Multiple Sclerosis patients' clinical symptoms do not correlate strongly with structural assessment done with traditional magnetic resonance images. However, its diagnosis and evaluation of the disease's progression are based on a combination of this imaging analysis complemented with clinical examination. Therefore, other biomarkers are necessary to better understand the disease. In this paper, we capitalize on machine learning techniques to classify relapsing-remitting multiple sclerosis patients and healthy volunteers based on machine learning techniques, and to identify relevant brain areas and connectivity measures for characterizing patients. To this end, we acquired magnetic resonance imaging data from relapsing-remitting multiple sclerosis patients and healthy subjects. Fractional anisotropy maps, structural and functional connectivity were extracted from the scans. Each of them were used as separate input features to construct support vector machine classifiers. A fourth input feature was created by combining structural and functional connectivity. Patients were divided in two groups according to their degree of disability and, together with the control group, three group pairs were formed for comparison. Twelve separate classifiers were built from the combination of these four input features and three group pairs. The classifiers were able to distinguish between patients and healthy subjects, reaching accuracy levels as high as 89% ± 2%. In contrast, the performance was noticeably lower when comparing the two groups of patients with different levels of disability, reaching levels below 63% ± 5%. The brain regions that contributed the most to the classification were the right occipital, left frontal orbital, medial frontal cortices and lingual gyrus. The developed classifiers based on MRI data were able to distinguish multiple sclerosis patients and healthy subjects reliably. Moreover, the resulting classification models identified brain regions, and functional and structural connections relevant for better understanding of the disease.

Value is often associated with reward, emphasizing its hedonic aspects. However, when circumstances change, value must also change (a compass outvalues gold, if you are lost). How are value representations in the brain reshaped under different behavioral goals? To answer this question, we devised a new task that decouples usefulness from its hedonic attributes, allowing us to study flexible goal-dependent mapping. Here, we show that, unlike sensory cortices, regions in the prefrontal cortex (PFC)—usually associated with value computation—remap their representation of perceptually identical items according to how useful the item has been to achieve a specific goal. Furthermore, we identify a coding scheme in the PFC that represents value regardless of the goal, thus supporting generalization across contexts. Our work questions the dominant view that equates value with reward, showing how a change in goals triggers a reorganization of the neural representation of value, enabling flexible behavior.

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Background: Multiple sclerosis exhibits specific neuropathological phenomena driving to both global and regional brain atrophy. At the clinical level, the disease is related to functional decline in cognitive domains as the working memory, processing speed, and verbal fluency. However, the compromise of social-cognitive abilities has concentrated some interest in recent years despite the available evidence suggesting the risk of disorganization in social life. Recent studies have used the MiniSEA test to assess the compromise of social cognition and have found relevant relationships with memory and executive functions, as well as with the level of global and regional brain atrophy. Objective: The present article aimed to identify structural changes related to socio-cognitive performance in a sample of patients with relapsing-remitting multiple sclerosis. Methods: 68 relapsing-remitting multiple sclerosis Chilean patients and 50 healthy control subjects underwent MRI scans and neuropsychological evaluation including social-cognition tasks. Total brain, white matter, and gray matter volumes were estimated. Also, voxel-based morphometry was applied to evaluate regional structural changes. Results: Patients exhibited lower scores in all neuropsychological tests. Social cognition exhibited a significant decrease in this group mostly related to the declining social perception. Normalized brain volume and white matter volume were significantly decreased when compared to healthy subjects. The regional brain atrophy analysis showed that changes in the insular cortex and medial frontal cortices are significantly related to the variability of social-cognitive performance among patients. Conclusions: In the present study, social cognition was only correlated with the deterioration of verbal fluency, despite the fact that previous studies have reported its link with memory and executive functions. The identification of specific structural correlates supports the comprehension of this phenomenon as an independent source of cognitive disability in these patients.

Brain–computer interfaces (BCIs), also known as brain–machine interfaces (BMIs), are a group of experimental procedures in which an external sensor is used to provide information about a specific brain process in order to change the measured quantity. A BCI acquires signals from the brain of a human or an animal using any one or more of these sensors, then selects or extracts specific features of interest from the signal and converts and then translates these into artificial output that can act on the body or the outside world. A BCI may influence human performance by replacing, restoring, supplementing, or enhancing brain function. In this chapter, we discuss the extant research in terms of experimental work and neuroscience understanding of the application of BCIs and neurofeedback systems in influencing human performance in different brain functions, namely, action, perception, cognition, and emotion, in healthy individuals, expert performers, and patients.