Spinal cord and brain atrophy patterns in neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Spinal cord and brain atrophy are common in neuromyelitis optica spectrum disorder (NMOSD) and relapsing-remitting multiple sclerosis (RRMS) but harbor distinct patterns accounting for disability and cognitive impairment. METHODS: This study included 209 NMOSD and 304 RRMS patients and 436 healthy controls. Non-negative matrix factorization was used to parse differences in spinal cord and brain atrophy at subject level into distinct patterns based on structural MRI. The weights of patterns were obtained using a linear regression model and associated with Expanded Disability Status Scale (EDSS) and cognitive scores. Additionally, patients were divided into cognitive impairment (CI) and cognitive preservation (CP) groups. RESULTS: Three patterns were observed in NMOSD: (1) Spinal Cord-Deep Grey Matter (SC-DGM) pattern was associated with high EDSS scores and decline of visuospatial memory function; (2) Frontal-Temporal pattern was associated with decline of language learning function; and (3) Cerebellum-Brainstem pattern had no observed association. Patients with CI had higher weights of SC-DGM pattern than CP group. Three patterns were observed in RRMS: (1) DGM pattern was associated with high EDSS scores, decreased information processing speed, and decreased language learning and visuospatial memory functions; (2) Frontal-Temporal pattern was associated with overall cognitive decline; and (3) Occipital pattern had no observed association. Patients with CI trended to have higher weights of DGM and Frontal-Temporal patterns than CP group. CONCLUSION: This study estimated the heterogeneity of spinal cord and brain atrophy patterns in NMOSD and RRMS patients at individual level, and evaluated the clinical relevance of these patterns, which may contribute to stratifying participants for targeted therapy.

Temporal and topological properties of dynamic networks reflect disability in patients with neuromyelitis optica spectrum disorders.

Approximately 36% of patients with neuromyelitis optica spectrum disorders (NMOSD) suffer from severe visual and motor disability (blindness or light perception or unable to walk) with abnormalities of whole-brain functional networks. However, it remains unclear how whole-brain functional networks and their dynamic properties are related to clinical disability in patients with NMOSD. Our study recruited 30 NMOSD patients (37.70 ± 11.99 years) and 45 healthy controls (HC, 41.84 ± 11.23 years). The independent component analysis, sliding-window approach and graph theory analysis were used to explore the static strength, time-varying and topological properties of large-scale functional networks and their associations with disability in NMOSD. Compared to HC, NMOSD patients showed significant alterations in dynamic networks rather than static networks. Specifically, NMOSD patients showed increased occurrence (fractional occupancy; P < 0.001) and more dwell times of the low-connectivity state (P < 0.001) with fewer transitions (P = 0.028) between states than HC, and higher fractional occupancy, increased dwell times of the low-connectivity state and lower transitions were related to more severe disability. Moreover, NMOSD patients exhibited altered small-worldness, decreased degree centrality and reduced clustering coefficients of hub nodes in dynamic networks, related to clinical disability. NMOSD patients exhibited higher occurrence and more dwell time in low-connectivity states, along with fewer transitions between states and decreased topological organizations, revealing the disrupted communication and coordination among brain networks over time. Our findings could provide new perspective to help us better understand the neuropathological mechanism of the clinical disability in NMOSD.

Independent and reproducible hippocampal radiomics biomarkers for multisite multiple sclerosis and neuromyelitis optica spectrum disorders.

OBJECTIVE: To investigate the abnormal radiomics features of the hippocampus in patients with multiple sclerosis (MS) and neuromyelitis optica spectrum disorders (NMOSD) and to explore the clinical implications of these features. METHODS: 752 participants were recruited in this retrospective multicenter study (7 centers), which included 236 MS, 236 NMOSD, and 280 normal controls (NC). Radiomics features of each side of the hippocampus were extracted, including intensity, shape, texture, and wavelet features (N = 431). To identify the variations in these features, two-sample t-tests were performed between the NMOSD vs. NC, MS vs. NC, and NMOSD vs. MS groups at each site. The statistical results from each site were then integrated through meta-analysis. To investigate the clinical significance of the hippocampal radiomics features, we conducted further analysis to examine the correlations between these features and clinical measures such as Expanded Disability Status Scale (EDSS), Brief Visuospatial Memory Test (BVMT), California Verbal Learning Test (CVLT), and Paced Auditory Serial Addition Task (PASAT). RESULTS: Compared with NC, patients with MS exhibited significant differences in 78 radiomics features (P < 0.05/862), with the majority of these being texture features. Patients with NMOSD showed significant differences in 137 radiomics features (P < 0.05/862), most of which were intensity features. The difference between MS and NMOSD patients was observed in 47 radiomics features (P < 0.05/862), mainly texture features. In patients with MS and NMOSD, the most significant features related to the EDSS were intensity and textural features, and the most significant features related to the PASAT were intensity features. Meanwhile, both disease groups observed a weak correlation between radiomics data and BVMT. CONCLUSION: Variations in the microstructure of the hippocampus can be detected through radiomics, offering a new approach to investigating the abnormal pattern of the hippocampus in MS and NMOSD.

LNAS: a clinically applicable deep-learning system for mediastinal enlarged lymph nodes segmentation and station mapping without regard to the pathogenesis using unenhanced CT images.

BACKGROUND: The accurate identification and evaluation of lymph nodes by CT images is of great significance for disease diagnosis, treatment, and prognosis. PURPOSE: To assess the lymph nodes' segmentation, size, and station by artificial intelligence (AI) for unenhanced chest CT images and evaluate its value in clinical scenarios. MATERIAL AND METHODS: This retrospective study proposed an end-to-end Lymph Nodes Analysis System (LNAS) consisting of three models: the Lymph Node Segmentation model (LNS), the Mediastinal Organ Segmentation model (MOS), and the Lymph Node Station Registration model (LNR). We selected a healthy chest CT image as the template image and annotated 14 lymph node station masks according to the IASLC to build the lymph node station mapping template. The exact contours and stations of the lymph nodes were annotated by two junior radiologists and reviewed by a senior radiologist. Patients aged 18 and above, who had undergone unenhanced chest CT and had at least one suspicious enlarged mediastinal lymph node in imaging reports, were included. Exclusions were patients who had thoracic surgeries in the past 2 weeks or artifacts on CT images affecting lymph node observation by radiologists. The system was trained on 6725 consecutive chest CTs that from Tianjin Medical University General Hospital, among which 6249 patients had suspicious enlarged mediastinal lymph nodes. A total of 519 consecutive chest CTs from Qilu Hospital of Shandong University (Qingdao) were used for external validation. The gold standard for each CT was determined by two radiologists and reviewed by one senior radiologist. RESULTS: The patient-level sensitivity of the LNAS system reached of 93.94% and 92.89% in internal and external test dataset, respectively. And the lesion-level sensitivity (recall) reached 89.48% and 85.97% in internal and external test dataset. For man-machine comparison, AI significantly apparently shortened the average reading time (p < 0.001) and had better lesion-level and patient-level sensitivities. CONCLUSION: AI improved the sensitivity lymph node segmentation by radiologists with an advantage in reading time.

Male and female are not the same: a multicenter study of static and dynamic functional connectivity in relapse-remitting multiple sclerosis in China.

Background: Sex-related effects have been observed in relapsing-remitting multiple sclerosis (RRMS), but their impact on functional networks remains unclear. Objective: To investigate the sex-related differences in connectivity strength and time variability within large-scale networks in RRMS. Methods: This is a multi-center retrospective study. A total of 208 RRMS patients (135 females; 37.55 ± 11.47 years old) and 228 healthy controls (123 females; 36.94 ± 12.17 years old) were included. All participants underwent clinical and MRI assessments. Independent component analysis was used to extract resting-state networks (RSNs). We assessed the connectivity strength using spatial maps (SMs) and static functional network connectivity (sFNC), evaluated temporal properties and dynamic functional network connectivity (dFNC) patterns of RSNs using dFNC, and investigated their associations with structural damage or clinical variables. Results: For static connectivity, only male RRMS patients displayed decreased SMs in the attention network and reduced sFNC between the sensorimotor network and visual or frontoparietal networks compared with healthy controls [P<0.05, false discovery rate (FDR) corrected]. For dynamic connectivity, three recurring states were identified for all participants: State 1 (sparse connected state; 42%), State 2 (middle-high connected state; 36%), and State 3 (high connected state; 16%). dFNC analyses suggested that altered temporal properties and dFNC patterns only occurred in females: female patients showed a higher fractional time (P<0.001) and more dwell time in State 1 (P<0.001) with higher transitions (P=0.004) compared with healthy females. Receiver operating characteristic curves revealed that the fraction time and mean dwell time of State 1 could significantly distinguish female patients from controls (area under the curve: 0.838-0.896). In addition, female patients with RRMS also mainly showed decreased dFNC in all states, particularly within cognitive networks such as the default mode, frontoparietal, and visual networks compared with healthy females (P < 0.05, FDR corrected). Conclusion: Our results observed alterations in connectivity strength only in male patients and time variability in female patients, suggesting that sex-related effects may play an important role in the functional impairment and reorganization of RRMS.

Serum neurofilament light chain predicts spinal cord atrophy in neuromyelitis optica spectrum disorder.

Levels of serum neurofilament light chain (sNfL) and serum glial fibrillary acidic protein (sGFAP) are useful biomarkers of disease activity and disability in neuromyelitis optica spectrum disorder (NMOSD). Here we investigated the association of sNfL and sGFAP levels with brain and spinal cord volumes in patients with NMOSD. Fifteen patients with NMOSD were enrolled in this prospective study. The median baseline level of sNfL was 42.2 (IQR, 16.1-72.6) pg/mL and decreased to 8.5 (IQR, 7.4-16.6) pg/mL at the end of the study. The reduction in sNfL was associated with a 7.5% loss of cervical spinal cord volume (CSCV) (p = 0.001). The levels of sGFAP reduced from 239.2 (IQR, 139.0-3393.3) pg/mL at baseline to 108.5 (IQR, 74.2-154.6) pg/mL. However, there was no strong correlation between sGFAP levels and CSCV changes during the follow-up period. Our data suggested that sNfL level is a useful biomarker for predicting spinal cord atrophy in patients with NMOSD.

Sex-specific associations of cardiovascular risk factors and coronary plaque composition for hemodynamically significant coronary artery stenosis: a coronary computed tomography angiography study.

BACKGROUND: It has been reported that there are sex differences in plaque composition and hemodynamically significant stenosis. This study aimed to explore the impact of sex on cardiovascular risk factors for specific plaque compositions and hemodynamically significant stenosis. METHODS: Data regarding demographics and cardiovascular risk factors were collected. Hemodynamically significant stenosis was identified by a computed tomography-derived fractional flow reserve of ≤ 0.8. Associations among cardiovascular risk factors, plaque composition, and hemodynamically significant stenosis were assessed using a multivariate binary logistic regression analysis across sexes. The discriminating capacity of diverse plaque components for hemodynamically significant stenosis was assessed by area under the receiver-operating characteristics curve with 95% confidence intervals. RESULTS: A total of 1164 patients (489 men and 675 women) were included. For men, hyperlipidemia and cigarette smoking were risk factors for each plaque component (all P < 0.05), and diabetes mellitus also predicted fibrotic components (P < 0.05). For women, risk factors for each plaque component were hypertension and diabetes mellitus (all P < 0.01). Nonetheless, hyperlipidemia (P < 0.05) was a specific risk factor for non-calcified components. Calcified components combined with fibrotic components showed superior discrimination of hemodynamically significant stenosis in men and calcified components alone in women (all P < 0.01). Hypertension (P < 0.01) was a risk factor for hemodynamically significant stenosis in women. In contrast, diabetes, hyperlipidemia, and cigarette smoking were risk factors for hemodynamically significant stenosis in men (all P < 0.05). CONCLUSIONS: In men, hemodynamically significant stenosis was predicted by a combination of calcified and fibrotic components with multiple risk factors. In women, hemodynamically significant stenosis was predicted by calcified components caused by a single risk factor. It might be a key point to improve prognosis by more precise risk management between men and women, which needs to be proved by further prospective trials.

Personalized estimates of brain cortical structural variability in individuals with Autism spectrum disorder: the predictor of brain age and neurobiology relevance.

BACKGROUND: Autism spectrum disorder (ASD) is a heritable condition related to brain development that affects a person's perception and socialization with others. Here, we examined variability in the brain morphology in ASD children and adolescent individuals at the level of brain cortical structural profiles and the level of each brain regional measure. METHODS: We selected brain structural MRI data in 600 ASDs and 729 normal controls (NCs) from Autism Brain Imaging Data Exchange (ABIDE). The personalized estimate of similarity between gray matter volume (GMV) profiles of an individual to that of others in the same group was assessed by using the person-based similarity index (PBSI). Regional contributions to PBSI score were utilized for brain age gap estimation (BrainAGE) prediction model establishment, including support vector regression (SVR), relevance vector regression (RVR), and Gaussian process regression (GPR). The association between BrainAGE prediction in ASD and clinical performance was investigated. We further explored the related inter-regional profiles of gene expression from the Allen Human Brain Atlas with variability differences in the brain morphology between groups. RESULTS: The PBSI score of GMV was negatively related to age regardless of the sample group, and the PBSI score was significantly lower in ASDs than in NCs. The regional contributions to the PBSI score of 126 brain regions in ASDs showed significant differences compared to NCs. RVR model achieved the best performance for predicting brain age. Higher inter-individual brain morphology variability was related to increased brain age, specific to communication symptoms. A total of 430 genes belonging to various pathways were identified as associated with brain cortical morphometric variation. The pathways, including short-term memory, regulation of system process, and regulation of nervous system process, were dominated mainly by gene sets for manno midbrain neurotypes. LIMITATIONS: There is a sample mismatch between the gene expression data and brain imaging data from ABIDE. A larger sample size can contribute to the model training of BrainAGE and the validation of the results. CONCLUSIONS: ASD has personalized heterogeneity brain morphology. The brain age gap estimation and transcription-neuroimaging associations derived from this trait are replenished in an additional direction to boost the understanding of the ASD brain.

Personalized estimates of morphometric similarity in multiple sclerosis and neuromyelitis optica spectrum disorders.

Brain morphometric alterations involve multiple brain regions on progression of the disease in multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD) and exhibit age-related degenerative changes during the pathological aging. Recent advance in brain morphometry as measured using MRI have leveraged Person-Based Similarity Index (PBSI) approach to assess the extent of within-diagnosis similarity or heterogeneity of brain neuroanatomical profiles between individuals of healthy populations and validate in neuropsychiatric disorders. Brain morphometric changes throughout the lifespan would be invaluable for understanding regional variability of age-related structural degeneration and the substrate of inflammatory demyelinating disease. Here, we aimed to quantify the neuroanatomical profiles with PBSI measures of cortical thickness (CT) and subcortical volumes (SV) in 263 MS, 207 NMOSD, and 338 healthy controls (HC) from six separate central datasets (aged 11-80). We explored the between-group comparisons of PBSI measures, as well as the advancing age and sex effects on PBSI measures. Compared to NMOSD, MS showed a lower extent of within-diagnosis similarity. Significant differences in regional contributions to PBSI score were observed in 29 brain regions between MS and NMOSD (P < 0.05/164, Bonferroni corrected), of which bilateral cerebellum in MS and bilateral parahippocampal gyrus in NMOSD represented the highest divergence between the two patient groups, with a high similarity effect within each group. The PBSI scores were generally lower with advancing age, but their associations showed different patterns depending on the age range. For MS, CT profiles were significantly negatively correlated with age until the early 30 s (ρ = -0.265, P = 0.030), while for NMOSD, SV profiles were significantly negatively correlated with age with 51 year-old and older (ρ = -0.365, P = 0.008). The current study suggests that PBSI approach could be used to quantify the variation in brain morphometric changes in CNS inflammatory demyelinating disease, and exhibited a greater neuroanatomical heterogeneity pattern in MS compared with NMOSD. Our results reveal that, as an MR marker, PBSI may be sensitive to distribute the disease-associated grey matter diversity and complexity. Disease-driven production of regionally selective and age stage-dependency changes in the neuroanatomical profile of MS and NMOSD should be considered to facilitate the prediction of clinical outcomes and assessment of treatment responses.

Cerebellar connectome alterations and associated genetic signatures in multiple sclerosis and neuromyelitis optica spectrum disorder.

BACKGROUND: The cerebellum plays key roles in the pathology of multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD), but the way in which these conditions affect how the cerebellum communicates with the rest of the brain (its connectome) and associated genetic correlates remains largely unknown. METHODS: Combining multimodal MRI data from 208 MS patients, 200 NMOSD patients and 228 healthy controls and brain-wide transcriptional data, this study characterized convergent and divergent alterations in within-cerebellar and cerebello-cerebral morphological and functional connectivity in MS and NMOSD, and further explored the association between the connectivity alterations and gene expression profiles. RESULTS: Despite numerous common alterations in the two conditions, diagnosis-specific increases in cerebellar morphological connectivity were found in MS within the cerebellar secondary motor module, and in NMOSD between cerebellar primary motor module and cerebral motor- and sensory-related areas. Both diseases also exhibited decreased functional connectivity between cerebellar motor modules and cerebral association cortices with MS-specific decreases within cerebellar secondary motor module and NMOSD-specific decreases between cerebellar motor modules and cerebral limbic and default-mode regions. Transcriptional data explained > 37.5% variance of the cerebellar functional alterations in MS with the most correlated genes enriched in signaling and ion transport-related processes and preferentially located in excitatory and inhibitory neurons. For NMOSD, similar results were found but with the most correlated genes also preferentially located in astrocytes and microglia. Finally, we showed that cerebellar connectivity can help distinguish the three groups from each other with morphological connectivity as predominant features for differentiating the patients from controls while functional connectivity for discriminating the two diseases. CONCLUSIONS: We demonstrate convergent and divergent cerebellar connectome alterations and associated transcriptomic signatures between MS and NMOSD, providing insight into shared and unique neurobiological mechanisms underlying these two diseases.

Brain age gap in neuromyelitis optica spectrum disorders and multiple sclerosis.

OBJECTIVE: To evaluate the clinical significance of deep learning-derived brain age prediction in neuromyelitis optica spectrum disorder (NMOSD) relative to relapsing-remitting multiple sclerosis (RRMS). METHODS: This cohort study used data retrospectively collected from 6 tertiary neurological centres in China between 2009 and 2018. In total, 199 patients with NMOSD and 200 patients with RRMS were studied alongside 269 healthy controls. Clinical follow-up was available in 85 patients with NMOSD and 124 patients with RRMS (mean duration NMOSD=5.8±1.9 (1.9-9.9) years, RRMS=5.2±1.7 (1.5-9.2) years). Deep learning was used to learn 'brain age' from MRI scans in the healthy controls and estimate the brain age gap (BAG) in patients. RESULTS: A significantly higher BAG was found in the NMOSD (5.4±8.2 years) and RRMS (13.0±14.7 years) groups compared with healthy controls. A higher baseline disability score and advanced brain volume loss were associated with increased BAG in both patient groups. A longer disease duration was associated with increased BAG in RRMS. BAG significantly predicted Expanded Disability Status Scale worsening in patients with NMOSD and RRMS. CONCLUSIONS: There is a clear BAG in NMOSD, although smaller than in RRMS. The BAG is a clinically relevant MRI marker in NMOSD and RRMS.

Personality and brain contribute to academic achievements of medical students.

There are many factors that influence the academic achievements of medical students, but how personality and brain modulate the academic achievements of medical students remains unclear. The study collected the personality, brain imaging, and academic data from 448 medical students at Tianjin Medical University with admission time between 2008 and 2017. Four types of academic achievements, including behavioral and social sciences, clinical sciences and skills, basic biomedical sciences, and scientific methods, were assessed by the academic records of 58 courses. Personality was evaluated by Tridimensional Personality Questionnaire and Neuroticism Extraversion Openness Personality Inventory. Brain structural and functional properties, including gray matter volume, spontaneous brain activity and functional connectivity, were computed based on magnetic resonance imaging (MRI). Linear regression was used to evaluate the associations between personality and academic achievements. A voxel-wise correlation was used to identify areas of the brain where structural and functional properties were associated with academic achievements. Mediation analysis was used to test whether brain properties and personality independently contribute to academic achievements. Our results showed that novelty seeking (NS) was negatively correlated, and conscientiousness was positively correlated with all types of academic achievements. Brain functional properties showed negatively correlated with academic achievement in basic biomedical sciences. However, we did not find any mediation effect of the brain functional properties on the association between personality (NS and conscientiousness) and academic achievement in basic biomedical sciences, nor mediation effect of the personality (NS and conscientiousness) on the association between brain functional properties and academic achievement in basic biomedical sciences. These findings suggest that specific personality (NS and conscientiousness) and brain functional properties independently contribute to academic achievements in basic biomedical sciences, and that modulation of these properties may benefit academic achievements among medical students.

Machine-learning-based contrast-enhanced computed tomography radiomic analysis for categorization of ovarian tumors.

Objectives: This study aims to evaluate the diagnostic performance of machine-learning-based contrast-enhanced CT radiomic analysis for categorizing benign and malignant ovarian tumors. Methods: A total of 1,329 patients with ovarian tumors were randomly divided into a training cohort (N=930) and a validation cohort (N=399). All tumors were resected, and pathological findings were confirmed. Radiomic features were extracted from the portal venous phase images of contrast-enhanced CT. The clinical predictors included age, CA-125, HE-4, ascites, and margin of tumor. Both radiomics model (including selected radiomic features) and mixed model (incorporating selected radiomic features and clinical predictors) were constructed respectively. Six classifiers [k-nearest neighbor (KNN), support vector machines (SVM), random forest (RF), logistic regression (LR), multi-layer perceptron (MLP), and eXtreme Gradient Boosting (XGBoost)] were used for each model. The mean relative standard deviation (RSD) and area under the receiver operating characteristic curve (AUC) were applied to evaluate and select the best classifiers. Then, the performances of the two models with selected classifiers were assessed in the validation cohort. Results: The MLP classifier with the least RSD (1.21 and 0.53, respectively) was selected as the best classifier in both radiomics and mixed models. The two models with MLP classifier performed well in the validation cohort, with the AUCs of 0.91 and 0.96 and with accuracies (ACCs) of 0.83 and 0.87, respectively. The Delong test showed that the AUC of mixed model was statistically different from that of radiomics model (p<0.001). Conclusions: Machine-learning-based CT radiomic analysis could categorize ovarian tumors with good performance preoperatively. The mixed model with MLP classifier may be a potential tool in clinical applications.

Expression of TRAF6 in peripheral blood B cells of patients with myasthenia gravis.

BACKGROUND: Tumor necrosis factor receptor-associated factor 6 (TRAF6) can regulate the activation of inflammatory signaling pathways by acting as an E3 ubiquitin ligase, which enhances B cell activation. This study aimed to evaluate the expression of TRAF6 in the peripheral blood B cells of myasthenia gravis (MG) patients and analyze the relationships between TRAF6 expression and clinical characteristics. METHOD: In our study, the expression level of TRAF6 in peripheral blood B cells of 89 patients was measured by flow cytometry compared with that of healthy subjects. The effects of disease severity, MG classification and immunotherapy on TRAF6 expression level were also analyzed. RESULTS: In our study, TRAF6 expression was elevated in CD19+ B cells and CD19+CD27+ memory B cells in generalized MG (GMG) patients compared with ocular MG (OMG) patients (p = 0.03 and p = 0.03, respectively). There was a significant positive correlation between the TRAF6 expression level and disease severity in both OMG patients and GMG patients (CD19+ B cells: OMG: p < 0.001, r = 0.89; GMG: p = 0.001, r = 0.59; CD29+CD27+ B cells: OMG: p = 0.001, r = 0.80; GMG: p = 0.048, r = 0.38). TRAF6 expression was significantly elevated in CD19+ B cells and CD19+CD27+ memory B cells in GMG with acute aggravation compared with GMG in MMS (p = 0.009 and p = 0.028, respectively). In the eleven MG patients who were followed, TRAF6 expression in B cells and memory B cells was significantly decreased after treatment (p = 0.03 and p < 0.01, respectively). CONCLUSION: TRAF6 is potentially a useful biomarker of inflammation in patients with MG, and might be used to evaluate the effectiveness of treatment.

MS or not MS: T2-weighted imaging (T2WI)-based radiomic findings distinguish MS from its mimics.

BACKGROUND: Ischemic vasculopathy, particularly small-vessel disease, may mimic multiple sclerosis (MS) located in the periventricular or subcortical region on magnetic resonance (MR) examinations and should be included in the differential diagnosis of MS-like lesions. OBJECTIVE: To evaluate the performance of a T2-weighted imaging (T2WI)-based radiomic signature to distinguish MS lesions from lesions corresponding to ischemic demyelination, which often mimics MS on MRI. METHODS: A retrospective study was conducted in 38 patients (627 lesions) with MS and 914 patients (2466 lesions) with lesions mimicking ischemic demyelination in the periventricular or subcortical region. All patients underwent 3 T MRI. A total of 472 radiomic features were extracted from the T2WI data of each patient. Intraclass correlation coefficients were used to select the features with excellent stability and repeatability. Then, we used the minimum-redundancy maximum-relevance (mRMR) and least absolute shrinkage and selection operator (LASSO) algorithms for feature selection. After feature selection, various classifiers (including logistic regression, decision tree, AdaBoost, random forest (RF), or support vector machine (SVM)) were trained. The performance of each classifier was validated in the test set by determining the area under the curve (AUC). RESULTS: Nine features were selected to distinguish MS lesions from the similar lesions of ischemic demyelination. The radiomic signature showed a significant difference between the MS and ischemic demyelination patients (p < 0.01). RF and SVM were overfitted. The LASSO logistic regression model was the best-performing radiomic model,with an AUC, accuracy, sensitivity, and specificity of 0.900 (95% CI: 0.883-0.918), 87.0%, 58.9% and 95.2%, respectively, in the training set and 0.828 (95% CI: 0.791-0.864), 87.7%, 53.6% and 94.4%, respectively, in the validation set. CONCLUSION: The T2WI-based radiomic signature can effectively differentiate MS patients from patients with MS-like lesions due to ischemic demyelination.

Genes associated with grey matter volume reduction in multiple sclerosis.

There is extensive grey matter volume (GMV) reduction in multiple sclerosis (MS), which may account for cognitive impairment in this disabling disorder. Although genome-wide association studies (GWASs) have identified hundreds of genes associated with MS, we know little about which genes associated with GMV reduction and cognitive decline in MS. In the present study, we aimed to uncover genes associated with GMV reduction in MS by performing cross-sample (1473 brain tissue samples) partial least squares regression between gene expression from 6 postmortem brains and case-control GMV difference of MS from a meta-analysis of 1391 patients and 1189 controls (discovery phase) and from the intergroup comparison between 69 patients and 70 controls (replication phase). We identified 623 genes whose brain spatial expression profiles were significantly associated with GMV reduction in MS. These genes showed significant enrichment for MS-related genes identified by GWAS; were functionally associated with ion channel, synaptic transmission, axon and neuron projection; and showed more significant cell type-specific expression in neurons than other cell types. More importantly, the identified genes showed significant enrichment for those genes with downregulated rather than upregulated expression in MS. The spatial distribution patterns of the expression of the identified genes showed more significant correlations with brain activation patterns of memory and language tasks. These findings indicate that grey matter atrophy in MS may be resulted from the joint effects of multiple genes that are associated with this disorder, especially genes with downregulated expression in MS.

Structural and functional hippocampal alterations in Multiple sclerosis and neuromyelitis optica spectrum disorder.

BACKGROUND: Hippocampal involvement may differ between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). OBJECTIVE: To investigate the morphometric, diffusion and functional alterations in hippocampus in MS and NMOSD and the clinical significance. METHODS: A total of 752 participants including 236 MS, 236 NMOSD and 280 healthy controls (HC) were included in this retrospective multi-center study. The hippocampus and subfield volumes, fractional anisotropy (FA) and mean diffusivity (MD), amplitude of low frequency fluctuation (ALFF) and degree centrality (DC) were analyzed, and their associations with clinical variables were investigated. RESULTS: The hippocampus showed significantly lower volume, FA and greater MD in MS compared to NMOSD and HC (p < 0.05), while no abnormal ALFF or DC was identified in any group. Hippocampal subfields were affected in both diseases, though subiculum, presubiculum and fimbria showed significantly lower volume only in MS (p < 0.05). Significant correlations between diffusion alterations, several subfield volumes and clinical variables were observed in both diseases, especially in MS (R = -0.444 to 0.498, p < 0.05). FA and MD showed fair discriminative power between MS and HC, NMOSD and HC (AUC > 0.7). CONCLUSIONS: Hippocampal atrophy and diffusion abnormalities were identified in MS and NMOSD, partly explaining how clinical disability and cognitive impairment are differentially affected.

Subtyping relapsing-remitting multiple sclerosis using structural MRI.

BACKGROUND AND PURPOSE: Subtyping relapsing-remitting multiple sclerosis (RRMS) patients may help predict disease progression and triage patients for treatment. We aimed to subtype RRMS patients by structural MRI and investigate their clinical significances. METHODS: 155 relapse-remitting MS (RRMS) and 210 healthy controls (HC) were retrospectively enrolled with structural 3DT1, diffusion tensor imaging (DTI) and resting-state functional MRI. Z scores of cortical and deep gray matter volumes (CGMV and DGMV) and white matter fractional anisotropy (WM-FA) in RRMS patients were calculated based on means and standard deviations of HC. We defined RRMS as "normal" (- 2 < z scores of both GMV and WM-FA), DGM (z scores of DGMV < - 2), and DGM-plus types (z scores of DGMV and [CGMV or WM-FA] < - 2) according to combinations of z scores compared to HC. Expanded disability status scale (EDSS), cognitive and functional MRI measurements, and conversion rate to secondary progressive MS (SPMS) at 5-year follow-up were compared between subtypes. RESULTS: 77 (49.7%) patients were "normal" type, 37 (23.9%) patients were DGM type and 34 (21.9%) patients were DGM-plus type. 7 (4.5%) patients who were not categorized into the above types were excluded. DGM-plus type had the highest EDSS. Both DGM and DGM-plus types had more severe cognitive impairment than "normal" type. Only DGM-plus type showed decreased functional MRI measures compared to HC. A higher conversion ratio to SPMS in DGM-plus type (55%) was identified compared to "normal" type (14%, p < 0.001) and DGM type (20%, p = 0.005). CONCLUSION: Three MRI-subtypes of RRMS were identified with distinct clinical and imaging features and different prognosis.

Brain MRI characteristics in neuromyelitis optica spectrum disorders: A large multi-center retrospective study in China.

PURPOSE: To investigate the brain MRI features in neuromyelitis optica spectrum disorders (NMOSD) and its clinical relevance in a large multi-center cohort in China. METHODS: 270 NMOSD patients were recruited from seven centers. The brain MRI were classified as normal, NMOSD-specific lesions, multiple sclerosis-like, nonspecific white matter changes. Brain volumes including whole brain, white, gray matter, cortex and subcortex gray matter volume were measured. The relationship between MRI measures, clinical disability and cognitive impairment were investigated. RESULTS: 98 patients (36.3%) had normal brain MRI; 48 patients (17.7%) had NMOSD-specific lesions located in dorsal brainstem, corticospinal tract corpus, callosum and periependymal lesions surrounding the ventricular system; 16 patients (6%) had multiple sclerosis-like lesions; and 108 patients (40%) had nonspecific white matter changes. NMOSD patients with brain lesions had a trend of lower subcortex gray matter volume compared to patients without lesions. 52.5% patients with normal brain MRI and 50.8% patients with abnormal brain MRI showed cognitive impairment. No significant differences were identified in brain volume between cognitive impairment and cognitive preserved groups. CONCLUSION: In this large multicenter NMOSD cohort, nonspecific white matter changes were the most common findings (40%). NMOSD patients with brain lesions demonstrated a trend of having lower brain volume than patients without lesions. Approximately 50% NMOSD patients presented cognitive impairment independent of brain lesions.

Differentiate aquaporin-4 antibody negative neuromyelitis optica spectrum disorders from multiple sclerosis by multimodal advanced MRI techniques.

BACKGROUND: It is clinically essential to distinguish aquaporin-4 antibody (AQP4-Ab) negative neuromyelitis optica spectrum disorders (NMOSD) and multiple sclerosis (MS) because of different therapeutic strategies. Since clinical and lesion features may not allow the distinction, we aimed to identify advanced imaging features that could improve the distinction between two disorders. METHODS: Multimodal imaging measures included fractional anisotropy, mean, axial, radial diffusivity (MD, AD, RD) and kurtosis (MK, AK, RK) from diffusion kurtosis imaging; functional connectivity strength (FCS) and density, regional homogeneity, amplitude of low frequency fluctuations from resting-state functional MRI; gray matter volume from structural MRI; and cerebral blood flow from arterial spin labeling imaging. Voxel-wise comparisons were performed to identify inter-group differences in imaging measures, and the performance of differentiating these two disorders was estimated by receiver operating characteristic curves. RESULTS: Compared to MS, patients with AQP4-Ab negative NMOSD showed decreased MD and AD but increased MK and AK in white matter regions; and reduced FCS in the occipital cortex (P < 0.05, FWE corrected). The joint-use of these five imaging measures distinguished the two disorders with an accuracy of 94% (P < 0.001, 95%CI = 0.84-0.98). Other imaging measures showed no significant differences between the two patient groups. CONCLUSIONS: The study showed less white matter damage and a more severe functional disconnection of the occipital cortex in patients with AQP4-Ab negative NMOSD compared to MS. The combined use of diffusion and functional connectivity could facilitate a better distinction between NMO and MS with seronegative AQP4-Ab in clinical management.