Structural and Functional Alterations in Visual Pathway After Optic Neuritis in MOG Antibody Disease: A Comparative Study With AQP4 Seropositive NMOSD

Background: Optic neuritis (ON) is an important clinical manifestation of neuromyelitis optic spectrum disease (NMOSD). Myelin oligodendrocyte glycoprotein (MOG) antibody-related and aquaporin 4 (AQP4) antibody-related ON show different disease patterns. The aim of this study was to explore the differences in structure and function of the visual pathway in patients with ON associated with MOG and AQP4 antibodies.Methods: In this prospective study, we recruited 52 subjects at Beijing Tiantan Hospital, including 11 with MOG Ig+ ON (MOG-ON), 13 with AQP4 Ig+ ON (AQP4-ON), and 28 healthy controls (HCs). Fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) of optic radiation (OR), primary visual cortex volume (V1), brain volume, and visual acuity (VA) were compared among groups. A multiple linear regression was used to explore associations between VA and predicted factors. In addition, we used optical coherence tomography (OCT) to examine thickness of the peripapillary retinal nerve fiber layer (pRNFL) and retinal ganglion cell complex (GCC) in a separate cohort consisting of 15 patients with ON (8 MOG-ON and 7 AQP4-ON) and 28 HCs.Results: Diffusion tensor imaging showed that the FA of OR was lower than controls in patients with AQP4-ON (p = 0.001) but not those with MOG-ON (p = 0.329) and was significantly different between the latter two groups (p = 0.005), while V1 was similar in patients with MOG-ON and AQP4-ON (p = 0.122), but was lower than controls in AQP4-ON (p = 0.002) but not those with MOG-ON (p = 0.210). The VA outcomes were better in MOG-ON than AQP4-ON, and linear regression analysis revealed that VA in MOG-ON and AQP4-ON was both predicted by the FA of OR (standard β = −0.467 and −0.521, p = 0.036 and 0.034). Both patients of MOG-ON and AQP4-ON showed neuroaxonal damage in the form of pRNFL and GCC thinning but showed no statistically significant difference (p = 0.556, 0.817).Conclusion: The structural integrity of OR in patients with MOG-ON, which is different from the imaging manifestations of AQP4-ON, may be a reason for the better visual outcomes of patients with MOG-ON

Automatic multiclass intramedullary spinal cord tumor segmentation on MRI with deep learning

Spinal cord tumors lead to neurological morbidity and mortality. Being able to obtain morphometric quantification (size, location, growth rate) of the tumor, edema, and cavity can result in improved monitoring and treatment planning. Such quantification requires the segmentation of these structures into three separate classes. However, manual segmentation of three-dimensional structures is time consuming, tedious and prone to intra- and inter-rater variability, motivating the development of automated methods. Here, we tailor a model adapted to the spinal cord tumor segmentation task. Data were obtained from 343 patients using gadolinium-enhanced T1-weighted and T2-weighted MRI scans with cervical, thoracic, and/or lumbar coverage. The dataset includes the three most common intramedullary spinal cord tumor types: astrocytomas, ependymomas, and hemangioblastomas. The proposed approach is a cascaded architecture with U-Net-based models that segments tumors in a two-stage process: locate and label. The model first finds the spinal cord and generates bounding box coordinates. The images are cropped according to this output, leading to a reduced field of view, which mitigates class imbalance. The tumor is then segmented. The segmentation of the tumor, cavity, and edema (as a single class) reached 76.7 ± 1.5% of Dice score and the segmentation of tumors alone reached 61.8 ± 4.0% Dice score. The true positive detection rate was above 87% for tumor, edema, and cavity. To the best of our knowledge, this is the first fully automatic deep learning model for spinal cord tumor segmentation. The multiclass segmentation pipeline is available in the Spinal Cord Toolbox (https://spinalcordtoolbox.com/). It can be run with custom data on a regular computer within seconds

Amide proton transfer-weighted imaging of pediatric brainstem glioma and its predicted value for H3 K27 alteration

BACKGROUND: Non-invasive determination of H3 K27 alteration of pediatric brainstem glioma (pedBSG) remains a clinical challenge. PURPOSE: To predict H3 K27-altered pedBSG using amide proton transfer-weighted (APTw) imaging. MATERIAL AND METHODS: This retrospective study included patients with pedBSG who underwent APTw imaging and had the H3 K27 alteration status determined by immunohistochemical staining. The presence or absence of foci of markedly increased APTw signal in the lesion was visually assessed. Quantitative APTw histogram parameters within the entire solid portion of tumors were extracted and compared between H3 K27-altered and wild-type groups using Student's t-test. The ability of APTw for differential diagnosis was evaluated using logistic regression. RESULTS: Sixty pedBSG patients included 48 patients with H3 K27-altered tumor (aged 2-48 years) and 12 patients with wild-type tumor (aged 3-53 years). Visual assessment showed that the foci of markedly increased APTw signal intensity were more common in the H3 K27-altered group than in wild-type group (60% vs. 16%, P = 0.007). Histogram parameters of APTw signal intensity in the H3 K27-altered group were significantly higher than those in the wild-type group (median, 2.74% vs. 2.22%, P = 0.02). The maximum (area under the receiver operating characteristic curve [AUC] = 0.72, P = 0.01) showed the highest diagnostic performance among histogram analysis. A combination of age, median and maximum APTw signal intensity could predict H3 K27 alteration with a sensitivity of 81%, specificity of 75% and AUC of 0.80. CONCLUSION: APTw imaging may serve as an imaging biomarker for H3 K27 alteration of pedBSGs

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. Methods: We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). Results: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes. Conclusion: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation

Different patterns of cerebral perfusion in SLE patients with and without neuropsychiatric manifestations

To investigate brain perfusion patterns in systemic lupus erythematosus (SLE) patients with and without neuropsychiatric systemic lupus erythematosus (NPSLE and non-NPSLE, respectively) and to identify biomarkers for the diagnosis of NPSLE using noninvasive three-dimensional (3D) arterial spin labeling (ASL). Thirty-one NPSLE and 24 non-NPSLE patients and 32 age- and sex-matched normal controls (NCs) were recruited. Three-dimensional ASL-MRI was applied to quantify cerebral perfusion. Whole brain, gray (GM) and white matter (WM), and voxel-based analysis (VBA) were performed to explore perfusion characteristics. Correlation analysis was performed to find the relationship between the perfusion measures, lesion volumes, and clinical variables. Receiver operating characteristic (ROC) analysis and support vector machine (SVM) classification were applied to differentiate NPSLE patients from non-NPSLE patients and healthy controls. Compared to NCs, NPSLE patients showed increased cerebral blood flow (CBF) within WM but decreased CBF within GM, while non-NPSLE patients showed increased CBF within both GM and WM. Compared to non-NPSLE patients, NPSLE patients showed significantly reduced CBF in the frontal gyrus, cerebellum, and corpus callosum. CBF within several brain regions such as cingulate and corpus callosum showed significant correlations with the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and the Systemic Lupus International Collaborating Clinics (SLICC) damage index scores. ROC analysis showed moderate performance in distinguishing NPSLE from non-NPSLE patients with AUCs > 0.7, while SVM analysis demonstrated that CBF within the corpus callosum achieved an accuracy of 83.6% in distinguishing NPSLE from non-NPSLE patients. Different brain perfusion patterns were observed between NPSLE and non-NPSLE patients. CBF measured by noninvasive 3D ASL could be a useful biomarker for the diagnosis and disease monitoring of NPSLE and non-NPSLE patients

Accelerating Brain 3D T1-Weighted Turbo Field Echo MRI Using Compressed Sensing-Sensitivity Encoding (CS-SENSE)

To evaluate the clinical application of the accelerated 3D T1-weighted turbo field echo (T1W-TFE) using the compressed sensing-sensitivity encoding (CS-SENSE) and identify the appropriate acceleration factor

Brain structural and functional alterations in MOG antibody disease

BACKGROUND: The impact of myelin oligodendrocyte glycoprotein antibody disease (MOGAD) on brain structure and function is unknown. OBJECTIVES: The aim of this study was to study the multimodal brain MRI alterations in MOGAD and to investigate their clinical significance. METHODS: A total of 17 MOGAD, 20 aquaporin-4 antibody seropositive neuromyelitis optica spectrum disorders (AQP4 + NMOSD), and 28 healthy controls (HC) were prospectively recruited. Voxel-wise gray matter (GM) volume, fractional anisotropy (FA), mean diffusivity (MD), and degree centrality (DC) were compared between groups. Clinical associations and differential diagnosis were determined using partial correlation and stepwise logistic regression. RESULTS: In comparison with HC, MOGAD had GM atrophy in frontal and temporal lobe, insula, thalamus, and hippocampus, and WM fiber disruption in optic radiation and anterior/posterior corona radiata; DC decreased in cerebellum and increased in temporal lobe. Compared to AQP4 + NMOSD, MOGAD presented lower GM volume in postcentral gyrus and decreased DC in cerebellum. Hippocampus/parahippocampus atrophy associated with Expanded Disability Status Scale (R = -0.55, p = 0.04) and California Verbal Learning Test (R = 0.62, p = 0.031). The differentiation of MOGAD from AQP4 + NMOSD achieved an accuracy of 95% using FA in splenium of corpus callosum and DC in occipital gyrus. CONCLUSION: Distinct structural and functional alterations were identified in MOGAD. Hippocampus/parahippocampus atrophy associated with clinical disability and cognitive impairment

Brain structural alterations in MOG antibody diseases: A comparative study with AQP4 seropositive NMOSD and MS

Background: Brain structural alterations and their clinical significance of myelin oligodendrocyte glycoprotein antibody disease (MOGAD) have not been determined. Methods: We recruited 35 MOGAD, 38 aquaporin 4 antibody positive neuromyelitis optica spectrum diseases (AQP4+ NMOSD), 37 multiple sclerosis (MS) and 60 healthy controls (HC) who underwent multimodal brain MRI from two centres. Brain lesions, volumes of the whole brain parenchyma, cortical and subcortical grey matter (GM), brainstem, cerebellum and cerebral white matter (WM) and diffusion measures (fractional anisotropy, FA and mean diffusivity, MD) were compared among the groups. Associations between the MRI measurements and the clinical variables were assessed by partial correlations. Logistic regression was performed to differentiate MOGAD from AQP4+ NMOSD and MS. Results: In MOGAD, 19 (54%) patients had lesions on MRI, with cortical/juxtacortical (68%) as the most common location. MOGAD and MS showed lower cortical and subcortical GM volumes than HC, while AQP4+ NMOSD only demonstrated a decreased cortical GM volume. MS demonstrated a lower cerebellar volume, a lower FA and an increased MD than MOGAD and HC. The subcortical GM volume was negatively correlated with Expanded Disability Status Scale in MOGAD (R=-0.51; p=0.004). A combination of MRI and clinical measures could achieve an accuracy of 85% and 93% for the classification of MOGAD versus AQP4+ NMOSD and MOGAD versus MS, respectively. Conclusion: MOGAD demonstrated cortical and subcortical atrophy without severe WM rarefaction. The subcortical GM volume correlated with clinical disability and a combination of MRI and clinical measures could separate MOGAD from AQP4+ NMOSD and MS