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.

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.

Altered hippocampal GABA and glutamate levels and uncoupling from functional connectivity in multiple sclerosis.

There is growing evidence for dysfunctional glutamatergic excitation and/or gamma-aminobutyric acid (GABA)ergic inhibition in patients with multiple sclerosis (MS). Cognitive impairment may occur during the early stages of MS and hippocampal abnormalities have been suggested as biomarkers. However, researchers have not clearly determined whether changes in hippocampal GABA and glutamate (Glu) levels are associated with cognitive impairment and aberrant neural activity in patients with MS. We used magnetic resonance spectroscopy to measure GABA+ and Glu levels in the left hippocampal region of 29 patients with relapsing-remitting MS and 29 healthy controls (HCs). Resting-state functional connectivity (FC) with the hippocampus was also examined. Compared to HCs, patients exhibited significantly lower GABA+ and Glu levels, which were associated with verbal and visuospatial memory deficits, respectively. Patients also showed decreased FC strengths between the hippocampus and several cortical regions, which are located within the default mode network. Moreover, hippocampal GABA+ levels and Glu/GABA+ ratios correlated with the FC strengths in HCs but not in patients with MS. This study describes a novel method for investigating the complex relationships among excitatory/inhibitory neurotransmitters, brain connectivity and cognition in health and disease. Strategies that modulate Glu and GABA neurotransmission may represent new therapeutic treatments for patients with MS.

Reduced GABA levels correlate with cognitive impairment in patients with relapsing-remitting multiple sclerosis.

OBJECTIVES: To investigate if brain gamma-aminobutyric acid (GABA) levels in patients with relapsing-remitting multiple sclerosis (RRMS) are abnormal compared with healthy controls, and their relationship to cognitive function in RRMS. METHODS: Twenty-eight RRMS patients and twenty-six healthy controls underwent magnetic resonance spectroscopy (MRS) at 3-T to detect GABA signals from posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC) and left hippocampus using the 'MEGAPoint Resolved Spectroscopy Sequence' (MEGA-PRESS) technique. All subjects also underwent a cognitive assessment. RESULTS: In RRMS patients, GABA+ were lower in the PCC (p = 0.036) and left hippocampus (p = 0.039) compared with controls, decreased GABA+ in the PCC and left hippocampus were associated with specific cognitive functions (r = -0.452, p = 0.016 and r = 0.451, p = 0.016 respectively); GABA+ in the mPFC were not significantly decreased or related to any cognitive scores (p > 0.05). CONCLUSIONS: This study demonstrates that abnormalities of the GABAergic system may be present in the pathogenesis of RRMS and suggests a potential link between regional GABA levels and cognitive impairment in patients with RRMS. KEY POINTS: • GABA levels may decrease in patients with RRMS. • Lower GABA levels correlated with worse cognitive performance in patients with RRMS. • Dysfunctional GABAergic neurotransmission may have a role in cognitive impairment in RRMS.

Accurate Differentiation of Spinal Tuberculosis and Spinal Metastases Using MR-Based Deep Learning Algorithms.

Purpose: To explore the application of deep learning (DL) methods based on T2 sagittal MR images for discriminating between spinal tuberculosis (STB) and spinal metastases (SM). Patients and Methods: A total of 121 patients with histologically confirmed STB and SM across four institutions were retrospectively analyzed. Data from two institutions were used for developing deep learning models and internal validation, while the remaining institutions' data were used for external testing. Utilizing MVITV2, EfficientNet-B3, ResNet101, and ResNet34 as backbone networks, we developed four distinct DL models and evaluated their diagnostic performance based on metrics such as accuracy (ACC), area under the receiver operating characteristic curve (AUC), F1 score, and confusion matrix. Furthermore, the external test images were blindly evaluated by two spine surgeons with different levels of experience. We also used Gradient-Class Activation Maps to visualize the high-dimensional features of different DL models. Results: For the internal validation set, MVITV2 outperformed other models with an accuracy of 98.7%, F1 score of 98.6%, and AUC of 0.98. Other models followed in this order: EfficientNet-B3 (ACC: 96.1%, F1 score: 95.9%, AUC: 0.99), ResNet101 (ACC: 85.5%, F1 score: 84.8%, AUC: 0.90), and ResNet34 (ACC: 81.6%, F1 score: 80.7%, AUC: 0.85). For the external test set, MVITV2 again performed excellently with an accuracy of 91.9%, F1 score of 91.5%, and an AUC of 0.95. EfficientNet-B3 came second (ACC: 85.9, F1 score: 91.5%, AUC: 0.91), followed by ResNet101 (ACC:80.8, F1 score: 80.0%, AUC: 0.87) and ResNet34 (ACC: 78.8, F1 score: 77.9%, AUC: 0.86). Additionally, the diagnostic accuracy of the less experienced spine surgeon was 73.7%, while that of the more experienced surgeon was 88.9%. Conclusion: Deep learning based on T2WI sagittal images can help discriminate between STB and SM, and can achieve a level of diagnostic performance comparable with that produced by experienced spine surgeons.

Expression characteristics of peripheral lymphocyte programmed death 1 and FoxP3+ Tregs in gastric cancer during surgery and chemotherapy.

BACKGROUND: Programmed death 1 (PD-1) and CD4+CD25+FoxP3+ expression in peripheral blood T-cells has been previously reported in various types of cancer. However, the specific variation tendency during surgery and chemotherapy, as well as their relationship in gastric cancer patients, still remain unclear. Understanding this aspect may provide some novel insights for future studies on tumor recurrence and tumor immune escape, and also serve as a reference for determining the optimal timing and dose of clinical anti-PD-1 antibodies. AIM: To observe and analyze the expression characteristics of peripheral lymphocyte PD-1 and FoxP3+ regulatory T cells (FoxP3+ Tregs) before and after surgery or chemotherapy in gastric cancer patients. METHODS: Twenty-nine stomach cancer patients undergoing chemotherapy after a D2 gastrectomy provided 10 mL peripheral blood samples at each phase of the perioperative period and during chemotherapy. This study also included 29 age-matched healthy donors as a control group. PD-1 expression was detected on lymphocytes, including CD4+CD8+CD45RO+, CD4+CD45RO+, and CD8+CD45RO+ lymphocytes as well as regulatory T cells. RESULTS: We observed a significant increase of PD-1 expression on immune subsets and a larger number of FoxP3+ Tregs in gastric cancer patients (P < 0.05). Following D2 gastrectomy, peripheral lymphocytes PD-1 expression and the number of FoxP3+ Tregs notably decrease (P < 0.05). However, during postoperative chemotherapy, we only observed a decrease in PD-1 expression on lymphocytes in the CD8+CD45RO+ and CD8+CD45RO+ populations. Additionally, linear correlation analysis indicated a positive correlation between PD-1 expression and the number of CD4+CD45RO+FoxP3high activated Tregs (aTregs) on the total peripheral lymphocytes (r = 0.5622, P < 0.0001). CONCLUSION: The observed alterations in PD-1 expression and the activation of regulatory T cells during gastric cancer treatment may offer novel insights for future investigations into tumor immune evasion and the clinical application of anti-PD-1 antibodies in gastric cancer.

Differential diagnosis of benign and malignant vertebral compression fractures: Comparison and correlation of radiomics and deep learning frameworks based on spinal CT and clinical characteristics.

PURPOSE: Differentiating benign from malignant vertebral compression fractures (VCFs) is a diagnostic dilemma in clinical practice. To improve the accuracy and efficiency of diagnosis, we evaluated the performance of deep learning and radiomics methods based on computed tomography (CT) and clinical characteristics in differentiating between Osteoporosis VCFs (OVCFs) and malignant VCFs (MVCFs). METHODS: We enrolled a total of 280 patients (155 with OVCFs and 125 with MVCFs) and randomly divided them into a training set (80%, n = 224) and a validation set (20%, n = 56). We developed three predictive models: a deep learning (DL) model, a radiomics (Rad) model, and a combined DL_Rad model, using CT and clinical characteristics data. The Inception_V3 served as the backbone of the DL model. The input data for the DL_Rad model consisted of the combined features of Rad and DCNN features. We calculated the receiver operating characteristic curve, area under the curve (AUC), and accuracy (ACC) to assess the performance of the models. Additionally, we calculated the correlation between Rad features and DCNN features. RESULTS: For the training set, the DL_Rad model achieved the best results, with an AUC of 0.99 and ACC of 0.99, followed by the Rad model (AUC: 0.99, ACC: 0.97) and DL model (AUC: 0.99, ACC: 0.94). For the validation set, the DL_Rad model (with an AUC of 0.97 and ACC of 0.93) outperformed the Rad model (with an AUC: 0.93 and ACC: 0.91) and the DL model (with an AUC: 0.89 and ACC: 0.88). Rad features achieved better classifier performance than the DCNN features, and their general correlations were weak. CONCLUSIONS: The Deep learnig model, Radiomics model, and Deep learning Radiomics model achieved promising results in discriminating MVCFs from OVCFs, and the DL_Rad model performed the best.

Identification of Origin for Spinal Metastases from MR Images: Comparison Between Radiomics and Deep Learning Methods.

OBJECTIVE: To determine whether spinal metastatic lesions originated from lung cancer or from other cancers based on spinal contrast-enhanced T1 (CET1) magnetic resonance (MR) images analyzed using radiomics (RAD) and deep learning (DL) methods. METHODS: We recruited and retrospectively reviewed 173 patients diagnosed with spinal metastases at two different centers between July 2018 and June 2021. Of these, 68 involved lung cancer and 105 were other types of cancer. They were assigned to an internal cohort of 149 patients, randomly divided into a training set and a validation set, and to an external cohort of 24 patients. All patients underwent CET1-MR imaging before surgery or biopsy. We developed two predictive algorithms: a DL model and a RAD model. We compared performance between models, and against human radiological assessment, via accuracy (ACC) and receiver operating characteristic (ROC) analyses. Furthermore, we analyzed the correlation between RAD and DL features. RESULTS: The DL model outperformed RAD model across the board, with ACC/ area under the receiver operating characteristic curve (AUC) values of 0.93/0.94 (DL) versus 0.84/0.93 (RAD) when applied to the training set from the internal cohort, 0.74/0.76 versus 0.72/0.75 when applied to the validation set, and 0.72/0.76 versus 0.69/0.72 when applied to the external test cohort. For the validation set, it also outperformed expert radiological assessment (ACC: 0.65, AUC: 0.68). We only found weak correlations between DL and RAD features. CONCLUSION: The DL algorithm successfully identified the origin of spinal metastases from pre-operative CET1-MR images, outperforming both RAD models and expert assessment by trained radiologists.

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.

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.