RESUMEN
Background: Differentiating neuromyelitis optica spectrum disorder (NMOSD) from multiple sclerosis (MS) is crucial in the field of diagnostics because, despite their similarities, the treatments for these two diseases are substantially different, and disease-modifying treatments for MS can worsen NMOSD. As brain magnetic resonance imaging (MRI) is an important tool to distinguish the two diseases, extensive research has been conducted to identify the defining characteristics of MRI images corresponding to these two diseases. However, the application of such research in clinical practice is still limited. In this study, we investigate the applicability of a deep learning-based algorithm for differentiating NMOSD from MS. Methods: In this study, we included 338 participants (213 patients with MS, 125 patients with NMOSD) who visited the Asan medical center between February 2009 and February 2020. A 3D convolutional neural network, which is a deep learning-based algorithm, was trained using fluid-attenuated inversion recovery images and clinical information of the participants. The performance of the final model in differentiating NMOSD from MS was evaluated and compared with that of two neurologists. Results: The deep learning-based model exhibited an area under the receiver operating characteristic curve of 0.82 (95% CI, 0.75-0.89). It differentiated NMOSD from MS with an accuracy of 71.1% (sensitivity = 87.8%, specificity = 61.6%), which is comparable to that exhibited by the neurologists. The intra-rater reliability of the two neurologists was moderate (κ = 0.47, 0.50), which was in contrast with the consistent classification of the deep learning-based model. Conclusion: The proposed model was verified to be capable of differentiating NMOSD from MS with accuracy comparable to that of neurologists, exhibiting the advantage of consistent classification. As a result, it can aid differential diagnosis between two important central nervous system inflammatory diseases in clinical practice.
RESUMEN
The purpose of this study was to investigate cortical interaction between brain regions in people with and without severe motor disability during brain-computer interface (BCI) operation through coherence analysis. Eighteen subjects, including six patients with cerebral palsy (CP) and three patients with amyotrophic lateral sclerosis (ALS), participated. The results showed (1) the existence of BCI performance difference caused by severe motor disability; (2) different coherence patterns between participants with and without severe motor disability during BCI operation and (3) effects of motor disability on cortical connections varying in the brain regions for the different frequency bands, indicating reduced cortical differentiation and specialisation. Participants with severe neuromuscular impairments, as compared with the able-bodied group, recruited more cortical regions to compensate for the difficulties caused by their motor disability, reflecting a less efficient operating strategy for the BCI task. This study demonstrated that coherence analysis can be applied to examine the ways cortical networks cooperate with each other during BCI tasks. PRACTITIONER SUMMARY: Few studies have investigated the electrophysiological underpinnings of differences in BCI performance. This study contributes by assessing neuronal synchrony among brain regions. Our findings revealed that severe motor disability causes more cortical areas to be recruited to perform the BCI task, indicating reduced cortical differentiation and specialisation.
