{'fraction_non_alphanumeric': 0.02097902097902098, 'fraction_numerical': 0.004581625271280444, 'mean_word_length': 5.491392801251957, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Promotion of neuronal recovery following experimental SCI via direct inhibition of glial scar formation.

Reestablishinguseful function after a permanent neurological injury has long been a sought after goal. One of the emerging strategies for restoring function following stroke, amputation, or spinal cord injury involves the use of neuroprosthetic devices. Such devices, for example a robotic arm, interface with the brain via electrodes and are controlled by the brain’s electrical activity. However, this field is still in its infancy and a number of important challenges must still be overcome. One of the most basic involves understanding the mechanisms by which a patient develops the ability to harness brain activity to control a device. Simply put, we have yet to fully delineate how the brain ‘learns’ to control a device without actually physically moving it. Recent work by Koralek et al is shedding light onto this process (Koralek AC, Jin X, Long II JD, et al. Corticalstriatal plasticity is necessary for learning intentional neuroprosthetic skills. Nature. 2012;483(7389): 331-335.). The learning of motor tasks involves corticostriatal circuits and plasticity in these circuits. However, the role of these circuits in abstract non-physical learning, or the type of learning necessary to master neuroprosthetic device control, was not well understood. To test the possibility that corticostriatal circuits are involved, Koralek et al developed an operant brain-machine interface task in rodents required to modulate the activity of the primary motor cortex, in the absence of physical movement, to control the pitch of an auditory tone. Rats were trained to control an auditory tone to achieve 1 of 2 pitches. The phases of learning for this abstract task mirrored those for physical tasks and modulation of motor cortex firing by the animals was observed during task performance. In addition, as with physical tasks (eg, proprioception), sensory input was necessary for learning, for when the tone was muted, learning was abolished. Using accelerometers and electromyography, it was confirmed that physical movement was not required to learn the abstract task despite firing of primary motor cortex. After using a series of behavioral tests to confirm that the neuroprosthetic actions of the rodents were intentional, and not habitual, the investigators turned their attention to studying the involvement of corticostriatal plasticity. In parallel to motor learning, dorsal striatal neuronal firing rates increased during late learning, especially during target reaching. Importantly, dorsal striatal firing demonstrated increased coupling to motor cortex activity during the course of late learning, indicating that plasticity was involved in this neuroprosthetic control task. Finally, using mutant mice lacking N-methyl-D-aspartate receptors on most striatal neurons, in which corticostriatal long-term potentiation is impaired, the investigators repeated task training. These mutant mice showed marked deficits in task learning, a lack of dorsal striatal neuronal firing, and abolished coupling between motor cortex and striatal activity. These findings indicated that plasticity in corticostriatal circuits and long-term potentiation is required not only for learning motor tasks, but also abstract tasks that do not require physical movement. These findings have numerous implications as investigators work toward an array of neuroprosthetic devices that can be controlled by a patient’s volitional brain activity. By identifying the circuitry vital for neuroprosthetic control, we may advance our understanding about where anatomically it is best to interface with the brain and how electrode arrays should be designed. Similarities between motor and abstract learning could assist in the development of learning tasks and strategies to be used during the neurorehabilitation process. Finally, the corticostriatal circuitry might serve as an ideal target of other neuroregenerative therapies (eg, stem cell transplantation) in order to maximize the capacity to generate functional recovery in the neurologically impaired.