Getting off track: Cortical feedback processing network modulated by continuous error signal during target-feedback mismatch

Performance monitoring and feedback processing - especially in the wake of erroneous outcomes - represent a crucial aspect of everyday life, allowing us to deal with imminent threats in the short term but also promoting necessary behavioral adjustments in the long term to avoid future conflicts. Over the last thirty years, research extensively analyzed the neural correlates of processing discrete error stimuli, unveiling the error-related negativity (ERN) and error positivity (Pe) as two main components of the cognitive response. However, the connection between the ERN/Pe and distinct stages of error processing, ranging from action monitoring to subsequent corrective behavior, remains ambiguous. Furthermore, mundane actions such as steering a vehicle already transgress the scope of discrete erroneous events and demand fine-tuned feedback control, and thus, the processing of continuous error signals - a topic scarcely researched at present. We analyzed two electroencephalography datasets to investigate the processing of continuous erroneous signals during a target tracking task, employing feedback in various levels and modalities. We observed significant differences between correct (slightly delayed) and erroneous feedback conditions in the larger one of the two datasets that we analyzed, both in sensor and source space. Furthermore, we found strong error-induced modulations that appeared consistent across datasets and error conditions, indicating a clear order of engagement of specific brain regions that correspond to individual components of error processing.

High frequency oscillations in epileptic and non-epileptic human hippocampus during a cognitive task

Hippocampal high-frequency electrographic activity (HFOs) represents one of the major discoveries not only in epilepsy research but also in cognitive science over the past few decades. A fundamental challenge, however, has been the fact that physiological HFOs associated with normal brain function overlap in frequency with pathological HFOs. We investigated the impact of a cognitive task on HFOs with the aim of improving differentiation between epileptic and non-epileptic hippocampi in humans. Hippocampal activity was recorded with depth electrodes in 15 patients with focal epilepsy during a resting period and subsequently during a cognitive task. HFOs in ripple and fast ripple frequency ranges were evaluated in both conditions, and their rate, spectral entropy, relative amplitude and duration were compared in epileptic and non-epileptic hippocampi. The similarity of HFOs properties recorded at rest in epileptic and non-epileptic hippocampi suggests that they cannot be used alone to distinguish between hippocampi. However, both ripples and fast ripples were observed with higher rates, higher relative amplitudes and longer durations at rest as well as during a cognitive task in epileptic compared with non-epileptic hippocampi. Moreover, during a cognitive task, significant reductions of HFOs rates were found in epileptic hippocampi. These reductions were not observed in non-epileptic hippocampi. Our results indicate that although both hippocampi generate HFOs with similar features that probably reflect non-pathological phenomena, it is possible to differentiate between epileptic and non-epileptic hippocampi using a simple odd-ball task.

Sparse EEG Source Localization Using LAPPS: Least Absolute l-P (0 < p < 1) Penalized Solution

OBJECTIVE

The electroencephalographic (EEG) inverse problem is ill-posed owing to the electromagnetism Helmholtz theorem and since there are fewer observations than the unknown variables. Apart from the strong background activities (ongoing EEG), evoked EEG is also inevitably contaminated by strong outliers caused by head movements or ocular movements during recordings.

METHODS

Considering the sparse activations during high cognitive processing, we propose a novel robust EEG source imaging algorithm, LAPPS (Least Absolute -P (0 < p < 1) Penalized Solution), which employs the -loss for the residual error to alleviate the effect of outliers and another -penalty norm (p=0.5) to obtain sparse sources while suppressing Gaussian noise in EEG recordings. The resulting optimization problem is solved using a modified ADMM algorithm.

RESULTS

Simulation study was performed to recover sparse signals of randomly selected sources using LAPPS and various methods commonly used for EEG source imaging including WMNE, -norm, sLORETA and FOCUSS solution. The simulation comparison quantitatively demonstrates that LAPPS obtained the best performances in all the conducted simulations for various dipoles configurations under various SNRs on a realistic head model. Moreover, in the localization of brain neural generators in a real visual oddball experiment, LAPPS obtained sparse activations consistent with previous findings revealed by EEG and fMRI.

CONCLUSION

This study demonstrates a potentially useful sparse method for EEG source imaging, creating a platform for investigating the brain neural generators.

SIGNIFICANCE

This method alleviates the effect of noise and recovers sparse sources while maintaining a low computational complexity due to the cheap matrix-vector multiplication.