Classification algorithms with multi-modal data fusion could accurately distinguish neuromyelitis optica from multiple sclerosis.

Neuromyelitis optica (NMO) exhibits substantial similarities to multiple sclerosis (MS) in clinical manifestations and imaging results and has long been considered a variant of MS. With the advent of a specific biomarker in NMO, known as anti-aquaporin 4, this assumption has changed; however, the differential diagnosis remains challenging and it is still not clear whether a combination of neuroimaging and clinical data could be used to aid clinical decision-making. Computer-aided diagnosis is a rapidly evolving process that holds great promise to facilitate objective differential diagnoses of disorders that show similar presentations. In this study, we aimed to use a powerful method for multi-modal data fusion, known as a multi-kernel learning and performed automatic diagnosis of subjects. We included 30 patients with NMO, 25 patients with MS and 35 healthy volunteers and performed multi-modal imaging with T1-weighted high resolution scans, diffusion tensor imaging (DTI) and resting-state functional MRI (fMRI). In addition, subjects underwent clinical examinations and cognitive assessments. We included 18 a priori predictors from neuroimaging, clinical and cognitive measures in the initial model. We used 10-fold cross-validation to learn the importance of each modality, train and finally test the model performance. The mean accuracy in differentiating between MS and NMO was 88%, where visible white matter lesion load, normal appearing white matter (DTI) and functional connectivity had the most important contributions to the final classification. In a multi-class classification problem we distinguished between all of 3 groups (MS, NMO and healthy controls) with an average accuracy of 84%. In this classification, visible white matter lesion load, functional connectivity, and cognitive scores were the 3 most important modalities. Our work provides preliminary evidence that computational tools can be used to help make an objective differential diagnosis of NMO and MS.

Validity and reliability of a Persian translation of the Minimal Assessment of Cognitive Function in Multiple Sclerosis (MACFIMS).

Cognitive dysfunction is common in multiple sclerosis (MS) and validated batteries are limited in languages other than English. We aimed to translate, cross-culturally adapt, validate, and assess reliability of Minimal Assessment of Cognitive Function in MS (MACFIMS) in Persian. The MACFIMS is a well-constructed battery in the MS literature. The battery was adapted to Persian in accordance with available guidelines. A total of 158 MS patients and 90 controls underwent neuropsychological assessment. For reliability assessment the battery was re-administered in a subset of 41 patients after a short interval using alternate forms to mitigate practice effects (approximately 10 days). Patients performed significantly worse than controls in all cognitive tests, supporting discriminant validity of our adapted battery. Approximately half of patients (46.2%) showed cognitive impairment as defined by the impairment in two or more tests. The Symbol Digit Modalities Test was the most robust test by ROC analysis. All tests showed acceptable to good level of reliability. This is the first validation of gold-standard cognitive testing in Persian. The Persian MACFIMS shows nearly the same psychometrics as its English counterpart.