Cortical microstructural changes in minimal hepatic encephalopathy: a gray matter-based spatial statistics study

Introduction

Minimal hepatic encephalopathy (MHE) corresponds to the earliest stage of hepatic encephalopathy (HE). Patients with MHE experience subtle neurocognitive deficits such as psychomotor impairment, shortened attention span, reduced concentration, impaired executive function, and memory loss, which are only detectable through neuropsychological or psychometric tests rather than routine clinical examination (1). The potential cognitive impairments place cirrhotic patients with MHE at a higher risk for motor vehicle accidents compared to those without MHE (2). Furthermore, MHE patients are more likely to progress to overt HE (OHE), an irreversible stage of HE associated with high mortality (3,4). Identification of early diagnostic biomarkers is paramount for the treatment and prognostic improvement of MHE patients (1,3,4).

Mounting histological evidence has revealed that MHE leads to impairments in cortical cytoarchitecture, including abnormal synaptic remodeling of cortical neurons (5) and decreased size and density of dendritic spines (6). In addition, neuroimaging evidence suggests that MHE causes changes in cortical morphology including decreased gray matter (GM) volume and cortical thickness in several regions such as the precuneus, insula, and superior temporal cortex, which is associated with cognitive impairment (7,8). Collectively, these findings indicate that cortical damage is an important characteristic of MHE that contributes to the mechanisms underlying neurocognitive dysfunction.

Recently, attempts have been made to detect abnormalities in the cortical microstructure of MHE patients using diffusion-weighted magnetic resonance imaging (dMRI), which can provide quantitative information noninvasively based on water diffusion characteristics (9). For example, one study using diffusion tensor imaging (DTI) found increased mean diffusivity and decreased fractional anisotropy (FA) in the fronto-temporal GM of MHE patients, indicating enhanced brain water content and impaired microstructural integrity (10). A previous diffusion kurtosis imaging (DKI) study further revealed decreased GM mean kurtosis in patients with MHE, particularly in the cingulate cortex, precuneus, and insular cortex (11). Notably, the aforementioned changes in GM have been correlated with the cognitive impairments of MHE patients (8,12-15). Thus, as previously reported, dMRI offers sensitive detection of cortical microstructural alterations in MHE and may be used to improve our understanding of the neurobiological basis of MHE-related cognitive impairment.

A recent advancement of dMRI is neurite orientation dispersion and density imaging (NODDI), a multicompartment model that can cope with complex fiber arrangements and differentiate between intraneurite-restricted (e.g., axonal and dendritic) and extraneurite-hindered (e.g., cell body and glial cell) diffusion (16). In contrast to DTI, NODDI can account for partial volume effects (PVEs) by filtering the cerebrospinal fluid (CSF) component of the diffusion signal (17). Moreover, compared with other diffusion models such as DKI, NODDI can address the presence of multiple diffusion environments in each imaging unit (18). Notably, NODDI offers a relatively direct depiction of intricate microstructure with biophysically meaningful parameters, including the neurite density index (NDI), orientation dispersion index (ODI, an indicator of the degree of neurite coherence), and isotropic volume fraction (ISOVF; the fraction of water diffusing freely without encountering membranes) (16). Extensive validation studies have shown a high correspondence between NODDI-based estimates of neurite density (19) and orientation dispersion (20) and their histological counterparts. Given its advantages, NODDI has been widely employed to probe changes in GM microstructure in a range of neurologic conditions, including Parkinson’s disease (21), Alzheimer’s disease (22), and amyotrophic lateral sclerosis (23); this has contributed to our understanding of the mechanisms underlying these neurological disorders (24).

Voxel-based analysis (VBA) is an automated exploratory method commonly used to analyze NODDI measurements in GM (23). VBA performs generalized spatial smoothing to reduce individual differences; however, this procedure can increase PVEs, making it difficult to obtain accurate measurements in GM (25). Unlike VBA, GM-based spatial statistics (GBSS) is a recently developed analogue of tract-based spatial statistics (TBSS), a method for voxel-wise analysis in white matter (WM). GBSS creates a skeleton at the center of the cortical GM and aggregates dMRl parameters in the surrounding regions, thereby minimizing WM- and CSF-derived PVEs (26). Therefore, GBSS efficiently harnesses the NODDI model and provides more robust between-group comparisons on cortical microstructure measurements (27). Current applications of GBSS include research on Alzheimer’s disease (28), autism spectrum disorder (29), and Parkinson’s disease (21), for which GBSS has helped identify subtle cortical abnormalities that may be alternative biomarkers (30). However, to date, no study has used GBSS to evaluate GM alterations in patients with MHE.

Existing research has demonstrated the feasibility of using NODDI-based TBSS to detect WM in patients with MHE (31). Here, we coupled GBSS with NODDI to evaluate alterations in the cortical microstructure of MHE patients and investigated correlations between cortical microstructure changes, disease development, and neurocognitive dysfunction in cirrhotic patients. Our study provides complementary information for understanding whole-brain microstructural changes in MHE. We present this article in accordance with the STROBE reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1903/rc).

Methods

Participants

This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (No. 2019KJCX008), and informed consent was provided by all participants. The participants included a group of 35 healthy controls (HC), a group of 41 cirrhotic patients without MHE (NHE), and a group of 21 cirrhotic patients with MHE. Patients with liver cirrhosis were recruited from the outpatient and inpatient departments of gastroenterology at Fujian Medical University Union Hospital, and the HC were recruited from the local community in Fuzhou. A detailed description of each participant’s demographic and clinical characteristics is presented in Table 1. No significant differences were found with respect to age, sex, or years of education among the three groups. The Psychometric Hepatic Encephalopathy Score (PHES) was used to assess the cognitive function of each participant, and it consisted of the following five subtests: number connection tests A and B, a serial-dotting test, a digit symbol test, and a line-tracing test. MHE was defined by a PHES ≤−5 (32).

Participants were excluded if they met any of the following criteria: (I) presence of other neuropsychiatric diseases; (II) use of psychotropic medications; (III) current diagnosis of OHE or any uncontrolled endocrine or metabolic condition (e.g., thyroid dysfunction); (IV) history of alcohol abuse within the past 6 months; and (V) contraindications for magnetic resonance imaging (MRI).

MRI data acquisition

All MRI images were obtained with a 3-T scanner (Prisma; Siemens Medical Systems, Erlangen, Germany). dMRI was performed using simultaneous multi-slice (SMS) echo-planar imaging (EPI) with the following parameters: repetition time (TR) =2,500 ms; echo time (TE) =81 ms; flip angle =90°; number of slices =72; field of view (FOV) =260×260 mm²; matrix =130×130; voxel size =2.0 mm × 2.0 mm × 2.0 mm; slice spacing =2.0 mm; 10 non-diffusion-weighted images (b=0 s/mm²) as well as 64 noncollinear directions with multiple b values (b=1,000, 2,000, and 3,000 s/mm²); SMS =4; and generalized autocalibrating partially parallel acquisitions (GRAPPA) =2.

Processing diffusion metrics

Diffusion images were corrected for eddy current, geometric distortions, and head movement using the EDDY and TOPUP toolboxes within the FMRIB Software Library (FSL) (33). The diffusion tensor model (34), which was applied to the corrected data with a b value of 1,000 s/mm², generated FA maps via a weighted linear least-squares method in Dipy (35). The NODDI (16) metrics, including the NDI, ODI, and ISOVF, were calculated using accelerated microstructure imaging via convex optimization (AMICO) (36).

Post-processing of GBSS

GBSS was performed using open-source scripts (https://github.com/arash-n/GBSS) (30) (Figure 1). The WM fraction maps were derived by segmenting FA maps into two-tissue classes with Atropos (37), whereas ISOVF maps represented the CSF fraction. After subtracting the WM fraction and CSF fraction from 1, the GM fraction maps were obtained. Subsequently, pseudo-T1-weighted images, crafted by amalgamating the WM and GM fraction maps, were used to construct a study-specific template via Advanced Normalization Tools (ANTs) (38). A cerebral cortex skeleton was created from the average GM fraction map, into which NODDI metrics were consolidated using FSL. The final GM skeleton included voxels with a GM fraction over 0.65 in at least 75% of the participants (30).

Figure 1 Processing pipeline of the GBSS. The process began with the computation of the GM fraction, achieved by subtracting the combined WM and CSF fractions from 1. Then, pseudo-T1-weighted images were produced by combining the weighted WM and GM fraction maps. Next, these pseudo-T1 weighted images from all subjects were merged to construct a group-specific template, to which the diffusion metrics were warped. Finally, the GM skeleton was created from the average GM fraction map, and the aligned metrics were projected onto the skeleton for analysis. GM skeleton is shown in green, with NDI and ODI overlaid in purple and red. CSF, cerebrospinal fluid; FA, fractional anisotropy; GBSS, gray matter-based spatial statistics; GM, gray matter; ISOVF, isotropic volume fraction; NDI, neurite density index; ODI, orientation dispersion index; WM, white matter.

Statistical analysis

Randomization and threshold-free cluster enhancement (TFCE) were performed within the FSL (39) to explore differences in NODDI metrics among the HC, NHE, and MHE groups. A comprehensive permutation test with 5,000 iterations was conducted with significant clusters identified at a family-wise error (FWE) corrected P value of <0.05. The relationship between the PHES and NODDI metrics was also investigated in the cirrhotic patients. All statistical analyses were adjusted for age, sex, and education. Significant clusters were mapped using the anatomical automatic labeling 3 (AAL3) atlas (40) in study-specific space.

Results

There were no significant differences in age (P=0.475), gender (P=0.281), or education level (P=0.950) among the three groups (Table 1). Compared to the HC and NHE groups, patients with MHE demonstrated poor neurocognitive performance, as indicated by significantly lower PHES (both P<0.001).

The GBSS analysis revealed inter-group NDI differences in some areas (FWE-corrected P<0.05), primarily including the default mode network (DMN)-related regions (such as the bilateral precuneus, bilateral posterior cingulate gyrus, left inferior parietal lobule, left medial superior frontal gyrus, and right hippocampus and parahippocampal gyrus), the sensorimotor cortex (such as the left postcentral and precentral gyrus, left rolandic operculum, left superior parietal lobule, and bilateral middle cingulate gyrus), the visual cortex (such as the bilateral lateral superior/middle/inferior occipital gyrus, cuneus, lingual gyrus, and fusiform gyrus), the auditory cortex (such as the bilateral superior temporal gyrus), the bilateral insula, the left middle and inferior frontal gyrus, and bilateral lateral temporal cortex (Figure 2, Table 2). With respect to ODI, no significant difference was observed across the three groups (FWE-corrected P>0.05).

Figure 2 Comparative analysis of NDI across three distinct groups: HC, NHE, and MHE. Clusters exhibiting statistically significant differences in NDI, indicated by P values below 0.05 after applying FWE-correction, are accentuated in gradients of red to yellow. The green structure represents the GM skeleton. FWE, family-wise error; GM, gray matter; HC, healthy controls; MHE, minimal hepatic encephalopathy; NDI, neurite density index; NHE, cirrhotic patients without minimal hepatic encephalopathy.

Both the MHE and NHE groups exhibited reduced NDI compared to HC. Specifically, the MHE group had lower NDI primarily in the DMN-related regions (such as the bilateral precuneus, bilateral posterior cingulate gyrus, left inferior parietal lobule, left medial superior frontal gyrus, and right hippocampus, and parahippocampal gyrus), the sensorimotor cortex (such as the left postcentral and precentral gyrus, left superior parietal lobule, and bilateral middle cingulate gyrus), the visual cortex (such as the bilateral lateral middle/inferior occipital gyrus, cuneus, lingual gyrus, and right fusiform gyrus), the auditory cortex (such as the bilateral superior temporal gyrus), the left insula, the left middle frontal gyrus, and bilateral lateral temporal cortex (FWE-corrected P<0.05) (Figure 3, Table 3). Interestingly, the NHE group also demonstrated NDI reduction in several regions (FWE-corrected P<0.05) shared with the MHE group, but the regions with significantly decreased NDI in the NHE group were less extensive than those in the MHE group (Figure 4, Table 4). In addition, compared with NHE, the MHE group exhibited decreased NDI in several regions (uncorrected P<0.0005, Figure S1); however, these differences were not statistically significant.

Figure 3 Differences in NDI between HC and MHE. Regions exhibiting statistically significant differences (P<0.05) following FWE-correction are denoted in red and yellow. The green structure represents the GM skeleton. FWE, family-wise error; GM, gray matter; HC, healthy controls; MHE, minimal hepatic encephalopathy; NDI, neurite density index.

Figure 4 Differences in NDI between HC and NHE. Clusters with P values <0.05 after the FWE correction are highlighted in red and yellow. The green structure represents the GM skeleton. FWE, family-wise error; GM, gray matter; HC, healthy controls; NDI, neurite density index; NHE, cirrhotic patients without minimal hepatic encephalopathy.

The NDI values in the right parahippocampal gyrus and lingual gyrus were significantly positively correlated with PHES scores among the cirrhotic patients (FWE-corrected P<0.05) (Figure 5).

Figure 5 Correlation between the PHES and NDI in cirrhotic patients. Regions with FWE-corrected P<0.05 are depicted by red and yellow clusters. Green outlines the GM skeleton. FWE, family-wise error; GM, gray matter; NDI, neurite density index; PHES, Psychometric Hepatic Encephalopathy Score.

Discussion

In this study, we adapted GBSS analysis and the NODDI model to assess cortical differences related to MHE, yielding the following findings: (I) the MHE group showed decreased NDI in several cortical regions, primarily including the DMN-related regions, left insula, left middle frontal gyrus, bilateral lateral temporal cortex, and sensorimotor, visual, and auditory cortices. (II) No significant difference in ODI was observed across the three groups. (III) The regions with significantly decreased NDI in the MHE group were more extensive than those in the NHE group. (IV) Reduced NDI was significantly correlated with neurocognitive deficits among the cirrhotic patients. Our findings shed light on the potential role of GBSS and NODDI in quantifying and understanding the pathophysiology of cortical microstructural alterations in MHE patients.

In a histologically validated study, the NDI was closely correlated with the density and number of dendritic trees (41); therefore, the regional reduction in NDI observed in our study could be interpreted as the loss of GM neurites in MHE patients. Consistently, a previous histopathological study of cortical pyramidal neurons reported that chronic HE-induced microglial proliferation and astrocyte swelling could increase the microenvironmental pressure and thus lead to dendritic spine reduction (42,43). Neurites, the primary structures of synaptic connectivity, constitute the computational circuitry of the brain and are closely associated with central functional efficiency (e.g., cognitive processes) (44). Neurite loss (reflected by decreased NDI) may be a structural correlate for cognitive impairment in MHE, which is supported by the correlation between NDI reduction and neurocognitive dysfunction observed in this study.

We further identified decreased NDI in several DMN-associated regions in the MHE patients. In agreement with our findings, previous resting-state functional MRI studies have confirmed DMN dysfunction in patients with MHE, reflected by abnormal intrinsic activity and disrupted functional connectivity, which deteriorates with the increasing severity of HE (45,46). The DMN, which deactivates during tasks that demand attention, is crucial in coordinating endogenous attention orienting (“top-down”) and exogenous attention reorienting (“bottom-up”) processes (47). Considering the inverse correlation between the DMN and attention network (48), cortical microstructural abnormalities in DMN-related regions may disrupt the coordination of attentional processes, potentially contributing to impaired attention, which is a principal characteristic of MHE (1).

We demonstrated that MHE was associated with reduced NDI in the left insula, left middle frontal gyrus, and bilateral lateral temporal cortex. These findings are consistent with previous MHE neuroimaging studies that demonstrated morphological changes (e.g., volume atrophy, tissue density reduction, and cortical thinning) in these regions, which were further associated with cognitive dysfunctions (8,12,49). Specifically, the left insula is involved in the maintenance of social and emotional working memory (50), the middle frontal gyrus participates in the integration and processing of spatial working memory (51), and the lateral temporal cortex is implicated in verbal memory encoding (52). Thus, the decreased NDI in these cortical regions may be related to the dysfunctions in the associated memory domains, which is frequently reported in MHE (1).

We also discovered microstructural changes in the sensorimotor, visual, and auditory cortices of MHE patients. Previous studies have described functional abnormalities within the sensorimotor, visual, and auditory regions [e.g., reduced synchronicity of neural activity (53) and disrupted functional connectivity (54)] of patients with MHE. Accordingly, reduced NDI may reflect the loss of cortical neurites, which could account for the corresponding and previously reported sensorimotor, visual, and auditory dysfunction in MHE (1).

Several limitations in this study should be acknowledged. First, with the GBSS pipeline, we generated the GM skeleton based on relatively low-resolution (2.0 mm × 2.0 mm × 2.0 mm) diffusion maps (FA and ISOVF), which could result in imperfect suppression of CSF- and WM-derived PVE (55). However, the potential impact of differing b value protocols for FA (b=0, 1,000 s/mm²) and ISOVF (b=1,000, 2,000, 3,000 s/mm²) is likely minimal, as FA effectively captures WM anisotropy even at b=1,000 s/mm², whereas multi-shell ISOVF ensures precise CSF identification (16,56). In the future, the incorporation of surface-based mapping and NODDI measurements could be used to address this issue (57). Additionally, metric-specific effects on the reconstruction of GM skeleton will be systematically compared, since DiPiero et al. found ODI-based segmentation improved differentiation of tissue types in infant brains compared to FA (58). Second, we did not evaluate the impact of several clinical factors (such as the etiology and severity of cirrhosis) on cortical microstructural changes, although these factors have been demonstrated to be associated with GM morphological changes (49). Future studies with subgroup analysis should specifically investigate the potential effects of these clinical factors on cortical microstructure and cognitive function in MHE patients. Third, NODDI focuses on accurately describing physical effects through model parameters while neglecting the exchange of water molecules between inter-compartments (24). Thus, the NODDI model needs to be refined by using more advanced acquisition and analysis protocols or by integrating additional dMRI techniques to fully characterize the dynamic processes of water molecules between different tissue components. Fourth, the T2 effect may also influence the signal intensity in dMRI (59), potentially leading to inaccuracies in the indicators obtained from DTI or NODDI fitting. Since there is ongoing debate regarding whether T2 changes in patients with HE (60-62), further research is warranted to investigate whether T2 affects our results. Fifth, since our study lacks scan-rescan reproducibility data, relevant research is needed to further validate our result. Finally, this study was based on a cross-sectional design with a small sample size. Further studies using a longitudinal design and larger sample sizes will be required to clarify the value of NODDI in predicting MHE-associated cognitive decline.

Conclusions

Our findings provide evidence for the microstructural modulation of cortical neurites as contributing to the latent biological basis of MHE-related cognitive impairments. Notably, prior research utilizing NODDI with TBSS revealed extensive NDI reductions in WM tracts, which are responsible for corresponding cognitive dysfunctions in MHE patients (31). Collectively, these studies demonstrate that MHE involves diffuse microstructural impairments across both GM and WM, which synergistically contribute to cognitive deficits such as attention impairment and psychomotor slowing. Our study thereby provides complementary information for understanding whole-brain microstructural changes in MHE, offering deeper insights into its underlying pathological mechanisms.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STROBE reporting checklist. Available at https://qims.amegroups.com/article/view/10.21037/qims-24-1903/rc

Funding: This work was funded by the National Natural Science Foundation of China (No. 82071900), the Fujian Province Joint Funds for the Innovation of Science and Technology (No. 2019Y9067), and the Fujian Provincial Natural Science Foundation of China (No. 2024J01599).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-24-1903/coif). 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. This study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. This study was approved by the Ethics Committee of Fujian Medical University Union Hospital (No. 2019KJCX008), and informed consent was provided by all participants.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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