Abstract
Electrical brain stimulation (EBS) is an appealing method to treat neurological disorders. To achieve optimal stimulation effects and a better understanding of the underlying brain mechanisms, neuroscientists have proposed computational modeling studies for a decade. Recently, multi-scale models that combine a volume conductor head model and multi-compartmental models of cortical neurons have been developed to predict stimulation effects on the macroscopic and microscopic levels more precisely. As the need for better computational models continues to increase, we overview here recent multi-scale modeling studies; we focused on approaches that coupled a simplified or high-resolution volume conductor head model and multi-compartmental models of cortical neurons, and constructed realistic fiber models using diffusion tensor imaging (DTI). Further implications for achieving better precision in estimating cellular responses are discussed.
Highlights
The stimulation current required to modulate cortical excitability can be delivered via invasive (deep brain stimulation (DBS), subdural, or epidural cortical stimulation) and noninvasive methods, depending upon whether a surgical procedure is required
While considerable literature has investigated brain stimulation effects using the magnitude of the stimulus-induced electric field, multi-scale models demonstrated that the spatial extent of excitation thresholds was not consistent with the distributions of the electric field
Numerical studies using a multi-compartmental model of neurons have shown computed neural responses, but they cannot provide extensive target sites and have little control over external stimulation parameters
Summary
Electrical brain stimulation (EBS) is an intriguing electrotherapy designed to regulate cortical excitability through a regulated current, and is used increasingly to treat various neurological disorders and as an adjunct to medical therapy for depression (Padberg and George, 2009; Nahas et al, 2010); chronic pain (Hanajima et al, 2002; Di Lazzaro et al, 2004; Holsheimer et al, 2007; Lefaucheur et al, 2010); rehabilitation (Brown et al, 2003, 2006; Canavero et al, 2006; Levy et al, 2008); Parkinson’s disease (Canavero et al, 2002; Hanajima et al, 2002; Pagni et al, 2008); essential tremor (Picillo et al, 2015); epilepsy (Nitsche and Paulus, 2009; Canavero, 2014); tinnitus (Tass et al, 2012), and other brain disorders (Canavero, 2009). As the importance of brain anatomy has been recognized, some high-resolution head models that reflect geometrical information from magnetic resonance imaging (MRI) have been proposed (Datta et al, 2009; Lee et al, 2012; Edwards et al, 2013; Truong et al, 2013; Windhoff et al, 2013; Kim et al, 2014; Shahid et al, 2014) These models hold promise for realistic electric field calculations that result thereby in more precise estimations of the brain areas affected. To simulate the effects of anatomical information on neuronal activation more precisely, numerical approaches that use cortical neuronal models that incorporate electrical and chemical information of biologically realistic neurons have been conducted, and the electricity calculated with head models is used as input to cortical neurons that simulate neural responses These integrations between neuronal and head models are referred to as multi-scale computational models, and they provide potential neural targets by brain stimulation.
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