The present study reports alterations of task-based functional brain connectivity in a group of 11 cosmonauts after a long-duration spaceflight, compared to a healthy control group not involved in the space program. To elicit the postural and locomotor sensorimotor mechanisms that are usually most significantly impaired when space travelers return to Earth, a plantar stimulation paradigm was used in a block design fMRI study. The motor control system activated by the plantar stimulation involved the pre-central and post-central gyri, SMA, SII/operculum, and, to a lesser degree, the insular cortex and cerebellum. While no post-flight alterations were observed in terms of activation, the network-based statistics approach revealed task-specific functional connectivity modifications within a broader set of regions involving the activation sites along with other parts of the sensorimotor neural network and the visual, proprioceptive, and vestibular systems. The most notable findings included a post-flight increase in the stimulation-specific connectivity of the right posterior supramarginal gyrus with the rest of the brain; a strengthening of connections between the left and right insulae; decreased connectivity of the vestibular nuclei, right inferior parietal cortex (BA40) and cerebellum with areas associated with motor, visual, vestibular, and proprioception functions; and decreased coupling of the cerebellum with the visual cortex and the right inferior parietal cortex. The severity of space motion sickness symptoms was found to correlate with a post- to pre-flight difference in connectivity between the right supramarginal gyrus and the left anterior insula. Due to the complex nature and rapid dynamics of adaptation to gravity alterations, the post-flight findings might be attributed to both the long-term microgravity exposure and to the readaptation to Earth’s gravity that took place between the landing and post-flight MRI session. Nevertheless, the results have implications for the multisensory reweighting and gravitational motor system theories, generating hypotheses to be tested in future research.

Including navigated repetitive transcranial magnetic stimulation in a conventional rehabilitation program positively influenced motor and functional recovery in study participants, demonstrating the clinical potential of the method. The results of this study will be used for designing a large-scale clinical trial.

For a long time, the cerebellum has been known to be a structure related to posture and equilibrium control. According to the anatomic structure of inputs and internal structure of the cerebellum, its role in learning was theoretically reasoned and experimentally proved. The hypothesis of an inverse internal model based on feedback-error learning mechanism combines feedforward control by the cerebellum and feedback control by the cerebral motor cortex. The cerebellar cortex is suggested to acquire internal models of the body and objects in the external world. During learning of a new tool the motor cortex receives feedback from the realized movement while the cerebellum produces only feedforward command. To realize a desired movement without feedback of the realized movement, the cerebellum needs to form an inverse model of the hand/arm system. This suggestion was supported by FMRi data. The role of cerebellum in learning new postural tasks mainly concerns reorganization of natural synergies. A learned postural pattern in dogs has been shown to be disturbed after lesions of the cerebral motor cortex or cerebellar nuclei. In humans, learning voluntary control of center of pressure position is greatly disturbed after cerebellar lesions. However, motor cortex and basal ganglia are also involved in the feedback learning postural tasks.

Although the results supported the feasibility of using robot-assisted gait therapy in the rehabilitation an individual with PD, further studies are needed to assess a potential advantage of the Lokomat system over conventional locomotor training for this population.

Genetic algorithms (GAs) are stochastic methods that are widely used in search and optimization. The breeding process is the main driving mechanism for GAs that leads the way to find the global optimum. And the initial phase of the breeding process starts with parent selection. The selection utilized in a GA is effective on the convergence speed of the algorithm. A GA can use different selection mechanisms for choosing parents from the population and in many applications the process generally depends on the fitness values of the individuals. Artificial neural networks (ANNs) are used to decide the appropriate parents by the new hybrid algorithm proposed in this study. And the use of neural networks aims to produce better offspring during the GA search. The neural network utilized in this algorithm tries to learn the structural patterns and correlations that enable two parents to produce high-fit offspring. In the breeding process, the first parent is selected based on the fitness value as usual. Then it is the neural network that decides the appropriate mate for the first parent chosen. Hence, the selection mechanism is not solely dependent on the fitness values in this study. The algorithm is tested with seven benchmark functions. It is observed from results of these tests that the new selection method leads genetic algorithm to converge faster.