Temporal interference (TI) stimulation was recently proposed that allows for the stimulation of deep brain structures with neocortical regions being minimally stimulated. For human brain modulation, TI current patterns are known to be considerably affected by the complex structures of the human head, and thus, it is hard to deliver TI current to a specific deep brain region. In this study, we optimized scalp electrode configurations and injection currents that can deliver maximum TI stimulation currents to a specific deep brain region, the head of the right hippocampus in this study, considering the real anatomical head structures of each individual. Three realistic finite element (FE) head models were employed for the optimization of TI stimulation. To generate TI current patterns, two pairs of scalp electrodes were selected, which carry two sinusoidally alternating currents with a small frequency difference. For every possible combination of electrode pairs, optimal injection currents delivering the maximal TI currents to the head of the right hippocampus were determined. The distribution of the optimized TI currents was then compared with that of the unoptimized TI currents and the conventional single frequency alternating current stimulation. Optimization of TI stimulation parameters allows for the delivery of the desired amount of TI current to the target region while effectively reducing the TI currents delivered to cortical regions compared to the other stimulation approaches. Inconsistency of the optimal stimulation conditions suggest that customized stimulation, considering the individual anatomical differences, is necessary for more effective transcranial TI stimulation. Customized transcranial TI stimulation based on the numerical field analysis is expected to enhance the overall effectiveness of noninvasive stimulation of the human deep brain structures.

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The present study demonstrated that recovery from low levels of blood glucose following a prolonged fasting period was associated with increased power in the theta and low alpha bands on a resting-state EEG and that this recovery was accompanied by improved attentional performance.

Epilepsy is a disease marked by hypersynchronous bursts of neuronal activity; therefore, identifying the network characteristics of the epileptic brain is important. Juvenile myoclonic epilepsy (JME) represents a common, idiopathic generalized epileptic syndrome, characterized by spike-and-wave discharge (SWD) electroencephalographic (EEG) waveforms. We compare herein the network properties of periods of SWD and baseline activity using graph theory. EEG data were obtained from 11 patients with JME. Functional cortical networks during SWD and baseline periods were estimated by calculating the coherence between all possible electrode pairs in the delta, theta, alpha, beta and gamma bands. Graph theoretical measures, including nodal degree, characteristic path length, clustering coefficient, and small-world index were then used to evaluate the characteristics of epileptic networks in JME. We also assessed short- and long-range connections between SWD and baseline networks. Compared to baseline, increased coherence was observed during SWD in all frequency bands. The nodal degree of the SWD network, particularly in the frontal region, was significantly higher compared to the baseline network. The clustering coefficient and small-world index were significantly lower in the theta and beta bands of the SWD versus baseline network, but the characteristic path length did not differ among networks. Long-range connections were increased during SWD, particularly between frontal and posterior brain regions. Our study suggests that SWD in JME is associated with increased local (particularly in frontal region) connectivity. Furthermore, the SWD network was associated with increased long-range connections, and reduced small-worldness, which may impair information processing during SWD.

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