Dorsolateral prefrontal cortex-based control with an implanted brain-computer interface.

The objective of this study was to test the feasibility of using the dorsolateral prefrontal cortex as a signal source for brain-computer interface control in people with severe motor impairment. We implanted two individuals with locked-in syndrome with a chronic brain-computer interface designed to restore independent communication. The implanted system (Utrecht NeuroProsthesis) included electrode strips placed subdurally over the dorsolateral prefrontal cortex. In both participants, counting backwards activated the dorsolateral prefrontal cortex consistently over the course of 47 and 22 months, respectively. Moreover, both participants were able to use this signal to control a cursor in one dimension, with average accuracy scores of 78 ± 9% (standard deviation) and 71 ± 11% (chance level: 50%), respectively. Brain-computer interface control based on dorsolateral prefrontal cortex activity is feasible in people with locked-in syndrome and may become of relevance for those unable to use sensorimotor signals for control.

High-density intracranial recordings reveal a distinct site in anterior dorsal precentral cortex that tracks perceived speech.

Various brain regions are implicated in speech processing, and the specific function of some of them is better understood than others. In particular, involvement of the dorsal precentral cortex (dPCC) in speech perception remains debated, and attribution of the function of this region is more or less restricted to motor processing. In this study, we investigated high-density intracranial responses to speech fragments of a feature film, aiming to determine whether dPCC is engaged in perception of continuous speech. Our findings show that dPCC exhibited preference to speech over other tested sounds. Moreover, the identified area was involved in tracking of speech auditory properties including speech spectral envelope, its rhythmic phrasal pattern and pitch contour. DPCC also showed the ability to filter out noise from the perceived speech. Comparing these results to data from motor experiments showed that the identified region had a distinct location in dPCC, anterior to the hand motor area and superior to the mouth articulator region. The present findings uncovered with high-density intracranial recordings help elucidate the functional specialization of PCC and demonstrate the unique role of its anterior dorsal region in continuous speech perception.

Give me a sign: decoding complex coordinated hand movements using high-field fMRI.

Decoding movements from the human cortex has been a topic of great interest for controlling an artificial limb in non-human primates and severely paralyzed people. Here we investigate feasibility of decoding gestures from the sensorimotor cortex in humans, using 7 T fMRI. Twelve healthy volunteers performed four hand gestures from the American Sign Language Alphabet. These gestures were performed in a rapid event related design used to establish the classifier and a slow event-related design, used to test the classifier. Single trial patterns were classified using a pattern-correlation classifier. The four hand gestures could be classified with an average accuracy of 63 % (range 35­95 %), which was significantly above chance (25 %). The hand region was, as expected, the most active region, and the optimal volume for classification was on average about 200 voxels, although this varied considerably across individuals. Importantly, classification accuracy correlated significantly with consistency of gesture execution. The results of our study demonstrate that decoding gestures from the hand region of the sensorimotor cortex using 7 T fMRI can reach very high accuracy, provided that gestures are executed in a consistent manner. Our results further indicate that the neuronal representation of hand gestures is robust and highly reproducible. Given that the most active foci were located in the hand region, and that 7 T fMRI has been shown to agree with electrocorticography, our results suggest that this confined region could serve to decode sign language gestures for intracranial brain­computer interfacing using surface grids.