title: Transcranial extracellular impedance control (tEIC) modulates behavioral performances.
abstract: Electric brain stimulations such as transcranial direct current stimulation (tDCS), transcranial random noise stimulation (tRNS), and transcranial alternating current stimulation (tACS) electrophysiologically modulate brain activity and as a result sometimes modulate behavioral performances. These stimulations can be viewed from an engineering standpoint as involving an artificial electric source (DC, noise, or AC) attached to an impedance branch of a distributed parameter circuit. The distributed parameter circuit is an approximation of the brain and includes electric sources (neurons) and impedances (volume conductors). Such a brain model is linear, as is often the case with the electroencephalogram (EEG) forward model. Thus, the above-mentioned current stimulations change the current distribution in the brain depending on the locations of the electric sources in the brain. Now, if the attached artificial electric source were to be replaced with a resistor, or even a negative resistor, the resistor would also change the current distribution in the brain. In light of the superposition theorem, which holds for any linear electric circuit, attaching an electric source is different from attaching a resistor; the resistor affects each active electric source in the brain so as to increase (or decrease in some cases of a negative resistor) the current flowing out from each source. From an electrophysiological standpoint, the attached resistor can only control the extracellular impedance and never causes forced stimulation; we call this technique transcranial extracellular impedance control (tEIC). We conducted a behavioral experiment to evaluate tEIC and found evidence that it had real-time enhancement and depression effects on EEGs and a real-time facilitation effect on reaction times. Thus, tEIC could be another technique to modulate behavioral performance.
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Ayumu
authors.initials: A
authors.lastname: Matani
authors.affiliation: Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Japan.
authors.forename: Masaaki
authors.initials: M
authors.lastname: Nakayama
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Mayumi
authors.initials: M
authors.lastname: Watanabe
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Yoshikazu
authors.initials: Y
authors.lastname: Furuyama
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Atsushi
authors.initials: A
authors.lastname: Hotta
authors.affiliation: Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
authors.forename: Shotaro
authors.initials: S
authors.lastname: Hoshino
mesh_terms: D000328:Adult; D001921:Brain / Q000502:physiology*; D003198:Computer Simulation; D017097:Electric Impedance; D004558:Electric Stimulation / Q000379:methods*; D004569:Electroencephalography; D005260:Female; D006801:Humans; D008297:Male; D008959:Models, Neurological; D011930:Reaction Time / Q000502:physiology*; D055815:Young Adult