Network‐based rTMS to modulate working memory: The difficult choice of effective parameters for online interventions

Abstract

Background: Online repetitive transcranialmagnetic stimulation (rTMS) has been shown to modulate working memory (WM) performance in a site-specific manner, with behavioral improvements due to stimulation of the dorsolateral prefrontal cortex (DLPFC), and impairment from stimulation to the lateral parietal cortex (LPC). Neurobehavioral studies have demonstrated that subprocesses of WM allowing for the maintenance and manipulation of information in the mind involve unique cortical networks. Despite promising evidence of modulatory effects of rTMS on WM, no studies have yet demonstrated distinct modulatory control of these two subprocesses. The current study therefore sought to explore this possibility through site-specific stimulation during an online task invoking both skills.Methods: Twenty-nine subjects completed a 4-day protocol, in which active or sham 5Hz rTMS was applied over the DLPFC and LPC in separate blocks of trials while participants performed tasks that required either maintenance alone, or both maintenance and manipulation (alphabetization) of information. Stimulation targets were defined individually based on fMRI activation and structural network properties. Stimulation amplitude was adjusted using electric field modeling to equate induced current in the target region across participants.

TAP: Targeting and analysis pipeline for optimization and verification of coil placement in transcranial magnetic stimulation

“…Our previously proposed E20, E50, and E100 metrics, which define the minimum E-field strength within the 20, 50, and 100 voxels with the strongest E-field in the ROI [2,36]. The computed dI/dt can be entered into the TMS device, typically as a percentage of the maximum stimulator output (%MSO), based on the approximately linear relationship between dI/dt and %MSO [2,36]. This relationship can generally be described by the formula where coefficients a and b can be fitted to empirical data obtained from TMS devices that provide both %MSO and dI/dt [A/μs] readout.…”

“…This relationship can generally be described by the formula where coefficients a and b can be fitted to empirical data obtained from TMS devices that provide both %MSO and dI/dt [A/μs] readout. In this case, dI/dt should be measured across the full range of intensity for a specific coil, for example, with 5% MSO steps [2,36]. For devices that do not display dI/dt, the most practical approach is to set b = 0, and compute from the TMS device maximum capacitor voltage, V C,max , corresponding to 100 %MSO, and the specific coil inductance L , both of which can be obtained from the manufacturer.…”

“…where coefficients ܽ and ܾ can be fitted to empirical data obtained from TMS devices that provide both %MSO and dI/dt [A/µs] readout. In this case, dI/dt should be measured across the full range of intensity for a specific coil, for example, with 5% MSO steps [2,36]. For devices that do not display dI/dt, the most practical approach is to set ܾ = 0, and compute…”

“…The desired directional or magnitude E-field strength in the ROI can be specified in TAP by one of several metrics: the maximal value as well as statistically more robust metrics, such as the 50 th , 75 th , 90 th , and 99 th percentiles, to avoid numerical E-field outliers. Our previously proposed E20, E50, and E100 metrics, which define the minimum E-field strength within the 20, 50, and 100 voxels with the strongest E-field in the ROI [2,36]. The computed dI/dt can be entered into the TMS device, typically as a percentage of the maximum stimulator output (%MSO), based on the approximately linear relationship between dI/dt and %MSO [2,36].…”

“…Prior to a TMS session, individualized computational modeling of head tissues and the electric field (E-field) induced in the ROI [17] allows for the identification of an optimal TMS coil placement on the scalp [12,18] as well as pulse intensity to deliver a specified E-field strength [2,36,44,45]. We previously developed the fast auxiliary dipole method (ADM) for software-assisted TMS targeting to maximize E-field delivery to an ROI [18], and ADM is now part of the SimNIBS E-field simulation software package.…”