Abnormalities in modular connectivity of functional brain networks and cognitive changes in patients with anti -N-methyl-D-aspartate receptor encephalitis

<jats:title>Abstract</jats:title>         <jats:p><jats:bold>Objective: </jats:bold>To explore potential mechanisms of cognitive changes in patients with anti-NMDAR encephalitis (ANMDARE) from intramodule and intermoduleeffects of brain functional networks. <jats:bold>Methods:</jats:bold> Resting-state functional MRI and T1-weighted imaging data were collected from 30 ANMDARE patientsand 30 healthy controls (HCs). Abrain functional matrix was constructed, and sparsity was established by module similarity. For both groups, changes in functional connectivity within and between modules was calculated, changes in whole-brain and module gray matter volumes were explored, and whole-brain functional topology was analyzed. Finally, the association of brain functional and structural changes with cognitive function in ANMDARE was further analyzed. <jats:bold>Results:</jats:bold> Compared to HCs, ANMDARE patients had enhanced connectivity within the modules that included the occipito-parietal-temporal and parahippocampal gyri. ANMDARE patients had significantly higher participation coefficients (PC) in the right inferior frontal gyrus than HCs and significantly lower PC in the left superior parietal lobule, left caudate nucleus, and right putamen. No statistically significant differences in gray matter volume and global topological properties were found between the two groups. No correlations were found between functional and structural brain indicators and the Cognitive Assessment Scale and the Emotional Deficit Scale. <jats:bold>Conclusions:</jats:bold> Changes in cognitive function in patients with ANMDARE are manifested by enhanced intramodular functional connectivity and intermodularconnectivity changes in the brain, with abnormal intramodular and extramodularconnectivity that do not maintain normal cognitive function.</jats:p>

section_content: Cognitive dysfunction is one of the core symptoms of anti -N-methyl-D-aspartate receptor encephalitis (ANMDARE). Most patients with ANMDARE who are treated not only continue to have neuropsychological de cits (depression, anxiety, etc. ) during recovery but also develop signi cant cognitive sequelae, with core de cits including memory and executive function. Despite adequate treatment, the recovery period is long, and neuropsychological sequelae of varying severity persist. Early diagnosis followed by treatment is an important clinical factor in the achievement of a favorable cognitive outcome in most patients. In addition, early cognitive recovery after the acute phase is of greater bene t than a time-delayed cognitive recovery period (Finke et al., 2012; Heine et al., 2021; McKeon et al., 2016; 2018). This study endeavors to provide further understanding of this disease by exploring the neurophysiopathological mechanisms underlying the changes in cognitive brain function in patients with ANMDARE. 

Advanced structural and functional imaging currently possesses some sensitivity to potential functional brain changes (Biswal et al., 1995). ANMDARE has been found to be associated with characteristic changes in functional connectivity (FC) and extensive changes in white matter integrity in multimodal MRI studies and is associated with cognitive impairment (Finke et al., 2013; Jinhui Wang et al., 2021). However, a large part of previous studies focused on only some brain regions to understand the functional changes of the disease. With the development of complex brain network analysis techniques, graph theoretic analysis incorporates the whole brain network to analyze the speed and e ciency of information transmission; e. g., by assessing modularity (Bullmore & Sporns, 2009). 

Modularity means that nodes within a module are closely connected and sparsely connected to nodes outside the module. Dense communication within a module improves the e ciency of information transmission in that module, and connections between modules integrate whole-brain information exchange (Achard et al., 2012). Modularity can be a better measure of the cognitive performance of brain networks. High network modularity quanti ed by resting-state FC is associated with individuals' working memory as well as situational memory (Chan et al., 2014; Stevens et al., 2012). In addition, extensive cognitive impairment occurs when important brain regions that maintain modular organization are lesioned (Warren et al., 2014). Stroke recovery is accompanied by an increase in network modularity, and modularity is associated with complex cognitive functions, including attention and memory (Siegel et al., 2018). 

The two fundamental processes of the brain that produce cognitive behavior are primarily the separation and integration of brain functions(Rong Wang et al., 2021), but the relationship between extensive brain impairment and the separation and integration of functions in ANMDARE has rarely been reported. Independent processing of cognitive behaviors by subsystems with similar brain functions is de ned as functional separation, and communication between different subsystems is designated functional integration (Shine, 2019). Different cognitive tasks require different separation and integration, and the restingstate functional organization of the brain has the intrinsic capacity to support different cognitive processes (Braun et al., 2015). The functional balance between separation and integration of brain networks can be quanti ed by identifying disease modularity(Rong Wang et al., 2021). Therefore, we studied the modularity alterations in the resting state of patients with ANMDARE to provide a more global explanation for the confusion between abnormal brain regions and cognitive impairment in ANMDARE.
section_name: Introduction

section_name: Materials And Methods

section_content: Thirty ANMDARE patients who met the diagnostic criteria from the inpatient or outpatient neurology department of the First A liated Hospital of Guangxi Medical University between April 2019 and July 2022 were recruited for this study. During this period, thirty HCs from the community matched for sex and age were recruited. According to the ANMDARE diagnostic criteria (Deng et al., 2019; Graus et al., 2016), patients who conformed to one of the six symptoms, namely, psychiatric abnormalities or cognitive impairment, seizures, impaired consciousness, autonomic dysfunction or central hyperventilation, motor impairment or involuntary movements, speech impairment, and detection of ANMDAR antibodies in the cerebrospinal uid were enrolled in the ANMDARE group. Patients with the following conditions were excluded from the study: comorbid diagnostic de nite psychiatric disorders, inability to cooperate in performing the examination, substance abuse, signi cant abnormalities in MRI structures and antibodies other than ANMDARE detected in cerebrospinal uid or serum. All subjects provided written informed consent. The study protocol was reviewed and approved by the Ethics Committee of the First Hospital of Guangxi Medical University. The Montreal Cognitive Assessment (MoCA), Hamilton Depression Inventory (HAMD24) and Hamilton Anxiety Inventory (HAMA) were used to assess cognition, depression and anxiety status of all subjects, respectively.
section_name: Participants

section_content: An Achieva 3. 0 T magnetic resonance scanner (Philips, The Netherlands) was used to collect resting-state functional MRI (rs-fMRI) and T1-weighted (T1W) imaging of all subjects. The technique used was echo-planar imaging (EPI) for rs-fMRI. Rs-fMRI parameter sequences were repetition time (TR) = 2000 ms; eld of view (FOV) = 220 × 220 mm²; echo time (TE) = 30 ms, 31 or 41 slices, slice thickness = 3. 5 or 5 mm, voxel size = 3. 44 × 3. 44 × 6. 00 mm, ip angle = 90°, slice gap = 1 mm, scan time = 6 min or 7. 5 min. 3D turbine eld echo sequences were used to acquire T1W data: TR = 7. 8 ms; FOV = 256 × 256 mm²; TE = 3. 46 ms, matrix size = 256×256, slice thickness = 2 mm, voxel size = 0. 89 × 0. 89 × 1 mm, ip angle = 9°, scan time = 27. 6 min or 26. 1 min. To control the effects of head movement and noise, all subjects wore brake helmets and noise-cancelling headphones, comfort was not affected, and subjects were told to stay awake.
section_name: MRI data acquisition

section_content: The DPABI toolbox based on statistical parametric mapping (SPM12) was used for rs-fMRI data preprocessing, all running on the MATLAB R2018b platform. 

The speci c steps were as follows: (1) DICOM to NifTI: all image data in DICOM format were converted to NifTI les; (2) remove timepoints: removal of the rst 10 volumes that may be affected by magnetic elds and lose stability and form artifacts; (3) slice timing: control temporal differences between slices to improve image data comparability; (4) realign: head motion correction, subjects with head rotation < 3º or horizontal head movement > 3 mm were excluded;

(5) normalization: volumetric alignment to Montreal Neurological Institute (MNI) space with a resolution of 3 mm × 3 mm × 3 mm after alignment; (6) smooth: a Gaussian kernel of size 6 mm × 6 mm × 6 mm full width at half maximum (FWHM) was used for data smoothing; (7)
section_name: MRI data preprocessing

section_content: The DPABI toolkit was used to build a functional brain network consisting of nodes and connected edges. A brain region refers to a node, and the connected edges between nodes are the FC between two brain regions. The nodes used in this study were derived from the Automated Anatomical Labeling Atlas(Rolls et al., 2015) (AAL90) and included only brain regions other than the brainstem and cerebellum. The strength of FC was the Pearson correlation coe cient of the functional time series of individual brain regions. Each subject's negative FC was assigned 0 to construct an undirected weighted FC matrix (90×90).
section_name: Functional network construction

section_content: To ensure the stability and accuracy of community delineation and to facilitate statistical comparison between two groups, we used a modular similaritybased network thresholding approach (Yu et al., 2020) and a data-driven multi-iterative generalization of the Louvain community detection algorithm to construct a community delineation template at the holistic level (Lancichinetti & Fortunato, 2012; Nour et al., 2019). 

Network thresholds were obtained based on modularity similarity as follows: (1) A single FC connection matrix was obtained from each subject, and the group-level connectivity matrix (GCM) was obtained by obtaining the mean FC values within the group (i. e., ANMDARE and HCs). ( 2 ) Thresholding of GCMs using a sparsity of 10%-50% with an interval of 1% was performed to obtain two sets of a series of group-level functional networks (GFNs). (3) Cluster detection of GFNs was performed using the algorithm proposed by Newman. Modular Q represents the degree to which the brain network divides community clusters clearly and without overlap, and generally, 0. 3 < Q < 0. 7 represented the nonrandom cluster structure and indicated a suitable degree of division (Newman, 2004). ( 4 ) According to the modular similarity-based network thresholding method proposed by Yu, Zhinan et al., at the group level, when sparsity s was used, Ms denoted the module lead-in of the node, and the corresponding element in matrix As was set to 1 if two nodes were in the same module; otherwise, it was set to 0. (5) The matrices Cp,q were obtained by comparing two sparsities (e. g., p and q) of the modular structure similarity and calculating the value of d for the community structure. The magnitude of Q determined whether the complex network obtained a nonrandom and clear modular structure, and d represented the similarity of the obtained modular structure at different sparsities. A smaller value of d was determined if the modular structure was more stable at sparsity degrees p and q. Therefore, the sparsity where the lower continuous d value was located indicates that it was optimal for network thresholding. To facilitate comparative statistics between the two groups, the sparsity overlapped by the two lower d values was used for analysis. 

As shown in Fig. 1, the larger the sparsity is, the smaller the Q value, and the Q value of ANMDARE is less than 0. 3, corresponding to a sparsity of 32%. Therefore, only a threshold range of 10-32% (steps 0. 5%) was used to calculate modularity similarity. As shown in Fig. 2, the normalized d-value contour plots for the two threshold ranges show that relatively low d-values occur in the sparsity range of 23. 5%-27% for ANMDARE, while low d-values occur in the threshold range of 24. 5%-28% for HCs. This suggests that the two groups have superior modular similarity in the sparsity of low d values. Therefore, we used a common sparsity value of 25% for both groups for the analysis. 

Module structuring was performed using the MATLAB2018 Brain Connectivity Toolbox (BCT, https://sites. google. com/site/bctnet/) as follows: (1) An undirected weighted network was constructed for each subject using a sparsity value of 25%. 

(2) The Louvain community algorithm was run 1000 times for each matrix to obtain 1000 partitions. (3) At the individual subject level, the probability of each node belonging to the same community among 1000 community partitions was calculated, and the matrix D was constructed using the consensus function. (4) The threshold value τ = 0. 2 was applied to matrix D. 

The Louvain community algorithm was applied to this threshold of matrix D running 1000 times to obtain another 1000 partitions. ( 5 ) Steps ( 3 )-( 4 ) were repeated until the consensus matrix formed a stable template for community partitioning at the overall level.
section_name: Community partitions of the functional brain network

section_content: The obtained overall cohort partitioning template was used for each subject, and the area under the curve (AUC) for Q was calculated for each subject in the 10%-32% (1% step) sparsity range to compare the AUC differences between the two sparsity ranges and the Q values at 25% sparsity. 

Network-based statistics (NBS) (Zalesky et al., 2010) were used to compare network information from ANMDARE and HC modules: (1) Statistics were performed for each edge, and edges with p < 0. 05 were retained; (2) The zero distribution (null distribution) was constructed using the method of permutation repeated 5000 times; (3) Based on the null distribution, p values were calculated, and p values < 0. 025 (one-tailed) were the statistical criterion. The connection components with p < 0. 025 were retained. The average strength of intermodule interaction (ASMI) represents the ratio of the sum of the strength of the actual number of connected edges of a module to the total number of all connected edges between modules, and it describes the magnitude of the strength of information exchange between two modules. The operations are performed in MATLAB R2018b based on the GRETNA toolkit. This study compares the interactions between the two groups of modules at a sparsity threshold of 25%. 

The volume of the whole brain and each module

An association between brain volume and FC was explored. We performed voxelwise two-sample t tests at the whole-brain and modular levels to compare differences in gray matter volume between patient and control groups using age, sex, education, and whole-brain volume as covariates [P < 0. 05, corrected for FWE]. 

Finally, multiple linear regression was used to explore the relationship between brain volume and FC in each module, with brain volume as an independent variable, mean FC within the NBS component as the dependent variable, and age, gender, education, and whole brain volume as covariates for the analysis.
section_name: Modularity Q, intramodule communication and modular interaction

section_content: The AUC of global and regional FC network metrics in the sparsity range of 10%-32% (in steps of 1%) was calculated using the GRETNA toolkit. The metrics contain small-world attributes (including normalized clustering coe cients (Gamma, γ), normalized feature shortest path length (Lambda, λ), and small-world index (Sigma, σ = γ/λ). Finally, the module participation coe cient (PC module) with a sparsity of 25% was calculated. A two-sample t test was used to compare the differences between the two groups.
section_name: Topological analysis of FC networks

section_content: General demographic data analysis was performed using SPSS 23. 0 software, and data are reported as the means ± standard deviation (mean ± SD) or medians ( rst quartile ~ third quartile) (Median (Q1 ~ Q3)). The chi-square test was used for gender comparisons. Two-sample t tests or nonparametric tests were used to assess between-group differences in age, education, MoCA, HAMA, and HAMD24 scores. Finally, values of the above abnormal brain region indicators in ANMDARE were extracted, and age, education and sex were used as covariates for partial correlation analysis to determine the relationship between abnormal brain region indicators and disease duration, MoCA, HAMA and HAMD24 scores. p < 0. 05 indicates a statistically signi cant difference.
section_name: Statistical analysis

section_name: Results

section_content: Statistical analysis revealed no signi cant differences in age and sex between the two groups of subjects. However, they were signi cantly lower in years of education and MoCA scores than HCs. Additionally, the HAMD24 and HAMA scores were signi cantly higher than those of HCs (Table 1 ). a p value was obtained using a chi-square test. 

b p value was obtained using a Mann-Whitney U test.
section_name: Demographic, cognitive and neuropsychological assessment data

section_content: Using the overlapping network density (25%), as shown in Fig. 1, Q values of the GFN were observed to be > 0. 3 in both groups, belonging to a nonrandom community structure. Module partitioning results showed that the entire brain FC network could be divided into 4 modules at the overall group level (Fig. 3a, Supplementary Table 1 ).
section_name: Community partitions at a sparsity of 25%

section_content: Compared with HCs, there was no signi cant difference in for modular Q at 10%-32% sparsity and modular Q at 25% sparsity in patients with ANMDARE (Table 2 ). No signi cant change in ASMI values was observed in ANMDARE for modules 1 to 4 compared with those of HCs (Supplementary Table 2 ). 

However, in Module 4, intramodule FC was increased compared to that of HCs (P = 0. 021, Fig. 3b, Supplementary Fig. 1 ), and there was no difference in intramodule FC in Module 1-Module 3 between groups.
section_name: FC of brain network modularity changes in 25% sparsity

section_content: There no signi cant differences in whole brain volumes and module 1-3 volumes in the ANMDARE group compared to HCs (FEW correction not passed, Supplementary Table 3 ). Right precuneus form module 4 volumes were lower in the ANMDARE group than in HCs (Cluster = 95, p < 0. 001, uncorrected; p = 0. 023, FEW corrected, Cluster = 1, Supplementary Table 3 ). 

Multiple linear regression showed no correlation between the volume of the whole brain and module 4 with module 4 FC in the NBS component (Supplementary Table 4 ).
section_name: VBM and the relationship with FC

section_content: As shown in 3, only the PC module calculated for ANMDARE group at 25% sparsity was signi cantly different compared to that of the HCs, and no signi cant differences were found between the ANMDARE group and HCs for other topological properties (Supplementary Table 5 ).
section_name: Between-group differences in topological properties of the ANMDARE and HCs groups

section_content: The results of the partial correlation analysis showed that there was no signi cant correlation between mean FC within the module-related NBS component or PC module in brain regions and MoCA, disease duration, HAMD24 and HAMA scores (p > 0. 05).
section_name: Correlation analysis

section_content: In this study, a modularity-based similarity approach was used to determine intermodule and intramodule comparisons between the two groups at 25% sparsity. The results revealed that FC abnormalities in the ANMDARE group were concentrated in the parieto-occipito-temporal region and the parahippocampal gyrus compared to those of HCs. In addition, abnormal FC between module regions may suggest altered brain function in maintaining cognition in patients with ANMDARE. 

In the ANMDARE group, the parieto-occipito-temporal regions, as well as the parahippocampal gyrus, were divided into a module due to similar strength of FC and signi cantly higher FC in these regions than in the HCs. Combined with cognitive scores that were signi cantly lower than those of HCs and emotional de cit scores that were signi cantly higher than those of HCs, this suggests the presence of cognitive impairment and emotional de cits in patients with ANMDARE, which may be related to changes in brain function. In a study of the relationship between changes in modularity and cognitive function in a population of adolescents, it was found that both the increased e ciency of whole-brain functional networks and the maturation of cognitive function were associated with the development of modular topology; that is, stronger intramodule connections and weaker intermodule connections (Baum et al., 2017). Furthermore, close connections within modules are designed to ensure local brain activity, while sparse connections between modules limit the expansion of redundant information (Kaiser et al., 2007), and enhanced close connections within modules may be compensation made to maintain the e ciency of whole brain networks. Studies in individual mice have also con rmed that when the dissemination of functional information in brain regions mediating short-term memory is disrupted or blocked, redundant pathways come into play and ine ciently continue to maintain function in the damaged areas (Chen et al., 2021). 

This suggests that patients with ANMDARE may have intramodular dysfunction. 

The occipital lobe occupies a certain percentage of FC modules. There are multiple U-shaped ber connections between the occipital lobe and part of the temporoparietal cortex, including the occipital vertical tract, frontal suboccipital tract, and optic radiation, which are involved in object recognition, motion perception, and face recognition (Palejwala et al., 2020). Reduced occipital brain activity was found to be associated with memory and cognitive impairment by comparing changes in brain activity in patients with schizophrenia vs. HCs (Zhang et al., 2018). The occipital lobe plays an important role in visuospatial cognition, and the processing of two-dimensional visual space has been shown to rely on the regulation of connections between the occipital cortex and the posterior parietal lobe, suggesting a possible link between cognitive activity involving the occipital lobe and the parietal lobe (Beffara et al., 2022). When the gray matter volume of the occipital, temporal and frontal lobes is atrophied, it can cause cognitive impairment in Parkinson's patients (Chen et al., 2019) ; therefore, the abnormal FC of the parieto-occipital temporal lobe in patients with ANMDARE may cause cognitive alterations. 

In addition, the most signi cantly closer connections between the right cuneus and the left precuneus are found in abnormally connected brain regions. The ability to regulate negative emotions is reduced in the presence of functional impairment in the precuneus (Ferri et al., 2016), and damage to this region may be associated with emotional de cits in patients with ANMDARE. It has also been reported in the literature that cortical atrophy in the cuneus and parietooccipital regions is present in patients with hallucinations in Lewy body dementia(Delli Pizzi et al., 2014), and we speculate that this may be related to the presence of hallucinations as a clinical manifestation in some ANMDARE cases. 

In addition, the parahippocampal gyrus, located inferiorly within the occipitotemporal lobe, is an important structure in which the hippocampus functions, with extensive cortical connections that play a role in higher cognitive functions, including memory encoding and retrieval and visuospatial processing (Lin et al., 2021). The results of the present study only revealed abnormal connections between the parahippocampal gyrus and the occipitoparietal lobe, and we speculate that the visuospatial cognitive impairment in ANMDARE may be due to abnormal functional changes between the parahippocampal gyrus and parietooccipital lobe. 

This study also found that compared to HCs, the PC module of the right inferior frontal gyrus increased and that of the parietal and basal ganglia regions decreased. PC is a measure that describes the depth to which a single node is embedded in its local module according to the optimal community clusters, which is the ratio of the number of connections a node has within its module to the number of connections within the entire network. An increase in PC can suggest a decrease in the number of intramodule connections or an increase in the number of intermodule connections, while a decrease in PC is an increase in the PC value that can indicate a decrease in the number of connections within a module or an increase in the number of connections between modules, while a decrease in PC value is a quantitative indication of an increase in the number of connections within a module or a decrease in the number of connections between modules. The comparison of module Q values between the two groups in this study was not statistically signi cant. We believe that the modularity Q value, which generally indicates the criteria for obtaining nonrandom and clear community clumps, was not signi cantly different between the two groups, which suggests the existence of comparable module delineation between the two groups. In contrast, the exploration between the two groups of gray matter volumes aimed to regress the linear relationship between gray matter volume changes and functional connectivity, and the results only revealed statistically signi cant results for the precuneus, which was not representative of gray matter volume changes in this brain region due to the voxel size of 1. This suggests that there were no signi cant differences in volume between the subjects involved and that there was no linear correlation with changes in FC. Unexpectedly, however, there were no changes in brain network topology in the ANMDARE group, and in combination with the analysis of previous studies that did not nd global topological changes in this disease population, we speculate that brain network topology in patients with this disease still retains the more plastic and e cient smallworld properties (Li et al., 2021) and could bene t more from early cognitive recovery in the recovery period after timely treatment (Heine et al., 2021). However, as no longitudinal studies have con rmed this, further studies are still needed to repeat the validation. 

The shortcomings of this study still need to be addressed in future studies. First, the years of education were relatively low in the patient group, and years of education, age and gender were regressed as covariates in this study. Second, the sample size is insu cient, and the accuracy and sensitivity of the results are limited accordingly. Third, this study was conducted only on a cross-sectional basis and lacks the modular baseline status of this patient group. Finally, the cognitive recovery of patients with ANMDARE is time dependent, and this study lacks further analysis of the disease duration subgroup.
section_name: Discussion

section_content: By exploring the modularity of ANMDARE in the resting state, there are alterations in enhanced FC within modules and small changes in connectivity between modules that may suggest an association with cognitive impairment in this patient population and may provide some help in understanding the pathophysiological mechanisms of the disease from a global perspective.
section_name: Conclusions

section_content: Acknowledgements We would like to express our gratitude to AJE (https://china. aje. com/cn) for the professional language services provided. 

Funding This was supported by funds including National Natural Science Foundation of China ( 81560223, 81660225 ) and Guangxi Provincial Natural Science Foundation ( 2018GXNSFAA050149 ). 

Competing Interests The authors have no relevant nancial or non-nancial interests to disclose.

section_content: Data Availability Data used in the present study can be made available request to the primary contact author or the corresponding author.

section_name: Supplementary Files

section_content: Author Contributions Dongying Huang, Jinou Zheng designed the study. Dongying Huang, Zhekun Li and Caitiao Lv acquired the data. Dongying Huang, Caitiao Lv and Zexiang Chen analyzed and interpreted the data. Dongying Huang and Zhekun Li wrote the manuscript. All authors read and approved the nal manuscript. 

Ethics approval This research was approved by the Ethics Committee of the First A iated Hospital of Guangxi Medical University. 

Consent to participate Informed consent was obtained prior to study participation by all subjects. 

Consent for publication Participants consented to the submission of non-identifable data for publication.

section_content: This is a list of supplementary les associated with this preprint. Click to download. SupplementaryMaterialsforAbnormalitiesinmodularconnectivityoffunctionalbrainnetworksandthecognitivechangesinpatientswithAntiNmethylDaspartatere
section_name: Supplementary Files