The association of high levels of autoantibodies to glutamic acid decarboxylase (GAD-ab) and stiff-person syndrome (SPS) is well known. However, the full spectrum of neurological syndromes associated with GAD-ab is not well established. In addition, these patients usually present type 1 diabetes mellitus (DM1) that could justify the presence of high GAD-ab levels. To clarify these issues, we reviewed the clinical and immunological features of patients in whom high GAD-ab levels were detected in a reference centre for DM1 and for the detection of antineuronal antibodies in suspected paraneoplastic neurological syndromes (PNS). High GAD-ab levels were defined as values > or =2000 U/ml by radioimmunoassay. Intrathecal synthesis (IS) of GAD-ab was calculated in paired serum/CSF samples. Values higher than the IgG index were considered indicators for positive GAD-ab-specific IS. High GAD-ab levels were identified in 61 patients, 22 (36%) had SPS, 17 (28%) cerebellar ataxia, 11 (18%) other neurological disorders (epilepsy -- four, PNS -- four; idiopathic limbic encephalitis -- two; myasthenia gravis -- one), and 11 (18%) isolated DM1. Patients with SPS and cerebellar ataxia had the same frequency of female gender (86% vs 94%), DM1 (59% vs 53%), CSF oligoclonal bands (35% vs 69%). Three of the four PNS patients, with paraneoplastic encephalomyelitis, a predominant gait cerebellar ataxia, and limbic encephalitis, had neuroendocrine carcinomas. GAD expression was confirmed in the two tumours in which the study was done. The fourth patient presented with paraneoplastic cerebellar degeneration antedating a lung adenocarcinoma. The frequency of increased IS of GAD-ab was 85% in SPS, 100% in cerebellar ataxia, and 86% in other neurological disorders. In conclusion, our study emphasizes that high GAD-ab levels associate with other neurological disorders besides SPS. Cerebellar ataxia, the second most common syndrome associated with high GAD-ab levels, shares with SPS the same demographic, clinical and immunological features. The demonstration of an increased IS of GAD-ab is important to confirm that the GAD autoimmunity is related to the neurological syndrome particularly when there is a concomitant DM1 that could justify the presence of high GAD-ab levels. Lastly, in patients who develop neurological syndromes that suggest a PNS, the finding of GAD-ab does not rule out this possibility and appropriate studies should be done to confirm an underlying cancer.

In this paper, we present a novel automated method for White Matter (WM) lesion segmentation of Multiple Sclerosis (MS) patient images. Our approach is based on a cascade of two 3D patch-wise convolutional neural networks (CNN). The first network is trained to be more sensitive revealing possible candidate lesion voxels while the second network is trained to reduce the number of misclassified voxels coming from the first network. This cascaded CNN architecture tends to learn well from small sets of training data, which can be very interesting in practice, given the difficulty to obtain manual label annotations and the large amount of available unlabeled Magnetic Resonance Imaging (MRI) data. We evaluate the accuracy of the proposed method on the public MS lesion segmentation challenge MICCAI2008 dataset, comparing it with respect to other state-of-the-art MS lesion segmentation tools. Furthermore, the proposed method is also evaluated on two private MS clinical datasets, where the performance of our method is also compared with different recent public available state-of-the-art MS lesion segmentation methods. At the time of writing this paper, our method is the best ranked approach on the MICCAI2008 challenge, outperforming the rest of 60 participant methods when using all the available input modalities (T1-w, T2-w and FLAIR), while still in the top-rank (3rd position) when using only T1-w and FLAIR modalities. On clinical MS data, our approach exhibits a significant increase in the accuracy segmenting of WM lesions when compared with the rest of evaluated methods, highly correlating (r ≥ 0.97) also with the expected lesion volume.

Objective We aimed to report the frequency and implications of antibodies to myelin oligodendrocyte glycoprotein (MOG-ab) in adults with demyelinating syndromes suspicious for neuromyelitis optica (NMO). Methods Samples from 174 patients (48 NMO, 84 longitudinally extensive myelitis (LETM), 39 optic neuritis (ON), and three acute disseminated encephalomyelitis (ADEM) who presented initially with isolated LETM) were retrospectively examined for AQP4-ab and MOG-ab using cell-based assays. Results MOG-ab were found in 17 (9.8%) patients, AQP4-ab in 59 (34%), and both antibodies in two (1.1%). Among the 17 patients with MOG-ab alone, seven (41%) had ON, five (29%) LETM, four (24%) NMO, and one (6%) ADEM. Compared with patients with AQP4-ab, those with MOG-ab were significantly younger (median: 27 vs. 40.5 years), without female predominance (53% vs. 90%), and the clinical course was more frequently monophasic (41% vs. 7%) with a benign outcome (median Expanded Disability Status Scale: 1.5 vs. 4.0). In eight patients with paired serum-cerebrospinal fluid (CSF) samples, five had MOG-ab in both samples and three only in serum. Antibody titres did not differ among clinical phenotypes or disease course. MOG-ab remained detectable in 12/14 patients (median follow-up: 23 months) without correlation between titres' evolution and outcome. Conclusion MOG-ab identify a subgroup of adult patients with NMO, LETM and ON that have better outcome than those associated with AQP4-ab. MOG-ab are more frequently detected in serum than CSF and the follow-up of titres does not correlate with outcome.

In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods. However, the accuracies of CNN methods tend to decrease significantly when evaluated on different image domains compared with those used for training, which demonstrates the lack of adaptability of CNNs to unseen imaging data. In this study, we analyzed the effect of intensity domain adaptation on our recently proposed CNN-based MS lesion segmentation method. Given a source model trained on two public MS datasets, we investigated the transferability of the CNN model when applied to other MRI scanners and protocols, evaluating the minimum number of annotated images needed from the new domain and the minimum number of layers needed to re-train to obtain comparable accuracy. Our analysis comprised MS patient data from both a clinical center and the public ISBI2015 challenge database, which permitted us to compare the domain adaptation capability of our model to that of other state-of-the-art methods. In both datasets, our results showed the effectiveness of the proposed model in adapting previously acquired knowledge to new image domains, even when a reduced number of training samples was available in the target dataset. For the ISBI2015 challenge, our one-shot domain adaptation model trained using only a single case showed a performance similar to that of other CNN methods that were fully trained using the entire available training set, yielding a comparable human expert rater performance. We believe that our experiments will encourage the MS community to incorporate its use in different clinical settings with reduced amounts of annotated data. This approach could be meaningful not only in terms of the accuracy in delineating MS lesions but also in the related reductions in time and economic costs derived from manual lesion labeling.