A method for recording resistance changes non-invasively during neuronal depolarization with a view to imaging brain activity with electrical impedance tomography

Advances in electrical impedance tomography-based brain imaging

Novel advances in the field of brain imaging have enabled the unprecedented clinical application of various imaging modalities to facilitate disease diagnosis and treatment. Electrical impedance tomography (EIT) is a functional imaging technique that measures the transfer impedances between electrodes on the body surface to estimate the spatial distribution of electrical properties of tissues. EIT offers many advantages over other neuroimaging technologies, which has led to its potential clinical use. This qualitative review provides an overview of the basic principles, algorithms, and system composition of EIT. Recent advances in the field of EIT are discussed in the context of epilepsy, stroke, brain injuries and edema, and other brain diseases. Further, we summarize factors limiting the development of brain EIT and highlight prospects for the field. In epilepsy imaging, there have been advances in EIT imaging depth, from cortical to subcortical regions. In stroke research, a bedside EIT stroke monitoring system has been developed for clinical practice, and data support the role of EIT in multi-modal imaging for diagnosing stroke. Additionally, EIT has been applied to monitor the changes in brain water content associated with cerebral edema, enabling the early identification of brain edema and the evaluation of mannitol dehydration. However, anatomically realistic geometry, inhomogeneity, cranium completeness, anisotropy and skull type, etc., must be considered to improve the accuracy of EIT modeling. Thus, the further establishment of EIT as a mature and routine diagnostic technique will necessitate the accumulation of more supporting evidence.

Optimisation of bioimpedance measurements of neuronal activity with an ex vivo preparation of Cancer pagurus peripheral nerves

BACKGROUND

In mammals, fast neural Electrical Impedance Tomography (EIT) can image the myelinated component of the compound action potentials (CAP) using a nerve cuff. If applied to unmyelinated fibres this has great potential to improve selective neuromodulation ("electroceuticals") to avoid off-target effects. Previously, bioimpedance recordings were averaged from unmyelinated crab leg nerve fibres, but the signal to noise ratio (SNR) needs improving.

NEW METHOD

Currently, functional non-invasive neuronal imaging is accomplished through surface electrodes or genetically expressed indicators that provide good spatial, but poor temporal, resolution. Here is an improved method for bioimpedance measurements from a model of unmyelinated fibres to enable optimisation through improvement of the 1) signal processing measurement paradigm, 2) neurophysiology, and 3) electrode-nerve interface.

RESULTS

For bioimpedance recordings, the recruitment and necessity of the CAP was quantified and saline significantly improved the SNR. An improved protocol resulted in averaging not being required, as sequentially recorded traces produced bioimpedance changes of -0.232 ± 0.064% that did not show phase or timing related artefacts.

COMPARISON WITH EXISTING METHOD

Here, two bioimpedance traces displayed an SNR of ≥3:1, while previously over >100 averages were required with greater inter-experimental variability. 10 paired traces were averaged for an SNR of ≥9:1, or near real-time measurement.

CONCLUSIONS

This method facilitates further studies aiming to enable non-invasive localization of fascicular activity in unmyelinated fibres within peripheral nerves. This technique could ultimately produce the first 3-D tomographic images to help guide selective neuromodulation using bioelectric devices.

Model of Impedance Changes in Unmyelinated Nerve Fibers

OBJECTIVE

Currently, there is no imaging method that is able to distinguish the functional activity inside nerves. Such a method would be essential for understanding peripheral nerve physiology and would allow precise neuromodulation of organs these nerves supply. Electrical impedance tomography (EIT) is a method that produces images of electrical impedance change (dZ) of an object by injecting alternating current and recording surface voltages. It has been shown to be able to image fast activity in the brain and large peripheral nerves. To image inside small autonomic nerves, mostly containing unmyelinated fibers, it is necessary to maximize SNR and optimize the EIT parameters. An accurate model of the nerve is required to identify these optimal parameters as well as to validate data obtained in the experiments.

METHODS

In this study, we developed two three-dimensional models of unmyelinated fibers: Hodgkin-Huxley (HH) squid giant axon (single and multiple) and mammalian C-nociceptor. A coupling feedback system was incorporated into the models to simulate direct and alternating current application and simultaneously record external field during action potential propagation.

RESULTS

Parameters of the developed models were varied to study their influence on the recorded impedance changes; the optimal parameters were identified. The negative dZ was found to monotonically decrease with frequency for both HH and C fiber models, in accordance with the experimental data.

CONCLUSION AND SIGNIFICANCE

The accurate realistic model of unmyelinated nerve allows the optimization of EIT parameters and matches literature and experimental results.

Empirical validation of statistical parametric mapping for group imaging of fast neural activity using electrical impedance tomography

Electrical impedance tomography (EIT) allows for the reconstruction of internal conductivity from surface measurements. A change in conductivity occurs as ion channels open during neural activity, making EIT a potential tool for functional brain imaging. EIT images can have  >10 000 voxels, which means statistical analysis of such images presents a substantial multiple testing problem. One way to optimally correct for these issues and still maintain the flexibility of complicated experimental designs is to use random field theory. This parametric method estimates the distribution of peaks one would expect by chance in a smooth random field of a given size. Random field theory has been used in several other neuroimaging techniques but never validated for EIT images of fast neural activity, such validation can be achieved using non-parametric techniques. Both parametric and non-parametric techniques were used to analyze a set of 22 images collected from 8 rats. Significant group activations were detected using both techniques (corrected p  <  0.05). Both parametric and non-parametric analyses yielded similar results, although the latter was less conservative. These results demonstrate the first statistical analysis of such an image set and indicate that such an analysis is an approach for EIT images of neural activity.

Investigation of potential artefactual changes in measurements of impedance changes during evoked activity: implications to electrical impedance tomography of brain function

Electrical impedance tomography (EIT) could provide images of fast neural activity in the adult human brain with a resolution of 1 ms and 1 mm by imaging impedance changes which occur as ion channels open during neuronal depolarization. The largest changes occur at dc and decrease rapidly over 100 Hz. Evoked potentials occur in this bandwidth and may cause artefactual apparent impedance changes if altered by the impedance measuring current. These were characterized during the compound action potential in the walking leg nerves of Cancer pagurus, placed on Ag/AgCl hook electrodes, to identify how to avoid artefactual changes during brain EIT. Artefact-free impedance changes (δZ) decreased with frequency from −0.045 ± 0.01% at 225 Hz to −0.02 ± 0.01% at 1025 Hz (mean ± 1 SD, n = 24 in 12 nerves) which matched changes predicted by a finite element model. Artefactual δZ reached c.300% and 50% of the genuine membrane impedance change at 225 Hz and 600 Hz respectively but decreased with frequency of the applied current and was negligible above 1 kHz. The proportional amplitude (δZ (%)) of the artefact did not vary significantly with the amplitude of injected current of 5–20 µA pp. but decreased significantly from −0.09 ± 0.024 to −0.03 ± 0.023% with phase of 0 to 45°. For fast neural EIT of evoked activity in the brain, artefacts may arise with applied current of >10 µA. Independence of δZ with respect to phase but not the amplitude of applied current controls for them; they can be minimized by randomizing the phase of the applied measuring current and excluded by recording at >1 kHz.

SQUID-detected ultra-low field MRI

MRI remains the premier method for non-invasive imaging of soft-tissue. Since the first demonstration of ULF MRI the trend has been towards ever higher magnetic fields. This is because the signal, and efficiency of Faraday detectors, increases with ever higher magnetic fields and corresponding Larmor frequencies. Nevertheless, there are many compelling reasons to continue to explore MRI at much weaker magnetic fields, the so-called ultra-low field or (ULF) regime. In the past decade many excellent proof-of-concept demonstrations of ULF MRI have been made. These include combined MRI and magnetoencephalography, imaging in the presence of metal, unique tissue contrast, and implementation in situations where a high magnetic field is simply impractical. These demonstrations have routinely used pulsed pre-polarization (at magnetic fields from ~10 to 100 mT) followed by read-out in a much weaker (1-100 μT) magnetic fields using the ultra-sensitive Superconducting Quantum Interference Device (SQUID) sensor. Even with pre-polarization and SQUID detection, ULF MRI suffers from many challenges associated with lower magnetization (i.e. signal) and inherently long acquisition times compared to conventional >1 T MRI. These are fundamental limitations imposed by the low measurement and gradient fields used. In this review article we discuss some of the techniques, potential applications, and inherent challenges of ULF MRI.

SQUID-detected ultra-low field MRI

MRI remains the premier method for non-invasive imaging of soft-tissue. Since the first demonstration of ULF MRI the trend has been towards ever higher magnetic fields. This is because the signal, and efficiency of Faraday detectors, increases with ever higher magnetic fields and corresponding Larmor frequencies. Nevertheless, there are many compelling reasons to continue to explore MRI at much weaker magnetic fields, the so-called ultra-low field or (ULF) regime. In the past decade many excellent proof-of-concept demonstrations of ULF MRI have been made. These include combined MRI and magnetoencephalography, imaging in the presence of metal, unique tissue contrast, and implementation in situations where a high magnetic field is simply impractical. These demonstrations have routinely used pulsed pre-polarization (at magnetic fields from ∼10 to 100mT) followed by read-out in a much weaker (1-100μT) magnetic fields using the ultra-sensitive Superconducting Quantum Interference Device (SQUID) sensor. Even with pre-polarization and SQUID detection, ULF MRI suffers from many challenges associated with lower magnetization (i.e. signal) and inherently long acquisition times compared to conventional >1T MRI. These are fundamental limitations imposed by the low measurement and gradient fields used. In this review article we discuss some of the techniques, potential applications, and inherent challenges of ULF MRI.

A novel method for recording neuronal depolarization with recording at 125-825 Hz: implications for imaging fast neural activity in the brain with electrical impedance tomography

Electrical impedance tomography (EIT) is a recently developed medical imaging method which has the potential to produce images of fast neuronal depolarization in the brain. Previous modelling suggested that applied current needed to be below 100 Hz but the signal-to-noise ratio (SNR) recorded with scalp electrodes during evoked responses was too low to permit imaging. A novel method in which contemporaneous evoked potentials are subtracted is presented with current applied at 225 Hz to cerebral cortex during evoked activity; although the signal is smaller than at DC by about 10×, the principal noise from the EEG is reduced by about 1000×, resulting in an improved SNR. It was validated with recording of compound action potentials in crab walking leg nerve where peak changes of -0.2% at 125 and 175 Hz tallied with biophysical modelling. In recording from rat cerebral cortex during somatosensory evoked responses, peak impedance decreases of -0.07 ± 0.006% (mean ± SE) with a SNR of >50 could be recorded at 225 Hz. This method provides a reproducible and artefact free means for recording resistance changes during neuronal activity which could form the basis for imaging fast neural activity in the brain.

Impedance changes recorded with scalp electrodes during visual evoked responses: implications for Electrical Impedance Tomography of fast neural activity

Electrical Impedance Tomography (EIT) is a recently developed medical imaging method which could enable fast neural imaging in the brain by recording the resistance changes which occur as ion channels open during neuronal depolarization. In published studies in animal models with intracranial electrodes, changes of 0.005 to 3% have been reported but the amplitude of changes in the human is not known. The purpose of this work was to determine if resistance changes could be recorded non-invasively in humans during evoked activity which could form the basis for EIT of fast neural activity. Resistance was recorded with scalp electrodes during 2 Hz pattern visual evoked responses over 10 min using an insensible 1 Hz square wave constant current of 0.1-1 mA. Significant resistance decreases of 0.0010+/-0.0005% (0.30+/-0.15 microV, signal-to-noise ratio (SNR) of 2:1, n=16 recordings over 6 subjects) (mean+/-SE) were recorded. These are in broad agreement with modelling which estimated changes of 0.0039+/-0.0034% (1.03+/-0.75 microV) using an anatomically realistic finite element model. This is the first demonstration of such changes in humans and so encourages the belief that EIT could be used for neural imaging. Unfortunately, the signal-to-noise ratio was not sufficient to permit imaging at present because recording over multiple injection sites needed for imaging would require impractically long recording times. However, in the future, invasive imaging with intracranial electrodes in animal models or humans and improved signal processing or recording may still enable imaging; this would constitute a significant advance in neuroscience technology.