Retinal nerve fiber layer atrophy and the presence of retinal periphlebitis are associated with disease activity, suggesting that retinal evaluation can be used as biomarkers of multiple sclerosis activity.

Our results suggest that the symptom of fatigue is associated with a disruption of brain networks involved in cognitive/attentional processes.

Background Studying axonal loss in the retina is a promising biomarker for multiple sclerosis (MS). Our aim was to compare optical coherence tomography (OCT) and Heidelberg retinal tomography (HRT) techniques to measure the thickness of the retinal nerve fiber layer (RNFL) in patients with MS, and to explore the relationship between changes in the RNFL thickness with physical and cognitive disability. We studied 52 patients with MS and 18 proportionally matched controls by performing neurological examination, neuropsychological evaluation using the Brief Repetitive Battery-Neuropsychology and RNFL thickness measurement using OCT and HRT. Results We found that both OCT and HRT could define a reduction in the thickness of the RNFL in patients with MS compared with controls, although both measurements were weakly correlated, suggesting that they might measure different aspects of the tissue changes in MS. The degree of RNFL atrophy was correlated with cognitive disability, mainly with the symbol digit modality test (r = 0.754, P < 0.001). Moreover, temporal quadrant RNFL atrophy measured with OCT was associated with physical disability. Conclusion In summary, both OCT and HRT are able to detect thinning of the RNFL, but OCT seems to be the most sensitive technique to identify changes associated with MS evolution. Multiple Sclerosis 2008; 14: 906-912.

GBM patients with CRET in early MRI and no fluorescent residual tissue had longer overall survival than patients with CRET and residual fluorescent tissue.

Semantic memory is the subsystem of human memory that stores knowledge of concepts or meanings, as opposed to life-specific experiences. How humans organize semantic information remains poorly understood. In an effort to better understand this issue, we conducted a verbal fluency experiment on 200 participants with the aim of inferring and representing the conceptual storage structure of the natural category of animals as a network. This was done by formulating a statistical framework for co-occurring concepts that aims to infer significant concept-concept associations and represent them as a graph. The resulting network was analyzed and enriched by means of a missing links recovery criterion based on modularity. Both network models were compared to a thresholded co-occurrence approach. They were evaluated using a random subset of verbal fluency tests and comparing the network outcomes (linked pairs are clustering transitions and disconnected pairs are switching transitions) to the outcomes of two expert human raters. Results show that the network models proposed in this study overcome a thresholded co-occurrence approach, and their outcomes are in high agreement with human evaluations. Finally, the interplay between conceptual structure and retrieval mechanisms is discussed.

No abstract

The fractal dimension (FD) is a quantitative parameter that characterizes the morphometric variability of a complex object. Among other applications, FD has been used to identify abnormalities of the human brain in conventional magnetic resonance imaging (MRI), including white matter abnormalities in patients with Multiple Sclerosis (MS). Extensive grey matter (GM) pathology has been recently identified in MS and it appears to be a key factor in long-term disability. The aim of the present work was to assess whether FD measurement of GM in T1 MRI sequences can identify GM abnormalities in patients with MS in the early phase of the disease. A voxel-based morphometry approach optimized for MS was used to obtain the segmented brain, where we later calculated the three-dimensional FD of the GM in MS patients and healthy controls. We found that patients with MS had a significant increase in the FD of the GM compared to controls. Such differences were present even in patients with short disease durations, including patients with first attacks of MS. In addition, the FD of the GM correlated with T1 and T2 lesion load, but not with GM atrophy or disability. The FD abnormalities of the GM here detected differed from the previously published FD of the white matter in MS, suggesting that different pathological processes were taking place in each structure. These results indicate that GM morphology is abnormal in patients with MS and that this alteration appears early in the course of the disease.

BackgroundThe aim of this study was to assess the diagnostic accuracy (sensitivity and specificity) of clinical, imaging and motor evoked potentials (MEP) for predicting the short-term prognosis of multiple sclerosis (MS).MethodsWe obtained clinical data, MRI and MEP from a prospective cohort of 51 patients and 20 matched controls followed for two years. Clinical end-points recorded were: 1) expanded disability status scale (EDSS), 2) disability progression, and 3) new relapses. We constructed computational classifiers (Bayesian, random decision-trees, simple logistic-linear regression-and neural networks) and calculated their accuracy by means of a 10-fold cross-validation method. We also validated our findings with a second cohort of 96 MS patients from a second center.ResultsWe found that disability at baseline, grey matter volume and MEP were the variables that better correlated with clinical end-points, although their diagnostic accuracy was low. However, classifiers combining the most informative variables, namely baseline disability (EDSS), MRI lesion load and central motor conduction time (CMCT), were much more accurate in predicting future disability. Using the most informative variables (especially EDSS and CMCT) we developed a neural network (NNet) that attained a good performance for predicting the EDSS change. The predictive ability of the neural network was validated in an independent cohort obtaining similar accuracy (80%) for predicting the change in the EDSS two years later.ConclusionsThe usefulness of clinical variables for predicting the course of MS on an individual basis is limited, despite being associated with the disease course. By training a NNet with the most informative variables we achieved a good accuracy for predicting short-term disability.