Modeling neuronal current MRI signal with human neuron

Can MRI Be Used as a Sensor to Record Neural Activity?

Magnetic resonance provides exquisite anatomical images and functional MRI monitors physiological activity by recording blood oxygenation. This review attempts to answer the following question: Can MRI be used as a sensor to directly record neural behavior? It considers MRI sensing of electrical activity in the heart and in peripheral nerves before turning to the central topic: recording of brain activity. The primary hypothesis is that bioelectric current produced by a nerve or muscle creates a magnetic field that influences the magnetic resonance signal, although other mechanisms for detection are also considered. Recent studies have provided evidence that using MRI to sense neural activity is possible under ideal conditions. Whether it can be used routinely to provide functional information about brain processes in people remains an open question. The review concludes with a survey of artificial intelligence techniques that have been applied to functional MRI and may be appropriate for MRI sensing of neural activity.

Magnetic resonance imaging with submillisecond temporal resolution

PURPOSE

To demonstrate an MRI technique-Submillisecond Periodic Event Encoded Dynamic Imaging (SPEEDI)-for capturing cyclic dynamic events with submillisecond temporal resolution.

METHODS

The SPEEDI technique is based on an FID or an echo signal in which each time point in the signal is used to sample a distinct k-space raster, followed by repeated FIDs or echoes to produce the remaining k-space data in each k-space raster. All acquisitions are synchronized with a cyclic event, resulting in a set of time-resolved images of the cyclic event with a temporal resolution determined by the dwell time. In SPEEDI, spatial encoding is accomplished by phase encoding. The SPEEDI technique was demonstrated in two experiments at 3 T to (1) visualize fast-changing electric currents that mimicked the waveform of an action potential, and (2) characterize rapidly decaying eddy currents in an MRI system, with a temporal resolution of 0.2 ms and 0.4 ms, respectively. In both experiments, compressed sensing was incorporated to reduce the scan times. Phase difference maps related to the dynamics of electric currents or eddy currents were then obtained.

RESULTS

In the first experiment, time-resolved phase maps resulting from the action potential-mimicking current waveform were successfully obtained and agreed well with theoretical calculations (normalized RMS error = 0.07). In the second experiment, spatially resolved eddy current phase maps revealed time constants (27.1 ± 0.2 ms, 41.1 ± 3.5 ms, and 34.8 ± 0.7 ms) that matched well with those obtained from an established method using point sources (26.4 ms, 41.2 ms and 34.8 ms). For both experiments, phase maps from fully sampled and compressed-sensing-accelerated k-space data exhibited a high structural similarity (> 0.8) despite a two-fold to three-fold acceleration.

CONCLUSIONS

We have illustrated that SPEEDI can provide submillisecond temporal resolution. This capability will likely lead to future exploration of ultrafast, cyclic biomedical processes using MRI.

Estimation of the minimum detectable phase change of surface coil for neural current MRI

Neuronal current magnetic resonance imaging (NC-MRI) is a new method in functional imaging of the brain that could cause the alteration in the phase of magnetic resonance signal. The phase variance is defined as the inverse of the signal to noise ratio (SNR). The intrinsic SNR of the MRI signal is characterized by the coil performance. We evaluated the relation between the geometry and the shape of coils in order to find the minimum detectable change in the signal phase and the possibility of direct detection of neuronal activity by MRI. Full wave equations were solved by the finite element method to calculate the SNR for circular, elliptical, and square shape surface coils. The simulation was repeated for Larmor frequencies of 64 MHz and 85.2 MHz and the coil sizes between 1.5 and 7.5 cm. Relative intrinsic signal to noise ratio (rISNR) of coils with a respect to a selected reference coil and a reference point in the sample was estimated. The circular coil had higher rISNR than other shapes. The increase of the strip width in the coils raised the rISNR 5-20%. For typical imaging parameters, rISNR reference was about 66 which led to a minimum detectable change in MRI signal phase of 0.87° (11.4 nT). It may also be reduced up to tenfold in a 1.5 cm circular coil. Detection of subtle phase signal change due to neuronal activity in surface coils needs a large amount of data acquisition and averaging, but it is intrinsically feasible.

Functional magnetic resonance electrical impedance tomography (fMREIT) sensitivity analysis using an active bidomain finite-element model of neural tissue

PURPOSE

A direct method of imaging neural activity was simulated to determine typical signal sizes.

METHODS

An active bidomain finite-element model was used to estimate approximate perturbations in MR phase data as a result of neural tissue activity, and when an external MR electrical impedance tomography imaging current was added to the region containing neural current sources.

RESULTS

Modeling-predicted, activity-related conductivity changes should produce measurable differential phase signals in practical MR electrical impedance tomography experiments conducted at moderate resolution at noise levels typical of high field systems. The primary dependence of MR electrical impedance tomography phase contrast on membrane conductivity changes, and not source strength, was demonstrated.

CONCLUSION

Because the injected imaging current may also affect the level of activity in the tissue of interest, this technique can be used synergistically with neuromodulation techniques such as deep brain stimulation, to examine mechanisms of action.

Detection of fast oscillating magnetic fields using dynamic multiple TR imaging and Fourier analysis

Neuronal oscillations produce oscillating magnetic fields. There have been trials to detect neuronal oscillations using MRI, but the detectability in in vivo is still in debate. Major obstacles to detecting neuronal oscillations are (i) weak amplitudes, (ii) fast oscillations, which are faster than MRI temporal resolution, and (iii) random frequencies and on/off intervals. In this study, we proposed a new approach for direct detection of weak and fast oscillating magnetic fields. The approach consists of (i) dynamic acquisitions using multiple times to repeats (TRs) and (ii) an expanded frequency spectral analysis. Gradient echo echo-planar imaging was used to test the feasibility of the proposed approach with a phantom generating oscillating magnetic fields with various frequencies and amplitudes and random on/off intervals. The results showed that the proposed approach could precisely detect the weak and fast oscillating magnetic fields with random frequencies and on/off intervals. Complex and phase spectra showed reliable signals, while no meaningful signals were observed in magnitude spectra. A two-TR approach provided an absolute frequency spectrum above Nyquist sampling frequency pixel by pixel with no a priori target frequency information. The proposed dynamic multiple-TR imaging and Fourier analysis are promising for direct detection of neuronal oscillations and potentially applicable to any pulse sequences.

Estimation of phase signal change in neuronal current MRI for evoke response of tactile detection with realistic somatosensory laminar network model

Magnetic field generated by neuronal activity could alter magnetic resonance imaging (MRI) signals but detection of such signal is under debate. Previous researches proposed that magnitude signal change is below current detectable level, but phase signal change (PSC) may be measurable with current MRI systems. Optimal imaging parameters like echo time, voxel size and external field direction, could increase the probability of detection of this small signal change. We simulate a voxel of cortical column to determine effect of such parameters on PSC signal. We extended a laminar network model for somatosensory cortex to find neuronal current in each segment of pyramidal neurons (PN). 60,000 PNs of simulated network were positioned randomly in a voxel. Biot-savart law applied to calculate neuronal magnetic field and additional phase. The procedure repeated for eleven neuronal arrangements in the voxel. PSC signal variation with the echo time and voxel size was assessed. The simulated results show that PSC signal increases with echo time, especially 100/80 ms after stimulus for gradient echo/spin echo sequence. It can be up to 0.1 mrad for echo time = 175 ms and voxel size = 1.48 × 1.48 × 2.18 mm(3). With echo time less than 25 ms after stimulus, it was just acquired effects of physiological noise on PSC signal. The absolute value of the signal increased with decrease of voxel size, but its components had complex variation. External field orthogonal to local surface of cortex maximizes the signal. Expected PSC signal for tactile detection in the somatosensory cortex increase with echo time and have no oscillation.

A method for MREIT-based source imaging: simulation studies

This paper aims to provide a method for using magnetic resonance electrical impedance tomography (MREIT) to visualize local conductivity changes associated with evoked neuronal activities in the brain. MREIT is an MRI-based technique for conductivity mapping by probing the magnetic flux density induced by an externally injected current through surface electrodes. Since local conductivity changes resulting from evoked neural activities are very small (less than a few %), a major challenge is to acquire exogenous magnetic flux density data exceeding a certain noise level. Noting that the signal-to-noise ratio is proportional to the square root of the number of averages, it is important to reduce the data acquisition time to get more averages within a given total data collection time. The proposed method uses a sub-sampled k-space data set in the phase-encoding direction to significantly reduce the data acquisition time. Since the sub-sampled data violates the Nyquist criteria, we only get a nonlinearly wrapped version of the exogenous magnetic flux density data, which is insufficient for conductivity imaging. Taking advantage of the sparseness of the conductivity change, the proposed method detects local conductivity changes by estimating the time-change of the Laplacian of the nonlinearly wrapped data.

Direct neural current imaging in an intact cerebellum with magnetic resonance imaging

The ability to detect neuronal currents with high spatiotemporal resolution using magnetic resonance imaging (MRI) is important for studying human brain function in both health and disease. While significant progress has been made, we still lack evidence showing that it is possible to measure an MR signal time-locked to neuronal currents with a temporal waveform matching concurrently recorded local field potentials (LFPs). Also lacking is evidence that such MR data can be used to image current distribution in active tissue. Since these two results are lacking even in vitro, we obtained these data in an intact isolated whole cerebellum of turtle during slow neuronal activity mediated by metabotropic glutamate receptors using a gradient-echo EPI sequence (TR=100ms) at 4.7T. Our results show that it is possible (1) to reliably detect an MR phase shift time course matching that of the concurrently measured LFP evoked by stimulation of a cerebellar peduncle, (2) to detect the signal in single voxels of 0.1mm(3), (3) to determine the spatial phase map matching the magnetic field distribution predicted by the LFP map, (4) to estimate the distribution of neuronal current in the active tissue from a group-average phase map, and (5) to provide a quantitatively accurate theoretical account of the measured phase shifts. The peak values of the detected MR phase shifts were 0.27-0.37°, corresponding to local magnetic field changes of 0.67-0.93nT (for TE=26ms). Our work provides an empirical basis for future extensions to in vivo imaging of neuronal currents.

Detecting neuronal currents with MRI: a human study

PURPOSE

Recently developed neuronal current magnetic resonance imaging aims to directly detect neuronal currents associated with brain activity, but controversial results have been reported in different studies on human subjects. Although there is no dispute that local neuronal currents produce weak transient magnetic fields that would attenuate local MR signal intensity, there is not yet consensus as to whether this attenuation is detectable with present magnetic resonance imaging techniques. This study investigates the magnitude of neuronal current-induced signal attenuation in human visual cortex.

THEORY

A temporally well-controlled visual stimulation paradigm with a known neuronal firing pattern in monkey visual cortex provides a means of detecting and testing the magnitude of the neuronal current-induced attenuation in neuronal current magnetic resonance imaging.

METHODS

Placing a series of acquisition windows to fully cover the entire response duration enables a thorough detection of any detectable MR signal attenuation induced by the stimulus-evoked neuronal currents.

RESULTS

No significant neuronal current-induced MR signal attenuation was observed in the putative V1 in any participated subjects.

CONCLUSION

The present magnetic resonance imaging technique is not sensitive enough to detect neuronal current-induced MR signal attenuation, and the upper limit of this attenuation was found to be less than 0.07% under the study condition.

Modeling the effect of dendritic input location on MEG and EEG source dipoles

The cerebral sources of magneto- and electroencephalography (MEG, EEG) signals can be represented by current dipoles. We used computational modeling of realistically shaped passive-membrane dendritic trees of pyramidal cells from the human cerebral cortex to examine how the spatial distribution of the synaptic inputs affects the current dipole. The magnitude of the total dipole moment vector was found to be proportional to the vertical location of the synaptic input. The dipole moment had opposite directions for inputs above and below a reversal point located near the soma. Inclusion of shunting-type inhibition either suppressed or enhanced the current dipole, depending on whether the excitatory and inhibitory synapses were on the same or opposite side of the reversal point. Relating the properties of the macroscopic current dipoles to dendritic current distributions can help to provide means for interpreting MEG and EEG data in terms of synaptic connection patterns within cortical areas.

Computational Modeling of Neuronal Current MRI Signals with Rat Somatosensory Cortical Neurons

Magnetic field generated by active neurons has recently been considered to determine location of neuronal activity directly with magnetic resonance imaging (MRI), but controversial results have been reported about detection of such small magnetic fields. In this study, multiple neuronal morphologies of rat tissue were modeled to investigate better estimation of MRI signal change produced by neuronal magnetic field (NMF). Ten pyramidal neurons from layer II to VI of rat somatosensory area with realistic morphology, biophysics, and neuronal density were modeled to simulate NMF of neuronal tissue, from which effects of NMF on MRI signals were obtained. Neuronal current MRI signals, which consist of relative magnitude signal change (RMSC) and phase signal change (PSC), were at least three and one orders of magnitude less than a tissue with single neuron type, respectively. Also, a reduction in voxel size could increase signal alterations. Furthermore, with selection of zenith angle of external main magnetic field related to tissue surface near to 90°, RMSC could be maximized. This value for PSC would be 90° for small voxel size and zero degree for large ones.

The Ionic DTI Model (iDTI) of Dynamic Diffusion Tensor Imaging (dDTI)

Measurements of water molecule diffusion along fiber tracts in CNS by diffusion tensor imaging (DTI) provides a static map of neural connections between brain centers, but does not capture the electrical activity along axons for these fiber tracts. Here, a modification of the DTI method is presented to enable the mapping of active fibers. It is termed dynamic diffusion tensor imaging (dDTI) and is based on a hypothesized “anisotropy reduction due to axonal excitation” (“AREX”). The potential changes in water mobility accompanying the movement of ions during the propagation of action potentials along axonal tracts are taken into account. Specifically, the proposed model, termed “ionic DTI model”, was formulated as follows.•

First, based on theoretical calculations, we calculated the molecular water flow accompanying the ionic flow perpendicular to the principal axis of fiber tracts produced by electrical conduction along excited myelinated and non-myelinated axons.

•

Based on the changes in molecular water flow we estimated the signal changes as well as the changes in fractional anisotropy of axonal tracts while performing a functional task.

•

The variation of fractional anisotropy in axonal tracts could allow mapping the active fiber tracts during a functional task.

Although technological advances are necessary to enable the robust and routine measurement of this electrical activity-dependent movement of water molecules perpendicular to axons, the proposed model of dDTI defines the vectorial parameters that will need to be measured to bring this much needed technique to fruition.

Magnetic resonance imaging of ionic currents in solution: the effect of magnetohydrodynamic flow

PURPOSE

Reliably detecting MRI signals in the brain that are more tightly coupled to neural activity than blood-oxygen-level-dependent fMRI signals could not only prove valuable for basic scientific research but could also enhance clinical applications such as epilepsy presurgical mapping. This endeavor will likely benefit from an improved understanding of the behavior of ionic currents, the mediators of neural activity, in the presence of the strong magnetic fields that are typical of modern-day MRI scanners.

THEORY

Of the various mechanisms that have been proposed to explain the behavior of ionic volume currents in a magnetic field, only one-magnetohydrodynamic flow-predicts a slow evolution of signals, on the order of a minute for normal saline in a typical MRI scanner.

METHODS

This prediction was tested by scanning a volume-current phantom containing normal saline with gradient-echo-planar imaging at 3 T.

RESULTS

Greater signal changes were observed in the phase of the images than in the magnitude, with the changes evolving on the order of a minute.

CONCLUSION

These results provide experimental support for the MHD flow hypothesis. Furthermore, MHD-driven cerebrospinal fluid flow could provide a novel fMRI contrast mechanism.

MRI signal phase oscillates with neuronal activity in cerebral cortex: implications for neuronal current imaging

Neuronal activity produces transient ionic currents that may be detectable using magnetic resonance imaging (MRI). We examined the feasibility of MRI-based detection of neuronal currents using computer simulations based on the laminar cortex model (LCM). Instead of simulating the activity of single neurons, we decomposed neuronal activity to action potentials (AP) and postsynaptic potentials (PSP). The geometries of dendrites and axons were generated dynamically to account for diverse neuronal morphologies. Magnetic fields associated with APs and PSPs were calculated during spontaneous and stimulated cortical activity, from which the neuronal current induced MRI signal was determined. We found that the MRI signal magnitude change (<0.1 ppm) is below currently detectable levels but that the signal phase change is likely to be detectable. Furthermore, neuronal MRI signals are sensitive to temporal and spatial variations in neuronal activity but independent of the intensity of neuronal activation. Synchronised neuronal activity produces large phase changes (in the order of 0.1 mrad). However, signal phase oscillates with neuronal activity. Consequently, MRI scans need to be synchronised with neuronal oscillations to maximise the likelihood of detecting signal phase changes due to neuronal currents. These findings inform the design of MRI experiments to detect neuronal currents.

Octopus visual system: a functional MRI model for detecting neuronal electric currents without a blood-oxygen-level-dependent confound

PURPOSE

Despite the efforts that have been devoted to detecting the transient magnetic fields generated by neuronal firing, the conclusion that a functionally relevant signal can be measured with MRI is still controversial. For human studies of neuronal current MRI (nc-MRI), the blood-oxygen-level-dependent (BOLD) effect remains an irresolvable confound. For tissue studies where hemoglobin is removed, natural sensory stimulation is not possible. This study investigates the feasibility of detecting a physiologically induced nc-MRI signal in vivo in a BOLD-free environment.

METHODS

The cephalopod mollusc Octopus bimaculoides has vertebrate-like eyes, large optic lobes (OLs), and blood that does not contain hemoglobin. Visually evoked potentials were measured in the octopus retina and OL by electroretinogram and local field potential. nc-MRI scans were conducted at 9.4 Tesla to capture these activities.

RESULTS

Electrophysiological recording detected strong responses in the retina and OL in vivo; however, nc-MRI failed to demonstrate any statistically significant signal change with a detection threshold of 0.2° for phase and 0.2% for magnitude. Experiments in a dissected eye-OL preparation yielded similar results.

CONCLUSION

These findings in a large hemoglobin-free nervous system suggest that sensory evoked neuronal magnetic fields are too weak for direct detection with current MRI technology.

Phase stability in fMRI time series: effect of noise regression, off-resonance correction and spatial filtering techniques

Although the majority of fMRI studies exploit magnitude changes only, there is an increasing interest regarding the potential additive information conveyed by the phase signal. This integrated part of the complex number furnished by the MR scanners can also be used for exploring direct detection of neuronal activity and for thermography. Few studies have explicitly addressed the issue of the available signal stability in the context of phase time-series, and therefore we explored the spatial pattern of frequency specific phase fluctuations, and evaluated the effect of physiological noise components (heart beat and respiration) on the phase signal. Three categories of retrospective noise reduction techniques were explored and the temporal signal stability was evaluated in terms of a physiologic noise model, for seven fMRI measurement protocols in eight healthy subjects at 3T, for segmented CSF, gray and white matter voxels. We confirmed that for most processing methods, an efficient use of the phase information is hampered by the fact that noise from physiological and instrumental sources contributes significantly more to the phase than to the magnitude instability. Noise regression based on the phase evolution of the central k-space point, RETROICOR, or an orthonormalized combination of these were able to reduce their impact, but without bringing phase stability down to levels expected from the magnitude signal. Similar results were obtained after targeted removal of scan-to-scan variations in the bulk magnetic field by the dynamic off-resonance in k-space (DORK) method and by the temporal off-resonance alignment of single-echo time series technique (TOAST). We found that spatial high-pass filtering was necessary, and in vivo a Gaussian filter width of 20mm was sufficient to suppress physiological noise and bring the phase fluctuations to magnitude levels. Stronger filters brought the fluctuations down to levels dictated by thermal noise contributions, and for 62.5mm(3) voxels the phase stability was as low as 5 mrad (0.27°). In conditions of low SNR(o) and high temporal sampling rate (short TR); we achieved an upper bound for the phase instabilities at 0.0017 ppm, which is close to the dHb contribution to the GM/WM phase contrast.

Are brain currents detectable by means of low-field NMR? A phantom study

A number of different methods have been developed in order to detect the spreading of neuronal currents by means of noninvasive imaging techniques. However, all of these are subjected to limitations in the temporal or spatial resolution. A new approach of neuronal current detection is based on the use of low-field nuclear magnetic resonance (LF-NMR) that records brain activity directly. In the following, we describe a phantom study in order to assess the feasibility of neuronal current detection using LF-NMR. In addition to that, necessary preliminary subject studies examining somatosensory evoked neuronal currents are presented. During the phantom study, the influences of two different neuronal time signals on (1)H-NMR signals were observed. The measurements were carried out by using a head phantom with an integrated current dipole to simulate neuronal activity. Two LF-NMR methods based on a DC and an AC (resonant) mechanism were utilized to study the feasibility of detecting both types of magnetic brain signals. Measurements were made inside an extremely magnetically shielded room by using a superconducting quantum interference device magnetometer system. The measurement principles were validated applying currents of higher intensity than those typical of the neuronal currents. Through stepwise reduction of the amplitude of the current dipole strength, the resolution limits of the two measuring procedures were found. The results indicate that it is necessary to improve the signal-to-noise ratio of the measurement system by at least a factor of 38 in order to detect typical human neuronal activity directly by means of LF-NMR. In addition to that, ways of achieving this factor are discussed.

Detection of neuronal current MRI in human without BOLD contamination

Controversial results regarding the detectability of neuronal current magnetic resonance imaging (ncMRI) have been reported in different studies on human subjects. In all the previous studies, the ncMRI signal was detected under a continuous and paradigm task-induced blood oxygen level dependent (BOLD) signal background. The aim of this study is to investigate the possibility of detecting ncMRI signal in human brain in the situation that task-induced BOLD background is absent or minimum. In this study, by adopting an event-related visuomotor paradigm with long interstimulus interval (=20 s), the ncMRI signal was detected when the BOLD signal fully returned to its baseline, and the potential BOLD background contamination was avoided effectively. The results showed that the visuomotor stimulation elicited BOLD activation in visual and motor cortices, but no significant ncMRI signal change (in magnitude) was detected in human brain. These experimental findings are consistent with theoretical predications.