Devin Adair

Modeling sequence and quasi-uniform assumption in computational neurostimulation.

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.

Tolerability of Repeated Application of Transcranial Electrical Stimulation with Limited Outputs to Healthy Subjects

BACKGROUND The safety and tolerability of limited output transcranial electrical stimulation (tES) in clinical populations support a non-significant risk designation. The tolerability of long-term use in a healthy population had remained untested. OBJECTIVE We tested the tolerability and compliance of two tES waveforms, transcranial direct current stimulation (tDCS) and modulated high frequency transcranial pulsed current stimulation (MHF-tPCS) compared to sham-tDCS, applied to healthy subjects for three to five days (17-20 minutes per day) per week for up to six weeks in a communal setting. MHF-tPCS consisted of asymmetric high-frequency pulses (7-11 kHz) having a peak amplitude of 10-20 mA peak, adjusted by subject, resulting in an average current of 5-7 mA. METHOD A total of 100 treatment blind healthy subjects were randomly assigned to one of three treatment groups: tDCS (n = 33), MHF-tPCS (n = 30), or sham-tDCS (n = 37). In order to test the role of waveform, electrode type and montage were fixed across tES and sham-tDCS arms: high-capacity self-adhering electrodes on the right lateral forehead and back of the neck. We conducted 1905 sessions (636 sham-tDCS, 623 tDCS, and 646 MHF-tPCS sessions) on study volunteers over a period of six weeks. RESULTS Common adverse events were primarily restricted to influences upon the skin and included skin tingling, itching, and mild burning sensations. The incidence of these events in the active tES treatment arms (MHF-tPCS, tDCS) was equivalent or significantly lower than their incidence in the sham-tDCS treatment arm. Other adverse events had a rarity (<5% incidence) that could not be significantly distinguished across the treatment groups. Some subjects were withdrawn from the study due to atypical headache (sham-tDCS n = 2, tDCS n = 2, and MHF-tPCS n = 3), atypical discomfort (sham-tDCS n = 0, tDCS n = 1, and MHF-tPCS n = 1), or atypical skin irritation (sham-tDCS n = 2, tDCS n = 8, and MHF-tPCS n = 1). The rate of compliance, elected sessions completed, for the MHF-tPCS group was significantly greater than the sham-tDCS groups compliance (p = 0.007). There were no serious adverse events in any treatment condition. CONCLUSION We conclude that repeated application of limited output tES across extended periods, limited to the hardware, electrodes, and protocols tested here, is well tolerated in healthy subjects, as previously observed in clinical populations.

Limited output transcranial electrical stimulation (LOTES-2017): Engineering principles, regulatory statutes, and industry standards for wellness, over-the-counter, or prescription devices with low risk

We present device standards for low-power non-invasive electrical brain stimulation devices classified as limited output transcranial electrical stimulation (tES). Emerging applications of limited output tES to modulate brain function span techniques to stimulate brain or nerve structures, including transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), and transcranial pulsed current stimulation (tPCS), have engendered discussion on how access to technology should be regulated. In regards to legal regulations and manufacturing standards for comparable technologies, a comprehensive framework already exists, including quality systems (QS), risk management, and (inter)national electrotechnical standards (IEC). In Part 1, relevant statutes are described for medical and wellness application. While agencies overseeing medical devices have broad jurisdiction, enforcement typically focuses on those devices with medical claims or posing significant risk. Consumer protections regarding responsible marketing and manufacture apply regardless. In Part 2 of this paper, we classify the electrical output performance of devices cleared by the United States Food and Drug Administration (FDA) including over-the-counter (OTC) and prescription electrostimulation devices, devices available for therapeutic or cosmetic purposes, and devices indicated for stimulation of the body or head. Examples include iontophoresis devices, powered muscle stimulators (PMS), cranial electrotherapy stimulation (CES), and transcutaneous electrical nerve stimulation (TENS) devices. Spanning over 13 FDA product codes, more than 1200 electrical stimulators have been cleared for marketing since 1977. The output characteristics of conventional tDCS, tACS, and tPCS techniques are well below those of most FDA cleared devices, including devices that are available OTC and those intended for stimulation on the head. This engineering analysis demonstrates that with regard to output performance and standing regulation, the availability of tDCS, tACS, or tPCS to the public would not introduce risk, provided such devices are responsibly manufactured and legally marketed. In Part 3, we develop voluntary manufacturer guidance for limited output tES that is aligned with current regulatory standards. Based on established medical engineering and scientific principles, we outline a robust and transparent technical framework for ensuring limited output tES devices are designed to minimize risks, while also supporting access and innovation. Alongside applicable medical and government activities, this voluntary industry standard (LOTES-2017) further serves an important role in supporting informed decisions by the public.

Inherent physiological artifacts in EEG during tDCS

&NA; Online imaging and neuromodulation is invalid if stimulation distorts measurements beyond the point of accurate measurement. In theory, combining transcranial Direct Current Stimulation (tDCS) with electroencephalography (EEG) is compelling, as both use non‐invasive electrodes and image‐guided dose can be informed by the reciprocity principle. To distinguish real changes in EEG from stimulation artifacts, prior studies applied conventional signal processing techniques (e.g. high‐pass filtering, ICA). Here, we address the assumptions underlying the suitability of these approaches. We distinguish physiological artifacts ‐ defined as artifacts resulting from interactions between the stimulation induced voltage and the body and so inherent regardless of tDCS or EEG hardware performance – from methodology‐related artifacts ‐ arising from non‐ideal experimental conditions or non‐ideal stimulation and recording equipment performance. Critically, we identify inherent physiological artifacts which are present in all online EEG‐tDCS: 1) cardiac distortion and 2) ocular motor distortion. In conjunction, non‐inherent physiological artifacts which can be minimized in most experimental conditions include: 1) motion and 2) myogenic distortion. Artifact dynamics were analyzed for varying stimulation parameters (montage, polarity, current) and stimulation hardware. Together with concurrent physiological monitoring (ECG, respiration, ocular, EMG, head motion), and current flow modeling, each physiological artifact was explained by biological source‐specific body impedance changes, leading to incremental changes in scalp DC voltage that are significantly larger than real neural signals. Because these artifacts modulate the DC voltage and scale with applied current, they are dose specific such that their contamination cannot be accounted for by conventional experimental controls (e.g. differing stimulation montage or current as a control). Moreover, because the EEG artifacts introduced by physiologic processes during tDCS are high dimensional (as indicated by Generalized Singular Value Decomposition‐ GSVD), non‐stationary, and overlap highly with neurogenic frequencies, these artifacts cannot be easily removed with conventional signal processing techniques. Spatial filtering techniques (GSVD) suggest that the removal of physiological artifacts would significantly degrade signal integrity. Physiological artifacts, as defined here, would emerge only during tDCS, thus processing techniques typically applied to EEG in the absence of tDCS would not be suitable for artifact removal during tDCS. All concurrent EEG‐tDCS must account for physiological artifacts that are a) present regardless of equipment used, and b) broadband and confound a broad range of experiments (e.g. oscillatory activity and event related potentials). Removal of these artifacts requires the recognition of their non‐stationary, physiology‐specific dynamics, and individualized nature. We present a broad taxonomy of artifacts (non/stimulation related), and suggest possible approaches and challenges to denoising online EEG‐tDCS stimulation artifacts. HighlightsWe identify inherent physiological artifacts during concurrent (online) tDCS‐EEG.Physiological artifacts include cardiac, ocular, motion, and myogenic artifacts.Dose response and current source independence of physiological artifacts are examined.FEM models explain physiological artifacts based on specific impedance changes.Typical signal processing methods cannot reliably remove physiological artifacts.

High-Definition transcranial direct current stimulation in early onset epileptic encephalopathy: a case study

ABSTRACT Primary objective: Early onset epileptic encephalopathy is characterized by high daily seizure-frequency, multifocal epileptic discharges, severe psychomotor retardation, and death at infancy. Currently, there are no effective treatments to alleviate seizure frequency and high-voltage epileptic discharges in these catastrophic epilepsy cases. The current study examined the safety and feasibility of High-Definition transcranial direct current stimulation (HD-tDCS) in reducing epileptiform activity in a 30-month-old child suffering from early onset epileptic encephalopathy. Design and Methods: HD-tDCS was administered over 10 intervention days spanning two weeks including pre- and post-intervention video-EEG monitoring. Results: There were no serious adverse events or side effects related to the HD-tDCS intervention. Frequency of clinical seizures was not significantly reduced. However, interictal sharp wave amplitudes were significantly lower during the post-intervention period versus baseline. Vital signs and blood biochemistry remained stable throughout the entire study. Conclusions: These exploratory findings support the safety and feasibility of 4 × 1 HD-tDCS in early onset epileptic encephalopathy and provide the first evidence of HD-tDCS effects on paroxysmal EEG features in electroclinical cases under the age of 36 months. Extending HD-tDCS treatment may enhance electrographic findings and clinical effects.

P128 Physiologic artifacts when combining EEG and tDCS

The field of non invasive brain stimulation (NIBS) has benefited from integration with imaging including magnetic resonance imaging (MRI) and electroencephalography (EEG). Several studies have reported on concurrent tDCS and EEG, and used signal processing of varying complexity (e.g. high-pass filtering to ICA) to remove “non-physiologic stimulation artifacts” – namely artifacts arising from non-ideal stimulation and recording amplifier performance. None has addressed “physiologic artifacts” which are defined here as non-stationary changes in artifacts resulting from interactions between the stimulation induced voltage and body. We identified and systematically characterized a series of tDCS induced physiologic and non-physiologic artifacts during concurrent EEG and High Definition (HD)-tDCS, and adapted subject-specific computational modeling to corroborate physiological EEG findings. Physiologic artifacts include (1) cardiac distortion; (2) ocular motor distortion; (3) movement (myogenic) distortion. In each case, the artifact was montage, intensity, and polarity specific; as such contamination from these physiologic artifacts cannot be accounted for by typical control experiments (e.g. EEG changes that are dose specific). High resolution finite element models explained artifact based on specific impedance changes. Importantly (a) physiologic artifacts are universal, they are nominally independent of device and so exist regardless of devices; (b) the broad-band nature of contamination may confound a broad range of experiments (e.g. oscillations, ERP); (c) removal of artifacts requires recognition of their peculiar dynamic and individualized nature.

P094 Method for EEG guided transcranial Electrical Stimulation without models

Objective There is a long interest in using EEG measurements to inform transcranial Electrical Stimulation (tES) but adoption is lacking. The conventional approach is to use anatomical head-models for both source localization (the EEG inverse problem) and current flow modeling (the tES forward model), but this approach is computationally demanding, requires an anatomical MRI, and strict assumptions about the target brain regions. We evaluate techniques whereby tES dose is derived from EEG without the need for an anatomical head model or assumptions. Approach The approaches are verified using a Finite Element Method (FEM) simulation of the EEG generated by a dipole, oriented either tangential or radial to the surface, and then simulating brain current flow produced by various model-free techniques including: (1) Voltage-to-voltage, (2) Voltage-to-Current; (3) Laplacian; and two Ad-Hoc techniques (4) Dipole sink-to-sink; and (5) Sink to Concentric Ring. These model-free approaches are compared to a numerically optimized dose that assumes perfect understanding of the dipole location and head anatomy. We vary the number of electrodes from a few to over three hundred, with focality or intensity as optimization criterion. Main results Our results demonstrate how simple Ad-Hoc approaches can achieve reasonable targeting for the case of a cortical dipole with 2–8 electrodes and no need for a model of the head. Significance For its simplicity and linearity, model-free EEG guided lends itself to broad adoption and can be applied to a static (tDCS), time-variant (e.g. tACS, tRNS, tPCS), or closed-loop tES. Figure options Download full-size image Download high-quality image (1118 K) Download as PowerPoint slide Figure options Download full-size image Download high-quality image (1499 K) Download as PowerPoint slide

Computer-Based Models of tDCS and tACS

Transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) are noninvasive neuromodulatory techniques that deliver low-intensity currents facilitating or inhibiting spontaneous neuronal activity. These techniques have a number of advantages that have been applied in clinical settings; in particular, tDCS/tACS dose in principle is easily customized by varying electrode number, position, size, shape, and current. However, the ability to leverage this customization depends on how tDCS/tACS dose modulate the underling brain current flow. This relationship is not simple and can benefit from the use of computational models of current flow, personalized to individual subjects and cases. Tools for modeling range from Finite Element Method models to stand-alone GUI based software for clinicians. Many software packages can load individual’s MRI scans, allowing individualized therapy design. However, the challenge remains to design and interpret these models while remaining aware of their limitations. Current flow models alone cannot “make dose decisions,” but rather inform the rational design of electrotherapy. This is evidenced in exemplary studies combining computer modeling and clinical data, several examples of which are outlined in this chapter. Though modeling software is now widely available, newer generations of algorithms promise more precision and flexibility, and thus it is predicted that with increased validation, dissemination, simplification and dissemination of modeling tools, computational forward models of neuromodulation will become useful tools to guide the optimization of clinical electrotherapy. Essential for this adoption and refinement is an appreciation by clinicians of the uses and limitations of computational models, and conversely understanding by engineers and programmers of what software functions are relevant to clinical practice.