HighlightsStability of recorded neural signals is crucial for the clinical viability of implantable brain-computer interfaces (BCIs).A fully implantable BCI offers a long-term robust and durable control signal in a person with amyotrophic lateral sclerosis.Increasing home use of the electrocorticography (ECoG)-BCI demonstrates user adoption of the BCI for communication.

Mental calculation is a complex mental procedure involving a frontoparietal network of brain regions. Functional MRI (fMRI) studies have revealed interesting characteristics of these regions, but the precise function of some areas remains elusive. In the present study, we used electrocorticographic (ECoG) recordings to chronometrically assess the neuronal processes during mental arithmetic. A calculation task was performed during presurgical 3T fMRI scanning and subsequent ECoG monitoring. Mental calculation induced an increase in fMRI blood oxygen level dependent signal in prefrontal, parietal and lower temporo-occipital regions. The group-fMRI result was subsequently used to cluster the implanted electrodes into anatomically defined regions of interest (ROIs). We observed remarkable differences in high frequency power profiles between ROIs, some of which were closely associated with stimulus presentation and others with the response. Upon stimulus presentation, occipital areas were the first to respond, followed by parietal and frontal areas, and finally by motor areas. Notably, we demonstrate that the fMRI activation in the middle frontal gyrus/precentral gyrus is associated with two subfunctions during mental calculation. This finding reveals the significance of the temporal dynamics of neural ensembles within regions with an apparent uniform function. In conclusion, our results shed more light on the spatiotemporal aspects of brain activation during a mental calculation task, and demonstrate that the use of fMRI data to cluster ECoG electrodes is a useful approach for ECoG group analysis.

Electrocorticography (ECoG)-based brain-computer interface (BCI) systems have emerged as a new signal platform for neuroprosthetic application. ECoG-based platforms have shown significant promise for clinical application due to the high level of information that can be derived from the ECoG signal, the signal's stability, and its intermediate nature of surgical invasiveness. However, before long-term BCI applications can be realized it will be important to also understand how the cortical physiology alters with age. Such understanding may provide an appreciation for how this may affect the control signals utilized by a chronic implant. In this study, we report on a large population of adult and pediatric invasively monitored subjects to determine the impact that age will have on surface cortical physiology. We evaluated six frequency bands--delta (<4 Hz), theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), low gamma band (30-50 Hz), and high gamma band (76-100 Hz)--to evaluate the effect of age on the magnitude of power change, cortical area of activation, and cortical networks. When significant trends are evaluated as a whole, it appears that the aging process appears to more substantively alter thalamocortical interactions leading to an increase in cortical inefficiency. Despite this, we find that higher gamma rhythms appear to be more anatomically constrained with age, while lower frequency rhythms appear to broaden in cortical involvement as time progresses. From an independent signal standpoint, this would favor high gamma rhythms' utilization as a separable signal that could be maintained chronically.

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.

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