Yuzhuo Su

Optimized multi-electrode stimulation increases focality and intensity at target

Transcranial direct current stimulation (tDCS) provides a non-invasive tool to elicit neuromodulation by delivering current through electrodes placed on the scalp. The present clinical paradigm uses two relatively large electrodes to inject current through the head resulting in electric fields that are broadly distributed over large regions of the brain. In this paper, we present a method that uses multiple small electrodes (i.e. 1.2 cm diameter) and systematically optimize the applied currents to achieve effective and targeted stimulation while ensuring safety of stimulation. We found a fundamental trade-off between achievable intensity (at the target) and focality, and algorithms to optimize both measures are presented. When compared with large pad-electrodes (approximated here by a set of small electrodes covering 25 cm(2)), the proposed approach achieves electric fields which exhibit simultaneously greater focality (80% improvement) and higher target intensity (98% improvement) at cortical targets using the same total current applied. These improvements illustrate the previously unrecognized and non-trivial dependence of the optimal electrode configuration on the desired electric field orientation and the maximum total current (due to safety). Similarly, by exploiting idiosyncratic details of brain anatomy, the optimization approach significantly improves upon prior un-optimized approaches using small electrodes. The analysis also reveals the optimal use of conventional bipolar montages: maximally intense tangential fields are attained with the two electrodes placed at a considerable distance from the target along the direction of the desired field; when radial fields are desired, the maximum-intensity configuration consists of an electrode placed directly over the target with a distant return electrode. To summarize, if a target location and stimulation orientation can be defined by the clinician, then the proposed technique is superior in terms of both focality and intensity as compared to previous solutions and is thus expected to translate into improved patient safety and increased clinical efficacy.

Spike Timing Amplifies the Effect of Electric Fields on Neurons: Implications for Endogenous Field Effects

Despite compelling phenomenological evidence that small electric fields (<5 mV/mm) can affect brain function, a quantitative and experimentally verified theory is currently lacking. Here we demonstrate a novel mechanism by which the nonlinear properties of single neurons “amplify” the effect of small electric fields: when concurrent to suprathreshold synaptic input, small electric fields can have significant effects on spike timing. For low-frequency fields, our theory predicts a linear dependency of spike timing changes on field strength. For high-frequency fields (relative to the synaptic input), the theory predicts coherent firing, with mean firing phase and coherence each increasing monotonically with field strength. Importantly, in both cases, the effects of fields on spike timing are amplified with decreasing synaptic input slope and increased cell susceptibility (millivolt membrane polarization per field amplitude). We confirmed these predictions experimentally using CA1 hippocampal neurons in vitro exposed to static (direct current) and oscillating (alternating current) uniform electric fields. In addition, we develop a robust method to quantify cell susceptibility using spike timing. Our results provide a precise mechanism for a functional role of endogenous field oscillations (e.g., gamma) in brain function and introduce a framework for considering the effects of environmental fields and design of low-intensity therapeutic neurostimulation technologies.

Automated MRI segmentation for individualized modeling of current flow in the human head

OBJECTIVE High-definition transcranial direct current stimulation (HD-tDCS) and high-density electroencephalography require accurate models of current flow for precise targeting and current source reconstruction. At a minimum, such modeling must capture the idiosyncratic anatomy of the brain, cerebrospinal fluid (CSF) and skull for each individual subject. Currently, the process to build such high-resolution individualized models from structural magnetic resonance images requires labor-intensive manual segmentation, even when utilizing available automated segmentation tools. Also, accurate placement of many high-density electrodes on an individual scalp is a tedious procedure. The goal was to develop fully automated techniques to reduce the manual effort in such a modeling process. APPROACH A fully automated segmentation technique based on Statical Parametric Mapping 8, including an improved tissue probability map and an automated correction routine for segmentation errors, was developed, along with an automated electrode placement tool for high-density arrays. The performance of these automated routines was evaluated against results from manual segmentation on four healthy subjects and seven stroke patients. The criteria include segmentation accuracy, the difference of current flow distributions in resulting HD-tDCS models and the optimized current flow intensities on cortical targets. MAIN RESULTS The segmentation tool can segment out not just the brain but also provide accurate results for CSF, skull and other soft tissues with a field of view extending to the neck. Compared to manual results, automated segmentation deviates by only 7% and 18% for normal and stroke subjects, respectively. The predicted electric fields in the brain deviate by 12% and 29% respectively, which is well within the variability observed for various modeling choices. Finally, optimized current flow intensities on cortical targets do not differ significantly. SIGNIFICANCE Fully automated individualized modeling may now be feasible for large-sample EEG research studies and tDCS clinical trials.

Effects of high-frequency stimulation on epileptiform activity in vitro : ON/OFF control paradigm

Purpose: To determine the effects of high‐frequency electrical stimulation on electrographic seizure activity during and after stimulation (ON‐effect and OFF‐effect).

Spectrum separation resolves partial-volume effect of MRSI as demonstrated on brain tumor scans.

Magnetic resonance spectroscopic imaging (MRSI) is currently used clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and to evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability because of partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity, and measurement noise. We address these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. This ‘spectrum separation’ method uses the non‐negative matrix factorization algorithm, which simultaneously decomposes the observed spectra of multiple voxels into abundance distributions and constituent spectra. The accuracy of the estimated abundances is validated on phantom data. The presented results on 20 clinical cases of brain tumor show reduced cross‐subject variability. This is reflected in improved discrimination between high‐grade and low‐grade gliomas, which demonstrates the physiological relevance of the extracted spectra. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool. Copyright

An automated method for high-definition transcranial direct current stimulation modeling

Targeted transcranial stimulation with electric currents requires accurate models of the current flow from scalp electrodes to the human brain. Idiosyncratic anatomy of individual brains and heads leads to significant variability in such current flows across subjects, thus, necessitating accurate individualized head models. Here we report on an automated processing chain that computes current distributions in the head starting from a structural magnetic resonance image (MRI). The main purpose of automating this process is to reduce the substantial effort currently required for manual segmentation, electrode placement, and solving of finite element models. In doing so, several weeks of manual labor were reduced to no more than 4 hours of computation time and minimal user interaction, while current-flow results for the automated method deviated by less than 27.9% from the manual method. Key facilitating factors are the addition of three tissue types (skull, scalp and air) to a state-of-the-art automated segmentation process, morphological processing to correct small but important segmentation errors, and automated placement of small electrodes based on easily reproducible standard electrode configurations. We anticipate that such an automated processing will become an indispensable tool to individualize transcranial direct current stimulation (tDCS) therapy.

A multiple electrode scheme for optimal non-invasive electrical stimulation

Transcranial electrical stimulation involves the delivery of weak electrical currents to the brain via scalp electrodes to elicit neuromodulatory effects. The current is conventionally passed through two large electrodes resulting in diffused electric fields. In this paper, we propose a novel paradigm in which multiple small electrodes with independent current controls are systematically optimized to yield targeted and effective stimulation under safety constraints. We employ the finite element method, in conjunction with a magnetic resonance imagery based model of the human head, to formulate a linear system relating the applied scalp current to the resulting electric field. Optimization techniques are then applied to derive stimulation parameters which maximize either intensity or focality at the target location. Results demonstrate that the optimal electrode configuration is strongly dependent on both the desired field orientation and the optimization criterion. The proposed scheme yields improvements of 98% in target intensity and 80% in focality compared to the conventional two-electrode montage. Additionally, the presented framework effectively optimizes electrode placement in the classical bipolar configuration, which is useful if only a single channel current source is available. Consequently, the proposed scheme promises to deliver increased efficacy and improved patient safety to clinical settings in which the target site is identified by a clinician.

A novel framework for AC field-effects on action potential coherence and phase

Small electric fields will polarize neurons by only a small amount for this reason small electric fields have previously been suggested to have no physiologically relevant effects. We propose a mechanism whereby AC extracellular fields incrementally polarize a neurons membrane and thus modulate the coherence and phase of synaptically driven action potentials. Knowing that a membrane polarizes in proportion to field strength (DeltaV = cE), and that spike timing changes linearly with increasing steady-state field strength (Deltat prop E), we make a number of quantitative predictions on the effects of AC extracellular fields on a neurons spike timing oscillating fields will shift firing times with mean falling within or the oscillatory cycle (the rising edge). This mean firing time advances with increasing field strength and decreasing injected ramp slope, i.e. it increases with cE/Vdot. This effect is proportional to the inverse of the driving synaptic membrane potential slope Deltat=DeltaV/Vdot dot cE/Vdot. The strength of coherence as measured by Rayleigh coefficient (vector strength) also increases with cE/Vdot. The predictions were verified in rat hippocampal A pyramidal neurons.

Spectral separation resolves partial volume effect in MRSI: A validation study

Magnetic resonance spectroscopic imaging (MRSI) is utilized clinically in conjunction with anatomical MRI to assess the presence and extent of brain tumors and evaluate treatment response. Unfortunately, the clinical utility of MRSI is limited by significant variability of in vivo spectra. Spectral profiles show increased variability due to partial coverage of large voxel volumes, infiltration of normal brain tissue by tumors, innate tumor heterogeneity and measurement noise. This study investigates spectral separation as a novel quantification tool, addressing these problems directly by quantifying the abundance (i.e. volume fraction) within a voxel for each tissue type instead of the conventional estimation of metabolite concentrations from spectral resonance peaks. Present results on 20 clinical cases of brain tumors show reduced cross-subject variability. This reduced variability leads to improved discrimination between high and low-grade gliomas, confirming the physiological relevance of the extracted spectra. Further validation on phantom data demonstrates the accuracy of the estimated abundances. These results show that the proposed spectral analysis method can improve the effectiveness of MRSI as a diagnostic tool.