Low-frequency oscillatory correlates of auditory predictive processing in cortical-subcortical networks: a MEG-study

Emerging evidence supports the role of neural oscillations as a mechanism for predictive information processing across large-scale networks. However, the oscillatory signatures underlying auditory mismatch detection and information flow between brain regions remain unclear. To address this issue, we examined the contribution of oscillatory activity at theta/alpha-bands (4–8/8–13 Hz) and assessed directed connectivity in magnetoencephalographic data while 17 human participants were presented with sound sequences containing predictable repetitions and order manipulations that elicited prediction-error responses. We characterized the spectro-temporal properties of neural generators using a minimum-norm approach and assessed directed connectivity using Granger Causality analysis. Mismatching sequences elicited increased theta power and phase-locking in auditory, hippocampal and prefrontal cortices, suggesting that theta-band oscillations underlie prediction-error generation in cortical-subcortical networks. Furthermore, enhanced feedforward theta/alpha-band connectivity was observed in auditory-prefrontal networks during mismatching sequences, while increased feedback connectivity in the alpha-band was observed between hippocampus and auditory regions during predictable sounds. Our findings highlight the involvement of hippocampal theta/alpha-band oscillations towards auditory prediction-error generation and suggest a spectral dissociation between inter-areal feedforward vs. feedback signalling, thus providing novel insights into the oscillatory mechanisms underlying auditory predictive processing

Improving the performance of translation wavelet transform using BMICA

Research has shown Wavelet Transform to be one of the best methods for denoising biosignals. Translation-Invariant form of this method has been found to be the best performance. In this paper however we utilize this method and merger with our newly created Independent Component Analysis method – BMICA. Different EEG signals are used to verify the method within the MATLAB environment. Results are then compared with those of the actual Translation-Invariant algorithm and evaluated using the performance measures Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Signal to Distortion Ratio (SDR), and Signal to Interference Ratio (SIR). Experiments revealed that the BMICA Translation-Invariant Wavelet Transform out performed in all four measures. This indicates that it performed superior to the basic Translation- Invariant Wavelet Transform algorithm producing cleaner EEG signals which can influence diagnosis as well as clinical studies of the brain

Complexity Analysis of Spontaneous Brain Activity in Attention-Deficit/Hyperactivity Disorder: Diagnostic Implications

Background: Attention-deficit/hyperactivity disorder (ADHD) is defined as the most common neurobehavioral disorder of childhood, but an objective diagnostic test is not available yet to date. Neurophychological, neuroimaging, and neurophysiological research offer ample evidence of brain and behavioral dysfunctions in ADHD, but these findings have not been useful as a diagnostic test. Methods: Whole-head magnetoencephalographic recordings were obtained from 14 diagnosed ADHD patients and 14 healthy children during resting conditions. Lempel-Ziv complexity (LZC) values were obtained for each channel and child and averaged in five sensor groups: anterior, central, left lateral, right lateral, and posterior. Results: Lempel-Ziv complexity scores were significantly higher in control subjects, with the maximum value in anterior region. Combining age and anterior complexity values allowed the correct classification of ADHD patients and control subjects with a 93% sensitivity and 79% specificity. Control subjects showed an age-related monotonic increase of LZC scores in all sensor groups, while children with ADHD exhibited a nonsignificant tendency toward decreased LZC scores. The age-related divergence resulted in a 100% specificity in children older than 9 years. Conclusions: Results support the role of a frontal hypoactivity in the diagnosis of ADHD. Moreover, the age-related divergence of complexity scores between ADHD patients and control subjects might reflect distinctive developmental trajectories. This interpretation of our results is in agreement with recent investigations reporting a delay of cortical maturation in the prefrontal corte

Disclosing large-scale directed functional connections in MEG with the multivariate phase slope index.

Abstract The phase slope index (PSI) is a method to disclose the direction of frequency-specific neural interactions from magnetoencephalographic (MEG) time series. A fundamental property of PSI is that of vanishing for linear mixing of independent neural sources. This property allows PSI to cope with the artificial instantaneous connectivity among MEG sensors or brain sources induced by the field spread. Nevertheless, PSI is limited by being a bivariate estimator of directionality as opposite to the multidimensional nature of brain activity as revealed by MEG. The purpose of this work is to provide a multivariate generalization of PSI. We termed this measure as the multivariate phase slope index (MPSI). In order to test the ability of MPSI in estimating the directionality, and to compare the MPSI results to those obtained by bivariate PSI approaches based on maximizing imaginary part of coherency and on canonical correlation analysis, we used extensive simulations. We proved that MPSI achieves the highest performance and that in a large number of simulated cases, the bivariate methods, as opposed to MPSI, do not detect a statistically significant directionality. Finally, we applied MPSI to assess seed-based directed functional connectivity in the alpha band from resting state MEG data of 61 subjects from the Human Connectome Project. The obtained results highlight a directed functional coupling in the alpha band between the primary visual cortex and several key regions of well-known resting state networks, e.g. dorsal attention network and fronto-parietal network