Slow cortical potential shifts preceding sensorimotor interactions

Beyond the Beta Rebound: Post-Task Responses in Oscillatory Activity follow Cessation of Working Memory Processes

Post-task responses (PTRs) are transitionary responses occurring for several seconds between the end of a stimulus/task and a period of rest. The most well-studied of these are beta band (13 - 30 Hz) PTRs in motor networks following movement, often called post-movement beta rebounds, which have been shown to differ in patients with schizophrenia and autism. Previous studies have proposed that beta PTRs reflect inhibition of task-positive networks to enable a return to resting brain activity, scaling with cognitive demand and reflecting cortical self-regulation. It is unknown whether PTRs are a phenomenon of the motor system, or whether they are a more general self-modulatory property of cortex that occur following cessation of higher cognitive processes as well as movement. To test this, we recorded magnetoencephalography (MEG) responses in 20 healthy participants to a working-memory task, known to recruit cortical networks associated with higher cognition. Our results revealed PTRs in the theta, alpha and beta bands across many regions of the brain, including the dorsal attention network (DAN) and lateral visual regions. These PTRs increased significantly (p < 0.05) in magnitude with working-memory load, an effect which is independent of oscillatory modulations occurring over the task period as well as those following individual stimuli. Furthermore, we showed that PTRs are functionally related to reaction times in left lateral visual (p < 0.05) and left parietal (p < 0.1) regions, while the oscillatory responses measured during the task period are not. Importantly, motor PTRs following button presses did not modulate with task condition, suggesting that PTRs in different networks are driven by different aspects of cognition. Our findings show that PTRs are not limited to motor networks but are widespread in regions which are recruited during the task. We provide evidence that PTRs have unique properties, scaling with cognitive load and correlating significantly with behaviour. Based on the evidence, we suggest that PTRs inhibit task-positive network activity to enable a transition to rest, however, further investigation is required to uncover their role in neuroscience and pathology.

Neurocognitive subprocesses of working memory performance

Working memory (WM) has been defined as the active maintenance and flexible updating of goal-relevant information in a form that has limited capacity and resists interference. Complex measures of WM recruit multiple subprocesses, making it difficult to isolate specific contributions of putatively independent subsystems. The present study was designed to determine whether neurophysiological indicators of proposed subprocesses of WM predict WM performance. We recruited 200 individuals defined by care-seeking status and measured neural responses using electroencephalography (EEG), while participants performed four WM tasks. We extracted spectral and time-domain EEG features from each task to quantify each of the hypothesized WM subprocesses: maintenance (storage of content), goal maintenance, and updating. We then used EEG measures of each subprocess as predictors of task performance to evaluate their contribution to WM. Significant predictors of WM capacity included contralateral delay activity and frontal theta, features typically associated with maintenance (storage of content) processes. In contrast, significant predictors of reaction time and its variability included contingent negative variation and the P3b, features typically associated with goal maintenance and updating. Broadly, these results suggest two principal dimensions that contribute to WM performance, tonic processes during maintenance contributing to capacity, and phasic processes during stimulus processing that contribute to response speed and variability. The analyses additionally highlight that reliability of features across tasks was greater (and comparable to that of WM performance) for features associated with stimulus processing (P3b and alpha), than with maintenance (gamma, theta and cross-frequency coupling).

Stacked autoencoders as new models for an accurate Alzheimer's disease classification support using resting-state EEG and MRI measurements

OBJECTIVE

This retrospective and exploratory study tested the accuracy of artificial neural networks (ANNs) at detecting Alzheimer's disease patients with dementia (ADD) based on input variables extracted from resting-state electroencephalogram (rsEEG), structural magnetic resonance imaging (sMRI) or both.

METHODS

For the classification exercise, the ANNs had two architectures that included stacked (autoencoding) hidden layers recreating input data in the output. The classification was based on LORETA source estimates from rsEEG activity recorded with 10-20 montage system (19 electrodes) and standard sMRI variables in 89 ADD and 45 healthy control participants taken from a national database.

RESULTS

The ANN with stacked autoencoders and a deep leaning model representing both ADD and control participants showed classification accuracies in discriminating them of 80%, 85%, and 89% using rsEEG, sMRI, and rsEEG + sMRI features, respectively. The two ANNs with stacked autoencoders and a deep leaning model specialized for either ADD or control participants showed classification accuracies of 77%, 83%, and 86% using the same input features.

CONCLUSIONS

The two architectures of ANNs using stacked (autoencoding) hidden layers consistently reached moderate to high accuracy in the discrimination between ADD and healthy control participants as a function of the rsEEG and sMRI features employed.

SIGNIFICANCE

The present results encourage future multi-centric, prospective and longitudinal cross-validation studies using high resolution EEG techniques and harmonized clinical procedures towards clinical applications of the present ANNs.

Classification of Healthy Subjects and Alzheimer's Disease Patients with Dementia from Cortical Sources of Resting State EEG Rhythms: A Study Using Artificial Neural Networks

Previous evidence showed a 75.5% best accuracy in the classification of 120 Alzheimer's disease (AD) patients with dementia and 100 matched normal elderly (Nold) subjects based on cortical source current density and linear lagged connectivity estimated by eLORETA freeware from resting state eyes-closed electroencephalographic (rsEEG) rhythms (Babiloni et al., 2016a). Specifically, that accuracy was reached using the ratio between occipital delta and alpha1 current density for a linear univariate classifier (receiver operating characteristic curves). Here we tested an innovative approach based on an artificial neural network (ANN) classifier from the same database of rsEEG markers. Frequency bands of interest were delta (2–4 Hz), theta (4–8 Hz Hz), alpha1 (8–10.5 Hz), and alpha2 (10.5–13 Hz). ANN classification showed an accuracy of 77% using the most 4 discriminative rsEEG markers of source current density (parietal theta/alpha 1, temporal theta/alpha 1, occipital theta/alpha 1, and occipital delta/alpha 1). It also showed an accuracy of 72% using the most 4 discriminative rsEEG markers of source lagged linear connectivity (inter-hemispherical occipital delta/alpha 2, intra-hemispherical right parietal-limbic alpha 1, intra-hemispherical left occipital-temporal theta/alpha 1, intra-hemispherical right occipital-temporal theta/alpha 1). With these 8 markers combined, an accuracy of at least 76% was reached. Interestingly, this accuracy based on 8 (linear) rsEEG markers as inputs to ANN was similar to that obtained with a single rsEEG marker (Babiloni et al., 2016a), thus unveiling their information redundancy for classification purposes. In future AD studies, inputs to ANNs should include other classes of independent linear (i.e., directed transfer function) and non-linear (i.e., entropy) rsEEG markers to improve the classification.

Classification of Single Normal and Alzheimer's Disease Individuals from Cortical Sources of Resting State EEG Rhythms

Previous studies have shown abnormal power and functional connectivity of resting state electroencephalographic (EEG) rhythms in groups of Alzheimer's disease (AD) compared to healthy elderly (Nold) subjects. Here we tested the best classification rate of 120 AD patients and 100 matched Nold subjects using EEG markers based on cortical sources of power and functional connectivity of these rhythms. EEG data were recorded during resting state eyes-closed condition. Exact low-resolution brain electromagnetic tomography (eLORETA) estimated the power and functional connectivity of cortical sources in frontal, central, parietal, occipital, temporal, and limbic regions. Delta (2-4 Hz), theta (4-8 Hz), alpha 1 (8-10.5 Hz), alpha 2 (10.5-13 Hz), beta 1 (13-20 Hz), beta 2 (20-30 Hz), and gamma (30-40 Hz) were the frequency bands of interest. The classification rates of interest were those with an area under the receiver operating characteristic curve (AUROC) higher than 0.7 as a threshold for a moderate classification rate (i.e., 70%). Results showed that the following EEG markers overcame this threshold: (i) central, parietal, occipital, temporal, and limbic delta/alpha 1 current density; (ii) central, parietal, occipital temporal, and limbic delta/alpha 2 current density; (iii) frontal theta/alpha 1 current density; (iv) occipital delta/alpha 1 inter-hemispherical connectivity; (v) occipital-temporal theta/alpha 1 right and left intra-hemispherical connectivity; and (vi) parietal-limbic alpha 1 right intra-hemispherical connectivity. Occipital delta/alpha 1 current density showed the best classification rate (sensitivity of 73.3%, specificity of 78%, accuracy of 75.5%, and AUROC of 82%). These results suggest that EEG source markers can classify Nold and AD individuals with a moderate classification rate higher than 80%.

The cerebral correlates of subliminal emotions: an eleoencephalographic study with emotional hybrid faces

In a high-resolution electroencephalographic study, participants evaluated the friendliness level of upright and inverted 'hybrid faces', i.e. facial photos containing a subliminal emotional core in the low spatial frequencies (< 6 cycles/image), superimposed on a neutral expression in the rest of the spatial frequencies. Upright happy and angry faces were judged as more friendly or less friendly than neutral faces, respectively. We observed the time course of cerebral correlates of these stimuli with event-related potentials (ERPs), confirming that hybrid faces elicited the posterior emotion-related and face-related components (P1, N170 and P2), previously shown to be engaged by non-subliminal emotional stimuli. In addition, these components were stronger in the right hemisphere and were both enhanced and delayed by face inversion. A frontal positivity (210-300 ms) was stronger for emotional than for neutral faces, and for upright than for inverted faces. Hence, hybrid faces represent an original approach in the study of subliminal emotions, which appears promising for investigating their electrophysiological correlates.

Resting state Rolandic mu rhythms are related to activity of sympathetic component of autonomic nervous system in healthy humans

We tested the hypothesis of a relationship between heart rate variability (HRV) and Rolandic mu rhythms in relaxed condition of resting state. Resting state eyes-closed electroencephalographic (EEG) and electrocardiographic (ECG) data were recorded (10-20 System) in 42 healthy adults. EEG rhythms of interest were high-frequency alpha (10.5-13Hz) and low-frequency beta (13-20Hz), which are supposed to form Rolandic mu rhythms. Rolandic and occipital (control) EEG sources were estimated by LORETA software. Results showed a statistically significant (p<0.05, corrected) negative correlation across all subjects between Rolandic cortical sources of low-frequency beta rhythms and the low-frequency band power (LF, 0.04-0.15Hz) of tachogram spectrum as an index of HRV. The lower the amplitude of Rolandic sources of low-frequency beta rhythms (as a putative sign of activity of somatomotor cortex), the higher the LF band power of tachogram spectrum (as a putative sign of sympathetic activity). This effect was specific as there was neither a similar correlation between these EEG rhythms and high-frequency band power of tachogram spectrum (as a putative sign of parasympathetic vagal activity) neither between occipital sources of low-frequency beta rhythms (as a putative sign of activity of visual cortex) and LF band power of tachogram spectrum. These results suggest that Rolandic low-frequency beta rhythms are related to sympathetic activity regulating heart rate, as a dynamic neurophysiologic oscillatory mechanism sub-serving the interaction between brain neural populations involved in somatomotor control and brain neural populations regulating ANS signals to heart for on-going homeostatic adaptations.

Single trial analysis of slow cortical potentials: a study on anticipation related potentials

OBJECTIVE

Abundant literature suggests the use of slow cortical potentials (SCPs) in a wide spectrum of basic and applied neuroscience areas. Due to their low signal to noise ratio, these potentials are often studied using grand-average analysis, which conceals trial-to-trial information. Moreover, most of the single trial analysis methods in the literature are based on classical electroencephalogram (EEG) features ([1-30] Hz) and are likely to be unsuitable for SCPs that have different signal properties (such as having the signal's spectral content in the range [0.2-0.7] Hz). In this paper we provide insights into the selection of appropriate parameters for spectral and spatial filtering.

APPROACH

We study anticipation related SCPs recorded using a web-browser application protocol and a full-band EEG (FbEEG) setup from 11 subjects on two different days.

MAIN RESULTS

We first highlight the role of a bandpass with [0.1-1.0] Hz in comparison with common practices (e.g., either with full dc, just a lowpass, or with a minimal highpass cut-off around 0.05 Hz). Secondly, we suggest that a combination of spatial-smoothing filter and common average reference (CAR) is more suitable than the spatial filters often reported in the literature (e.g., re-referencing to an electrode, Laplacian or CAR alone). Thirdly, with the help of these preprocessing steps, we demonstrate the generalization capabilities of linear classifiers across several days (AUC of 0.88 ± 0.05 on average with a minimum of 0.81 ± 0.03 and a maximum of 0.97 ± 0.01). We also report the possibility of further improvements using a Bayesian fusion technique applied to electrode-specific classifiers.

SIGNIFICANCE

We believe the suggested spatial and spectral preprocessing methods are advantageous for grand-average and single trial analysis of SCPs obtained from EEG, MEG as well as for electrocorticogram. The use of these methods will impact basic neurophysiological studies as well as the use of SCPs in the design of neuroprosthetics.

Low frequency oscillations in rat posterior parietal cortex are differentially activated by cues and distractors

The posterior parietal cortex (PPC) is hypothesized to detect visual cues among competing distractors. Anatomical and neurophysiologic evidence indicates that the rat PPC is part of a network of brain areas involved in directed attention, specifically when new task parameters or conditions are introduced. Here, we test the hypothesis that changes in the local field potential (LFP) of the PPC of rats performing a sustained attention task reflect aspects of detection. Two event-related potentials were observed during detection: the P300 response and the contingent negative variation (CNV). Spectrogram analysis also indicated a detection-specific increase in alpha power in the retention interval of this task. This is consistent with observations from human studies, which indicate that tasks requiring a subject to withhold a response produced a pronounced synchronization of alpha rhythms during the delay, and desynchronization during retrieval. We also found cycles of alpha synchrony and desynchrony in response to a periodic distractor. These cycles were most pronounced in the initial trial block of the distractor when the false alarm rate was highest, and as task performance improved these cycles significantly diminished. This result suggests that alpha cycling in the PPC represent neural activity critical for learning to inhibit distractors. The occurrence of alpha synchronization and desynchronization to attention-demanding stimuli, in addition to the P300 and CNV responses observed during detection, is evidence that rat PPC is involved in sustained attention, particularly in the presence of distractors.

Inhibitory effect of voluntary movement preparation on cutaneous heat pain and laser-evoked potentials

In our study, preparation of voluntary movement was used to physiologically activate the motor cortex areas and the effect of this activation on CO(2) laser-evoked potentials (LEPs) was explored. LEPs were recorded from 31 scalp electrodes in 10 healthy subjects after painful stimulation of the right C6-C7 skin dermatomes. LEP stimuli were delivered in the time interval between a visual warning stimulus followed after 1 s. by an imperative stimulus. The imperative stimulus triggered: (i) no task in the baseline condition (Pain); (ii) flexion-extension movements of the second finger of the right hand in the movement condition (Pain + Movement); (iii) cognitive task (mathematic computation) in the distraction condition (Pain + Cognition). The experimental conditions were also repeated during application of laser stimuli on the left C6-C7 skin dermatomes. Compared with the baseline condition (no task required), during preparation of right-hand voluntary movement there was a significant reduction in LEP amplitude and subjective pain rating after right- but not after left-hand stimulation, which suggests that the observed effect cannot be attributed to a nonspecific reduction in attention toward painful stimulus. During preparation of a cognitive task, LEP amplitude was reduced compared to baseline. Our results represent the first neurophysiological suggestion that physiological activation of the motor cortex, occurring during movement preparation, inhibits cortical pain processing by a centrifugal mechanism.

Distraction affects frontal alpha rhythms related to expectancy of pain: An EEG study

Previous electroencephalographic (EEG) evidence has shown event-related desynchronization (ERD) of alpha rhythms before predictable painful stimuli, as a possible neural concomitant of attentional preparatory processes (Babiloni, C., Brancucci, A., Babiloni, F., Capotosto, P., Carducci, F., Cincotti, F., Arendt-Nielsen, L., Chen, A.C., Rossini, P.M., 2003. Anticipatory cortical responses during the expectancy of a predictable painful stimulation. A high-resolution electroencephalography study. Eur. J. Neurosci. 18 (6) 1692-700). This study tested the hypothesis that alpha ERD before predictable painful stimuli is reduced as an effect of distraction. A visual warning stimulus preceded a laser painful stimulation, which was strictly followed by visual imperative stimuli. In the Pain (control) condition, no task was required after the imperative stimuli. In the Pain + Movement condition, subjects had to perform a movement of the right index finger. In the Pain + Cognition condition, they had to mentally perform an arithmetical task. EEG data were recorded in 10 subjects from 30 electrodes. Artifact-free recordings were spatially enhanced by surface Laplacian transformation. Alpha ERD was computed at three alpha sub-bands according to subjects' individual alpha frequency peak (i.e., about 6-8 Hz, 8-10 Hz, 10-12 Hz). Compared to the control condition, the subjects reported a significantly lower stimulus intensity perception and unpleasantness in the Pain + Movement and Pain + Cognition conditions. In addition, there was a cancellation of the alpha 3 ERD (i.e., about 10-12 Hz) in Pain + Cognition condition and even a generation of a statistically significant alpha 3 ERS in Pain + Movement condition. These effects were maximum over fronto-central midline. These results suggest that distraction during the expectancy of pain is related to a reduced neural desynchronization of fronto-central midline alpha rhythms (i.e., reduced cortical activation) towards an overt hyper-synchronization (cortical idling).