Brain ischemia inhibits immune function systemically, with resulting infectious complications. Whether in stroke different immune alterations occur in brain and periphery and whether analogous mechanisms operate in these compartments remains unclear. Here we show that in patients with ischemic stroke and in mice subjected to middle cerebral artery occlusion, natural killer (NK) cells display remarkably distinct temporal and transcriptome profiles in the brain as compared to the periphery. The activation of catecholaminergic and hypothalamic-pituitary-adrenal axis leads to splenic atrophy and contraction of NK cell numbers in the periphery through a modulated expression of SOCS3, whereas cholinergic innervation-mediated suppression of NK cell responses in the brain involves RUNX3. Importantly, pharmacological or genetic ablation of innervation preserved NK cell function and restrained post-stroke infection. Thus, brain ischemia compromises NK cell-mediated immune defenses through mechanisms that differ in the brain versus the periphery, and targeted inhibition of neurogenic innervation limits post-stroke infection.

The spinal cord is frequently affected by atrophy and/or lesions in multiple sclerosis (MS) patients. Segmentation of the spinal cord and lesions from MRI data provides measures of damage, which are key criteria for the diagnosis, prognosis, and longitudinal monitoring in MS. Automating this operation eliminates inter-rater variability and increases the efficiency of large-throughput analysis pipelines. Robust and reliable segmentation across multi-site spinal cord data is challenging because of the large variability related to acquisition parameters and image artifacts. In particular, a precise delineation of lesions is hindered by a broad heterogeneity of lesion contrast, size, location, and shape. The goal of this study was to develop a fully-automatic framework — robust to variability in both image parameters and clinical condition — for segmentation of the spinal cord and intramedullary MS lesions from conventional MRI data of MS and non-MS cases. Scans of 1,042 subjects (459 healthy controls, 471 MS patients, and 112 with other spinal pathologies) were included in this multi-site study (n=30). Data spanned three contrasts (T1-, T2-, and T2*-weighted) for a total of 1,943 volumes and featured large heterogeneity in terms of resolution, orientation, coverage, and clinical conditions. The proposed cord and lesion automatic segmentation approach is based on a sequence of two Convolutional Neural Networks (CNNs). To deal with the very small proportion of spinal cord and/or lesion voxels compared to the rest of the volume, a first CNN with 2D dilated convolutions detects the spinal cord centerline, followed by a second CNN with 3D convolutions that segments the spinal cord and/or lesions. CNNs were trained independently with the Dice loss. When compared against manual segmentation, our CNN-based approach showed a median Dice of 95% vs. 88% for PropSeg (p≤0.05), a state-of-the-art spinal cord segmentation method. Regarding lesion segmentation on MS data, our framework provided a Dice of 60%, a relative volume difference of −15%, and a lesion-wise detection sensitivity and precision of 83% and 77%, respectively. In this study, we introduce a robust method to segment the spinal cord and intramedullary MS lesions on a variety of MRI contrasts. The proposed framework is open-source and readily available in the Spinal Cord Toolbox.

In patients with NMO, WM tract integrity disruption was identified in both CP and CI groups. GM atrophy, particularly in the DGM, was only found in the CI group. Hippocampal volume is the main MRI predictor of cognition in NMO.

Purpose: The metabolic patterns of 18 F-fluoro-2-deoxy-d-glucose positron emission tomography ( 18 F-FDG-PET) in autoimmune encephalitis associated with leucine-rich glioma-inactivated 1 antibody (LGI1 AE) are still unclear. We performed a cohort study to investigate the clinical metabolic characteristics and diagnostic value based on 18 F-FDG-PET in patients with LGI1 AE.Materials and Methods: A total of 34 patients including 18 patients (53%) in the acute phase and 16 patients (47%) in the chronic phase who were diagnosed with LGI1 AE were retrospectively analyzed from October 2014 to June 2018 at the Department of Neurology in Beijing Tiantan Hospital, the Capital Medical University. The clinical data were collected by searching through electronic medical records.Results: The initial 18 F-FDG-PET scan indicated a significant abnormal metabolic pattern in 31 LGI1 AE patients (91%), whereas only 20 patients (59%) showed an abnormal MRI signal (P < 0.05). The 18 F-FDG-PET metabolic pattern was reversible after treatment; most of the patients showed an almost normal uptake of 18 F-FDG-PET after discharge. Regarding the spatial distribution, the abnormal metabolic pattern in LGI1 AE subjects exhibiting hypermetabolism was specifically located in the basal ganglia (BG) and medial temporal lobe (MTL). BG hypermetabolism was observed in 28 subjects (82%), and 68% of patients showed MTL hypermetabolism. A total of 17 patients (50%) exhibited faciobrachial dystonic seizures (FBDS), and the remaining subjects showed non-FBDS symptoms (50 and 50%). BG-only hypermetabolism was detected in seven subjects in the FBDS subgroup (7/16) but in only one subject in the non-FBDS subgroup (1/15) (44 vs. 7%, P < 0.05). Conclusion:18 F-FDG-PET imaging was more sensitive than MRI in the diagnosis of LGI1 AE. Isolated BG hypermetabolism was more frequently observed in subjects with FBDS, suggesting the potential involvement of the BG.

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Normal brain MRI findings were observed in half of the patients. Lesions in the hippocampus were the most common MR imaging abnormal finding. The presence of hippocampal lesions is the main MR imaging predictor for poor prognosis in patients with anti--methyl-D-aspartate receptor encephalitis.

The brain connectome of multiple sclerosis (MS) has been investigated by several previous studies; however, it is still unknown how the network changes in clinically isolated syndrome (CIS), the earliest stage of MS, and how network alterations on a functional level relate to the structural level in MS disease. Here, we investigated the topological alterations of both the structural and functional connectomes in 41 CIS and 32 MS patients, compared to 35 healthy controls, by combining diffusion tensor imaging and resting-state functional MRI with graph analysis approaches. We found that the structural connectome showed a deviation from the optimal pattern as early as the CIS stage, while the functional connectome only showed local changes in MS patients, not in CIS. When comparing two patient groups, the changes appear more severe in MS. Importantly, the disruptions of structural and functional connectomes in patients occurred in the same direction and locally correlated in sensorimotor component. Finally, the extent of structural network changes was correlated with several clinical variables in MS patients. Together, the results suggested early disruption of the structural brain connectome in CIS patients and provided a new perspective for investigating the relationship of the structural and functional alterations in MS.

Spinal cord lesions detected on MRI hold important diagnostic and prognostic value for multiple sclerosis. Previous attempts to correlate lesion burden with clinical status have had limited success, however, suggesting that lesion location may be a contributor. Our aim was to explore the spatial distribution of multiple sclerosis lesions in the cervical spinal cord, with respect to clinical status. We included 642 suspected or confirmed multiple sclerosis patients (31 clinically isolated syndrome, and 416 relapsingremitting, 84 secondary progressive, and 73 primary progressive multiple sclerosis) from 13 clinical sites. Cervical spine lesions were manually delineated on T 2-and T 2 *-weighted axial and sagittal MRI scans acquired at 3 or 7 T. With an automatic publiclyavailable analysis pipeline we produced voxelwise lesion frequency maps to identify predilection sites in various patient groups characterized by clinical subtype, Expanded Disability Status Scale score and disease duration. We also measured absolute and normalized lesion volumes in several regions of interest using an atlas-based approach, and evaluated differences within and between groups. The lateral funiculi were more frequently affected by lesions in progressive subtypes than in relapsing in voxelwise analysis (P 5 0.001), which was further confirmed by absolute and normalized lesion volumes (P 5 0.01). The central cord area was more often affected by lesions in primary progressive than relapse-remitting patients (P 5 0.001). Between white and grey matter, the absolute lesion volume in the white matter was greater than in the grey matter in all phenotypes (P 5 0.001); however when normalizing by each region, normalized lesion volumes were comparable between white and grey matter in primary progressive patients. Lesions appearing in the lateral funiculi and central cord area were significantly correlated with Expanded Disability Status Scale score (P 5 0.001). High lesion frequencies were observed in patients with a more aggressive disease course, rather than long disease duration. Lesions located in the lateral funiculi and central cord area of the cervical spine may influence clinical status in multiple sclerosis. This work shows the added value of cervical spine lesions, and provides an avenue for evaluating the distribution of spinal cord lesions in various patient groups.