Task-free electrocorticography frequency mapping of the motor cortex

ObjectiveElectrocortical stimulation mapping (ESM) is the current gold standard for functional mapping of the eloquent cortex prior to epilepsy surgery. The procedure is, however, time-consuming and quite demanding for patients. Electrocorticography frequency mapping (ECoG mapping) has been suggested as an adjunct method. Here, we investigated whether it is possible to perform mapping of motor regions using ECoG data of spontaneous movements.MethodsUsing the video registration of seven epilepsy patients who underwent electrocorticography and ESM, we selected periods of spontaneous hand and arm movements and periods of rest. Frequency analysis was performed, and electrodes showing a significant change in power (4–7, 8–14, 15–25, 26–45 or 65–95 Hz) were compared with those being identified as relevant for hand and/or arm movement by ESM.ResultsAll frequency bands showed a high specificity (>0.80), and the 65–95 Hz frequency band additionally had a high sensitivity (0.82) for identifying ESM positive electrodes.ConclusionsOur data show a good match between ECoG mapping of spontaneous movements and ESM data.SignificanceThe accurate match suggests that ECoG mapping of the motor cortex using spontaneous movements may be a valuable complement to ESM, especially when other options requiring patient cooperation fail.     

Sleep spindles are locally modulated by training on a brain–computer interface

The learning of a motor task is known to be improved by sleep, and sleep spindles are thought to facilitate this learning by enabling synaptic plasticity. In this study subjects implanted with electrocorticography (ECoG) arrays for long-term epilepsy monitoring were trained to control a cursor on a computer screen by modulating either the high-gamma or mu/beta power at a single electrode located over the motor or premotor area. In all trained subjects, spindle density in posttraining sleep was increased with respect to pretraining sleep in a remarkably spatially specific manner. The pattern of increased spindle activity reflects the functionally specific regions that were involved in learning of a highly novel and salient task during wakefulness, supporting the idea that sleep spindles are involved in learning to use a motor-based brain–computer interface device.The need for sleep is ubiquitous in the animal kingdom, but the purpose of sleep is still poorly understood and controversial in many respects. Nevertheless, it is now widely accepted that sleep, and especially nonrapid eye movement (NREM) sleep, plays an active role in learning and memory consolidation (1). NREM epochs are easily identified in the cortical field potential as periods dominated by high-amplitude, low-frequency oscillations (2). Sleep spindles are one of the most prominent and recognizable of these oscillations, and as such they are commonly used to classify NREM sleep stages. These brief 12- to 15-Hz oscillations are generated by the reticular nucleus of the thalamus and grouped by the slow oscillation (<1 Hz) in the neocortex (3). The reticular nucleus is involved in gating sensory inputs and it is hypothesized that sleep spindles prevent incoming sensory information from reaching the neocortex during NREM sleep (4). This dissociation could provide a window for uninterrupted replay of recently instantiated memories and thus support sleep-dependent memory consolidation. There is now a large body of evidence linking sleep spindles to learning and memory in both humans and animals (5–7). For example, increases in spindle density have been correlated with learning a declarative memory task (5), with retention of verbal memories (6), and with relevant recall of a remote memory (7). Additionally, spindles are correlated with sharp-wave complexes in the hippocampus (8, 9) and are associated with stored-trace reactivation in the neocortex (10). It has been hypothesized that sleep spindles actually facilitate learning by establishing a cortical state that is conducive to synaptic plasticity, and therefore, to sleep-dependent memory consolidation (11, 12). A great deal of emphasis has been placed on the role of sleep spindles in the hippocampal–neocortical dialogue (8, 9, 13), and thus consolidation of hippocampal-dependent memories. However, there is ample data to suggest that spindles are also important for motor and procedural learning (14–17).This theory is complicated by the fact that, whereas learning requires changes to specific circuits, spindles have traditionally been considered global events arising from a single generator. However, several studies have shown that this assumption requires reevaluation. Nishida and Walker (17) showed that following procedural task learning, spindles were increased in the learning hemisphere with respect to the nonlearning hemisphere. More recently there has been a report that spindles recorded by magnetoencephalography, in contrast to those recorded by EEG, arise from multiple asynchronous generators (18), a result which may be explained by the interaction of core and matrix thalmocortical projections (19). The globally synchronous nature of spindles was further challenged by Nir et al. (20) who used depth electrodes implanted in medial brain areas to show that the majority of sleep spindles are not synchronous across the brain but rather localized to a single brain area. However, the extent of the locality of spindles within a brain area has not yet been demonstrated. Furthermore, it has not been shown that local groups of spindles are independently modulated by behavior.In this study, subjects implanted with subdural electrode grids for epilepsy monitoring were trained to control a computer cursor by modulating the activity at a single electrode. Here we show that after training on a brain–computer interface (BCI) the rate of sleep spindle occurrence in posttraining sleep is increased in a remarkably localized pattern around the functionally specific regions that were involved in task performance.