Two new papers on Forward Modeling Methods
Both papers appear in a special issue of Progress in Brain Research.
Modeling sequence and quasi-uniform assumption in computational neurostimulation
Bikson M, Truong DQ, Mourdoukoutas A, Aboseria M, Khadka N, Adair D, Rahman A
Abstract: Computational neurostimulation aims to develop mathematical constructs that link the application of neuromodulation with changes in behavior and cognition. This process is critical but daunting for technical challenges and scientific unknowns. The overarching goal of this review is to address how this complex task can be made tractable. We describe a framework of sequential modeling steps to achieve this: (1) current flow models, (2) cell polarization models, (3) network and information processing models, and (4) models of the neuroscientific correlates of behavior. Each step is explained with a specific emphasis on the assumptions underpinning underlying sequential implementation. We explain the further implementation of the quasi-uniform assumption to overcome technical limitations and unknowns. We specifically focus on examples in electrical stimulation, such as transcranial direct current stimulation. Our approach and conclusions are broadly applied to immediate and ongoing efforts to deploy computational neurostimulation.
Multilevel computational models for predicting the cellular effects of noninvasive brain stimulation
Rahman A, Lafon B, Bikson M
Abstract: Since 2000, there has been rapid acceleration in the use of tDCS in both clinical and cognitive neuroscience research, encouraged by the simplicity of the technique (two electrodes and a battery powered stimulator) and the perception that tDCS protocols can be simply designed by placing the anode over the cortex to “excite,” and the cathode over cortex to “inhibit.” A specific and predictive understanding of tDCS needs experimental data to be placed into a quantitative framework. Biologically constrained computational models provide a useful framework within which to interpret results from empirical studies and generate novel, testable hypotheses. Although not without caveats, computational models provide a tool for exploring cognitive and brain processes, are amenable to quantitative analysis, and can inspire novel empirical work that might be difficult to intuit simply by examining experimental results. We approach modeling the effects of tDCS on neurons from multiple levels: modeling the electric field distribution, modeling single-compartment effects, and finally with multicompartment neuron models.