Theta-gamma phase-amplitude coupling: physiological basics, analysis methods, and perspectives of translation into clinical practice

Studying rhythmic neural synchronization (cross-frequency coupling in various ranges) is an emerging topic in present-day neurophysiology. One of the best-studied cross-frequency couplings is theta-gamma phase-amplitude coupling that contributes to the cognitive function and may vary in patients with several conditions associated with cognitive impairment. Changes in theta-gamma coupling can be registered in a wide range of diseases associated with cognitive decline. The review covers the physiological basics of theta-gamma coupling, its registration and calculation, correlation with cognitive test results in healthy volunteers, and changes in patients. We have discussed the results of the preliminary studies of frequency-dependent non-invasive brain stimulation based on theta-gamma coupling

Elimination of signals tilting caused by B₀ field inhomogeneity using 2D-lineshape reference deconvolution

An efficient approach for reference deconvolution of two-dimensional spectra aiming at the correction of static field inhomogeneity was established. In comparison to known techniques, a great improvement was achieved using the cross-section along the diagonal of the reference peak instead of its full 2D line shape. The method is termed pseudo-2D diagonal deconvolution. The approach developed allows suppressing the two-dimensional peaks tilting caused by the magnetic field inhomogeneity, while keeping the signal-to-noise ratio constant. Long-known method of 2D reference deconvolution (true-2D reference deconvolution) was also applied for comparison. The neutral and resolution-enhancing pseudo-2D deconvolutions were successfully applied for the resolution of complex overlapping multiplets and for measuring small scalar coupling constants. The new algorithm for the elimination of shape distortion of two-dimensional peaks showed to be promising in the perspective of an automated analysis of 2D correlation NMR spectra

Variability of Neuronal Responses: Types and Functional Significance in Neuroplasticity and Neural Darwinism

In many cases, neural signals detected under the same external experimental conditions significantly change from trial to trial. The variability phenomenon, which complicates extraction of reproducible results and is ignored in many studies by averaging, has attracted attention of researchers in recent years. In this paper, we classify possible types of variability based on its functional significance and describe features of each type. We describe the key adaptive significance of variability at the neural network level and the degeneracy phenomenon that may be important for learning processes in connection with the principle of neuronal group selection

Optimization of the Navigated TMS Mapping Algorithm for Accurate Estimation of Cortical Muscle Representation Characteristics

Navigated transcranial magnetic stimulation (nTMS) mapping of cortical muscle representations allows noninvasive assessment of the state of a healthy or diseased motor system, and monitoring changes over time. These applications are hampered by the heterogeneity of existing mapping algorithms and the lack of detailed information about their accuracy. We aimed to find an optimal motor evoked potential (MEP) sampling scheme in the grid-based mapping algorithm in terms of the accuracy of muscle representation parameters. The abductor pollicis brevis (APB) muscles of eight healthy subjects were mapped three times on consecutive days using a seven-by-seven grid with ten stimuli per cell. The effect of the MEP variability on the parameter accuracy was assessed using bootstrapping. The accuracy of representation parameters increased with the number of stimuli without saturation up to at least ten stimuli per cell. The detailed sampling showed that the between-session representation area changes in the absence of interventions were significantly larger than the within-session fluctuations and thus could not be explained solely by the trial-to-trial variability of MEPs. The results demonstrate that the number of stimuli has no universally optimal value and must be chosen by balancing the accuracy requirements with the mapping time constraints in a given problem

Brain Activations and Functional Connectivity Patterns Associated with Insight-Based and Analytical Anagram Solving

Insight is one of the most mysterious problem-solving phenomena involving the sudden emergence of a solution, often preceded by long unproductive attempts to find it. This seemingly unexplainable generation of the answer, together with the role attributed to insight in the advancement of science, technology and culture, stimulate active research interest in discovering its neuronal underpinnings. The present study employs functional Magnetic resonance imaging (fMRI) to probe and compare the brain activations occurring in the course of solving anagrams by insight or analytically, as judged by the subjects. A number of regions were activated in both strategies, including the left premotor cortex, left claustrum, and bilateral clusters in the precuneus and middle temporal gyrus. The activated areas span the majority of the clusters reported in a recent meta-analysis of insight-related fMRI studies. At the same time, the activation patterns were very similar between the insight and analytical solutions, with the only difference in the right sensorimotor region probably explainable by subject motion related to the study design. Additionally, we applied resting-state fMRI to study functional connectivity patterns correlated with the individual frequency of insight anagram solutions. Significant correlations were found for the seed-based connectivity of areas in the left premotor cortex, left claustrum, and left frontal eye field. The results stress the need for optimizing insight paradigms with respect to the accuracy and reliability of the subjective insight/analytical solution classification. Furthermore, the short-lived nature of the insight phenomenon makes it difficult to capture the associated neural events with the current experimental techniques and motivates complementing such studies by the investigation of the structural and functional brain features related to the individual differences in the frequency of insight-based decisions

Feasibility of Non-Gaussian Diffusion Metrics in Chronic Disorders of Consciousness

Diagnostic accuracy of different chronic disorders of consciousness (DOC) can be affected by the false negative errors in up to 40% cases. In the present study, we aimed to investigate the feasibility of a non-Gaussian diffusion approach in chronic DOC and to estimate a sensitivity of diffusion kurtosis imaging (DKI) metrics for the differentiation of vegetative state/unresponsive wakefulness syndrome (VS/UWS) and minimally conscious state (MCS) from a healthy brain state. We acquired diffusion MRI data from 18 patients in chronic DOC (11 VS/UWS, 7 MCS) and 14 healthy controls. A quantitative comparison of the diffusion metrics for grey (GM) and white (WM) matter between the controls and patient group showed a significant (p < 0.05) difference in supratentorial WM and GM for all evaluated diffusion metrics, as well as for brainstem, corpus callosum, and thalamus. An intra-subject VS/UWS and MCS group comparison showed only kurtosis metrics and fractional anisotropy differences using tract-based spatial statistics, owing mainly to macrostructural differences on most severely lesioned hemispheres. As a result, we demonstrated an ability of DKI metrics to localise and detect changes in both WM and GM and showed their capability in order to distinguish patients with a different level of consciousness