Microscopic investigation of the resonant mechanism for the implementation of nc-MRI at ultra-low field MRI

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.

Conductance-based models and the fragmentation problem: A case study based on hippocampal CA1 pyramidal cell models and epilepsy

Epilepsy has been a central topic in computational neuroscience, and in silico models have shown to be excellent tools to integrate and evaluate findings from animal and clinical settings. Among the different languages and tools for computational modeling development, NEURON stands out as one of the most used and mature neurosimulators. However, despite the vast quantity of models developed with NEURON, a fragmentation problem is evident in the great majority of models related to the same type of cell or cell properties. This fragmentation causes a lack of interoperability between the models because of differences in parameters. The problem is not related to the neurosimulator, which is prepared to reuse elements of other models, but related to decisions made during the model development, when it is not uncommon to adjust parameter values according to the necessities of the study. Here, this problem is presented by studying computational models related to temporal lobe epilepsy and the definitions of hippocampal CA1 pyramidal cells. The current assessment aims to highlight the implications of fragmentation for reliable modeling and the need to adopt a framework that allows a better interoperability between different models. This article is part of the Special Issue "NEWroscience 2018".

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.

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.

Is it possible to detect dendrite currents using presently available magnetic resonance imaging techniques?

The action currents of a dendrite, peripheral nerve or skeletal muscle create their own magnetic field. Many investigators have attempted to detect neural and dendritic currents directly using magnetic resonance imaging that can cause the phase of the spins to change. Our goal in this paper is to use the calculated magnetic field of a dendrite to estimate the resulting phase shift in the magnetic resonance signal. The field produced by a dense collection of simultaneously active dendrites may be just detectable under the most ideal circumstances, but in almost every realistic case the field cannot be detected using current MRI technology.

Magnetic resonance imaging of oscillating electrical currents

Functional MRI has become an important tool of researchers and clinicians who seek to understand patterns of neuronal activation that accompany sensory and cognitive processes. However, the interpretation of fMRI images rests on assumptions about the relationship between neuronal firing and hemodynamic response that are not firmly grounded in rigorous theory or experimental evidence. Further, the blood-oxygen-level-dependent effect, which correlates an MRI observable to neuronal firing, evolves over a period that is 2 orders of magnitude longer than the underlying processes that are thought to cause it. Here, we instead demonstrate experiments to directly image oscillating currents by MRI. The approach rests on a resonant interaction between an applied rf field and an oscillating magnetic field in the sample and, as such, permits quantitative, frequency-selective measurements of current density without spatial or temporal cancellation. We apply this method in a current loop phantom, mapping its magnetic field and achieving a detection sensitivity near the threshold required for the detection of neuronal currents. Because the contrast mechanism is under spectroscopic control, we are able to demonstrate how ramped and phase-modulated spin-lock radiation can enhance the sensitivity and robustness of the experiment. We further demonstrate the combination of these methods with remote detection, a technique in which the encoding and detection of an MRI experiment are separated by sample flow or translation. We illustrate that remotely detected MRI permits the measurement of currents in small volumes of flowing water with high sensitivity and spatial resolution.

Effect of peripheral nerve action currents on magnetic resonance imaging

Many researchers have attempted to detect neural currents directly using magnetic resonance imaging (MRI). The action currents of a peripheral nerve create their own magnetic field that can cause the phase of the spins to change. Our goal in this paper is to use the measured magnetic field of a nerve to estimate the resulting phase shift in the magnetic resonance signal. We examine three cases: the squid giant axon, the frog sciatic nerve, and the human median nerve. In each case, the phase shift is much less than one degree, and will be very difficult to measure with current technology.

Detection of peripheral nerve and skeletal muscle action currents using magnetic resonance imaging

Many researchers have attempted to detect neural currents directly using magnetic resonance imaging (MRI). The action currents of a peripheral nerve or skeletal muscle create their own magnetic field that can cause the phase of the spins to change. Our goal in this paper is to use the measured magnetic field of a nerve or muscle to estimate the resulting phase shift in the magnetic resonance signal. We examine four cases: the squid giant axon, the frog sciatic nerve, the human median nerve, and the rat EDL muscle. In each case, the phase shift is much less than one tenth of one degree, and will be very difficult to measure with current technology.

What's new in neuroimaging methods?

The rapid advancement of neuroimaging methodology and its growing availability has transformed neuroscience research. The answers to many questions that we ask about how the brain is organized depend on the quality of data that we are able to obtain about the locations, dynamics, fluctuations, magnitudes, and types of brain activity and structural changes. In this review an attempt is made to take a snapshot of the cutting edge of a small component of the very rapidly evolving field of neuroimaging. For each area covered, a brief context is provided along with a summary of a few of the current developments and issues. Then, several outstanding papers, published in the past year or so, are described, providing an example of the directions in which each area is progressing. The areas covered include functional magnetic resonance imaging (fMRI), voxel-based morphometry (VBM), diffusion tensor imaging (DTI), electroencephalography (EEG), magnetoencephalography (MEG), optical imaging, and positron emission tomography (PET). More detail is included on fMRI; its subsections include fMRI interpretation, new fMRI contrasts, MRI technology, MRI paradigms and processing, and endogenous oscillations in fMRI.

Neuronal current detection with low-field magnetic resonance: simulations and methods

The noninvasive detection of neuronal currents in active brain networks [or direct neuronal imaging (DNI)] by means of nuclear magnetic resonance (NMR) remains a scientific challenge. Many different attempts using NMR scanners with magnetic fields >1 T (high-field NMR) have been made in the past years to detect phase shifts or magnitude changes in the NMR signals. However, the many physiological (i.e., the contemporarily BOLD effect, the weakness of the neuronal-induced magnetic field, etc.) and technical limitations (e.g., the spatial resolution) in observing the weak signals have led to some contradicting results. In contrast, only a few attempts have been made using low-field NMR techniques. As such, this paper was aimed at reviewing two recent developments in this front. The detection schemes discussed in this manuscript, the resonant mechanism (RM) and the DC method, are specific to NMR instrumentations with main fields below the earth magnetic field (50 microT), while some even below a few microteslas (ULF-NMR). However, the experimental validation for both techniques, with differentiating sensitivity to the various neuronal activities at specific temporal and spatial resolutions, is still in progress and requires carefully designed magnetic field sensor technology. Additional care should be taken to ensure a stringent magnetic shield from the ambient magnetic field fluctuations. In this review, we discuss the characteristics and prospect of these two methods in detecting neuronal currents, along with the technical requirements on the instrumentation.