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IntroductionNeurostimulation applied from deep brain stimulation (DBS) electrodes is an effective therapeutic intervention in patients suffering from intractable drug-resistant epilepsy when resective surgery is contraindicated or failed. Inhibitory DBS to suppress seizures and associated epileptogenic biomarkers could be performed with high-frequency stimulation (HFS), typically between 100 and 165 Hz, to various deep-seated targets, such as the Mesio-temporal lobe (MTL), which leads to changes in brain rhythms, specifically in the hippocampus. The most prominent alterations concern high-frequency oscillations (HFOs), namely an increase in ripples, a reduction in pathological Fast Ripples (FRs), and a decrease in pathological interictal epileptiform discharges (IEDs).Materials and methodsIn the current study, we use Temporal Interference (TI) stimulation to provide a non-invasive DBS (130 Hz) of the MTL, specifically the hippocampus, in both mouse models of epilepsy, and scale the method using human cadavers to demonstrate the potential efficacy in human patients. Simulations for both mice and human heads were performed to calculate the best coordinates to reach the hippocampus.ResultsThis non-invasive DBS increases physiological ripples, and decreases the number of FRs and IEDs in a mouse model of epilepsy. Similarly, we show the inability of 130 Hz transcranial current stimulation (TCS) to achieve similar results. We therefore further demonstrate the translatability to human subjects via measurements of the TI stimulation vs. TCS in human cadavers. Results show a better penetration of TI fields into the human hippocampus as compared with TCS.SignificanceThese results constitute the first proof of the feasibility and efficiency of TI to stimulate at depth an area without impacting the surrounding tissue. The data tend to show the sufficiently focal character of the induced effects and suggest promising therapeutic applications in epilepsy.

Increasing evidence suggests that sleep spindles are involved in memory consolidation, but few studies have investigated the effects of learning on brain responses associated with spindles in humans. Here we used simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) during sleep to assess haemodynamic brain responses related to spindles after learning. Twenty young healthy participants were scanned with EEG/fMRI during (i) a declarative memory face sequence learning task, (ii) subsequent sleep, and (iii) recall after sleep (learning night). As a control condition an identical EEG/fMRI scanning protocol was performed after participants over-learned the face sequence task to complete mastery (control night). Results demonstrated increased responses in the fusiform gyrus both during encoding before sleep and during successful recall after sleep, in the learning night compared to the control night. During sleep, a larger response in the fusiform gyrus was observed in the presence of fast spindles during the learning as compared to the control night. Our findings support a cortical reactivation during fast spindles of brain regions previously involved in declarative learning and subsequently activated during memory recall, thereby promoting the cortical consolidation of memory traces.

Sleep deprivation (SD) leads to impairments in cognitive function. Here, we tested the hypothesis that cognitive changes in the sleep-deprived brain can be explained by information processing within and between large-scale cortical networks. We acquired functional magnetic resonance imaging (fMRI) scans of 20 healthy volunteers during attention and executive tasks following a regular night of sleep, a night of SD, and a recovery nap containing nonrapid eye movement (NREM) sleep. Overall, SD was associated with increased cortex-wide functional integration, driven by a rise of integration within cortical networks. The ratio of within versus between network integration in the cortex increased further in the recovery nap, suggesting that prolonged wakefulness drives the cortex towards a state resembling sleep. This balance of integration and segregation in the sleep-deprived state was tightly associated with deficits in cognitive performance. This was a distinct and better marker of cognitive impairment than conventional indicators of homeostatic sleep pressure, as well as the pronounced thalamocortical connectivity changes that occurs towards falling asleep. Importantly, restoration of the balance between segregation and integration of cortical activity was also related to performance recovery after the nap, demonstrating a bidirectional effect. These results demonstrate that intra- and interindividual differences in cortical network integration and segregation during task performance may play a critical role in vulnerability to cognitive impairment in the sleep-deprived state.