Differentiation of Anti-NMDAR Encephalitis and Autoimmune Limbic Encephalitis Using Histogram Analysis based on Multiparametric MRI

Differentiation of Anti-NMDAR Encephalitis and Autoimmune Limbic Encephalitis Using Histogram Analysis based on Multiparametric MRI | 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 Differentiation of Anti-NMDAR Encephalitis and Autoimmune Limbic Encephalitis Using Histogram Analysis based on Multiparametric MRI Jinglan Wang, Qiu Bi, Wanmin Tan, Changshan Wu, Yaqi Yang, Chongming Song, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8504405/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 Objective: To evaluate the combined diagnostic value of clinical features, conventional MRI findings, and histogram metrics derived from multiparametric MRI (mp-MRI) in differentiating anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis from autoimmune limbic encephalitis (ALE). Methods: This retrospective study analyzed baseline clinical and brain MRI data from 76 anti-NMDAR encephalitis and 59 ALE patients. Bilateral hippocampi were manually delineated as regions of interest, and histogram metrics were extracted from fluid-attenuated inversion recovery (FLAIR), T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Clinical characteristics, conventional MRI features, and histogram metrics were compared between the two groups. Independent predictors were identified using logistic regression. Diagnostic models based on clinical variables, conventional MRI, and single-modality or multimodal histogram metrics were constructed and assessed using area under the curve (AUC). Results: Independent clinical predictors included age, prodromal symptoms, psychiatric or behavioral abnormalities, memory impairment, cerebrospinal fluid nucleated cell count, and elevated IgM levels (all P < 0.05). For conventional MRI, bilateral hippocampal volume and abnormal hippocampal signal served as independent predictors (all P < 0.05). The clinical model (AUC = 0.858), conventional MRI model (AUC = 0.770), and mp-MRI histogram model (AUC = 0.737) each outperformed single-modality histogram models. Integrating histogram metrics with clinical and conventional MRI features significantly enhanced diagnostic performance, with the combined mp-MRI model achieving the highest accuracy (AUC = 0.947). Conclusion: Histogram metrics derived from mp-MRI are promising biomarkers for differentiating anti-NMDAR encephalitis from ALE, and the integration with clinical and conventional MRI indicators in combined models significantly improves diagnostic accuracy. Anti-NMDAR encephalitis Autoimmune limbic encephalitis Magnetic resonance imaging Histogram analysis Differential diagnosis Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Autoimmune encephalitis (AE) comprises a group of neuroinflammatory disorders mediated by autoantibodies. As an increasingly important condition in clinical practice, its incidence has risen markedly in recent years[ 1 ]. Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and autoimmune limbic encephalitis (ALE) represent two major subtypes of AE[ 2 , 3 ]. In the early stages of disease, both often present with overlapping symptoms, such as psychiatric manifestations and seizures, which complicates differential diagnosis[ 4 – 6 ]. Furthermore, anti-NMDAR encephalitis is predominantly associated with ovarian teratomas and typically responds well to immunotherapy and tumor resection, whereas ALE is more frequently linked to malignancies such as small cell lung cancer or thymoma[ 7 , 8 ]. Accurate differentiation therefore holds critical clinical significance, as these conditions differ considerably in etiology, therapeutic strategies, and prognosis[ 9 ]. Imaging abnormalities on conventional MRI are relatively uncommon in anti-NMDAR encephalitis; when present, they may manifest as diffuse cortical or subcortical hyperintensities on T2-FLAIR sequences. In contrast, ALE more often exhibits unilateral or bilateral involvement of the hippocampus or basal ganglia[ 10 ]. As one of the regions with the highest density of NMDA receptor expression, the hippocampus has been a key region of interest (ROI) in prior imaging studies of anti-NMDAR encephalitis[ 11 , 12 ]. Conventional MRI plays an important role in the diagnosis of AE and detection of structural abnormalities, yet its utility in differentiating specific subtypes remains limited, especially in atypical presentations. Histogram analysis, an objective and reproducible imaging technique, quantitatively describes the grayscale distribution of voxel intensities within a given ROI, thereby reflecting microstructural and pathophysiological changes[ 13 ]. In recent years, it has shown promising value in tumor characterization, grading, and prognosis prediction[ 13 – 15 ]. However, its application in differentiating encephalitis subtypes has been rarely explored. Therefore, this study aims to integrate clinical features, conventional MRI characteristics, and histogram metrics derived from multiparametric MRI (mp-MRI) to develop both single-modality and multimodal diagnostic models for differentiating anti-NMDAR encephalitis from ALE. 2. Materials and Methods 2.1 Study Participants This retrospective study was approved by the local ethics review committee, and the requirement for written informed consent was waived. A total of 157 patients diagnosed with either anti-NMDAR encephalitis or ALE at our institution between March 2017 and August 2023 were screened. The inclusion criteria were: (1) meeting the diagnostic criteria for anti-NMDAR encephalitis and ALE proposed by Graus and Dalmau in 2016[ 16 ]; and (2) availability of complete MRI examinations, including diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. Exclusion criteria were as follows: (1) coexisting central nervous system disorders (e.g., cerebral infarction, tumors, etc.); (2) incomplete clinical data; and (3) suboptimal MRI image quality. Specifically, 8 patients were excluded due to concomitant neurological diseases, 11 due to insufficient clinical information, and 3 due to poor image quality. Ultimately, 135 patients were enrolled, including 76 with anti-NMDAR encephalitis and 59 with ALE. 2.2 Clinical Data Demographic information (age and sex) and clinical characteristics were collected for all participants. Clinical variables included prodromal symptoms and major neurological manifestations such as seizures, disturbances of consciousness, cognitive dysfunction, psychiatric or behavioral abnormalities, speech impairment, movement disorders, and memory decline. Initial laboratory findings at admission were also recorded, including: (1) hematological parameters—white blood cell (WBC) count, neutrophil count, monocyte count, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and serum sodium levels; and (2) cerebrospinal fluid (CSF) parameters—intracranial pressure, nucleated cell count, protein, glucose, chloride, creatine kinase (CPK), lactate dehydrogenase (LDH), and immunoglobulin levels (IgA, IgG, IgM). 2.3 MRI Acquisition All MRI examinations were performed using 1.5 T scanners (Ingenia, Philips Healthcare, Netherlands; or Signa Artist, GE Healthcare, USA), each equipped with an 8-channel head coil. Each participant underwent routine cranial MRI in the supine position, including axial T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and DWI sequences (b = 0 and 1000 s/mm²). For Philips systems, ADC maps were automatically generated on the console, whereas for GE systems, ADC maps were reconstructed on the AW post-processing workstation. Due to the extended study interval and the use of multiple MRI platforms, acquisition parameters varied slightly; detailed parameters are provided in Supplementary Table S1 . 2.4 Image Analysis Two neuroradiologists with 15 and 3 years of experience independently reviewed and interpreted all MRI data, blinded to clinical subtype. Conventional MRI characteristics were assessed and documented, including lesion distribution (frontal, parietal, temporal, and occipital lobes; insula; periventricular region; thalamus; basal ganglia; hippocampus; amygdala; cerebellum) and enhancement patterns (lesional, leptomeningeal, or dural enhancement). Prior to histogram analysis, all images were preprocessed using 3D Slicer (version 5.8.1, https://www.slicer.org/ ) with intensity inhomogeneity correction and z-score standardization. A neuroradiologist with 3 years of experience manually delineated bilateral hippocampal regions of interest (ROIs) on axial images from T2WI, FLAIR, and ADC sequences using FireVoxel software (version 476, https://www.firevoxel.org/ ) , as illustrated in Figs. 1 and 2 . Extracted histogram metrics included hippocampal volume (from T2WI) and the following for each sequence: minimum, maximum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy. 2.5 Statistical Analysis Statistical analyses were conducted using SPSS Statistics (Version 27.0, IBM) and GraphPad Prism (Version 10.1.2). A P -value < 0.05 was considered statistically significant. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequency (percentage). The Kolmogorov–Smirnov test was used to assess normality. Depending on data distribution, independent-sample t-tests or Mann–Whitney U tests were used to compare continuous variables, and Chi-square or Fisher’s exact tests were used for categorical variables. Univariate and multivariate logistic regression analyses were performed to identify independent predictors o f anti-NMDAR encephalitis versus ALE to guide model development. Model performance was evaluated using receiver operating characteristic (ROC) curves, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off values (determined by the maximum Youden index) were calculated. 3. Results 3.1 Clinical Features The mean age of patients with anti-NMDAR encephalitis (30 ± 17 years) was significantly lower than that of patients with ALE (45 ± 18 years; P < 0.001). Significant between-group differences were observed in age, prodromal symptoms, psychiatric or behavioral abnormalities, memory decline, WBC count, NLR, CSF nucleated cell count, and elevated CSF IgM levels (all P < 0.05). No statistically significant differences were found for sex, seizures, disturbances of consciousness, cognitive dysfunction, speech disorders, movement disorders, MLR, sodium levels, intracranial pressure, or other CSF parameters (protein, glucose, chloride, CPK, LDH, IgA, or IgG) (all P > 0.05). A comprehensive summary of demographic and clinical characteristics is presented in Table 1 . 3.2 Conventional MRI Characteristics Bilateral hippocampal volumes were significantly larger in the anti-NMDAR encephalitis group [5.451 (4.699–6.215) cm³] compared with the ALE group [4.901 (4.121–5.591) cm³] ( P = 0.003). In contrast, the proportion of hippocampal and amygdala abnormalities was significantly higher in the ALE group than in the anti-NMDAR encephalitis group (all P 0.05). Among the 76 patients with anti-NMDAR encephalitis, 74 underwent contrast-enhanced MRI; among the 59 patients with ALE, 50 underwent contrast-enhanced MRI. No significant between-group differences were identified in lesional enhancement, leptomeningeal enhancement, or dural enhancement (all P > 0.05). Detailed conventional MRI findings for both groups are summarized in Table 2 . 3.3 Histogram Metrics Comparisons of histogram metrics extracted from T2WI, FLAIR, and ADC sequences are presented in Figure 3 and Supplementary Table S1 . For histogram metrics derived from FLAIR, the anti-NMDAR encephalitis group demonstrated significantly higher minimum values but lower SD, variance, and CV compared with the ALE group (all P < 0.05). For histogram metrics derived from T2WI, the anti-NMDAR encephalitis group similarly exhibited significantly lower SD, variance, and CV than the ALE group (all P < 0.05). For histogram metrics derived from ADC, minimum values and entropy were significantly higher in the anti-NMDAR encephalitis group (both P < 0.05). Overall, dispersion metrics (SD, variance, CV) on FLAIR and T2WI were consistently lower in the anti-NMDAR encephalitis group, indicating greater hippocampal signal homogeneity. Higher minimum values and entropy on ADC may reflect differing water diffusion properties between groups. No significant differences were found in other metrics—such as skewness, kurtosis, or most percentile values (all P > 0.05). 3.4 Model Construction and Performance Comparison The results of univariate and multivariate logistic regression for clinical, conventional MRI, and MRI histogram metrics are listed in Tables 3 to Table 5 , respectively. Independent clinical predictors of anti-NMDAR encephalitis versus ALE included age, prodromal symptoms, psychiatric or behavioral abnormalities, memory decline, CSF nucleated cell count, and elevated IgM levels (all P < 0.05) (Table 3). Bilateral hippocampal volume and hippocampal MRI abnormalities were identified as independent conventional MRI predictors (all P < 0.05) (Table 4).For histogram models, CV served as the independent predictor for both FLAIR and T2WI-based models (all P < 0.05), while minimum value and entropy were identified as independent predictors in the ADC-based model (all P < 0.05) (Table 5). Diagnostic performance metrics and ROC curves for all predictive models are presented in Table 6 and Figure 4 , respectively. The clinical model (AUC = 0.858) and the conventional MRI model (AUC = 0.770) performed better than the individual histogram models (FLAIR: 0.671; T2WI: 0.604; ADC: 0.679; mp-MRI: 0.737). When the clinical and conventional MRI models were each combined with the four histogram models, diagnostic performance improved markedly (AUCs: 0.899, 0.896, 0.936, and 0.947). The mp-MRI combined model achieved the highest diagnostic accuracy among all models. 3.5 Subgroup Analysis Subgroup analyses were conducted using histogram models constructed separately for the left and right hippocampi. Comparisons of histogram metrics for each hippocampus across MRI sequences are provided in Supplementary Tables S3 and S4 . Corresponding univariate and multivariate logistic regression results are shown in Supplementary Tables S5 and S6 . Except for the ADC histogram model based on the right hippocampus—where CV and Perc.75 were identified as independent predictors—the findings were consistent with those derived from bilateral hippocampal analyses. Predictive models constructed using these independent predictors demonstrated that mp-MRI histogram models generally provided superior performance across subgroups ( Supplementary Table S7, Supplementary Figure S1 ). However, no statistically significant differences in AUCs were observed among the subgroup models ( Supplementary Figure S2 ). 4. Discussion This study identified several independent predictors for differentiating anti-NMDAR encephalitis from ALE, including clinical features such as age, prodromal symptoms, and elevated IgM levels, as well as bilateral hippocampal volume, and hippocampal abnormalities on conventional MRI. In terms of diagnostic performance, clinical models, conventional MRI models, and mp-MRI histogram models all outperformed single-modality histogram models, with the mp-MRI combined model achieving the highest discriminative accuracy. To our knowledge, this is the first study to comprehensively evaluate the combined diagnostic value of clinical features, conventional neuroimaging findings, and multi-sequence MRI histogram metrics in differentiating anti-NMDAR encephalitis from ALE. These results may contribute to improving early diagnostic accuracy, refining subtype classification, and supporting tailored clinical management. Consistent with previous studies, patients with anti-NMDAR encephalitis in our cohort were generally younger and presented with a higher frequency of psychiatric behavioral abnormalities, whereas ALE patients were older and more likely to exhibit memory decline[ 4 – 6 ]. Anti-NMDAR encephalitis also demonstrated a higher incidence of infectious prodromal symptoms, such as headache and fever, possibly related to infection-associated triggers. Prior research has shown that herpes simplex virus encephalitis can evolve into AE, particularly anti-NMDAR encephalitis[ 17 ]. Although NLR and MLR have been proposed as inflammatory biomarkers in immune-related diseases[ 18 ], these markers did not differ between the two groups in our study, suggesting insufficient specificity for differential diagnosis. Interestingly, anti-NMDAR encephalitis was associated with elevated CSF IgM levels and higher CSF nucleated cell counts, differing from prior findings comparing AE with viral encephalitis[ 19 ]. These differences may reflect distinct neuroinflammatory pathways among encephalitis subtypes and highlight the value of integrating CSF immune markers into differential diagnosis. Previous research indicates that both anti-NMDAR encephalitis and ALE may involve the hippocampus, and many MRI-based or deep learning studies have used the hippocampus as a region of interest with promising results[ 11 , 12 ]. In our study, hippocampal and amygdalar abnormalities were more frequent in ALE, consistent with its clinical presentation dominated by memory impairment[ 20 ]. Notably, anti-NMDAR encephalitis is often characterized by functional network disruptions—such as altered hippocampal connectivity—whereas ALE more commonly displays structural T2 hyperintensities in the limbic system[ 6 , 21 , 22 ]. These findings underscore differences in underlying pathological mechanisms despite shared anatomical involvement. Although leptomeningeal enhancement was observed in a subset of both patient groups, consistent with prior studies[ 23 ], it did not provide diagnostic value in differentiating the two conditions. Given the small sample size, however, the possibility of its discriminative potential warrants further investigation[ 24 ]. In addition, the significantly smaller hippocampal volumes in the ALE group are consistent with reports of neuronal loss and hippocampal atrophy in ALE[ 25 ] and may also relate to older age and differing disease stages. Conventional MRI findings in anti-NMDAR encephalitis are frequently normal or only mildly abnormal[ 26 ], and some patients with ALE may also have negative imaging results[ 27 ]. Histogram analysis, by quantifying voxel intensity characteristics, provides an objective method to detect subtle changes not readily apparent on routine imaging. Its utility has been demonstrated in tumor grading, disease classification, and prognosis estimation[ 15 , 28 ]. In our study, histogram analysis revealed important differences in signal homogeneity and water diffusion properties between the two conditions. The anti-NMDAR encephalitis group showed lower dispersion metrics (SD, variance, CV) on T2WI and FLAIR, suggesting more homogeneous hippocampal signal intensity. This may reflect the predominantly functional and synaptic impairments, as well as diffuse edema, seen in anti-NMDAR encephalitis rather than focal tissue destruction[ 29 ]. In contrast, ALE demonstrated lower ADC minimum values, suggestive of more severe focal cellular degeneration, necrosis, or dense inflammatory infiltration, consistent with its characteristic pathological features[ 30 ]. The lower ADC entropy observed in ALE suggests a more uniform microstructural pattern despite the macroscopic heterogeneity seen on T2WI/FLAIR. Conversely, anti-NMDAR encephalitis—despite appearing macroscopically homogeneous—exhibited higher ADC entropy, indicating more complex microstructural alterations that may reflect widespread immune-mediated neuronal dysfunction[ 31 , 32 ]. These findings support a conceptual distinction: ALE is characterized by macroscopic heterogeneity but microscopically homogeneous structural injury[ 30 ], whereas anti-NMDAR encephalitis shows macroscopic homogeneity but microscopically heterogeneous functional or synaptic pathology[ 33 , 34 ]. By combining clinical indicators, conventional MRI features, and mp-MRI histogram metrics, diagnostic performance improved significantly. This aligns with the current trend toward multimodal imaging analysis, in which quantitative features enhance diagnostic power beyond qualitative assessment[ 35 , 36 ]. Although previous studies have reported lateralization differences in hippocampal structure or function[ 37 – 39 ], our subgroup analyses did not identify significant AUC differences between left- and right-based models, suggesting that hippocampal lateralization does not materially affect differentiation between anti-NMDAR encephalitis and ALE. This finding is consistent with the bilateral hippocampal involvement reported in some clinical cases[ 40 ] and indicates that future research should focus on microstructural imaging biomarkers rather than lateralized patterns. Despite these strengths, several limitations must be acknowledged. First, this was a single-center retrospective study with a relatively small sample size, which may introduce selection bias. Large-scale, multicenter prospective studies are required to validate these findings in the future. Second, although imaging data underwent standardized preprocessing, variability in MRI scanners and protocols may still affect model performance. Finally, manual hippocampal segmentation may introduce subjective variability; future work incorporating automated segmentation techniques may improve reproducibility and efficiency. 5. Conclusion Histogram analysis offers a promising approach for differentiating anti-NMDAR encephalitis from ALE, and may serve as a valuable imaging biomarker. Compared with single-modality analysis, multimodal assessment more comprehensively captures heterogeneous hippocampal characteristics and provides stronger predictive and discriminative capabilities. When combined with clinical indicators and conventional MRI findings, histogram features significantly enhance diagnostic performance, yielding high sensitivity and specificity that support early diagnosis and precise disease management. Future multicenter studies and advancements in automated imaging analysis are expected to further strengthen the utility of MRI histogram analysis in differentiating autoimmune encephalitis subtypes and contribute to more reliable clinical decision-making. Declarations This retrospective study has been approved by the local ethics committee. Consent to participate Written informed consent was waived because of the retrospective nature of the study. Consent for publication Written informed consent for publication was obtained from all participants. Competing interests The authors declare no competing interests. Author Contribution J. Wang wrote the initial draft..Q. Bi designed the study..W. Tan collected the data..C.Wu collected the data..Y. Yang collected the data..C. Song analyzed the data..S.Liu analyzed the data..L. Wu designed the study..All authors reviewed and approved the final manuscript.. References Segal Y, Rotschild O, Mina Y, Maayan Eshed G, Levinson T, Paran Y, et al (2024) Epidemiology of autoimmune encephalitis and comparison to infectious causes—Experience from a tertiary center. Ann Clin Transl Neurol 11:2337–2349. https://doi.org/10.1002/acn3.52147 Dalmau J, Graus F (2018) Antibody-Mediated Encephalitis. N Engl J Med 378:840–851. https://doi.org/10.1056/NEJMra1708712 Van Steenhoven RW, de Vries JM, Bruijstens AL, Paunovic M, Nagtzaam MM, Franken SC, et al (2023) Mimics of Autoimmune Encephalitis. Neurol Neuroimmunol Neuroinflamm 10:e200148. https://doi.org/10.1212/NXI.0000000000200148 Dalmau J, Armangué T, Planagumà J, Radosevic M, Mannara F, Leypoldt F, et al (2019) An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: mechanisms and models. Lancet Neurol 18:1045–1057. https://doi.org/10.1016/S1474-4422(19)30244-3 Aem B, Maam de B, Sl S, E M-H, J B, M P, et al (2021) Anti-NMDAR Encephalitis in the Netherlands, Focusing on Late-Onset Patients and Antibody Test Accuracy. Neurology(R) neuroimmunology & neuroinflammation 9:. https://doi.org/10.1212/NXI.0000000000001127 Zhang T-Y, Cai M-T, Zheng Y, Lai Q-L, Shen C-H, Qiao S, et al (2021) Anti-Alpha-Amino-3-Hydroxy-5-Methyl-4-Isoxazolepropionic Acid Receptor Encephalitis: A Review. Front Immunol 12:652820. https://doi.org/10.3389/fimmu.2021.652820 Titulaer MJ, McCracken L, Gabilondo I, Armangué T, Glaser C, Iizuka T, et al (2013) Treatment and prognostic factors for long-term outcome in patients with anti-N-Methyl-D-Aspartate (NMDA) receptor encephalitis: a cohort study. Lancet Neurol 12:157–165. https://doi.org/10.1016/S1474-4422(12)70310-1 Radja GK, Cavanna AE (2013) Treatment of VGKC complex antibody-associated limbic encephalitis: a systematic review. J Neuropsychiatry Clin Neurosci 25:264–271. https://doi.org/10.1176/appi.neuropsych.13020022 Masciocchi S, Businaro P, Scaranzin S, Morandi C, Franciotta D, Gastaldi M (2024) General features, pathogenesis, and laboratory diagnostics of autoimmune encephalitis. Crit Rev Clin Lab Sci 61:45–69. https://doi.org/10.1080/10408363.2023.2247482 Yu X, Fang Y, Sun L, Yang J, Hong J-S, Sun B, et al (2025) Imaging biomarkers in antibody-mediated autoimmune encephalitis. Quant Imaging Med Surg 15:8372–8394. https://doi.org/10.21037/qims-2025-131 Xiang Y, Zeng C, Liu B, Tan W, Wu J, Hu X, et al (2022) Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI. J Magn Reson Imaging 55:1082–1092. https://doi.org/10.1002/jmri.27909 Xiang Y, Dong X, Zeng C, Liu J, Liu H, Hu X, et al (2022) Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China. Front Immunol 13:913703. https://doi.org/10.3389/fimmu.2022.913703 Liu X, Han T, Wang Y, Liu H, Sun Q, Xue C, et al (2024) Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas. Neuroradiology 66:531–541. https://doi.org/10.1007/s00234-024-03319-w Sun Y, Wang C (2024) Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions. Comput Biol Med 172:108196. https://doi.org/10.1016/j.compbiomed.2024.108196 Kang Y, Choi SH, Kim Y-J, Kim KG, Sohn C-H, Kim J-H, et al (2011) Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High- b -Value Diffusion-weighted MR Imaging—Correlation with Tumor Grade. Radiology 261:882–890. https://doi.org/10.1148/radiol.11110686 Graus F, Titulaer MJ, Balu R, Benseler S, Bien CG, Cellucci T, et al (2016) A clinical approach to diagnosis of autoimmune encephalitis. Lancet Neurol 15:391–404. https://doi.org/10.1016/S1474-4422(15)00401-9 Armangue T, Spatola M, Vlagea A, Mattozzi S, Cárceles-Cordon M, Martinez-Heras E, et al (2018) Frequency, syndromes, risk factors, and outcome of autoimmune encephalitis following herpes simplex encephalitis: a prospective observational study and a retrospective analysis of cases. Lancet Neurol 17:760–772. https://doi.org/10.1016/S1474-4422(18)30244-8 Liu Z, Li Y, Wang Y, Zhang H, Lian Y, Cheng X (2022) The Neutrophil-to-Lymphocyte and Monocyte-to-Lymphocyte Ratios Are Independently Associated With the Severity of Autoimmune Encephalitis. Front Immunol 13:911779. https://doi.org/10.3389/fimmu.2022.911779 Hu X, Cheng S (2024) The role of immunoglobulin in cerebrospinal fluid on the differential diagnosis of autoimmune encephalitis and viral encephalitis in children. BMC Pediatr 24:386. https://doi.org/10.1186/s12887-024-04824-w Mueller C, Langenbruch L, Rau JMH, Brix T, Strippel C, Dik A, et al (2022) Neuropsychological Performance in Autoimmune Limbic Encephalitis: Evidence from an Immunotherapy-Naïve Cohort. Arch Clin Neuropsychol 37:738–752. https://doi.org/10.1093/arclin/acac001 Hartung TJ, Cooper G, Jünger V, Komnenić D, Ryan L, Heine J, et al (2024) The T1-weighted/T2-weighted ratio as a biomarker of anti-NMDA receptor encephalitis. J Neurol Neurosurg Psychiatry 95:366–373. https://doi.org/10.1136/jnnp-2023-332069 Kuchling J, Jurek B, Kents M, Kreye J, Geis C, Wickel J, et al (2024) Impaired functional connectivity of the hippocampus in translational murine models of NMDA-receptor antibody associated neuropsychiatric pathology. Mol Psychiatry 29:85–96. https://doi.org/10.1038/s41380-023-02303-9 Chang H, Ma J, Feng K, Feng N, Wang X, Sun J, et al (2023) Elevated blood and cerebrospinal fluid biomarkers of microglial activation and blood‒brain barrier disruption in anti-NMDA receptor encephalitis. J Neuroinflammation 20:172. https://doi.org/10.1186/s12974-023-02841-7 Neo S, Yeo T, Chen Z, Ngiam NHW, Lim ET-X, Tan K, et al (2020) Acute radiological features facilitate diagnosis and prognosis of anti-N-methyl-d-aspartate receptor (NMDAR) and anti-voltage-gated potassium channel (VGKC) encephalitis in adults. J Neurol Sci 419:117216. https://doi.org/10.1016/j.jns.2020.117216 Sapana T, Li W, Tian F, Yan W, Dou B, Hua S, et al (2023) A case report of anti-GAD65 antibody-positive autoimmune encephalitis in children associated with autoimmune polyendocrine syndrome type-II and literature review. Front Immunol 14:1274672. https://doi.org/10.3389/fimmu.2023.1274672 Khatib L, Pique J, Ciano-Petersen NL, Criton G, Birzu C, Aubart M, et al (2025) Abnormal Brain MRI in Anti-NMDA Receptor Encephalitis: Clinical and Prognostic Implications. Neurology® Neuroimmunology & Neuroinflammation 12:e200378. https://doi.org/10.1212/NXI.0000000000200378 Yuan L, Li X, Cui T, Chen Q, Ai L (2025) Uncommon Bilateral Frontoparietal Cortex Hypermetabolism Revealed by 18 F-FDG PET/CT in a Patient With Anti-LGI1 Encephalitis. Clin Nucl Med 50:951–952. https://doi.org/10.1097/RLU.0000000000005824 Su Y, Cheng R, Guo J, Zhang M, Wang J, Ji H, et al (2024) Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis. BMC Cancer 24:805. https://doi.org/10.1186/s12885-024-12571-5 Ding Y, Zhou Z, Chen J, Peng Y, Wang H, Qiu W, et al (2021) Anti-NMDAR encephalitis induced in mice by active immunization with a peptide from the amino-terminal domain of the GluN1 subunit. J Neuroinflammation 18:53. https://doi.org/10.1186/s12974-021-02107-0 Pitsch J, van Loo KMJ, Gallus M, Dik A, Kamalizade D, Baumgart A-K, et al (2021) CD8+ T-Lymphocyte-Driven Limbic Encephalitis Results in Temporal Lobe Epilepsy. Ann Neurol 89:666–685. https://doi.org/10.1002/ana.26000 Bai Y, Liu Z, Qian T, Peng Y, Ma H, Hu H, et al (2023) Single-nucleus RNA sequencing unveils critical regulators in various hippocampal neurons for anti-N-methyl-D-aspartate receptor encephalitis. Brain Pathol 33:e13156. https://doi.org/10.1111/bpa.13156 Fortea A, Ortuño M, Masias M, Guasp M, De la Serna E, Armangue T, et al (2025) Brain Metabolite Levels in the Post-Acute Stage of Anti-NMDA Receptor Encephalitis and Schizophrenia: A Longitudinal Case-Control Study. Biol Psychiatry S0006-3223(25)01316–2. https://doi.org/10.1016/j.biopsych.2025.07.006 Jamet Z, Mergaux C, Meras M, Bouchet D, Villega F, Kreye J, et al (2024) NMDA receptor autoantibodies primarily impair the extrasynaptic compartment. Brain 147:2745–2760. https://doi.org/10.1093/brain/awae163 Gong X, Li W, Luo C, Tian Y, Wang J, Gao Y, et al (2025) Neurochemical and molecular characteristics of altered brain functional activity in the anti-NMDAR encephalitis. Neurobiol Dis 215:107050. https://doi.org/10.1016/j.nbd.2025.107050 Gugger JJ, Kulick-Soper CV, Sinha N, Jaskir M, Hadar PN, Josyula M, et al (2025) Evaluation of limbic microstructural abnormalities in temporal lobe epilepsy: A neurite orientation distribution and density imaging study. Epilepsia. https://doi.org/10.1111/epi.18488 Lam HW, Patodia S, Zeicu C, Lim YM, Mrzyglod A, Scott C, et al (2024) Quantitative cellular pathology of the amygdala in temporal lobe epilepsy and correlation with magnetic resonance imaging volumetry, tissue microstructure, and sudden unexpected death in epilepsy risk factors. Epilepsia 65:2368–2385. https://doi.org/10.1111/epi.18033 Peyvandi Karizbodagh M, Sadr-Nabavi A, Hami J, Mohammadipour A, Khoshdel-Sarkarizi H, Kheradmand H, et al (2021) Developmental regulation and lateralization of N-methyl-d-aspartate (NMDA) receptors in the rat hippocampus. Neuropeptides 89:102183. https://doi.org/10.1016/j.npep.2021.102183 Hänsel M, Reichmann H, Haehner A, Schmitz-Peiffer H, Schneider H (2025) Hippocampal dysfunction after autoimmune encephalitis depending on the antibody type. J Neurol 272:175. https://doi.org/10.1007/s00415-024-12742-1 Nemati SS, Sadeghi L, Dehghan G, Sheibani N (2023) Lateralization of the hippocampus: A review of molecular, functional, and physiological properties in health and disease. Behav Brain Res 454:114657. https://doi.org/10.1016/j.bbr.2023.114657 Qin X, Yang H, Zhu F, Wang Q, Shan W (2021) Clinical Character of CASPR2 Autoimmune Encephalitis: A Multiple Center Retrospective Study. Front Immunol 12:652864. https://doi.org/10.3389/fimmu.2021.652864 Tables Tables 1 to 6 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files supplementary.docx Table1to6.docx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8504405","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":575091248,"identity":"dd65d950-2d51-4745-b974-56a6060a7fb8","order_by":0,"name":"Jinglan Wang","email":"","orcid":"","institution":"First Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jinglan","middleName":"","lastName":"Wang","suffix":""},{"id":575091249,"identity":"12a7c6e8-65b8-40c8-824f-db44a06059a9","order_by":1,"name":"Qiu Bi","email":"","orcid":"","institution":"First People's Hospital of Yunnan 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Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYFAC5oYDHwwk5NjY2w8Qq4Wx4eCMAgtjPp4zCcRrYeb5UJE4T8LBgDgNBjcSGw/wGEikt0kwJDD8qNhGlJaGAxIGErlt0o0HGHvO3CasxQykxQCkReZAAjNjG7FaEoAOY5NIMCBBywEDiQTitdifedhwsMFAwrANGMgHifKLZHvy4c9//tTJy7e3H3zwo4IILSjgAInqR8EoGAWjYBTgAgAyD0AyXRLpzwAAAABJRU5ErkJggg==","orcid":"","institution":"First People's Hospital of Yunnan Province","correspondingAuthor":true,"prefix":"","firstName":"Li","middleName":"","lastName":"Wu","suffix":""}],"badges":[],"createdAt":"2026-01-03 04:53:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8504405/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8504405/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":100560934,"identity":"28af2928-876a-4bd9-90b4-ae9ef3bca5cf","added_by":"auto","created_at":"2026-01-19 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08:43:54","extension":"xml","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128724,"visible":true,"origin":"","legend":"","description":"","filename":"3891e79ee6b24a57baee8202f89eb0571structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/dbdd281b686234bed97e48de.xml"},{"id":100560861,"identity":"5332f9a4-fa1d-4e41-ba49-f5b9b76a847a","added_by":"auto","created_at":"2026-01-19 08:43:51","extension":"html","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":141615,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/a7c5ebbfee34370d597b84e0.html"},{"id":100561347,"identity":"b8f6fcfe-6b3e-4dc3-99f4-595b8a3df8d6","added_by":"auto","created_at":"2026-01-19 08:43:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":368766,"visible":true,"origin":"","legend":"\u003cp\u003eA 15-year-old female with anti-NMDAR encephalitis. The bilateral hippocampus appears normal on conventional MRI axial images (A-C), with green areas representing the regions of interest (ROIs) of bilateral hippocampus (D-F), and histograms of FLAIR, T2WI, and ADC (G-I).\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/19975d47d4b5064d5565724d.png"},{"id":100561360,"identity":"ff57cc64-bb63-4006-83d2-297b5d99fb51","added_by":"auto","created_at":"2026-01-19 08:44:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":382571,"visible":true,"origin":"","legend":"\u003cp\u003eA 33-year-old male with anti-LGI1 encephalitis. The bilateral hippocampus appears abnormal on conventional MRI axial images (A-C), with green areas representing the regions of interest (ROIs) of bilateral hippocampus (D-F), and histograms of FLAIR, T2WI, and ADC (G-I).\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/6e92bfc9878454b7aa148dd3.png"},{"id":100560871,"identity":"f30fb196-72cc-4c1e-be3f-3941e553147a","added_by":"auto","created_at":"2026-01-19 08:43:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":205044,"visible":true,"origin":"","legend":"\u003cp\u003eBox diagrams of MRI histograms characteristics for bilateral hippocampal derived from different MRI sequences between the Limbic encephalitis group and the Anti-NMDAR encephalitis group. \u0026nbsp;*Indicates significantly different.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/8d7623ac20cdcf5ea183e03b.png"},{"id":100561341,"identity":"f9a0772f-38a0-4028-a6b5-cf9dd261c720","added_by":"auto","created_at":"2026-01-19 08:43:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":21471,"visible":true,"origin":"","legend":"\u003cp\u003eReceiver operating characteristic curves of individual models (A、B) and combined models (C).\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/1e006735fdc1ee2726a829f8.png"},{"id":102131775,"identity":"4b747bd7-6fb4-4a0b-9d18-2127d2b4db86","added_by":"auto","created_at":"2026-02-08 06:24:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1708550,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/07918bf0-1da1-472d-a42d-00040a2b6be0.pdf"},{"id":100561370,"identity":"68afb704-ffe0-438a-8a64-4e71072ee9e1","added_by":"auto","created_at":"2026-01-19 08:44:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":182749,"visible":true,"origin":"","legend":"","description":"","filename":"supplementary.docx","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/36b3767844576d0dbd7f5222.docx"},{"id":100594720,"identity":"2ed00636-2903-4596-971c-6622af4fc1f5","added_by":"auto","created_at":"2026-01-19 13:44:22","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":24361,"visible":true,"origin":"","legend":"","description":"","filename":"Table1to6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8504405/v1/0a72bf35e4b11a0d87486222.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Differentiation of Anti-NMDAR Encephalitis and Autoimmune Limbic Encephalitis Using Histogram Analysis based on Multiparametric MRI ","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAutoimmune encephalitis (AE) comprises a group of neuroinflammatory disorders mediated by autoantibodies. As an increasingly important condition in clinical practice, its incidence has risen markedly in recent years[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis and autoimmune limbic encephalitis (ALE) represent two major subtypes of AE[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In the early stages of disease, both often present with overlapping symptoms, such as psychiatric manifestations and seizures, which complicates differential diagnosis[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, anti-NMDAR encephalitis is predominantly associated with ovarian teratomas and typically responds well to immunotherapy and tumor resection, whereas ALE is more frequently linked to malignancies such as small cell lung cancer or thymoma[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Accurate differentiation therefore holds critical clinical significance, as these conditions differ considerably in etiology, therapeutic strategies, and prognosis[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImaging abnormalities on conventional MRI are relatively uncommon in anti-NMDAR encephalitis; when present, they may manifest as diffuse cortical or subcortical hyperintensities on T2-FLAIR sequences. In contrast, ALE more often exhibits unilateral or bilateral involvement of the hippocampus or basal ganglia[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. As one of the regions with the highest density of NMDA receptor expression, the hippocampus has been a key region of interest (ROI) in prior imaging studies of anti-NMDAR encephalitis[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Conventional MRI plays an important role in the diagnosis of AE and detection of structural abnormalities, yet its utility in differentiating specific subtypes remains limited, especially in atypical presentations.\u003c/p\u003e \u003cp\u003eHistogram analysis, an objective and reproducible imaging technique, quantitatively describes the grayscale distribution of voxel intensities within a given ROI, thereby reflecting microstructural and pathophysiological changes[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In recent years, it has shown promising value in tumor characterization, grading, and prognosis prediction[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. However, its application in differentiating encephalitis subtypes has been rarely explored.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to integrate clinical features, conventional MRI characteristics, and histogram metrics derived from multiparametric MRI (mp-MRI) to develop both single-modality and multimodal diagnostic models for differentiating anti-NMDAR encephalitis from ALE.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Participants\u003c/h2\u003e \u003cp\u003e This retrospective study was approved by the local ethics review committee, and the requirement for written informed consent was waived. A total of 157 patients diagnosed with either anti-NMDAR encephalitis or ALE at our institution between March 2017 and August 2023 were screened. The inclusion criteria were: (1) meeting the diagnostic criteria for anti-NMDAR encephalitis and ALE proposed by Graus and Dalmau in 2016[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]; and (2) availability of complete MRI examinations, including diffusion-weighted imaging (DWI) and apparent diffusion coefficient (ADC) maps. Exclusion criteria were as follows: (1) coexisting central nervous system disorders (e.g., cerebral infarction, tumors, etc.); (2) incomplete clinical data; and (3) suboptimal MRI image quality. Specifically, 8 patients were excluded due to concomitant neurological diseases, 11 due to insufficient clinical information, and 3 due to poor image quality. Ultimately, 135 patients were enrolled, including 76 with anti-NMDAR encephalitis and 59 with ALE.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Clinical Data\u003c/h2\u003e \u003cp\u003eDemographic information (age and sex) and clinical characteristics were collected for all participants. Clinical variables included prodromal symptoms and major neurological manifestations such as seizures, disturbances of consciousness, cognitive dysfunction, psychiatric or behavioral abnormalities, speech impairment, movement disorders, and memory decline. Initial laboratory findings at admission were also recorded, including: (1) hematological parameters\u0026mdash;white blood cell (WBC) count, neutrophil count, monocyte count, neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), and serum sodium levels; and (2) cerebrospinal fluid (CSF) parameters\u0026mdash;intracranial pressure, nucleated cell count, protein, glucose, chloride, creatine kinase (CPK), lactate dehydrogenase (LDH), and immunoglobulin levels (IgA, IgG, IgM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 MRI Acquisition\u003c/h2\u003e \u003cp\u003eAll MRI examinations were performed using 1.5 T scanners (Ingenia, Philips Healthcare, Netherlands; or Signa Artist, GE Healthcare, USA), each equipped with an 8-channel head coil. Each participant underwent routine cranial MRI in the supine position, including axial T2-weighted imaging (T2WI), fluid-attenuated inversion recovery (FLAIR), and DWI sequences (b\u0026thinsp;=\u0026thinsp;0 and 1000 s/mm\u0026sup2;). For Philips systems, ADC maps were automatically generated on the console, whereas for GE systems, ADC maps were reconstructed on the AW post-processing workstation. Due to the extended study interval and the use of multiple MRI platforms, acquisition parameters varied slightly; detailed parameters are provided in \u003cb\u003eSupplementary Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Image Analysis\u003c/h2\u003e \u003cp\u003eTwo neuroradiologists with 15 and 3 years of experience independently reviewed and interpreted all MRI data, blinded to clinical subtype. Conventional MRI characteristics were assessed and documented, including lesion distribution (frontal, parietal, temporal, and occipital lobes; insula; periventricular region; thalamus; basal ganglia; hippocampus; amygdala; cerebellum) and enhancement patterns (lesional, leptomeningeal, or dural enhancement).\u003c/p\u003e \u003cp\u003ePrior to histogram analysis, all images were preprocessed using 3D Slicer (version 5.8.1, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.slicer.org/\u003c/span\u003e\u003cspan address=\"https://www.slicer.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e with intensity inhomogeneity correction and z-score standardization. A neuroradiologist with 3 years of experience manually delineated bilateral hippocampal regions of interest (ROIs) on axial images from T2WI, FLAIR, and ADC sequences using FireVoxel software (version 476, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.firevoxel.org/\u003c/span\u003e\u003cspan address=\"https://www.firevoxel.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e, as illustrated in Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Extracted histogram metrics included hippocampal volume (from T2WI) and the following for each sequence: minimum, maximum, mean, Perc.01, Perc.05, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.95, Perc.99, standard deviation (SD), variance, coefficient of variation (CV), skewness, kurtosis, and entropy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003eStatistical analyses were conducted using SPSS Statistics (Version 27.0, IBM) and GraphPad Prism (Version 10.1.2). A \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant. Continuous variables were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical variables were presented as frequency (percentage). The Kolmogorov\u0026ndash;Smirnov test was used to assess normality. Depending on data distribution, independent-sample t-tests or Mann\u0026ndash;Whitney U tests were used to compare continuous variables, and Chi-square or Fisher\u0026rsquo;s exact tests were used for categorical variables. Univariate and multivariate logistic regression analyses were performed to identify independent predictors o\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ef\u003c/span\u003e anti-NMDAR encephalitis versus ALE to guide model development. Model performance was evaluated using receiver operating characteristic (ROC) curves, and the area under the curve (AUC), sensitivity, specificity, and optimal cut-off values (determined by the maximum Youden index) were calculated.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Clinical Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean age of patients with anti-NMDAR encephalitis (30 \u0026plusmn; 17 years) was significantly lower than that of patients with ALE (45 \u0026plusmn; 18 years; \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). Significant between-group differences were observed in age, prodromal symptoms, psychiatric or behavioral abnormalities, memory decline, WBC count, NLR, CSF nucleated cell count, and elevated CSF IgM levels (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). No statistically significant differences were found for sex, seizures, disturbances of consciousness, cognitive dysfunction, speech disorders, movement disorders, MLR, sodium levels, intracranial pressure, or other CSF parameters (protein, glucose, chloride, CPK, LDH, IgA, or IgG) (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). A comprehensive summary of demographic and clinical characteristics is presented in \u003cstrong\u003eTable 1\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Conventional MRI Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBilateral hippocampal volumes were significantly larger in the anti-NMDAR encephalitis group [5.451 (4.699\u0026ndash;6.215) cm\u0026sup3;] compared with the ALE group [4.901 (4.121\u0026ndash;5.591) cm\u0026sup3;] (\u003cem\u003eP\u003c/em\u003e = 0.003). In contrast, the proportion of hippocampal and amygdala abnormalities was significantly higher in the ALE group than in the anti-NMDAR encephalitis group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.001). No significant differences were observed in other brain regions such as the frontal or parietal lobes (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Among the 76 patients with anti-NMDAR encephalitis, 74 underwent contrast-enhanced MRI; among the 59 patients with ALE, 50 underwent contrast-enhanced MRI. No significant between-group differences were identified in lesional enhancement, leptomeningeal enhancement, or dural enhancement (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05). Detailed conventional MRI findings for both groups are summarized in \u003cstrong\u003eTable 2\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Histogram Metrics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparisons of histogram metrics extracted from T2WI, FLAIR, and ADC sequences are presented in \u003cstrong\u003eFigure 3\u003c/strong\u003e and \u003cstrong\u003eSupplementary Table S1\u003c/strong\u003e. For histogram metrics derived from FLAIR, the anti-NMDAR encephalitis group demonstrated significantly higher minimum values but lower SD, variance, and CV compared with the ALE group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For histogram metrics derived from T2WI, the anti-NMDAR encephalitis group similarly exhibited significantly lower SD, variance, and CV than the ALE group (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For histogram metrics derived from ADC, minimum values and entropy were significantly higher in the anti-NMDAR encephalitis group (both \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05).\u003c/p\u003e\n\u003cp\u003eOverall, dispersion metrics (SD, variance, CV) on FLAIR and T2WI were consistently lower in the anti-NMDAR encephalitis group, indicating greater hippocampal signal homogeneity. Higher minimum values and entropy on ADC may reflect differing water diffusion properties between groups. No significant differences were found in other metrics\u0026mdash;such as skewness, kurtosis, or most percentile values (all \u003cem\u003eP\u003c/em\u003e \u0026gt; 0.05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Model Construction and Performance Comparison\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of univariate and multivariate logistic regression for clinical, conventional MRI, and MRI histogram metrics are listed in \u003cstrong\u003eTables 3 to Table 5\u003c/strong\u003e, respectively. Independent clinical predictors of anti-NMDAR encephalitis versus ALE included age, prodromal symptoms, psychiatric or behavioral abnormalities, memory decline, CSF nucleated cell count, and elevated IgM levels (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 3). Bilateral hippocampal volume and hippocampal MRI abnormalities were identified as independent conventional MRI predictors (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 4).For histogram models, CV served as the independent predictor for both FLAIR and T2WI-based models (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05), while minimum value and entropy were identified as independent predictors in the ADC-based model (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) (Table 5).\u003c/p\u003e\n\u003cp\u003eDiagnostic performance metrics and ROC curves for all predictive models are presented in \u003cstrong\u003eTable 6\u003c/strong\u003e and \u003cstrong\u003eFigure 4\u003c/strong\u003e, respectively. The clinical model (AUC = 0.858) and the conventional MRI model (AUC = 0.770) performed better than the individual histogram models (FLAIR: 0.671; T2WI: 0.604; ADC: 0.679; mp-MRI: 0.737). When the clinical and conventional MRI models were each combined with the four histogram models, diagnostic performance improved markedly (AUCs: 0.899, 0.896, 0.936, and 0.947). The mp-MRI combined model achieved the highest diagnostic accuracy among all models.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Subgroup Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted using histogram models constructed separately for the left and right hippocampi. Comparisons of histogram metrics for each hippocampus across MRI sequences are provided in \u003cstrong\u003eSupplementary Tables S3 and S4\u003c/strong\u003e. Corresponding univariate and multivariate logistic regression results are shown in \u003cstrong\u003eSupplementary Tables S5 and S6\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003eExcept for the ADC histogram model based on the right hippocampus\u0026mdash;where CV and Perc.75 were identified as independent predictors\u0026mdash;the findings were consistent with those derived from bilateral hippocampal analyses. Predictive models constructed using these independent predictors demonstrated that mp-MRI histogram models generally provided superior performance across subgroups (\u003cstrong\u003eSupplementary Table S7, Supplementary Figure S1\u003c/strong\u003e). However, no statistically significant differences in AUCs were observed among the subgroup models (\u003cstrong\u003eSupplementary Figure S2\u003c/strong\u003e).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis study identified several independent predictors for differentiating anti-NMDAR encephalitis from ALE, including clinical features such as age, prodromal symptoms, and elevated IgM levels, as well as bilateral hippocampal volume, and hippocampal abnormalities on conventional MRI. In terms of diagnostic performance, clinical models, conventional MRI models, and mp-MRI histogram models all outperformed single-modality histogram models, with the mp-MRI combined model achieving the highest discriminative accuracy. To our knowledge, this is the first study to comprehensively evaluate the combined diagnostic value of clinical features, conventional neuroimaging findings, and multi-sequence MRI histogram metrics in differentiating anti-NMDAR encephalitis from ALE. These results may contribute to improving early diagnostic accuracy, refining subtype classification, and supporting tailored clinical management.\u003c/p\u003e \u003cp\u003eConsistent with previous studies, patients with anti-NMDAR encephalitis in our cohort were generally younger and presented with a higher frequency of psychiatric behavioral abnormalities, whereas ALE patients were older and more likely to exhibit memory decline[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Anti-NMDAR encephalitis also demonstrated a higher incidence of infectious prodromal symptoms, such as headache and fever, possibly related to infection-associated triggers. Prior research has shown that herpes simplex virus encephalitis can evolve into AE, particularly anti-NMDAR encephalitis[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although NLR and MLR have been proposed as inflammatory biomarkers in immune-related diseases[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], these markers did not differ between the two groups in our study, suggesting insufficient specificity for differential diagnosis. Interestingly, anti-NMDAR encephalitis was associated with elevated CSF IgM levels and higher CSF nucleated cell counts, differing from prior findings comparing AE with viral encephalitis[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. These differences may reflect distinct neuroinflammatory pathways among encephalitis subtypes and highlight the value of integrating CSF immune markers into differential diagnosis.\u003c/p\u003e \u003cp\u003ePrevious research indicates that both anti-NMDAR encephalitis and ALE may involve the hippocampus, and many MRI-based or deep learning studies have used the hippocampus as a region of interest with promising results[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In our study, hippocampal and amygdalar abnormalities were more frequent in ALE, consistent with its clinical presentation dominated by memory impairment[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Notably, anti-NMDAR encephalitis is often characterized by functional network disruptions\u0026mdash;such as altered hippocampal connectivity\u0026mdash;whereas ALE more commonly displays structural T2 hyperintensities in the limbic system[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings underscore differences in underlying pathological mechanisms despite shared anatomical involvement. Although leptomeningeal enhancement was observed in a subset of both patient groups, consistent with prior studies[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], it did not provide diagnostic value in differentiating the two conditions. Given the small sample size, however, the possibility of its discriminative potential warrants further investigation[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In addition, the significantly smaller hippocampal volumes in the ALE group are consistent with reports of neuronal loss and hippocampal atrophy in ALE[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] and may also relate to older age and differing disease stages.\u003c/p\u003e \u003cp\u003eConventional MRI findings in anti-NMDAR encephalitis are frequently normal or only mildly abnormal[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], and some patients with ALE may also have negative imaging results[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Histogram analysis, by quantifying voxel intensity characteristics, provides an objective method to detect subtle changes not readily apparent on routine imaging. Its utility has been demonstrated in tumor grading, disease classification, and prognosis estimation[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In our study, histogram analysis revealed important differences in signal homogeneity and water diffusion properties between the two conditions. The anti-NMDAR encephalitis group showed lower dispersion metrics (SD, variance, CV) on T2WI and FLAIR, suggesting more homogeneous hippocampal signal intensity. This may reflect the predominantly functional and synaptic impairments, as well as diffuse edema, seen in anti-NMDAR encephalitis rather than focal tissue destruction[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. In contrast, ALE demonstrated lower ADC minimum values, suggestive of more severe focal cellular degeneration, necrosis, or dense inflammatory infiltration, consistent with its characteristic pathological features[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The lower ADC entropy observed in ALE suggests a more uniform microstructural pattern despite the macroscopic heterogeneity seen on T2WI/FLAIR. Conversely, anti-NMDAR encephalitis\u0026mdash;despite appearing macroscopically homogeneous\u0026mdash;exhibited higher ADC entropy, indicating more complex microstructural alterations that may reflect widespread immune-mediated neuronal dysfunction[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. These findings support a conceptual distinction: ALE is characterized by macroscopic heterogeneity but microscopically homogeneous structural injury[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], whereas anti-NMDAR encephalitis shows macroscopic homogeneity but microscopically heterogeneous functional or synaptic pathology[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBy combining clinical indicators, conventional MRI features, and mp-MRI histogram metrics, diagnostic performance improved significantly. This aligns with the current trend toward multimodal imaging analysis, in which quantitative features enhance diagnostic power beyond qualitative assessment[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Although previous studies have reported lateralization differences in hippocampal structure or function[\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], our subgroup analyses did not identify significant AUC differences between left- and right-based models, suggesting that hippocampal lateralization does not materially affect differentiation between anti-NMDAR encephalitis and ALE. This finding is consistent with the bilateral hippocampal involvement reported in some clinical cases[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] and indicates that future research should focus on microstructural imaging biomarkers rather than lateralized patterns.\u003c/p\u003e \u003cp\u003eDespite these strengths, several limitations must be acknowledged. First, this was a single-center retrospective study with a relatively small sample size, which may introduce selection bias. Large-scale, multicenter prospective studies are required to validate these findings in the future. Second, although imaging data underwent standardized preprocessing, variability in MRI scanners and protocols may still affect model performance. Finally, manual hippocampal segmentation may introduce subjective variability; future work incorporating automated segmentation techniques may improve reproducibility and efficiency.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eHistogram analysis offers a promising approach for differentiating anti-NMDAR encephalitis from ALE, and may serve as a valuable imaging biomarker. Compared with single-modality analysis, multimodal assessment more comprehensively captures heterogeneous hippocampal characteristics and provides stronger predictive and discriminative capabilities. When combined with clinical indicators and conventional MRI findings, histogram features significantly enhance diagnostic performance, yielding high sensitivity and specificity that support early diagnosis and precise disease management. Future multicenter studies and advancements in automated imaging analysis are expected to further strengthen the utility of MRI histogram analysis in differentiating autoimmune encephalitis subtypes and contribute to more reliable clinical decision-making.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThis retrospective study has been approved by the local ethics committee.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was waived because of the retrospective nature of the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent for publication was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eJ. Wang wrote the initial draft..Q. Bi designed the study..W. Tan collected the data..C.Wu collected the data..Y. Yang collected the data..C. Song analyzed the data..S.Liu analyzed the data..L. Wu designed the study..All authors reviewed and approved the final manuscript..\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eSegal Y, Rotschild O, Mina Y, Maayan Eshed G, Levinson T, Paran Y, et al (2024) Epidemiology of autoimmune encephalitis and comparison to infectious causes\u0026mdash;Experience from a tertiary center. Ann Clin Transl Neurol 11:2337\u0026ndash;2349. https://doi.org/10.1002/acn3.52147\u003c/li\u003e\n\u003cli\u003eDalmau J, Graus F (2018) Antibody-Mediated Encephalitis. N Engl J Med 378:840\u0026ndash;851. https://doi.org/10.1056/NEJMra1708712\u003c/li\u003e\n\u003cli\u003eVan Steenhoven RW, de Vries JM, Bruijstens AL, Paunovic M, Nagtzaam MM, Franken SC, et al (2023) Mimics of Autoimmune Encephalitis. Neurol Neuroimmunol Neuroinflamm 10:e200148. https://doi.org/10.1212/NXI.0000000000200148\u003c/li\u003e\n\u003cli\u003eDalmau J, Armangu\u0026eacute; T, Planagum\u0026agrave; J, Radosevic M, Mannara F, Leypoldt F, et al (2019) An update on anti-NMDA receptor encephalitis for neurologists and psychiatrists: mechanisms and models. Lancet Neurol 18:1045\u0026ndash;1057. https://doi.org/10.1016/S1474-4422(19)30244-3\u003c/li\u003e\n\u003cli\u003eAem B, Maam de B, Sl S, E M-H, J B, M P, et al (2021) Anti-NMDAR Encephalitis in the Netherlands, Focusing on Late-Onset Patients and Antibody Test Accuracy. Neurology(R) neuroimmunology \u0026amp; neuroinflammation 9:. https://doi.org/10.1212/NXI.0000000000001127\u003c/li\u003e\n\u003cli\u003eZhang T-Y, Cai M-T, Zheng Y, Lai Q-L, Shen C-H, Qiao S, et al (2021) Anti-Alpha-Amino-3-Hydroxy-5-Methyl-4-Isoxazolepropionic Acid Receptor Encephalitis: A Review. Front Immunol 12:652820. https://doi.org/10.3389/fimmu.2021.652820\u003c/li\u003e\n\u003cli\u003eTitulaer MJ, McCracken L, Gabilondo I, Armangu\u0026eacute; T, Glaser C, Iizuka T, et al (2013) Treatment and prognostic factors for long-term outcome in patients with anti-N-Methyl-D-Aspartate (NMDA) receptor encephalitis: a cohort study. Lancet Neurol 12:157\u0026ndash;165. https://doi.org/10.1016/S1474-4422(12)70310-1\u003c/li\u003e\n\u003cli\u003eRadja GK, Cavanna AE (2013) Treatment of VGKC complex antibody-associated limbic encephalitis: a systematic review. J Neuropsychiatry Clin Neurosci 25:264\u0026ndash;271. https://doi.org/10.1176/appi.neuropsych.13020022\u003c/li\u003e\n\u003cli\u003eMasciocchi S, Businaro P, Scaranzin S, Morandi C, Franciotta D, Gastaldi M (2024) General features, pathogenesis, and laboratory diagnostics of autoimmune encephalitis. Crit Rev Clin Lab Sci 61:45\u0026ndash;69. https://doi.org/10.1080/10408363.2023.2247482\u003c/li\u003e\n\u003cli\u003eYu X, Fang Y, Sun L, Yang J, Hong J-S, Sun B, et al (2025) Imaging biomarkers in antibody-mediated autoimmune encephalitis. Quant Imaging Med Surg 15:8372\u0026ndash;8394. https://doi.org/10.21037/qims-2025-131\u003c/li\u003e\n\u003cli\u003eXiang Y, Zeng C, Liu B, Tan W, Wu J, Hu X, et al (2022) Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI. J Magn Reson Imaging 55:1082\u0026ndash;1092. https://doi.org/10.1002/jmri.27909\u003c/li\u003e\n\u003cli\u003eXiang Y, Dong X, Zeng C, Liu J, Liu H, Hu X, et al (2022) Clinical Variables, Deep Learning and Radiomics Features Help Predict the Prognosis of Adult Anti-N-methyl-D-aspartate Receptor Encephalitis Early: A Two-Center Study in Southwest China. Front Immunol 13:913703. https://doi.org/10.3389/fimmu.2022.913703\u003c/li\u003e\n\u003cli\u003eLiu X, Han T, Wang Y, Liu H, Sun Q, Xue C, et al (2024) Whole-tumor histogram analysis of postcontrast T1-weighted and apparent diffusion coefficient in predicting the grade and proliferative activity of adult intracranial ependymomas. Neuroradiology 66:531\u0026ndash;541. https://doi.org/10.1007/s00234-024-03319-w\u003c/li\u003e\n\u003cli\u003eSun Y, Wang C (2024) Brain tumor detection based on a novel and high-quality prediction of the tumor pixel distributions. Comput Biol Med 172:108196. https://doi.org/10.1016/j.compbiomed.2024.108196\u003c/li\u003e\n\u003cli\u003eKang Y, Choi SH, Kim Y-J, Kim KG, Sohn C-H, Kim J-H, et al (2011) Gliomas: Histogram Analysis of Apparent Diffusion Coefficient Maps with Standard- or High- \u003cem\u003eb\u003c/em\u003e -Value Diffusion-weighted MR Imaging\u0026mdash;Correlation with Tumor Grade. Radiology 261:882\u0026ndash;890. https://doi.org/10.1148/radiol.11110686\u003c/li\u003e\n\u003cli\u003eGraus F, Titulaer MJ, Balu R, Benseler S, Bien CG, Cellucci T, et al (2016) A clinical approach to diagnosis of autoimmune encephalitis. Lancet Neurol 15:391\u0026ndash;404. https://doi.org/10.1016/S1474-4422(15)00401-9\u003c/li\u003e\n\u003cli\u003eArmangue T, Spatola M, Vlagea A, Mattozzi S, C\u0026aacute;rceles-Cordon M, Martinez-Heras E, et al (2018) Frequency, syndromes, risk factors, and outcome of autoimmune encephalitis following herpes simplex encephalitis: a prospective observational study and a retrospective analysis of cases. Lancet Neurol 17:760\u0026ndash;772. https://doi.org/10.1016/S1474-4422(18)30244-8\u003c/li\u003e\n\u003cli\u003eLiu Z, Li Y, Wang Y, Zhang H, Lian Y, Cheng X (2022) The Neutrophil-to-Lymphocyte and Monocyte-to-Lymphocyte Ratios Are Independently Associated With the Severity of Autoimmune Encephalitis. Front Immunol 13:911779. https://doi.org/10.3389/fimmu.2022.911779\u003c/li\u003e\n\u003cli\u003eHu X, Cheng S (2024) The role of immunoglobulin in cerebrospinal fluid on the differential diagnosis of autoimmune encephalitis and viral encephalitis in children. BMC Pediatr 24:386. https://doi.org/10.1186/s12887-024-04824-w\u003c/li\u003e\n\u003cli\u003eMueller C, Langenbruch L, Rau JMH, Brix T, Strippel C, Dik A, et al (2022) Neuropsychological Performance in Autoimmune Limbic Encephalitis: Evidence from an Immunotherapy-Na\u0026iuml;ve Cohort. Arch Clin Neuropsychol 37:738\u0026ndash;752. https://doi.org/10.1093/arclin/acac001\u003c/li\u003e\n\u003cli\u003eHartung TJ, Cooper G, J\u0026uuml;nger V, Komnenić D, Ryan L, Heine J, et al (2024) The T1-weighted/T2-weighted ratio as a biomarker of anti-NMDA receptor encephalitis. J Neurol Neurosurg Psychiatry 95:366\u0026ndash;373. https://doi.org/10.1136/jnnp-2023-332069\u003c/li\u003e\n\u003cli\u003eKuchling J, Jurek B, Kents M, Kreye J, Geis C, Wickel J, et al (2024) Impaired functional connectivity of the hippocampus in translational murine models of NMDA-receptor antibody associated neuropsychiatric pathology. Mol Psychiatry 29:85\u0026ndash;96. https://doi.org/10.1038/s41380-023-02303-9\u003c/li\u003e\n\u003cli\u003eChang H, Ma J, Feng K, Feng N, Wang X, Sun J, et al (2023) Elevated blood and cerebrospinal fluid biomarkers of microglial activation and blood‒brain barrier disruption in anti-NMDA receptor encephalitis. J Neuroinflammation 20:172. https://doi.org/10.1186/s12974-023-02841-7\u003c/li\u003e\n\u003cli\u003eNeo S, Yeo T, Chen Z, Ngiam NHW, Lim ET-X, Tan K, et al (2020) Acute radiological features facilitate diagnosis and prognosis of anti-N-methyl-d-aspartate receptor (NMDAR) and anti-voltage-gated potassium channel (VGKC) encephalitis in adults. J Neurol Sci 419:117216. https://doi.org/10.1016/j.jns.2020.117216\u003c/li\u003e\n\u003cli\u003eSapana T, Li W, Tian F, Yan W, Dou B, Hua S, et al (2023) A case report of anti-GAD65 antibody-positive autoimmune encephalitis in children associated with autoimmune polyendocrine syndrome type-II and literature review. Front Immunol 14:1274672. https://doi.org/10.3389/fimmu.2023.1274672\u003c/li\u003e\n\u003cli\u003eKhatib L, Pique J, Ciano-Petersen NL, Criton G, Birzu C, Aubart M, et al (2025) Abnormal Brain MRI in Anti-NMDA Receptor Encephalitis: Clinical and Prognostic Implications. Neurology\u0026reg; Neuroimmunology \u0026amp; Neuroinflammation 12:e200378. https://doi.org/10.1212/NXI.0000000000200378\u003c/li\u003e\n\u003cli\u003eYuan L, Li X, Cui T, Chen Q, Ai L (2025) Uncommon Bilateral Frontoparietal Cortex Hypermetabolism Revealed by 18 F-FDG PET/CT in a Patient With Anti-LGI1 Encephalitis. Clin Nucl Med 50:951\u0026ndash;952. https://doi.org/10.1097/RLU.0000000000005824\u003c/li\u003e\n\u003cli\u003eSu Y, Cheng R, Guo J, Zhang M, Wang J, Ji H, et al (2024) Differentiation of glioma and solitary brain metastasis: a multi-parameter magnetic resonance imaging study using histogram analysis. BMC Cancer 24:805. https://doi.org/10.1186/s12885-024-12571-5\u003c/li\u003e\n\u003cli\u003eDing Y, Zhou Z, Chen J, Peng Y, Wang H, Qiu W, et al (2021) Anti-NMDAR encephalitis induced in mice by active immunization with a peptide from the amino-terminal domain of the GluN1 subunit. J Neuroinflammation 18:53. https://doi.org/10.1186/s12974-021-02107-0\u003c/li\u003e\n\u003cli\u003ePitsch J, van Loo KMJ, Gallus M, Dik A, Kamalizade D, Baumgart A-K, et al (2021) CD8+ T-Lymphocyte-Driven Limbic Encephalitis Results in Temporal Lobe Epilepsy. Ann Neurol 89:666\u0026ndash;685. https://doi.org/10.1002/ana.26000\u003c/li\u003e\n\u003cli\u003eBai Y, Liu Z, Qian T, Peng Y, Ma H, Hu H, et al (2023) Single-nucleus RNA sequencing unveils critical regulators in various hippocampal neurons for anti-N-methyl-D-aspartate receptor encephalitis. Brain Pathol 33:e13156. https://doi.org/10.1111/bpa.13156\u003c/li\u003e\n\u003cli\u003eFortea A, Ortu\u0026ntilde;o M, Masias M, Guasp M, De la Serna E, Armangue T, et al (2025) Brain Metabolite Levels in the Post-Acute Stage of Anti-NMDA Receptor Encephalitis and Schizophrenia: A Longitudinal Case-Control Study. Biol Psychiatry S0006-3223(25)01316\u0026ndash;2. https://doi.org/10.1016/j.biopsych.2025.07.006\u003c/li\u003e\n\u003cli\u003eJamet Z, Mergaux C, Meras M, Bouchet D, Villega F, Kreye J, et al (2024) NMDA receptor autoantibodies primarily impair the extrasynaptic compartment. Brain 147:2745\u0026ndash;2760. https://doi.org/10.1093/brain/awae163\u003c/li\u003e\n\u003cli\u003eGong X, Li W, Luo C, Tian Y, Wang J, Gao Y, et al (2025) Neurochemical and molecular characteristics of altered brain functional activity in the anti-NMDAR encephalitis. Neurobiol Dis 215:107050. https://doi.org/10.1016/j.nbd.2025.107050\u003c/li\u003e\n\u003cli\u003eGugger JJ, Kulick-Soper CV, Sinha N, Jaskir M, Hadar PN, Josyula M, et al (2025) Evaluation of limbic microstructural abnormalities in temporal lobe epilepsy: A neurite orientation distribution and density imaging study. Epilepsia. https://doi.org/10.1111/epi.18488\u003c/li\u003e\n\u003cli\u003eLam HW, Patodia S, Zeicu C, Lim YM, Mrzyglod A, Scott C, et al (2024) Quantitative cellular pathology of the amygdala in temporal lobe epilepsy and correlation with magnetic resonance imaging volumetry, tissue microstructure, and sudden unexpected death in epilepsy risk factors. Epilepsia 65:2368\u0026ndash;2385. https://doi.org/10.1111/epi.18033\u003c/li\u003e\n\u003cli\u003ePeyvandi Karizbodagh M, Sadr-Nabavi A, Hami J, Mohammadipour A, Khoshdel-Sarkarizi H, Kheradmand H, et al (2021) Developmental regulation and lateralization of N-methyl-d-aspartate (NMDA) receptors in the rat hippocampus. Neuropeptides 89:102183. https://doi.org/10.1016/j.npep.2021.102183\u003c/li\u003e\n\u003cli\u003eH\u0026auml;nsel M, Reichmann H, Haehner A, Schmitz-Peiffer H, Schneider H (2025) Hippocampal dysfunction after autoimmune encephalitis depending on the antibody type. J Neurol 272:175. https://doi.org/10.1007/s00415-024-12742-1\u003c/li\u003e\n\u003cli\u003eNemati SS, Sadeghi L, Dehghan G, Sheibani N (2023) Lateralization of the hippocampus: A review of molecular, functional, and physiological properties in health and disease. Behav Brain Res 454:114657. https://doi.org/10.1016/j.bbr.2023.114657\u003c/li\u003e\n\u003cli\u003eQin X, Yang H, Zhu F, Wang Q, Shan W (2021) Clinical Character of CASPR2 Autoimmune Encephalitis: A Multiple Center Retrospective Study. Front Immunol 12:652864. https://doi.org/10.3389/fimmu.2021.652864\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 6 are available in the Supplementary Files section.\u003c/p\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":"Anti-NMDAR encephalitis, Autoimmune limbic encephalitis, Magnetic resonance imaging, Histogram analysis, Differential diagnosis","lastPublishedDoi":"10.21203/rs.3.rs-8504405/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8504405/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e\u003cbr\u003e\n \u0026nbsp;To evaluate the combined diagnostic value of clinical features, conventional MRI findings, and histogram metrics derived from multiparametric MRI (mp-MRI) in differentiating anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis from autoimmune limbic encephalitis (ALE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e\u003cbr\u003e\n \u0026nbsp;This retrospective study analyzed baseline clinical and brain MRI data from 76 anti-NMDAR encephalitis and 59 ALE patients. Bilateral hippocampi were manually delineated as regions of interest, and histogram metrics were extracted from fluid-attenuated inversion recovery (FLAIR), T2-weighted imaging (T2WI), and apparent diffusion coefficient (ADC) maps. Clinical characteristics, conventional MRI features, and histogram metrics were compared between the two groups. Independent predictors were identified using logistic regression. Diagnostic models based on clinical variables, conventional MRI, and single-modality or multimodal histogram metrics were constructed and assessed using area under the curve (AUC).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e\u003cbr\u003e\n \u0026nbsp;Independent clinical predictors included age, prodromal symptoms, psychiatric or behavioral abnormalities, memory impairment, cerebrospinal fluid nucleated cell count, and elevated IgM levels (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). For conventional MRI, bilateral hippocampal volume and abnormal hippocampal signal served as independent predictors (all \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05). The clinical model (AUC = 0.858), conventional MRI model (AUC = 0.770), and mp-MRI histogram model (AUC = 0.737) each outperformed single-modality histogram models. Integrating histogram metrics with clinical and conventional MRI features significantly enhanced diagnostic performance, with the combined mp-MRI model achieving the highest accuracy (AUC = 0.947).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e\u003cbr\u003e\n \u0026nbsp;Histogram metrics derived from mp-MRI are promising biomarkers for differentiating anti-NMDAR encephalitis from ALE, and the integration with clinical and conventional MRI indicators in combined models significantly improves diagnostic accuracy.\u003c/p\u003e","manuscriptTitle":"Differentiation of Anti-NMDAR Encephalitis and Autoimmune Limbic Encephalitis Using Histogram Analysis based on Multiparametric MRI","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 08:29:22","doi":"10.21203/rs.3.rs-8504405/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":"4e9066a0-4fda-4255-91ac-a27e27f2415e","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-07T18:39:59+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 08:29:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8504405","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8504405","identity":"rs-8504405","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}