathy (HE) is a common neurological complication in patients with cirrhosis, manifesting as a spectrum of neuropsychiatric symptoms ranging from subtle cognitive impairment to coma [ 1 ]. With the aging of the global population, the differential diagnosis between HE and neurodegenerative disorders has become increasingly challenging due to overlapping clinical presentations [ 2 ]. Traditional diagnostic approaches primarily rely on clinical assessment and blood biochemical markers such as ammonia levels, but these methods are limited by subjectivity and insufficient sensitivity [ 3 ]. Among various neuroimaging biomarkers, the globus pallidus (GP) has garnered significant attention in HE research. Magnetic resonance imaging (MRI) studies have demonstrated that 70%-90% of HE patients exhibit bilateral symmetric hyperintensity in the GP on T1-weighted images, a characteristic finding attributed to manganese deposition resulting from impaired hepatic clearance [ 4 ]. This pallidal abnormality appears to be independent of ammonia levels, suggesting a distinct pathophysiological mechanism and highlighting the GP as a critical region in HE neurotoxicity [ 5 ]. While conventional structural MRI captures macroscopic changes, it offers limited sensitivity to microstructural alterations. Neurite Orientation Dispersion and Density Imaging (NODDI) is an advanced diffusion MRI technique that enables non-invasive quantification of brain tissue microstructure through parameters including the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI) [ 6 ]. These metrics provide more refined biological markers for investigating HE-related cerebral microstructural abnormalities. Recent studies have begun applying NODDI to explore microstructural changes in HE, though most have focused primarily on white matter alterations [ 7 ]. Gray-matter Based Spatial Statistics (GBSS) is a voxel-based analysis method specifically designed to investigate spatial patterns of gray matter microstructure [ 8 ]. A recent study combining NODDI with GBSS successfully identified cortical microstructural abnormalities in patients with minimal hepatic encephalopathy [ 9 ]. However, this research did not specifically focus on the GP—a region known to be particularly vulnerable in HE—nor did it systematically examine the correlation between cerebral microstructural parameters and blood biochemical indices. Given these research gaps, the study aims to: (ⅰ) assess brain microstructural alterations in patients with HE using NODDI combined with GBSS methodology; (ⅱ) analyze correlations between the NODDI parameters of the GP in the HE group and blood biochemical indices to provide novel imaging evidence for understanding the pathophysiological mechanisms underlying HE. Methods Participants The study was approved by the local Ethics Committee (no. L2023005) and all participants provided written informed consent (version V1.0; dated August 9, 2023). Thirty-three patients clinically diagnosed with decompensated cirrhosis between December 2023 and June 2025 were enrolled in this study, and 31 healthy volunteers were included as the health control(HC) group. The inclusion criteria for the HE group were as follows: (ⅰ) Clinical diagnosis of decompensated cirrhosis; (ⅱ) Imaging evidence of cirrhosis, portal hypertension, and ascites as shown by imaging examinations. The inclusion criteria for the control group were: (ⅰ) routine health examination results indicating good health with no known significant systemic organ diseases; (ⅱ) laboratory tests (including complete blood count, liver and kidney function, coagulation profile, and other major biochemical indices) within normal ranges. The exclusion criteria for both groups included: (ⅰ) contraindications to MRI (e.g., presence of cardiac pacemakers, cochlear implants, or metallic implants) that prevent the completion of scanning; (ⅱ) poor image quality rendering the data unsuitable for subsequent analysis; (ⅲ) incomplete data, with failure to complete all study procedures (such as MRI scanning and blood biochemical tests); (ⅳ) long-term abuse of substances or medications that may affect the central nervous system (e.g., antipsychotics, sedatives). MRI data acquisition MRI examinations were performed on a 3.0-T scanner (SIGNA Architect 3.0 T, GE Healthcare, US) with a combined head and neck coil. diffusion-weighted magnetic resonance imaging (dMRI) was performed using a spin-echo planar imaging sequence with the following parameters: repetition time (TR) = 4286 ms ; echo time (TE) = 113.5 ms; field of view (FOV) = 240×240 mm 2 ; matrix = 128×128; slice thickness = 3 m; non-diffusion weighted images (b = 0 s/mm 2 ) as well as 30 noncollinear directions with multiple b values (b = 1000, 2500 s/mm 2 ). T1-weighted structural images were acquired using a 3D Gradient-Echo (GRE) Brain Volume (known as BRAVO) sequences with the following parameters: TR/TE = 6.3/2.4 ms; FOV = 256×256×160 mm 3 ; voxel size = 0.5×0.5×0.5 mm 3 ; acquisition matrix; slice thickness = 6mm. Image Preprocessing Diffusion-weighted data processing First, the MRIcroGL software ( https://www.nitrc.org/projects/mricrogl ) was performed to convert all raw data in DICOM format into NIFTI format, and further anonymize patient data. Then, the dMRI were processed using the EDDY and TOPUP tools from the FMRIB Software Library (FSL) to perform eddy current correction, geometric distortion correction, and head motion correction. Following these preprocessing steps, the diffusion tensor model was applied to the corrected data, and fractional anisotropy (FA) maps were generated using the weighted linear least squares method implemented in the Dipy library. Subsequently, the Accelerated Microstructure Imaging via Convex Optimization (AMICO) approach was utilized to compute parameters associated with the NODDI model. This analysis yielded key metrics, including the Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and free water fraction(FWF)). Post-processing of GBSS GBSS was performed to analyze the microstructural changes of gray matter using scripts available online ( https://github.com/arash-n/GBSS ). The specific steps are illustrated in Fig. 1 . Firstly, gray matter fraction maps were derived in the native diffusion space by subtracting FWF and white matter fractions from unity in each voxel. The FWF were obtained from NODDI, while the white matter fractions were estimated using two-tissue class segmentation of FA images with Atropos. A study-specific pseudo-T1 template was created through iterative group averaging of subject-level pseudo-T1 images, which were derived from the weighted fusion of white matter and gray matter fraction maps. The preprocessed outputs were then input into the gbss_1_reg.sh script for affine registration, followed by non-linear spatial registration to this custom template. This process generated a suite of registered derivatives, including gray matter fraction maps, warped pseudo-T1 images, and warped gray matter, NDI, and ODI maps. Subsequently, the gbss_2_skel.sh script was employed to process these registered maps and extract skeletonized features, namely NDI_skeleton, ODI_skeleton, and GM_skeleton, along with a mean gray matter map. Skeletonization was constrained by a by a mask derived from the thresholded average gray matter fraction map to preserve region-specific structural boundaries. Finally, the skeletonized products were refined using the gbss_3_fill.sh script, which applied a voxel-wise nearest-neighbor smoothing algorithm to produce fully filled, spatially complete NDI_filling and ODI_filling metrics for subsequent downstream GBSS statistical analysis. Blood Biochemical Examination All patients underwent Blood Biochemical Examination, including liver synthetic function (Albumin, Prothrombin Time [PT], International Normalized Ratio [INR]), cholestasis (Indirect Bilirubin)), and other relevant factors (D-dimer, Hemoglobin concentration, and Blood Ammonia). Statistical Analysis Randomization and threshold-free cluster enhancement (TFCE) were performed within the FSL to explore differences in NDI_filling and ODI_filling metrics between the HE and HC group. A nonparametric permutation test with 1,000 iterations was conducted. Family-wise error (FWE) correction was performed to address multiple comparisons, with a significant threshold set at P FWE <0.05. The relationship between NODDI parameters of the GP in the HE group and blood biochemical indices was also investigated using the Pearson correlation analysis. P value < 0.05 was considered significant. Statistical analyses were performed using Python Software. Results Significant group differences between the HE and HC group in terms of NDI In patients with HE, a significantly decreased NDI was found in some regions of the gray matter, primarily the right frontal cortex (including the right medial superior frontal gyrus, right anterior cingulate gyrus, right superior frontal gyrus, right supplementary motor area, and right inferior frontal gyrus), bilateral parietal cortex (including the bilateral precuneus, bilateral superior parietal gyri, right angular gyrus, and right inferior parietal gyrus), left occipital regions (left cuneus, left calcarine gyrus, left fusiform gyrus, and left middle occipital gyrus), left temporal cortex (including the left middle temporal gyrus and left temporal pole), right insula, bilateral middle cingulate gyri, bilateral posterior cingulate gyrus, right hippocampus, bilateral parahippocampal gyri, left thalamus, right putamen and bilateral cerebellum. In addition to these primary clusters, several other regions showed isolated voxels or very small clusters of alteration (see Fig. 2 , Table 1 ). Table 1 Brain regions with significant differences in neurite density index (NDI) between the hepatic encephalopathy (HE) and health control (HC) groups Cluster Voxels Coordinates P peak value AAL3 atllas X Y Z 1 969 -9 274 51 0.008 Frontal_Sup_Medial_R (Voxels: 511, AAL ID: 24); Cingulum_Mid_R (Voxels: 225, AAL ID: 34); Cingulum_Ant_R (Voxels: 139, AAL ID: 32); Frontal_Sup_R (Voxels: 54, AAL ID: 4); Supp_Motor_Area_R (Voxels: 22, AAL ID: 20); Cingulum_Mid_L (Voxels: 10, AAL ID: 33); Frontal_Mid_R (Voxels: 2, AAL ID: 8); Frontal_Sup_Medial_L (Voxels: 2, AAL ID: 23) 2 789 -35 249 19 0.005 Insula_R (Voxels: 216, AAL ID: 30); Putamen_R (Voxels: 80, AAL ID: 74); Frontal_Inf_Orb_R (Voxels: 27, AAL ID: 16) 3 737 -1 204 -18 0.01 Cerebelum_4_5_L (Voxels: 174, AAL ID: 97); Cerebelum_4_5_R (Voxels: 91, AAL ID: 98); Cerebelum_6_L (Voxels: 61, AAL ID: 99); Vermis_8 (Voxels: 57, AAL ID: 114); Vermis_4_5 (Voxels: 54, AAL ID: 111); Fusiform_L (Voxels: 38, AAL ID: 55); Cerebelum_8_L (Voxels: 23, AAL ID: 103); Vermis_6 (Voxels: 22, AAL ID: 112); Cerebelum_8_R (Voxels: 19, AAL ID: 104); Cerebelum_6_R (Voxels: 12, AAL ID: 100); Vermis_9 (Voxels: 12, AAL ID: 115); Cerebelum_Crus2_R (Voxels: 2, AAL ID: 94); Vermis_7 (Voxels: 1, AAL ID: 113); ParaHippocampal_L (Voxels: 1, AAL ID: 39) 4 398 -6 181 28 0.015 Cuneus_L (Voxels: 135, AAL ID: 45); Precuneus_L (Voxels: 119, AAL ID: 67); Cingulum_Post_L (Voxels: 28, AAL ID: 35); Calcarine_L (Voxels: 19, AAL ID: 43); Precuneus_R (Voxels: 18, AAL ID: 68); Cingulum_Post_R (Voxels: 4, AAL ID: 36); Calcarine_R (Voxels: 2, AAL ID: 44) 5 252 40 173 26 0.015 Temporal_Mid_L (Voxels: 26, AAL ID: 85); Occipital_Mid_L (Voxels: 4, AAL ID: 51) 6 210 -9 176 45 0.024 Precuneus_L (Voxels: 67, AAL ID: 67); Precuneus_R (Voxels: 58, AAL ID: 68); Parietal_Sup_R (Voxels: 27, AAL ID: 60) 7 116 22 195 -26 0.022 Cerebelum_Crus1_L (Voxels: 48, AAL ID: 91); Cerebelum_6_L (Voxels: 41, AAL ID: 99) 8 112 10 171 51 0.02 Parietal_Sup_L (Voxels: 24, AAL ID: 59); Precuneus_L (Voxels: 18, AAL ID: 67) 9 72 6 221 14 0.01 Thalamus_L (Voxels: 72, AAL ID: 77) 10 54 -25 266 7 0.032 Frontal_Inf_Orb_R (Voxels: 3, AAL ID: 16); Rectus_R (Voxels: 2, AAL ID: 28) 11 52 -10 250 71 0.043 Supp_Motor_Area_R (Voxels: 52, AAL ID: 20) 12 26 2 188 32 0.033 Precuneus_L (Voxels: 26, AAL ID: 67) 13 22 -7 239 70 0.046 Supp_Motor_Area_R (Voxels: 22, AAL ID: 20) 14 22 15 180 59 0.033 Parietal_Sup_L (Voxels: 19, AAL ID: 59) 15 18 -58 194 32 0.041 Angular_R (Voxels: 17, AAL ID: 66); SupraMarginal_R (Voxels: 1, AAL ID: 64) 16 13 -32 233 -3 0.046 Hippocampus_R (Voxels: 13, AAL ID: 38) 17 13 20 173 54 0.042 No AAL region matched 18 10 22 250 -2 0.043 Temporal_Pole_Sup_L (Voxels: 5, AAL ID: 83); Amygdala_L (Voxels: 1, AAL ID: 41) 19 10 5 195 6 0.04 Lingual_L (Voxels: 8, AAL ID: 47); Calcarine_L (Voxels: 2, AAL ID: 43) 20 8 -46 280 21 0.046 Frontal_Mid_R (Voxels: 6, AAL ID: 8); Frontal_Inf_Tri_R (Voxels: 2, AAL ID: 14) 21 6 -54 193 43 0.048 Parietal_Inf_R (Voxels: 6, AAL ID: 62) 22 4 -45 270 22 0.048 Frontal_Inf_Tri_R (Voxels: 4, AAL ID: 14) 23 4 -29 214 -1 0.048 ParaHippocampal_R (Voxels: 4, AAL ID: 40) 24 4 -28 217 0 0.045 Hippocampus_R (Voxels: 4, AAL ID: 38) 25 4 -31 219 -3 0.049 ParaHippocampal_R (Voxels: 4, AAL ID: 40) 26 3 -28 224 -2 0.049 Hippocampus_R (Voxels: 3, AAL ID: 38) 27 2 23 249 0 0.049 No AAL region matched 28 2 -14 196 6 0.048 Lingual_R (Voxels: 2, AAL ID: 48) 29 2 2 198 35 0.049 Cingulum_Post_L (Voxels: 1, AAL ID: 35); Precuneus_L (Voxels: 1, AAL ID: 67) 30 1 -34 237 -6 0.05 Hippocampus_R (Voxels: 1, AAL ID: 38) 31 1 -14 198 6 0.049 Lingual_R (Voxels: 1, AAL ID: 48) 32 1 -31 233 -5 0.049 Hippocampus_R (Voxels: 1, AAL ID: 38) 33 1 -12 198 6 0.049 Lingual_R (Voxels: 1, AAL ID: 48) 34 1 -47 271 23 0.049 Frontal_Inf_Tri_R (Voxels: 1, AAL ID: 14) 35 1 -31 213 -3 0.05 ParaHippocampal_R (Voxels: 1, AAL ID: 40) 36 1 -31 238 -5 0.048 Hippocampus_R (Voxels: 1, AAL ID: 38) The GM regions that the cluster involves were identified according to the AAL3 atlas in the FSL software program. P values are shown after FWE correction. AAL3, anatomical automatic labeling 3; FSL, FMRIB Software Library; FWE, family-wise error; GM, gray matter; L, left; R, right. Significant group differences between the HE and HC group in terms of ODI In patients with HE, a significantly increased ODI was found within the posterior cerebellum and cerebellar vermis. (see Table 2 ). Table 2 Brain regions with significant differences in orientation dispersion index (ODI) between the hepatic encephalopathy (HE) and health control (HC) groups Cluster Voxels Coordinates P peak value AAL3 atllas X Y Z 1 16 -2 208 -29 0.008 Vermis_10 (Voxels: 7, AAL ID: 116); Cerebelum_9_R (Voxels: 6, AAL ID: 106); Vermis_9 (Voxels: 2, AAL ID: 115) 2 6 0 202 -33 0.005 Cerebelum_9_L (Voxels: 5, AAL ID: 105); Vermis_9 (Voxels: 1, AAL ID: 115) The GM regions that the cluster involves were identified according to the AAL3 atlas in the FSL software program. P values are shown after FWE correction. AAL3, anatomical automatic labeling 3; FSL, FMRIB Software Library; FWE, family-wise error; GM, gray matter; L, left; R, right. Correlation Between NODDI Parameters of the GP and Blood Biochemical Indices Pearson correlation analyses demonstrated that the NDI of the right GP was positively correlated with indirect bilirubin (r = 0.496, p = 0.016), prothrombin time (PT) (r = 0.505, p = 0.020), and prothrombin international normalized ratio (INR) (r = 0.508, p = 0.019) (see Fig. 3 ). Additionally, the ODI of the left GP was positively correlated with hemoglobin concentration (r = 0.402, p = 0.046) (see Fig. 4 ). Discussion In this study, we observed widespread neurite microstructural alterations in patients with HE, quantified using NODDI. HE patients showed significantly decreased NDI across multiple gray matter regions including frontal, temporal, parietal, occipital cortices as well as subcortical structures (thalamus, hippocampus, insula), and increased ODI primarily in cerebellar regions. Furthermore, correlations between GP and blood biochemical indices were also identified. These results provide novel biophysical insights into the neuropathology of HE, suggesting a complex interplay of neuroinflammation, edema, and metabolic toxicity. The most prominent finding was the extensive reduction of NDI in the HE group, with the largest clusters located in the right medial superior frontal gyrus, right insula, and bilateral cingulate gyri. NDI serves as a sensitive marker for the intracellular volume fraction of axons and dendrites [ 10 ]. The reduction of NDI in these regions likely reflects a combination of neurite atrophy and the expansion of the extracellular space due to low-grade cerebral edema [ 11 ]. These regions, which play a central role in executive function, attention, and emotional regulation, may reflect the underlying neuronal damage and dysfunction in HE, consistent with cognitive decline observed in these patients [ 12 , 13 ]. Interestingly, a recent GBSS-NODDI study [ 9 ] similarly identified reduced NDI patterns in patients with minimal hepatic encephalopathy (MHE), demonstrating significant NDI decreases specifically in the left insula and left middle frontal gyrus. These regions involved in higher-order cognitive and multisensory integration. The consistency of these findings across both HE and MHE populations confirms that microstructural degeneration, detectable by NODDI, is a central feature of HE-related brain injury, beginning even at the clinically covert (MHE) stage. It is noteworthy that the spatial distribution of the most significant alterations differed between the studies, which may reflect the progression of neuropathology. In our HE group, the largest effect sizes were located in the right medial superior frontal gyrus, right insula, and bilateral cingulate gyrus. In contrast, the changes reported in MHE exhibited a left-hemisphere predominance. This pattern suggests a potential trajectory: early, subtle damage may preferentially affect or be more easily detected in left-hemispheric hubs of the executive and salience networks. As liver dysfunction progresses and clinical symptoms emerge, the damage becomes more bilateral and widespread, involving key midline structures, such as the cingulate cortex, and critical right hemisphere regions associated with attention and self-regulation, such as the medial prefrontal cortex. The HE group also exhibited lower NDI in the precuneus compared to the HC group. The involvement of the medial prefrontal cortex and the precuneus highlights the vulnerability of the Default Mode Network (DMN). Consistent with our findings, previous resting-state functional MRI studies [ 14 , 15 ] have confirmed the presence of dysfunction in the DMN in patients with HE, characterized by decreased amplitude of low-frequency fluctuation (ALFF) and attenuated functional connectivity. Disruption of the DMN is consistently associated with the cognitive deficits observed in HE, such as impaired attention and self-referential processing [ 16 , 17 ]. Our results extend these findings by pinpointing the biophysical basis of this dysfunction: a loss of dendritic density or integrity. Furthermore, the significant involvement of the insula (part of the Salience Network) supports the "network switching" hypothesis, where structural damage prevents the brain from effectively toggling between central executive and default mode states, leading to the behavioral rigidity seen in patients [ 18 ]. We observed a distinct pattern in the cerebellum, characterized by increased ODI in the posterior cerebellum and vermis. While reduced NDI indicates tissue loss, an increased ODI reflects a more disorganized or complex neurite configuration [ 6 ]. In the context of HE, this increased complexity might represent a maladaptive response or "disorganized branching" in the face of ongoing neurotoxicity. The posterior cerebellum is increasingly recognized for its role in non-motor cognitive processes, including emotional regulation and attention [ 19 , 20 ]. The coexistence of reduced NDI and increased ODI suggests that the cerebellum undergoes active, albeit pathological, remodeling as part of a whole-brain network failure. The novel and clinically relevant finding of this study is the dissociation between NDI and ODI alterations within the GP in relation to blood biochemical markers. This supports the notion that HE involves widespread pathophysiological mechanisms, including metabolic, inflammatory, and vascular derangements, which in turn affect neuronal microstructure [ 21 ]. We observed that the NDI of the right GP was positively correlated with markers of liver dysfunction (indirect bilirubin, PT, and INR). Typically, neurodegeneration involves a reduction in neurite density. However, the GP is the preferential site for manganese (Mn) deposition and ammonium accumulation in patients with chronic liver disease, often manifesting as T1-weighted hyperintensity [ 22 , 23 ]. This accumulation often leads to astrocyte hypertrophy, which increases the apparent intracellular volume fraction[ 21 , 24 ]. Thus, the "pseudo-increase" in NDI likely reflects toxic cellular swelling rather than healthy neurite growth[ 25 ]. Conversely, the left GP ODI showed a positive correlation with hemoglobin concentration. Anemia is a common systemic complication of cirrhosis that exacerbates cerebral hypoxia [ 26 ]. Our finding suggests that lower hemoglobin levels (more severe anemia) are associated with reduced ODI, representing a simplification of dendritic arborization or loss of synaptic complexity [ 27 , 28 ]. This suggests a "double-hit" mechanism: toxic metabolic accumulation (bilirubin/manganese) drives cellular swelling and increases NDI, while systemic factors like anemia and hypoxia lead to a breakdown of structural complexity (reduced ODI) [ 29 ]. Limitations Several limitations in this study should be considered. First, the clinical heterogeneity of decompensated cirrhosis patients, including varying etiologies and liver dysfunction severity, may have introduced confounding factors. Future studies with larger, stratified cohorts are needed to enhance data homogeneity. Second, the sample size was relatively small for an exploratory neuroimaging analysis, which may limit the statistical power to detect subtle cortical alterations. Third, the cross-sectional design precludes the establishment of causal relationships between systemic biochemical changes and microstructural decay. Longitudinal research is essential to evaluate the efficacy of NODDI metrics in predicting disease progression. Finally, the 3.0T MRI protocol may limit spatial resolution for smaller subcortical structures. Future studies using high-resolution, multi-shell diffusion protocols could further refine these findings. Despite these limitations, the present results provide meaningful insights into HE-related microstructural alterations. Conclusions This study employed the NODDI to characterize gray matter microstructural pathology in HE, revealing widespread neurite loss and cerebellar disorganization. The most significant finding was the dissociated correlation of GP NDI and ODI with distinct systemic biomarkers, supporting a “double-hit” model of neurotoxicity. In conclusion, NODDI-derived parameters offer sensitive and biologically specific biomarkers that bridge systemic biochemistry and cerebral microstructure, holding promise for improving the early detection and mechanistic evaluation of HE. Declarations Acknowledgements: This work was supported by Luoyang City Core Technology Breakthrough Public Welfare Special Project (grant number 230218A). Data Sharing Statement: The data could be shared upon request. Please contact the correspondent author. Conflicts of Interest: The authors have no conflicts of interest to declare. Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. 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Neuroimage. 2021;238:118234; https://doi.org/10.1016/j.neuroimage.2021.118234 . Jespersen SN, Bjarkam CR, Nyengaard JR, Chakravarty MM, Hansen B, Vosegaard T, et al. Neurite density from magnetic resonance diffusion measurements at ultrahigh field: comparison with light microscopy and electron microscopy. Neuroimage. 2010;49(1):205–16; https://doi.org/10.1016/j.neuroimage.2009.08.053 . Tariq M, Schneider T, Alexander DC, Gandini Wheeler-Kingshott CA, Zhang H. Bingham-NODDI: Mapping anisotropic orientation dispersion of neurites using diffusion MRI. Neuroimage. 2016;133:207–23; https://doi.org/10.1016/j.neuroimage.2016.01.046 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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04:45:04","extension":"xml","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":102866,"visible":true,"origin":"","legend":"","description":"","filename":"84636cce31d44f9289f783e9acef50751structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/9c15ec87eefada510aa31cb6.xml"},{"id":100751145,"identity":"a5c334e8-0e65-4341-b3e2-5657573b5663","added_by":"auto","created_at":"2026-01-21 04:44:38","extension":"html","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":113429,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/8e9fbf48263a880e78a8f5c9.html"},{"id":100751147,"identity":"dc2a3f27-b2fe-4642-824a-9006da5c7de9","added_by":"auto","created_at":"2026-01-21 04:44:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":118012,"visible":true,"origin":"","legend":"\u003cp\u003eProcessing pipeline of the Gray Matter-Boundary Structural Skeleton (GBSS). FWF, free water fraction; NDI, neurite density index; ODI, orientation dispersion index; FA, fractional anisotropy; GM, gray matter\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/7b412878497ec7d5166b44f9.png"},{"id":100751250,"identity":"c578372a-1bfa-4f61-b9ad-fdb6f54cba4b","added_by":"auto","created_at":"2026-01-21 04:45:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":376132,"visible":true,"origin":"","legend":"\u003cp\u003eBetween-group differences in neurite density index (NDI). Yellow clusters reflect GM decreases in the Hepatic encephalopathy (HE) group. The color bar represents the \u003cem\u003ep value\u003c/em\u003e. Significance is indicated by \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, FWE corrected.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/9718281272a9623e08716092.png"},{"id":100751164,"identity":"86a537d1-f101-4f56-9351-706997f37bbf","added_by":"auto","created_at":"2026-01-21 04:44:51","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":122821,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between right globus pallidus neurite density index (NDI) and (a) indirect bilirubin, (b) prothrombin time (PT), and (c) international normalized ratio (INR).\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/17164110f570990f3b18e893.png"},{"id":100751180,"identity":"2a8dfa4c-67c8-47d2-9c0a-6c884e5356db","added_by":"auto","created_at":"2026-01-21 04:44:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":180859,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelations between left globus pallidus orientation dispersion index (ODI) and hemoglobin concentration.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/e652f3a9d7689c6b1b633fa0.png"},{"id":100804069,"identity":"d508c0af-62c4-43d8-a6c4-3aeead2fae00","added_by":"auto","created_at":"2026-01-21 14:35:53","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1615331,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8537235/v1/604d5347-40c4-4475-bfed-ae111e2f8df3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Gray Matter Microstructural Alterations and their Correlation with Systemic Biomarkers in Hepatic Encephalopathy: A NODDI Study Using Gray-matter Based Spatial Statistics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHepatic encephalopathy (HE) is a common neurological complication in patients with cirrhosis, manifesting as a spectrum of neuropsychiatric symptoms ranging from subtle cognitive impairment to coma [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. With the aging of the global population, the differential diagnosis between HE and neurodegenerative disorders has become increasingly challenging due to overlapping clinical presentations [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Traditional diagnostic approaches primarily rely on clinical assessment and blood biochemical markers such as ammonia levels, but these methods are limited by subjectivity and insufficient sensitivity [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong various neuroimaging biomarkers, the globus pallidus (GP) has garnered significant attention in HE research. Magnetic resonance imaging (MRI) studies have demonstrated that 70%-90% of HE patients exhibit bilateral symmetric hyperintensity in the GP on T1-weighted images, a characteristic finding attributed to manganese deposition resulting from impaired hepatic clearance [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. This pallidal abnormality appears to be independent of ammonia levels, suggesting a distinct pathophysiological mechanism and highlighting the GP as a critical region in HE neurotoxicity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWhile conventional structural MRI captures macroscopic changes, it offers limited sensitivity to microstructural alterations. Neurite Orientation Dispersion and Density Imaging (NODDI) is an advanced diffusion MRI technique that enables non-invasive quantification of brain tissue microstructure through parameters including the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI) [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. These metrics provide more refined biological markers for investigating HE-related cerebral microstructural abnormalities. Recent studies have begun applying NODDI to explore microstructural changes in HE, though most have focused primarily on white matter alterations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eGray-matter Based Spatial Statistics (GBSS) is a voxel-based analysis method specifically designed to investigate spatial patterns of gray matter microstructure [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. A recent study combining NODDI with GBSS successfully identified cortical microstructural abnormalities in patients with minimal hepatic encephalopathy [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, this research did not specifically focus on the GP\u0026mdash;a region known to be particularly vulnerable in HE\u0026mdash;nor did it systematically examine the correlation between cerebral microstructural parameters and blood biochemical indices.\u003c/p\u003e \u003cp\u003eGiven these research gaps, the study aims to: (ⅰ) assess brain microstructural alterations in patients with HE using NODDI combined with GBSS methodology; (ⅱ) analyze correlations between the NODDI parameters of the GP in the HE group and blood biochemical indices to provide novel imaging evidence for understanding the pathophysiological mechanisms underlying HE.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003e The study was approved by the local Ethics Committee (no. L2023005) and all participants provided written informed consent (version V1.0; dated August 9, 2023).\u003c/p\u003e \u003cp\u003eThirty-three patients clinically diagnosed with decompensated cirrhosis between December 2023 and June 2025 were enrolled in this study, and 31 healthy volunteers were included as the health control(HC) group. The inclusion criteria for the HE group were as follows: (ⅰ) Clinical diagnosis of decompensated cirrhosis; (ⅱ) Imaging evidence of cirrhosis, portal hypertension, and ascites as shown by imaging examinations. The inclusion criteria for the control group were: (ⅰ) routine health examination results indicating good health with no known significant systemic organ diseases; (ⅱ) laboratory tests (including complete blood count, liver and kidney function, coagulation profile, and other major biochemical indices) within normal ranges. The exclusion criteria for both groups included: (ⅰ) contraindications to MRI (e.g., presence of cardiac pacemakers, cochlear implants, or metallic implants) that prevent the completion of scanning; (ⅱ) poor image quality rendering the data unsuitable for subsequent analysis; (ⅲ) incomplete data, with failure to complete all study procedures (such as MRI scanning and blood biochemical tests); (ⅳ) long-term abuse of substances or medications that may affect the central nervous system (e.g., antipsychotics, sedatives).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMRI data acquisition\u003c/h3\u003e\n\u003cp\u003eMRI examinations were performed on a 3.0-T scanner (SIGNA Architect 3.0 T, GE Healthcare, US) with a combined head and neck coil. diffusion-weighted magnetic resonance imaging (dMRI) was performed using a spin-echo planar imaging sequence with the following parameters: repetition time (TR)\u0026thinsp;=\u0026thinsp;4286 ms ; echo time (TE)\u0026thinsp;=\u0026thinsp;113.5 ms; field of view (FOV)\u0026thinsp;=\u0026thinsp;240\u0026times;240 mm\u003csup\u003e2\u003c/sup\u003e; matrix\u0026thinsp;=\u0026thinsp;128\u0026times;128; slice thickness\u0026thinsp;=\u0026thinsp;3 m; non-diffusion weighted images (b\u0026thinsp;=\u0026thinsp;0 s/mm\u003csup\u003e2\u003c/sup\u003e) as well as 30 noncollinear directions with multiple b values (b\u0026thinsp;=\u0026thinsp;1000, 2500 s/mm\u003csup\u003e2\u003c/sup\u003e). T1-weighted structural images were acquired using a 3D Gradient-Echo (GRE) Brain Volume (known as BRAVO) sequences with the following parameters: TR/TE\u0026thinsp;=\u0026thinsp;6.3/2.4 ms; FOV\u0026thinsp;=\u0026thinsp;256\u0026times;256\u0026times;160 mm\u003csup\u003e3\u003c/sup\u003e; voxel size\u0026thinsp;=\u0026thinsp;0.5\u0026times;0.5\u0026times;0.5 mm\u003csup\u003e3\u003c/sup\u003e; acquisition matrix; slice thickness\u0026thinsp;=\u0026thinsp;6mm.\u003c/p\u003e\n\u003ch3\u003eImage Preprocessing\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eDiffusion-weighted data processing\u003c/h2\u003e \u003cp\u003eFirst, the MRIcroGL software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/mricrogl\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/mricrogl\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was performed to convert all raw data in DICOM format into NIFTI format, and further anonymize patient data. Then, the dMRI were processed using the EDDY and TOPUP tools from the FMRIB Software Library (FSL) to perform eddy current correction, geometric distortion correction, and head motion correction. Following these preprocessing steps, the diffusion tensor model was applied to the corrected data, and fractional anisotropy (FA) maps were generated using the weighted linear least squares method implemented in the Dipy library. Subsequently, the Accelerated Microstructure Imaging via Convex Optimization (AMICO) approach was utilized to compute parameters associated with the NODDI model. This analysis yielded key metrics, including the Neurite Density Index (NDI), Orientation Dispersion Index (ODI), and free water fraction(FWF)).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePost-processing of GBSS\u003c/h3\u003e\n\u003cp\u003eGBSS was performed to analyze the microstructural changes of gray matter using scripts available online (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/arash-n/GBSS\u003c/span\u003e\u003cspan address=\"https://github.com/arash-n/GBSS\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The specific steps are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Firstly, gray matter fraction maps were derived in the native diffusion space by subtracting FWF and white matter fractions from unity in each voxel. The FWF were obtained from NODDI, while the white matter fractions were estimated using two-tissue class segmentation of FA images with Atropos. A study-specific pseudo-T1 template was created through iterative group averaging of subject-level pseudo-T1 images, which were derived from the weighted fusion of white matter and gray matter fraction maps. The preprocessed outputs were then input into the gbss_1_reg.sh script for affine registration, followed by non-linear spatial registration to this custom template. This process generated a suite of registered derivatives, including gray matter fraction maps, warped pseudo-T1 images, and warped gray matter, NDI, and ODI maps. Subsequently, the gbss_2_skel.sh script was employed to process these registered maps and extract skeletonized features, namely NDI_skeleton, ODI_skeleton, and GM_skeleton, along with a mean gray matter map. Skeletonization was constrained by a by a mask derived from the thresholded average gray matter fraction map to preserve region-specific structural boundaries. Finally, the skeletonized products were refined using the gbss_3_fill.sh script, which applied a voxel-wise nearest-neighbor smoothing algorithm to produce fully filled, spatially complete NDI_filling and ODI_filling metrics for subsequent downstream GBSS statistical analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBlood Biochemical Examination\u003c/h2\u003e \u003cp\u003eAll patients underwent Blood Biochemical Examination, including liver synthetic function (Albumin, Prothrombin Time [PT], International Normalized Ratio [INR]), cholestasis (Indirect Bilirubin)), and other relevant factors (D-dimer, Hemoglobin concentration, and Blood Ammonia).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eRandomization and threshold-free cluster enhancement (TFCE) were performed within the FSL to explore differences in NDI_filling and ODI_filling metrics between the HE and HC group. A nonparametric permutation test with 1,000 iterations was conducted. Family-wise error (FWE) correction was performed to address multiple comparisons, with a significant threshold set at P\u003csub\u003eFWE\u003c/sub\u003e\u0026lt;0.05.\u003c/p\u003e \u003cp\u003eThe relationship between NODDI parameters of the GP in the HE group and blood biochemical indices was also investigated using the Pearson correlation analysis. P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant. Statistical analyses were performed using Python Software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSignificant group differences between the HE and HC group in terms of NDI\u003c/h2\u003e \u003cp\u003eIn patients with HE, a significantly decreased NDI was found in some regions of the gray matter, primarily the right frontal cortex (including the right medial superior frontal gyrus, right anterior cingulate gyrus, right superior frontal gyrus, right supplementary motor area, and right inferior frontal gyrus), bilateral parietal cortex (including the bilateral precuneus, bilateral superior parietal gyri, right angular gyrus, and right inferior parietal gyrus), left occipital regions (left cuneus, left calcarine gyrus, left fusiform gyrus, and left middle occipital gyrus), left temporal cortex (including the left middle temporal gyrus and left temporal pole), right insula, bilateral middle cingulate gyri, bilateral posterior cingulate gyrus, right hippocampus, bilateral parahippocampal gyri, left thalamus, right putamen and bilateral cerebellum. In addition to these primary clusters, several other regions showed isolated voxels or very small clusters of alteration (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions with significant differences in neurite density index (NDI) between the hepatic encephalopathy (HE) and health control (HC) groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVoxels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003csub\u003epeak value\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAAL3 atllas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e969\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrontal_Sup_Medial_R (Voxels: 511, AAL ID: 24); Cingulum_Mid_R (Voxels: 225, AAL ID: 34); Cingulum_Ant_R (Voxels: 139, AAL ID: 32); Frontal_Sup_R (Voxels: 54, AAL ID: 4); Supp_Motor_Area_R (Voxels: 22, AAL ID: 20); Cingulum_Mid_L (Voxels: 10, AAL ID: 33); Frontal_Mid_R (Voxels: 2, AAL ID: 8); Frontal_Sup_Medial_L (Voxels: 2, AAL ID: 23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eInsula_R (Voxels: 216, AAL ID: 30); Putamen_R (Voxels: 80, AAL ID: 74); Frontal_Inf_Orb_R (Voxels: 27, AAL ID: 16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCerebelum_4_5_L (Voxels: 174, AAL ID: 97); Cerebelum_4_5_R (Voxels: 91, AAL ID: 98); Cerebelum_6_L (Voxels: 61, AAL ID: 99); Vermis_8 (Voxels: 57, AAL ID: 114); Vermis_4_5 (Voxels: 54, AAL ID: 111); Fusiform_L (Voxels: 38, AAL ID: 55); Cerebelum_8_L (Voxels: 23, AAL ID: 103); Vermis_6 (Voxels: 22, AAL ID: 112); Cerebelum_8_R (Voxels: 19, AAL ID: 104); Cerebelum_6_R (Voxels: 12, AAL ID: 100); Vermis_9 (Voxels: 12, AAL ID: 115); Cerebelum_Crus2_R (Voxels: 2, AAL ID: 94); Vermis_7 (Voxels: 1, AAL ID: 113); ParaHippocampal_L (Voxels: 1, AAL ID: 39)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCuneus_L (Voxels: 135, AAL ID: 45); Precuneus_L (Voxels: 119, AAL ID: 67); Cingulum_Post_L (Voxels: 28, AAL ID: 35); Calcarine_L (Voxels: 19, AAL ID: 43); Precuneus_R (Voxels: 18, AAL ID: 68); Cingulum_Post_R (Voxels: 4, AAL ID: 36); Calcarine_R (Voxels: 2, AAL ID: 44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemporal_Mid_L (Voxels: 26, AAL ID: 85); Occipital_Mid_L (Voxels: 4, AAL ID: 51)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrecuneus_L (Voxels: 67, AAL ID: 67); Precuneus_R (Voxels: 58, AAL ID: 68); Parietal_Sup_R (Voxels: 27, AAL ID: 60)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCerebelum_Crus1_L (Voxels: 48, AAL ID: 91); Cerebelum_6_L (Voxels: 41, AAL ID: 99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParietal_Sup_L (Voxels: 24, AAL ID: 59); Precuneus_L (Voxels: 18, AAL ID: 67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eThalamus_L (Voxels: 72, AAL ID: 77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrontal_Inf_Orb_R (Voxels: 3, AAL ID: 16); Rectus_R (Voxels: 2, AAL ID: 28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupp_Motor_Area_R (Voxels: 52, AAL ID: 20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePrecuneus_L (Voxels: 26, AAL ID: 67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSupp_Motor_Area_R (Voxels: 22, AAL ID: 20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParietal_Sup_L (Voxels: 19, AAL ID: 59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAngular_R (Voxels: 17, AAL ID: 66); SupraMarginal_R (Voxels: 1, AAL ID: 64)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 13, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo AAL region matched\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTemporal_Pole_Sup_L (Voxels: 5, AAL ID: 83); Amygdala_L (Voxels: 1, AAL ID: 41)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLingual_L (Voxels: 8, AAL ID: 47); Calcarine_L (Voxels: 2, AAL ID: 43)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e280\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrontal_Mid_R (Voxels: 6, AAL ID: 8); Frontal_Inf_Tri_R (Voxels: 2, AAL ID: 14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParietal_Inf_R (Voxels: 6, AAL ID: 62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrontal_Inf_Tri_R (Voxels: 4, AAL ID: 14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParaHippocampal_R (Voxels: 4, AAL ID: 40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 4, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParaHippocampal_R (Voxels: 4, AAL ID: 40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 3, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo AAL region matched\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLingual_R (Voxels: 2, AAL ID: 48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCingulum_Post_L (Voxels: 1, AAL ID: 35); Precuneus_L (Voxels: 1, AAL ID: 67)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 1, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLingual_R (Voxels: 1, AAL ID: 48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 1, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLingual_R (Voxels: 1, AAL ID: 48)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFrontal_Inf_Tri_R (Voxels: 1, AAL ID: 14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eParaHippocampal_R (Voxels: 1, AAL ID: 40)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHippocampus_R (Voxels: 1, AAL ID: 38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe GM regions that the cluster involves were identified according to the AAL3 atlas in the FSL software program. P values are shown after FWE correction. AAL3, anatomical automatic labeling 3; FSL, FMRIB Software Library; FWE, family-wise error; GM, gray matter; L, left; R, right.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSignificant group differences between the HE and HC group in terms of ODI\u003c/h2\u003e \u003cp\u003eIn patients with HE, a significantly increased ODI was found within the posterior cerebellum and cerebellar vermis. (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBrain regions with significant differences in orientation dispersion index (ODI) between the hepatic encephalopathy (HE) and health control (HC) groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCluster\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVoxels\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003csub\u003epeak value\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAAL3 atllas\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eX\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eY\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eZ\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e208\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eVermis_10 (Voxels: 7, AAL ID: 116); Cerebelum_9_R (Voxels: 6, AAL ID: 106); Vermis_9 (Voxels: 2, AAL ID: 115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCerebelum_9_L (Voxels: 5, AAL ID: 105); Vermis_9 (Voxels: 1, AAL ID: 115)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe GM regions that the cluster involves were identified according to the AAL3 atlas in the FSL software program. P values are shown after FWE correction. AAL3, anatomical automatic labeling 3; FSL, FMRIB Software Library; FWE, family-wise error; GM, gray matter; L, left; R, right.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation Between NODDI Parameters of the GP and Blood Biochemical Indices\u003c/h2\u003e \u003cp\u003ePearson correlation analyses demonstrated that the NDI of the right GP was positively correlated with indirect bilirubin (r\u0026thinsp;=\u0026thinsp;0.496, p\u0026thinsp;=\u0026thinsp;0.016), prothrombin time (PT) (r\u0026thinsp;=\u0026thinsp;0.505, p\u0026thinsp;=\u0026thinsp;0.020), and prothrombin international normalized ratio (INR) (r\u0026thinsp;=\u0026thinsp;0.508, p\u0026thinsp;=\u0026thinsp;0.019) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, the ODI of the left GP was positively correlated with hemoglobin concentration (r\u0026thinsp;=\u0026thinsp;0.402, p\u0026thinsp;=\u0026thinsp;0.046) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we observed widespread neurite microstructural alterations in patients with HE, quantified using NODDI. HE patients showed significantly decreased NDI across multiple gray matter regions including frontal, temporal, parietal, occipital cortices as well as subcortical structures (thalamus, hippocampus, insula), and increased ODI primarily in cerebellar regions. Furthermore, correlations between GP and blood biochemical indices were also identified. These results provide novel biophysical insights into the neuropathology of HE, suggesting a complex interplay of neuroinflammation, edema, and metabolic toxicity.\u003c/p\u003e \u003cp\u003eThe most prominent finding was the extensive reduction of NDI in the HE group, with the largest clusters located in the right medial superior frontal gyrus, right insula, and bilateral cingulate gyri. NDI serves as a sensitive marker for the intracellular volume fraction of axons and dendrites [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. The reduction of NDI in these regions likely reflects a combination of neurite atrophy and the expansion of the extracellular space due to low-grade cerebral edema [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. These regions, which play a central role in executive function, attention, and emotional regulation, may reflect the underlying neuronal damage and dysfunction in HE, consistent with cognitive decline observed in these patients [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInterestingly, a recent GBSS-NODDI study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] similarly identified reduced NDI patterns in patients with minimal hepatic encephalopathy (MHE), demonstrating significant NDI decreases specifically in the left insula and left middle frontal gyrus. These regions involved in higher-order cognitive and multisensory integration. The consistency of these findings across both HE and MHE populations confirms that microstructural degeneration, detectable by NODDI, is a central feature of HE-related brain injury, beginning even at the clinically covert (MHE) stage. It is noteworthy that the spatial distribution of the most significant alterations differed between the studies, which may reflect the progression of neuropathology. In our HE group, the largest effect sizes were located in the right medial superior frontal gyrus, right insula, and bilateral cingulate gyrus. In contrast, the changes reported in MHE exhibited a left-hemisphere predominance. This pattern suggests a potential trajectory: early, subtle damage may preferentially affect or be more easily detected in left-hemispheric hubs of the executive and salience networks. As liver dysfunction progresses and clinical symptoms emerge, the damage becomes more bilateral and widespread, involving key midline structures, such as the cingulate cortex, and critical right hemisphere regions associated with attention and self-regulation, such as the medial prefrontal cortex.\u003c/p\u003e \u003cp\u003eThe HE group also exhibited lower NDI in the precuneus compared to the HC group. The involvement of the medial prefrontal cortex and the precuneus highlights the vulnerability of the Default Mode Network (DMN). Consistent with our findings, previous resting-state functional MRI studies [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] have confirmed the presence of dysfunction in the DMN in patients with HE, characterized by decreased amplitude of low-frequency fluctuation (ALFF) and attenuated functional connectivity. Disruption of the DMN is consistently associated with the cognitive deficits observed in HE, such as impaired attention and self-referential processing [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Our results extend these findings by pinpointing the biophysical basis of this dysfunction: a loss of dendritic density or integrity. Furthermore, the significant involvement of the insula (part of the Salience Network) supports the \"network switching\" hypothesis, where structural damage prevents the brain from effectively toggling between central executive and default mode states, leading to the behavioral rigidity seen in patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe observed a distinct pattern in the cerebellum, characterized by increased ODI in the posterior cerebellum and vermis. While reduced NDI indicates tissue loss, an increased ODI reflects a more disorganized or complex neurite configuration [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In the context of HE, this increased complexity might represent a maladaptive response or \"disorganized branching\" in the face of ongoing neurotoxicity. The posterior cerebellum is increasingly recognized for its role in non-motor cognitive processes, including emotional regulation and attention [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The coexistence of reduced NDI and increased ODI suggests that the cerebellum undergoes active, albeit pathological, remodeling as part of a whole-brain network failure.\u003c/p\u003e \u003cp\u003eThe novel and clinically relevant finding of this study is the dissociation between NDI and ODI alterations within the GP in relation to blood biochemical markers. This supports the notion that HE involves widespread pathophysiological mechanisms, including metabolic, inflammatory, and vascular derangements, which in turn affect neuronal microstructure [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. We observed that the NDI of the right GP was positively correlated with markers of liver dysfunction (indirect bilirubin, PT, and INR). Typically, neurodegeneration involves a reduction in neurite density. However, the GP is the preferential site for manganese (Mn) deposition and ammonium accumulation in patients with chronic liver disease, often manifesting as T1-weighted hyperintensity [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This accumulation often leads to astrocyte hypertrophy, which increases the apparent intracellular volume fraction[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Thus, the \"pseudo-increase\" in NDI likely reflects toxic cellular swelling rather than healthy neurite growth[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConversely, the left GP ODI showed a positive correlation with hemoglobin concentration. Anemia is a common systemic complication of cirrhosis that exacerbates cerebral hypoxia [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Our finding suggests that lower hemoglobin levels (more severe anemia) are associated with reduced ODI, representing a simplification of dendritic arborization or loss of synaptic complexity [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This suggests a \"double-hit\" mechanism: toxic metabolic accumulation (bilirubin/manganese) drives cellular swelling and increases NDI, while systemic factors like anemia and hypoxia lead to a breakdown of structural complexity (reduced ODI) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations in this study should be considered. First, the clinical heterogeneity of decompensated cirrhosis patients, including varying etiologies and liver dysfunction severity, may have introduced confounding factors. Future studies with larger, stratified cohorts are needed to enhance data homogeneity. Second, the sample size was relatively small for an exploratory neuroimaging analysis, which may limit the statistical power to detect subtle cortical alterations. Third, the cross-sectional design precludes the establishment of causal relationships between systemic biochemical changes and microstructural decay. Longitudinal research is essential to evaluate the efficacy of NODDI metrics in predicting disease progression. Finally, the 3.0T MRI protocol may limit spatial resolution for smaller subcortical structures. Future studies using high-resolution, multi-shell diffusion protocols could further refine these findings. Despite these limitations, the present results provide meaningful insights into HE-related microstructural alterations.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study employed the NODDI to characterize gray matter microstructural pathology in HE, revealing widespread neurite loss and cerebellar disorganization. The most significant finding was the dissociated correlation of GP NDI and ODI with distinct systemic biomarkers, supporting a \u0026ldquo;double-hit\u0026rdquo; model of neurotoxicity.\u003c/p\u003e \u003cp\u003eIn conclusion, NODDI-derived parameters offer sensitive and biologically specific biomarkers that bridge systemic biochemistry and cerebral microstructure, holding promise for improving the early detection and mechanistic evaluation of HE.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Luoyang City Core Technology Breakthrough Public Welfare Special Project (grant number 230218A).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eData Sharing Statement:\u0026nbsp;\u003c/em\u003eThe data could be shared upon request. Please contact the correspondent author.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflicts of Interest:\u0026nbsp;\u003c/em\u003eThe authors have no conflicts of interest to declare.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthical Statement:\u0026nbsp;\u003c/em\u003eThe authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki. This study was approved by Ethics Committee (no. L2023005) and all participants provided written informed consent (version V1.0; dated August 9, 2023). All participants signed written informed consent before undergoing MRI scans.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eVilstrup H, Amodio P, Bajaj J, Cordoba J, Ferenci P, Mullen KD, et al. 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Neuroimage. 2016;133:207\u0026ndash;23; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.neuroimage.2016.01.046\u003c/span\u003e\u003cspan address=\"10.1016/j.neuroimage.2016.01.046\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hepatic Encephalopathy, Neurite Orientation Dispersion and Density Imaging (NODDI), Gray-matter Based Spatial Statistics (GBSS), Globus Pallidus, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-8537235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8537235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eHepatic encephalopathy (HE) involves complex neurobiological changes that are often difficult to quantify using conventional MRI. This study aims to utilize Neurite Orientation Dispersion and Density Imaging (NODDI) combined with Gray-matter Based Spatial Statistics (GBSS) to characterize microstructural alterations in patients with HE and explore their relationship with clinical biochemical markers, specifically within the globus pallidus (GP).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThirty-three patients with HE and 31 healthy controls underwent 3T MRI including a multi-shell diffusion protocol for NODDI. GBSS was performed to assess differences in the Neurite Density Index (NDI) and Orientation Dispersion Index (ODI). Pearson correlation analyzed relationships between GP NODDI parameters and blood biochemical indices.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHE patients exhibited significantly decreased NDI across widespread cortical and subcortical regions (frontal, parietal, temporal, cingulate, insula, thalamus) and increased ODI in the posterior cerebellum/vermis. Crucially, the NDI of the right GP showed positive correlations with indirect bilirubin, prothrombin time, and INR (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while the ODI of the left GP positively correlated with hemoglobin concentration (p\u0026thinsp;=\u0026thinsp;0.046).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eNODDI reveals extensive neurite loss and cerebellar disorganization in HE. The dissociated correlation patterns of GP NDI and ODI with distinct blood markers suggest a \u0026ldquo;double-hit\u0026rdquo; pathophysiological model: toxic metabolite accumulation may drive cellular swelling (increased NDI), while systemic factors like anemia may reduce structural complexity (decreased ODI). These findings highlight NODDI as a sensitive tool for monitoring the progression and metabolic impact of HE.\u003c/p\u003e","manuscriptTitle":"Gray Matter Microstructural Alterations and their Correlation with Systemic Biomarkers in Hepatic Encephalopathy: A NODDI Study Using Gray-matter Based Spatial Statistics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-21 04:38:25","doi":"10.21203/rs.3.rs-8537235/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"676152cc-ab5e-403b-8882-cc6a0966843c","owner":[],"postedDate":"January 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-16T21:24:31+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-21 04:38:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8537235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8537235","identity":"rs-8537235","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}