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

On the Comparisons of Decorrelation Approaches for Non-Gaussian Neutral Vector Variables

As a typical non-Gaussian vector variable, a neutral vector variable contains nonnegative elements only, and its l₁-norm equals one. In addition, its neutral properties make it significantly different from the commonly studied vector variables (e.g., the Gaussian vector variables). Due to the aforementioned properties, the conventionally applied linear transformation approaches [e.g., principal component analysis (PCA) and independent component analysis (ICA)] are not suitable for neutral vector variables, as PCA cannot transform a neutral vector variable, which is highly negatively correlated, into a set of mutually independent scalar variables and ICA cannot preserve the bounded property after transformation. In recent work, we proposed an efficient nonlinear transformation approach, i.e., the parallel nonlinear transformation (PNT), for decorrelating neutral vector variables. In this article, we extensively compare PNT with PCA and ICA through both theoretical analysis and experimental evaluations. The results of our investigations demonstrate the superiority of PNT for decorrelating the neutral vector variables

Optimal Resource Allocation Using Deep Learning-Based Adaptive Compression For Mhealth Applications

In the last few years the number of patients with chronic diseases that require constant monitoring increases rapidly; which motivates the researchers to develop scalable remote health applications. Nevertheless, transmitting big real-time data through a dynamic network limited by the bandwidth, end-to-end delay and transmission energy; will be an obstacle against having an efficient transmission of the data. The problem can be resolved by applying data reduction techniques on the vital signs at the transmitter side and reconstructing the data at the receiver side (i.e. the m-Health center). However, a new problem will be introduced which is the ability to receive the vital signs at the server side with an acceptable distortion rate (i.e. deformation of vital signs because of inefficient data reduction). In this thesis, we integrate efficient data reduction with wireless networking to deliver an adaptive compression with an acceptable distortion, while reacting to the wireless network dynamics such as channel fading and user mobility. A Deep Learning (DL) approach was used to implement an adaptive compression technique to compress and reconstruct the vital signs in general and specifically the Electroencephalogram Signal (EEG) with the minimum distortion. Then, a resource allocation framework was introduced to minimize the transmission energy along with the distortion of the reconstructed signa

“What” and “when” predictions modulate auditory processing in a mutually congruent manner

Introduction: Extracting regularities from ongoing stimulus streams to form predictions is crucial for adaptive behavior. Such regularities exist in terms of the content of the stimuli and their timing, both of which are known to interactively modulate sensory processing. In real-world stimulus streams such as music, regularities can occur at multiple levels, both in terms of contents (e.g., predictions relating to individual notes vs. their more complex groups) and timing (e.g., pertaining to timing between intervals vs. the overall beat of a musical phrase). However, it is unknown whether the brain integrates predictions in a manner that is mutually congruent (e.g., if “beat” timing predictions selectively interact with “what” predictions falling on pulses which define the beat), and whether integrating predictions in different timing conditions relies on dissociable neural correlates. Methods: To address these questions, our study manipulated “what” and “when” predictions at different levels – (local) interval-defining and (global) beat-defining – within the same stimulus stream, while neural activity was recorded using electroencephalogram (EEG) in participants (N = 20) performing a repetition detection task. Results: Our results reveal that temporal predictions based on beat or interval timing modulated mismatch responses to violations of “what” predictions happening at the predicted time points, and that these modulations were shared between types of temporal predictions in terms of the spatiotemporal distribution of EEG signals. Effective connectivity analysis using dynamic causal modeling showed that the integration of “what” and “when” predictions selectively increased connectivity at relatively late cortical processing stages, between the superior temporal gyrus and the fronto-parietal network. Discussion: Taken together, these results suggest that the brain integrates different predictions with a high degree of mutual congruence, but in a shared and distributed cortical network. This finding contrasts with recent studies indicating separable mechanisms for beat-based and memory-based predictive processing

Neuroelectromagnetic signatures of the reproduction of supra-second durations

AbstractWhen participants are asked to reproduce an earlier presented duration, EEG recordings typically show a slow potential that develops over the fronto-central regions of the brain and is assumed to be generated in the supplementary motor area (SMA). This contingent negative variation (CNV) has been linked to anticipation, preparation and formation of temporal judgment (Macar, Vidal, and Casini, 1999, Experimental Brain Research, 125(3), 271–80). Although the interpretation of the CNV amplitude is problematic (Kononowicz and Van Rijn, (2011), Frontiers in Integrative Neuroscience, 5(48); Ng, Tobin, and Penney, 2011, Frontiers in Integrative Neuroscience, 5(77)), the observation of this slow potential is extremely robust, and thus one could assume that magnetic recordings of brain activity should show similar activity patterns. However, interval timing studies using durations shorter than one second did not provide unequivocal evidence as to whether CNV has a magnetic counterpart (CMV). As interval timing has been typically associated with durations longer than one second, participants in this study were presented intervals of 2, 3 or 4s that had to be reproduced in setup similar to the seminal work of Elbert et al. (1991, Psychophysiology, 28(6), 648–55) while co-recording EEG and MEG.The EEG data showed a clear CNV during the standard and the reproduction interval. In the reproduction interval the CNV steadily builds up from the onset of interval for both stimulus and response locked data. The MEG data did not show a CNV-resembling ramping of activity, but only showed a pre-movement magnetic field (preMMF) that originated from the SMA, occurring approximately 0.6s before the termination of the timed interval. These findings support the notion that signatures of timing are more straightforwardly measured using EEG, and show that the measured MEG signal from the SMA is constrained to the end of reproduction interval, before the voluntary movement.Moreover, we investigated a link between timing behavior and the early iCNV and late CNV amplitudes to evaluate the hypothesis that these amplitudes reflect the accumulation of temporal pulses. Larger iCNV amplitudes predicted shorter reproduced durations. This effect was more pronounced for the 2s interval reproduction, suggesting that preparatory strategies depend on the length of reproduced interval. Similarly to Elbert et al. (1991, Psychophysiology, 28(6), 648–55), longer reproductions were associated with smaller CNV amplitudes, both between conditions and across participants within the same condition. As the temporal accumulation hypothesis predicts the inverse, these results support the proposal by Van Rijn et al. (2011, Frontiers in Integrative Neuroscience, 5) that the CNV reflects other temporally driven processes such as temporal expectation and preparation rather than temporal accumulation itself

Oscillatory signatures of unimodal, bimodal, and cross-modal sensory working memory

Neural oscillatory activity is an essential brain mechanism that enables and subserves a vast range of cognitive functions. Studying them non-invasively through electroencephalography (EEG) has proven to be an effective method of discovering associations between oscillations in different frequency bands and various cognitive functions. Studying the oscillatory dynamics of human working memory (WM) \u2013 a core component of human higher cognitive functioning \u2013 has been particularly fruitful, leading to insights about the mental processes, frequency bands, and brain areas involved. In addition to frequency band specificity, the application of source reconstruction methods has led to further insights by revealing specific brain areas associated with WM related processing. In the present study, we focused on the oscillatory power dynamics during sensory working memory (SWM) in auditory and tactile modalities in the alpha band. In a delayed comparison two alternative forced choice task participants received two seque ntial stimuli and had to respond whether the intensity of the second stimulus was stronger than that of the first stimulus. In three related EEG experiments we examined SWM processing under unimodal (stimulation in one modality), bimodal (stimulation in both modalities simultaneously), and cross -modal (sequential stimulation of the modalities) conditions. An additional non -WM control condition allowed us to explore not only the differences between auditory and tactile WM, but also the effects of the WM task itself on the delay period oscillatory activity within each sensory modality. Our results showed that, while the bimodal stimulation condition led to behavioral enhancement, an increased stimulus difference was necessary to maintain the same level of performance also in the cross-modal conditions. Localizing the oscillatory activity in the alpha band (8 \u2013 12Hz) revealed a clear disinhibitory effect over the somatosensory cortex during the early and the late delay period, while the mid-delay did not show any differences in SWM between the two modalities. A similar, albeit weaker , effect was observed over the auditory cortices. A right parietal reduction of alpha power emerged during the late delay when a tactile stimulus had to be compared cross-modally. This suggests the involvement of parietal somatosensory association cortex in the cross-modal transformation of the tactile stimulus. Lastly, the differences between cortical source distributions when contrasting unimodal and cross-modal conditions demonstrated that late delay effects do not reflect only anticipatory effects due to the upcoming modality, but also reflect the influence of the stimulus modality kept in WM. Contrasting the bimodal condition with the unimodal ones revealed a parametric beta band effect in a right parietal area during the early delay only in the bimodal condition, which suggests that beta oscillations might play a role in multimodal integration under SWM conditions. A second effect during the early delay period was observed in the theta (4 \u2013 7Hz) band. An early effect appeared when contrasting conditions in which the first stimuli were identical while the second stimuli differed across the conditions. This result suggests that the early delay period is already shaped by the anticipated comparison context. The clearest differences in the contrast between WM and non-WM task were observed in theta and gamma bands. Source localizing the condition differences suggested the involvement of hippocampal and fronto-central areas in carrying out the WM task. Furthermore, sensory cortices of the respective modality conditions showed the highest levels of connectivity with the rest of the brain during the late delay, further highlighting the involvement of gamma band oscillations in SWM related processing. Overall, this study demonstrates that the results obtained when studying SWM related processing strongly depend on the sensory modality examined and the type of WM task employed. Any observations with regard to SWM related oscillatory power dynamics should be explored in multiple contexts before drawing any generalized conclusions