Causal phase-dependent control of non-spatial attention in human prefrontal cortex.

Non-spatial attention is a fundamental cognitive mechanism that allows organisms to orient the focus of conscious awareness towards sensory information that is relevant to a behavioural goal while shifting it away from irrelevant stimuli. It has been suggested that attention is regulated by the ongoing phase of slow excitability fluctuations of neural activity in the prefrontal cortex, a hypothesis that has been challenged with no consensus. Here we developed a behavioural and non-invasive stimulation paradigm aiming at modulating slow excitability fluctuations of the inferior frontal junction. Using this approach, we show that non-spatial attention can be selectively modulated as a function of the ongoing phase of exogenously modulated excitability states of this brain structure. These results demonstrate that non-spatial attention relies on ongoing prefrontal excitability states, which are probably regulated by slow oscillatory dynamics, that orchestrate goal-oriented behaviour.

Modulation of Visual Contrast Sensitivity with tRNS across the Visual System, Evidence from Stimulation and Simulation.

Transcranial random noise stimulation (tRNS) has been shown to significantly improve visual perception. Previous studies demonstrated that tRNS delivered over cortical areas acutely enhances visual contrast detection of weak stimuli. However, it is currently unknown whether tRNS-induced signal enhancement could be achieved within different neural substrates along the retino-cortical pathway. In three experimental sessions, we tested whether tRNS applied to the primary visual cortex (V1) and/or to the retina improves visual contrast detection. We first measured visual contrast detection threshold (VCT; N = 24, 16 females) during tRNS delivery separately over V1 and over the retina, determined the optimal tRNS intensities for each individual (ind-tRNS), and retested the effects of ind-tRNS within the sessions. We further investigated whether we could reproduce the ind-tRNS-induced modulation on a different session (N = 19, 14 females). Finally, we tested whether the simultaneous application of ind-tRNS to the retina and V1 causes additive effects. Moreover, we present detailed simulations of the induced electric field across the visual system. We found that at the group level tRNS decreases VCT compared with baseline when delivered to the V1. Beneficial effects of ind-tRNS could be replicated when retested within the same experimental session but not when retested in a separate session. Applying tRNS to the retina did not cause a systematic reduction of VCT, regardless of whether the individually optimized intensity was considered or not. We also did not observe consistent additive effects of V1 and retina stimulation. Our findings demonstrate significant tRNS-induced modulation of visual contrast processing in V1 but not in the retina.

Current Directions in the Auricular Vagus Nerve Stimulation II - An Engineering Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging electroceutical technology in the field of bioelectronic medicine with applications in therapy. Artificial modulation of the afferent vagus nerve - a powerful entrance to the brain - affects a large number of physiological processes implicating interactions between the brain and body. Engineering aspects of aVNS determine its efficiency in application. The relevant safety and regulatory issues need to be appropriately addressed. In particular, in silico modeling acts as a tool for aVNS optimization. The evolution of personalized electroceuticals using novel architectures of the closed-loop aVNS paradigms with biofeedback can be expected to optimally meet therapy needs. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the scope of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on engineering aspects - a discussion of physiological aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Current Directions in the Auricular Vagus Nerve Stimulation I - A Physiological Perspective.

Electrical stimulation of the auricular vagus nerve (aVNS) is an emerging technology in the field of bioelectronic medicine with applications in therapy. Modulation of the afferent vagus nerve affects a large number of physiological processes and bodily states associated with information transfer between the brain and body. These include disease mitigating effects and sustainable therapeutic applications ranging from chronic pain diseases, neurodegenerative and metabolic ailments to inflammatory and cardiovascular diseases. Given the current evidence from experimental research in animal and clinical studies we discuss basic aVNS mechanisms and their potential clinical effects. Collectively, we provide a focused review on the physiological role of the vagus nerve and formulate a biology-driven rationale for aVNS. For the first time, two international workshops on aVNS have been held in Warsaw and Vienna in 2017 within the framework of EU COST Action "European network for innovative uses of EMFs in biomedical applications (BM1309)." Both workshops focused critically on the driving physiological mechanisms of aVNS, its experimental and clinical studies in animals and humans, in silico aVNS studies, technological advancements, and regulatory barriers. The results of the workshops are covered in two reviews, covering physiological and engineering aspects. The present review summarizes on physiological aspects - a discussion of engineering aspects is provided by our accompanying article (Kaniusas et al., 2019). Both reviews build a reasonable bridge from the rationale of aVNS as a therapeutic tool to current research lines, all of them being highly relevant for the promising aVNS technology to reach the patient.

Functionalized anatomical models for EM-neuron Interaction modeling.

The understanding of interactions between electromagnetic (EM) fields and nerves are crucial in contexts ranging from therapeutic neurostimulation to low frequency EM exposure safety. To properly consider the impact of in vivo induced field inhomogeneity on non-linear neuronal dynamics, coupled EM-neuronal dynamics modeling is required. For that purpose, novel functionalized computable human phantoms have been developed. Their implementation and the systematic verification of the integrated anisotropic quasi-static EM solver and neuronal dynamics modeling functionality, based on the method of manufactured solutions and numerical reference data, is described. Electric and magnetic stimulation of the ulnar and sciatic nerve were modeled to help understanding a range of controversial issues related to the magnitude and optimal determination of strength-duration (SD) time constants. The results indicate the importance of considering the stimulation-specific inhomogeneous field distributions (especially at tissue interfaces), realistic models of non-linear neuronal dynamics, very short pulses, and suitable SD extrapolation models. These results and the functionalized computable phantom will influence and support the development of safe and effective neuroprosthetic devices and novel electroceuticals. Furthermore they will assist the evaluation of existing low frequency exposure standards for the entire population under all exposure conditions.

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.

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.

The effect of physiological noise in phase functional magnetic resonance imaging: from blood oxygen level-dependent effects to direct detection of neuronal currents.

Recently, the possibility to use both magnitude and phase image sets for the statistical evaluation of fMRI has been proposed, with the prospective of increasing both statistical power and the spatial specificity. In the present work, several issues that affect the spatial and temporal stability in fMRI phase time series in the presence of physiologic noise processes are reviewed, discussed and illustrated by experiments performed at 3 T. The observed phase value is a fingerprint of the underlying voxel averaged magnetic field variations. Those related to physiological processes can be considered static or dynamic in relation to the temporal scale of a 2D acquisition and will play out on different spatial scales as well: globally across the entire images slice, and locally depending on the constituents and their relative fractions inside the MRI voxel. The 'static' respiration-induced effects lead to magneto-mechanic scan-to-scan variations in the global magnetic field but may also contribute to local BOLD fluctuations due to respiration-related variations in arterial carbon dioxide. Likewise, the 'dynamic' cardiac-related effects will lead to global susceptibility effects caused by pulsatile motion of the brain as well as local blood pressure-related changes in BOLD and changes in blood flow velocity. Finally, subject motion may lead to variations in both local and global tissue susceptibility that will be especially pronounced close to air cavities. Since dissimilar manifestations of physiological processes can be expected in phase and in magnitude images, a direct relationship between phase and magnitude scan-to-scan fluctuations cannot be assumed a priori. Therefore three different models were defined for the phase stability, each dependent on the relation between phase and magnitude variations and the best will depend on the underlying noise processes. By experiments on healthy volunteers at rest, we showed that phase stability depends on the type of post-processing and can be improved by reducing the low-frequency respiration-induced mechano-magnetic effects. Although the manifestations of physiological noise were in general more pronounced in phase than in magnitude images, due to phase wraps and global Bo effects, we suggest that a phase stability similar to that found in magnitude could theoretically be achieved by adequate correction methods. Moreover, as suggested by our experimental data regarding BOLD-related phase effects, phase stability could even supersede magnitude stability in voxels covering dense microvascular networks with BOLD-related fluctuations as the dominant noise contributor. In the interest of the quality of both BOLD-based and nc-MRI methods, future studies are required to find alternative methods that can improve phase stability, designed to match the temporal and spatial scale of the underlying neuronal activity.