Neuronal oscillations in the EEG under varying cognitive load: a comparative study between slow waves and faster oscillations

Non-zero mean alpha oscillations revealed with computational model and empirical data

Ongoing oscillations and evoked responses are two main types of neuronal activity obtained with diverse electrophysiological recordings (EEG/MEG/iEEG/LFP). Although typically studied separately, they might in fact be closely related. One possibility to unite them is to demonstrate that neuronal oscillations have non-zero mean which predicts that stimulus- or task-triggered amplitude modulation of oscillations can contribute to the generation of evoked responses. We validated this mechanism using computational modelling and analysis of a large EEG data set. With a biophysical model, we indeed demonstrated that intracellular currents in the neuron are asymmetric and, consequently, the mean of alpha oscillations is non-zero. To understand the effect that neuronal currents exert on oscillatory mean, we varied several biophysical and morphological properties of neurons in the network, such as voltage-gated channel densities, length of dendrites, and intensity of incoming stimuli. For a very large range of model parameters, we observed evidence for non-zero mean of oscillations. Complimentary, we analysed empirical rest EEG recordings of 90 participants (50 young, 40 elderly) and, with spatio-spectral decomposition, detected at least one spatially-filtred oscillatory component of non-zero mean alpha oscillations in 93% of participants. In order to explain a complex relationship between the dynamics of amplitude-envelope and corresponding baseline shifts, we performed additional simulations with simple oscillators coupled with different time delays. We demonstrated that the extent of spatial synchronisation may obscure macroscopic estimation of alpha rhythm modulation while leaving baseline shifts unchanged. Overall, our results predict that amplitude modulation of neural oscillations should at least partially explain the generation of evoked responses. Therefore, inference about changes in evoked responses with respect to cognitive conditions, age or neuropathologies should be constructed while taking into account oscillatory neuronal dynamics.

Test-Retest Reliability of Time-Domain EEG Features to Assess Cognitive Load Using a Wireless Dry-Electrode System

Human Machine Interfaces (HMIs) can provide critical support and improve daily task functionality for prosthesis users or social interaction for patients with locked-in syndrome using an assistive communication device. One goal in the development of sophisticated HMIs is to reduce the cognitive load (CL) they place on the user to promote the use of the technology. Electroencephalogram (EEG)-derived measures collected with wired wet-electrode systems have been used to assess CL in laboratory environments and have demonstrated acceptable test-retest reliability. Assessment of CL during real-world unconstrained HMI operation, however, requires the use of a wireless dry-electrode EEG system which provides easier electrode application and untethered movement. However, the test-retest reliability of wireless dry-electrode systems to quantify CL has not been explored. Ensuring the consistent capture of CL-related signals across multiple sessions is critical if these devices are to be used to assess how improvements in HMIs affect CL. Therefore, the current study used a wireless dry-electrode EEG system to compare Evoked Response Potential (ERP) features of a simple auditory oddball task to measure CL during two separate testing sessions a week apart. ERPs of 11 subjects were recorded while participants performed a virtual task at two difficulty levels. A significant correlation was found between the P300 component of the ERPs and subjective ratings of CL during both testing sessions. Furthermore, there was a statistically significant test-retest reliability for this same ERP feature and similar signal-to-noise ratios (SNRs) across sessions.Clinical Relevance- This is an initial step in validating wireless dry-electrode EEG systems to assess cognitive load across multiple sessions. The evidence presented is critical if dry-wireless EEG systems are to be used to identify aspects of HMIs that reduce CL in clinical and real-life environments. Assessing CL in unconstrained environments can better inform clinicians and technology developers in their design of future HMIs.

Towards Intelligent Data Analytics: A Case Study in Driver Cognitive Load Classification

One debatable issue in traffic safety research is that the cognitive load by secondary tasks reduces primary task performance, i.e., driving. In this paper, the study adopted a version of the n-back task as a cognitively loading secondary task on the primary task, i.e., driving; where drivers drove in three different simulated driving scenarios. This paper has taken a multimodal approach to perform 'intelligent multivariate data analytics' based on machine learning (ML). Here, the k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) are used for driver cognitive load classification. Moreover, physiological measures have proven to be sophisticated in cognitive load identification, yet it suffers from confounding factors and noise. Therefore, this work uses multi-component signals, i.e., physiological measures and vehicular features to overcome that problem. Both multiclass and binary classifications have been performed to distinguish normal driving from cognitive load tasks. To identify the optimal feature set, two feature selection algorithms, i.e., sequential forward floating selection (SFFS) and random forest have been applied where out of 323 features, a subset of 42 features has been selected as the best feature subset. For the classification, RF has shown better performance with F1-score of 0.75 and 0.80 than two other algorithms. Moreover, the result shows that using multicomponent features classifiers could classify better than using features from a single source.

Interictal epileptiform discharge effects on neuropsychological assessment and epilepsy surgical planning

Both animal research and human research suggest that interictal epileptiform discharges (IEDs) may affect cognition, although the significance of such findings remains controversial. We review a wide range of literature with bearing on this topic and present relevant epilepsy surgery cases, which suggest that the effects of IEDs may be substantial and informative for surgical planning. In the first case, we present a patient with epilepsy with left anterior temporal lobe (TL) seizure onset who experienced frequent IEDs during preoperative neuropsychological assessment. Cognitive results strongly lateralized to the left TL. Because the patient failed performance validity tests and appeared amnestic for verbal materials inconsistent with his work history, selected neuropsychological tests were repeated 6 weeks later. Scores improved one to two standard deviations over the initial evaluation and because of this improvement, were only mildly suggestive of left TL impairment. The second case involves another patient with documented left TL epilepsy who experienced epileptiform activity while undergoing neurocognitive testing and simultaneous ambulatory EEG recording. This patient's verbal memory performance was impaired during the period that IEDs were present but near normal when such activity was absent. Overall, although the presence of IEDs may be helpful in confirming laterality of seizure onset, frequent IEDs might disrupt focal cognitive functions and distort accurate measurement of neuropsychological ability, interfering with accurate characterization of surgical risks and benefits. Such transient effects on daily performance may also contribute to significant functional compromise. We include a discussion of the manner in which IED effects during presurgical assessment can hinder individual patient presurgical planning as well as distort outcome research (e.g., IEDs occurring during presurgical assessment may lead to an underestimation of postoperative neuropsychological decline).

Spontaneous activity in the waiting brain: a marker of impulsive choice in attention-deficit/hyperactivity disorder?

•

In controls very low frequency (VLF) EEG attenuated during task and waiting periods.

•

In ADHD there was less attenuation during tasks and none at all during waiting.

•

Degree of waiting attenuation correlated with parent's ratings of impulsive choice.

•

Aberrant waiting VLF EEG may be a neural marker for impulsive choice in ADHD.

Background

Spontaneous very low frequency oscillations (VLFO), seen in the resting brain, are attenuated when individuals are working on attention demanding tasks or waiting for rewards (Hsu et al., 2013). Individuals with attention-deficit/hyperactivity disorder (ADHD) display excess VLFO when working on attention tasks. They also have difficulty waiting for rewards. Here we examined the waiting brain signature in ADHD and its association with impulsive choice.

Methods

DC-EEG from 21 children with ADHD and 21 controls (9–15 years) were collected under four conditions: (i) resting; (ii) choosing to wait; (iii) being “forced” to wait; and (iv) working on a reaction time task. A questionnaire measured two components of impulsive choice.

Results

Significant VLFO reductions were observed in controls within anterior brain regions in both working and waiting conditions. Individuals with ADHD showed VLFO attenuation while working but to a reduced level and none at all when waiting. A closer inspection revealed an increase of VLFO activity in temporal regions during waiting. Excess VLFO activity during waiting was associated with parents’ ratings of temporal discounting and delay aversion.

Conclusions

The results highlight the potential role for waiting-related spontaneous neural activity in the pathophysiology of impulsive decision-making of ADHD.

Modeling multiple time scale firing rate adaptation in a neural network of local field potentials

In response to stimulus changes, the firing rates of many neurons adapt, such that stimulus change is emphasized. Previous work has emphasized that rate adaptation can span a wide range of time scales and produce time scale invariant power law adaptation. However, neuronal rate adaptation is typically modeled using single time scale dynamics, and constructing a conductance-based model with arbitrary adaptation dynamics is nontrivial. Here, a modeling approach is developed in which firing rate adaptation, or spike frequency adaptation, can be understood as a filtering of slow stimulus statistics. Adaptation dynamics are modeled by a stimulus filter, and quantified by measuring the phase leads of the firing rate in response to varying input frequencies. Arbitrary adaptation dynamics are approximated by a set of weighted exponentials with parameters obtained by fitting to a desired filter. With this approach it is straightforward to assess the effect of multiple time scale adaptation dynamics on neural networks. To demonstrate this, single time scale and power law adaptation were added to a network model of local field potentials. Rate adaptation enhanced the slow oscillations of the network and flattened the output power spectrum, dampening intrinsic network frequencies. Thus, rate adaptation may play an important role in network dynamics.

From oscillatory transcranial current stimulation to scalp EEG changes: a biophysical and physiological modeling study

Both biophysical and neurophysiological aspects need to be considered to assess the impact of electric fields induced by transcranial current stimulation (tCS) on the cerebral cortex and the subsequent effects occurring on scalp EEG. The objective of this work was to elaborate a global model allowing for the simulation of scalp EEG signals under tCS. In our integrated modeling approach, realistic meshes of the head tissues and of the stimulation electrodes were first built to map the generated electric field distribution on the cortical surface. Secondly, source activities at various cortical macro-regions were generated by means of a computational model of neuronal populations. The model parameters were adjusted so that populations generated an oscillating activity around 10 Hz resembling typical EEG alpha activity. In order to account for tCS effects and following current biophysical models, the calculated component of the electric field normal to the cortex was used to locally influence the activity of neuronal populations. Lastly, EEG under both spontaneous and tACS-stimulated (transcranial sinunoidal tCS from 4 to 16 Hz) brain activity was simulated at the level of scalp electrodes by solving the forward problem in the aforementioned realistic head model. Under the 10 Hz-tACS condition, a significant increase in alpha power occurred in simulated scalp EEG signals as compared to the no-stimulation condition. This increase involved most channels bilaterally, was more pronounced on posterior electrodes and was only significant for tACS frequencies from 8 to 12 Hz. The immediate effects of tACS in the model agreed with the post-tACS results previously reported in real subjects. Moreover, additional information was also brought by the model at other electrode positions or stimulation frequency. This suggests that our modeling approach can be used to compare, interpret and predict changes occurring on EEG with respect to parameters used in specific stimulation configurations.