Intrinsic Connectivity Networks, patterns of correlated activity emerging from “resting-state” BOLD time series, are increasingly being associated with cognitive, clinical, and behavioral aspects, and compared with patterns of activity elicited by specific tasks. We study the reconfiguration of brain networks between task and resting-state conditions by a machine learning approach, to highlight the Intrinsic Connectivity Networks (ICNs) which are more affected by the change of network configurations in task vs. rest. To this end, we use a large cohort of publicly available data in both resting and task-based fMRI paradigms. By applying a battery of different supervised classifiers relying only on task-based measurements, we show that the highest accuracy to predict ICNs is reached with a simple neural network of one hidden layer. In addition, when testing the fitted model on resting state measurements, such architecture yields a performance close to 90% for areas connected to the task performed, which mainly involve the visual and sensorimotor cortex, whilst a relevant decrease of the performance is observed in the other ICNs. On one hand, our results confirm the correspondence of ICNs in both paradigms (task and resting) thus opening a window for future clinical applications to subjects whose participation in a required task cannot be guaranteed. On the other hand it is shown that brain areas not involved in the task display different connectivity patterns in the two paradigms.

Neuroimaging classification procedures between normal and pathological subjects are sparse and highly dependent of an expert's clinical criterion. Here, we aimed to investigate whether possible brain structural network differences in the shiverer mouse mutant, a relevant animal model of myelin related diseases, can reflect intrinsic individual brain properties that allow the automatic discrimination between the shiverer and normal subjects. Common structural networks properties between shiverer (C3Fe.SWV Mbpshi/Mbpshi, n = 6) and background control (C3HeB.FeJ, n = 6) mice are estimated and compared by means of three diffusion weighted MRI (DW-MRI) fiber tractography algorithms and a graph framework. Firstly, we found that brain networks of control group are significantly more clustered, modularized, efficient and optimized than those of the shiverer group, which presented significantly increased characteristic path length. These results are in line with previous structural/functional complex brain networks analysis that have revealed topologic differences and brain network randomization associated to specific states of human brain pathology. In addition, by means of network measures spatial representations and discrimination analysis, we show that it is possible to classify with high accuracy to which group each subject belongs, providing also a probability value of being a normal or shiverer subject as an individual anatomical classifier. The obtained correct predictions (e.g., around 91.6–100%) and clear spatial subdivisions between control and shiverer mice, suggest that there might exist specific network subspaces corresponding to specific brain disorders, supporting also the point of view that complex brain network analyses constitutes promising tools in the future creation of interpretable imaging biomarkers.

Establishing bridges between the findings from cognitive neurosciences and teaching practice has not been systematically achieved. However, many researchers interested in this area agree on the positive impact that knowledge on how the brain learns has on teaching practices and educational policies. For more than 15 years, the Laboratory for Educational Neurosciences from the Cuban Centre for Neurosciences has collected evidence on basic numerical capacities and their association with learning mathematics, taking into account different levels of analysis that consider biology, cognition and education. Researchers in this laboratory have developed a conceptual, methodological and instrumental platform based on the experimental evidence they have systematically obtained. This platform has resulted in the design and validation of tools and resources for learning mathematics in the classroom with the intervention of the teachers. RESUMENEstablecer puentes entre los hallazgos de las neurociencias cognitivas y la práctica docente es un propósito que no ha logrado ser concretado de forma sistemática. Sin embargo, muchos investigadores interesados en la temática concuerdan en el impacto positivo del conocimiento acerca de cómo el cerebro aprende sobre las prácticas docentes y las políticas educativas. Durante más de 15 años, el laboratorio de Neurociencias Educacionales del Centro de Neurociencias de Cuba ha obtenido un cuerpo de evidencias acerca de las capacidades numéricas básicas y su relación con el aprendizaje de las matemáticas, focalizándose en las relaciones entre los niveles biológico, cognitivo y educacional. Investigadores de este laboratorio han desa-rrollado una plataforma conceptual, metodológica e instrumental basada en las ARTICLE HISTORY

Background: Although gait patterns disturbances are known to be related to cognitive decline, there is no consensus on the possibility of predicting one from the other. It is necessary to find the optimal gait features, experimental protocols, and computational algorithms to achieve this purpose. Purposes: To assess the efficacy of the Stable Sparse Classifiers procedure (SSC) for discriminating young and older adults, as well as healthy and cognitively impaired elderly groups from their gait patterns. To identify the walking tasks or combinations of tasks and specific spatio-temporal gait features (STGF) that allow the best prediction with SSC. Methods: A sample of 125 participants (40 young- and 85 older-adults) was studied. They underwent assessment with five neuropsychological tests that explore different cognitive domains. A summarized cognitive index (MDCog), based on the Mahalanobis distance from normative data, was calculated. The sample was divided into three groups (young adults, healthy and cognitively impaired elderly adults) using k-means clustering of MDCog in addition to Age. The participants executed four walking tasks (normal, fast, easy- and hard-dual tasks) and their gait patterns, measured with a body-fixed Inertial Measurement Unit, were used to calculate 16 STGF and dual-task costs. SSC was then employed to predict which group the participants belonged to. The classification performance was assessed using the area under the receiver operating curves (AUC). The set of STGF features and tasks producing the most accurate classifications were identified. Results: The comparison between the three groups revealed significant differences for all STGF in all tasks, while the global AUC of the classification using SSC was 0.87. The classification between the groups of elderly people revealed that the combination of the easy dual-task and the fast walking task had the best prediction performance (AUC = 0.86). Gait variability in step and stride time and the RMS value of vertical acceleration were the features with the largest predictive power. SSC prediction accuracy was better than the accuracies obtained with linear discriminant analysis and support vector machine classifiers. Conclusions: The study corroborated that the changes in gait patterns can be used to discriminate between young and older adults and more importantly between healthy and cognitively impaired adults. A subset of gait tasks and STGF optimal for achieving this goal with SSC were identified, with the latter method superior to other classification techniques.