It is unclear whether additionally recruited sensorimotor areas in the ipsilesional and contralesional hemisphere and the cerebellum can compensate for lost neuronal functions after stroke. The objective of this study was to investigate how increased recruitment of secondary sensorimotor areas is associated with quality of motor control after stroke. In seventeen patients (three females, fourteen males; age: 59.9 ± 12.6 years), cortical activation levels were determined with functional magnetic resonance imaging (fMRI) in 12 regions of interest during a finger flexion–extension task in weeks 6 and 29 after stroke. At the same time points and by using 3D kinematics, the quality of motor control was assessed by smoothness of the grasp aperture during a reach-to-grasp task, quantified by normalized jerk. Ipsilesional premotor cortex, insula and cerebellum, as well as the contralesional supplementary motor area, insula and cerebellum, correlated significantly and positively with the normalized jerk of grasp aperture at week 6 after stroke. A positive trend towards this correlation was observed in week 29. This study suggests that recruitment of secondary motor areas at 6 weeks after stroke is highly associated with increased jerk during reaching and grasping. As jerk represents the change in acceleration, the recruitment of additional sensorimotor areas seems to reflect a type of control in which deviations from an optimal movement pattern are continuously corrected. This relationship suggests that additional recruitment of sensorimotor areas after stroke may not correspond to restitution of motor function, but more likely to adaptive motor learning strategies to compensate for motor impairments.
The high reliability of decoding mouth movements suggests that attempted mouth movements are a promising candidate for BCI in paralyzed people.
Brain-computer interfaces aim to provide people with paralysis with the possibility to use their neural signals to control devices. For communication, most BCIs are based on the selection of letters from a (digital) letter board to spell words and sentences. Visual mental imagery of letters could offer a new, fast and intuitive way to spell in a BCI-communication solution. Here we provide a proof of concept for the decoding of visually imagined characters from the early visual cortex using 7 Tesla functional MRI. Sixteen healthy participants visually imagined three different characters for 3, 5 and 7 s in a slow event-related design. Using single-trial classification, we were able to decode the characters with an average accuracy of 54%, which is significantly above chance level (33%). Furthermore, the imagined characters were classifiable shortly after cue onset and remained classifiable with prolonged imagery. These properties, combined with the cortical location of the early visual cortex and its decodable activity, encourage further research on intracranial interfacing using surface electrodes to bring us closer to such a visual imagery based BCI communication solution.
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