Neuromodulation: Transcranial electric stimulation seen from within the brain
Computer models can make transcranial electric stimulation a better tool for research and therapy.
- Duke University, United States
The human brain seems well protected, encased within the skull. Yet something as simple as placing a pair of wet sponges onto someone's head and sending a weak electric current between them can actually alter the brain's activity. A refined version of this method – known as transcranial electric stimulation – has attracted considerable interest and is now being used to probe the workings of the brain and develop treatments for medical conditions such as depression, epilepsy or stroke.
Transcranial electric stimulation (or TES for short) has parallels with conventional drug treatments in the sense that delivering an electric field to the brain is analogous to delivering drug molecules into the body. So, just as it is important to know how the human body affects an administered drug (a field of research that is known as pharmacokinetics), in TES we need to know how much of the current applied to the scalp actually enters the brain, and where this current goes.
The 'pharmacokinetics of TES' remains contentious (Underwood, 2016), but is important for several reasons. First, it allows us to relate findings from experiments in which brain tissue from animals is stimulated directly to findings obtained via noninvasive applications in people. Second, it helps researchers optimize the process in order to target specific regions of the brain. Third, it enables researchers to compensate for the differences between individuals, and to standardize the exposure that they receive.
The only established approach for estimating the dose of TES delivered to an individual relies on a three-dimensional model of the subject's head that includes its different tissues and the attached electrodes, which is fed into a computer simulation (Figure 1). Such models have been available for some time (Datta et al., 2009), but they had been validated only partially and indirectly in humans or other primates (Edwards et al., 2013; Lee et al., 2015). Moreover, there are uncertainties about the electric properties of the tissues in these models.
Figure 1
Computational model of the electric field and current produced in an individual's head during transcranial electric stimulation.
(A) Electrodes (white and orange rectangles) are attached to the scalp and electric current is applied; the model of the head shown here is derived from a structural MRI scan. (B) Simulation showing the electric current per unit area (current density) in a section of the brain during transcranial stimulation: this image shows the scalp (outermost layer), skull, cerebrospinal fluid, gray matter and white matter. The highest current density values in the brain (blue) are 100-fold lower than those in the scalp (red). The high resistance of the skull means that the majority of the current is shunted in the scalp. The cerebrospinal fluid is highly conductive and this takes current away from the brain too. (C) Simulation showing the electric field on the surface of the brain. For this configuration, the electric field is strongest between the two electrodes. The model was created and visualized with the free SimNIBS software package (http://simnibs.de; Windhoff et al., 2013).
Now, in eLife, Lucas Parra and colleagues – including Yu Huang and Anli Liu as joint first authors – report how they have addressed these issues by combining elaborate computational modeling with recordings taken within the brains of ten people undergoing surgery for epilepsy (Huang et al., 2017). This sample size markedly exceeds that of other similar measurements (Opitz et al., 2016), and the three-dimensional models used are highly sophisticated too. Leveraging this setup, Huang et al. provide the most extensive and direct estimates of the TES electric field to date. They also confirm that computational models of TES can accurately recreate the electric field generated in a real brain.
Huang et al. – who are based at City College of the City University of New York, New York University School of Medicine and the Mayo Clinic – provide practical insights that should help others to implement the models as well. For accurate results, the individual scan should capture the entire head, from neck to crown. This is not the convention in clinical imaging, which currently only focuses on the brain, but Huang et al. get round this limitation by splicing the bottom portion of a standard model of a head onto the individual scans. To do this, the images must be properly cropped and morphed, though this feature has yet to be added to publicly available electric field modeling software.
Including a compartment for the cerebrospinal fluid (the colorless liquid that surrounds the brain) also makes the models more accurate. Appropriate imaging and image analysis methods are required to capture this layer as well as the skull, which are both quite thin (see Figure 1B). However, modelers can breathe a sigh of relief, because the data suggest that the different layers within the skull can be omitted from the models without significantly impacting their accuracy. The way that conductivity changes depending on the orientation of the current in the brain's white matter can similarly be ignored, at least for the mostly outer regions of the brain explored so far by Huang et al.
This work also underscores the present limitations of modeling. It is still uncertain exactly what values for tissue conductivity should be used, and whether it is acceptable to use the same values for everyone. Addressing this question requires further studies likely involving a range of techniques. For example, there are promising efforts to measure tissue conductivities directly during surgery (Koessler et al., 2017), or with other noninvasive techniques (Chauhan et al., 2017).
Even without making the absolute electric field estimates more accurate, existing modeling approaches and software appear suitable for measuring the relative strength of stimulation across brain regions, and predicting how an individual's anatomy might affect this. Indeed, the National Institutes of Health now requires that researchers applying for certain grants "use realistic head modeling" to characterize what electric field is delivered across the brain (NIH, 2017). All in all, it seems that the time is now right for wider adoption of 'pharmacokinetics' of transcranial brain stimulation.
References
-
Electric field model of transcranial electric stimulation in nonhuman primates: correspondence to individual motor thresholdIEEE Transactions on Biomedical Engineering 62:2095–2105.https://doi.org/10.1109/TBME.2015.2425406
Article and author information
Author details
Publication history
Copyright
© 2017, Peterchev
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,800
- views
-
- 259
- downloads
-
- 8
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Neuromodulation: Transcranial electric stimulation seen from within the brain
eLife 6:e25812.
https://doi.org/10.7554/eLife.25812
Further reading
-
- Neuroscience
Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.
-
- Neuroscience
Non-linear summation of synaptic inputs to the dendrites of pyramidal neurons has been proposed to increase the computation capacity of neurons through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration has been documented extensively in principal neurons, mediated by several voltage-dependent conductances. It has also been reported in parvalbumin-positive hippocampal basket cells, in dendrites innervated by feedback excitatory synapses. Whether other interneurons, which support feed-forward or feedback inhibition of principal neuron dendrites, also exhibit local non-linear integration of synaptic excitation is not known. Here, we use patch-clamp electrophysiology, and two-photon calcium imaging and glutamate uncaging, to show that supralinear dendritic integration of near-synchronous spatially clustered glutamate-receptor mediated depolarization occurs in NDNF-positive neurogliaform cells and oriens-lacunosum moleculare interneurons in the mouse hippocampus. Supralinear summation was detected via recordings of somatic depolarizations elicited by uncaging of glutamate on dendritic fragments, and, in neurogliaform cells, by concurrent imaging of dendritic calcium transients. Supralinearity was abolished by blocking NMDA receptors (NMDARs) but resisted blockade of voltage-gated sodium channels. Blocking L-type calcium channels abolished supralinear calcium signalling but only had a minor effect on voltage supralinearity. Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in dendrite-projecting inhibitory cells of the hippocampus.
