Optimal use of EEG recordings to target active brain areas with transcranial electrical stimulation

Abstract

To demonstrate causal relationships between brain and behavior, investigators would like to guide brain stimulation using measurements of neural activity. Particularly promising in this context are electroencephalography (EEG) and transcranial electrical stimulation (TES), as they are linked by a reciprocity principle which, despite being known for decades, has not led to a formalism for relating EEG recordings to optimal stimulation parameters. Here we derive a closed-form expression for the TES configuration that optimally stimulates (i.e., targets) the sources of recorded EEG, without making assumptions about source location or distribution. We also derive a duality between TES targeting and EEG source localization, and demonstrate that in cases where source localization fails, so does the proposed targeting. Numerical simulations with multiple head models confirm these theoretical predictions and quantify the achieved stimulation in terms of focality and intensity. We show that constraining the stimulation currents automatically selects optimal montages that involve only a few (4–7) electrodes, with only incremental loss in performance when targeting focal activations. The proposed technique allows brain scientists and clinicians to rationally target the sources of observed EEG and thus overcomes a major obstacle to the realization of individualized or closed-loop brain stimulation.

Can transcranial electric stimulation with multiple electrodes reach deep targets?

“…Based on this observation, a number of investigators have proposed multi-electrode methods to maximize intensity or focality on a desired target in the brain [4,12–17 ]. The approach uses several small “high-definition” electrodes and can be combined with any arbitrary waveform.…”

Does 10-Hz cathodal oscillating current of the parieto-occipital lobe modulate target detection?

“…Anatomical variation including scalp-brain distance, gyral folding of the cerebral cortex, and thickness of corticospinal fluid layer and skull can have a significant impact on the effects of transcranial current stimulation (Nitsche et al, 2008;Opitz et al, 2015). In the future, computational models might help overcome these inter-individual differences by allowing researchers to select the optimal set of stimulation parameters for each individual based on models of current flow (Truong et al, 2014;Dmochowski et al, 2017). However, the fact that tCS can cause non-linear effects at the neurophysiological and behavioral level (Bonaiuto, 2015), and the difficulty in obtaining data in vivo to validate the computational models (Bai et al, 2013), necessitate further model refinements and development.…”

Unification of optimal targeting methods in Transcranial Electrical Stimulation

“…If no current injection limits are imposed or they are too loose to be neglected, the LS or WLS solutions can be presented by a well-known closed formula (Dmochowski et al, 2011;Fernández-Corazza et al, 2016;Salman et al, 2016). One option to account for the total current budget constraint without the need of numerical solvers is to apply a scaling factor to the closed formula (as in (Dmochowski et al, 2017;Fernández-Corazza et al, 2016)) and, as we show later, the solution is still optimal. Another option is to consider the total and per electrode current limits and solve the problem using a numerical optimization algorithm such as LASSO (Dmochowski et al, 2011), MATLAB convex optimization (Dmochowski et al, 2011) or genetic algorithms (Otal et al, 2016;Ruffini et al, 2014).…”