Patterns Of Electrical Activity Research Articles

Can electroencephalography (EEG) identify the different dimensions of pain in fibromyalgia? A pilot study

BackgroundElectroencephalography (EEG) is a promising tool for identifying the physiological biomarkers of fibromyalgia (FM). Evidence suggests differences in power band and density between individuals with FM and healthy controls. EEG changes appear to be associated with pain intensity; however, their relationship with the quality of pain has not been examined. We aimed to investigate whether abnormal EEG in the frontal and central points of the 10–20 EEG system in individuals with FM is associated with pain’s sensory-discriminative and affective-motivational dimensions. The association between EEG and the two dimensions of emotional disorders (depression and anxiety) was also investigated.MethodsIn this cross-sectional pilot study, pain experience (pain rating index [PRI]) and two dimensions of emotional disorders (depression and anxiety) were assessed using the McGill Pain Questionnaire (PRI-sensory and PRI-affective) and Hospital Anxiety and Depression Scale (HADS), respectively. In quantitative EEG analysis, the relative spectral power of each frequency band (delta, theta, alpha, and beta) was identified in the frontal and central points during rest.ResultsA negative correlation was found between the relative spectral power for the delta bands in the frontal (r= -0.656; p = 0.028) and central points (r= -0.624; p = 0.040) and the PRI-affective scores. A positive correlation was found between the alpha bands in the frontal (r = 0.642; p = 0.033) and central points (r = 0.642; p = 0.033) and the PRI-affective scores. A negative correlation between the delta bands in the central points and the anxiety subscale of the HADS (r = -0.648; p = 0.031) was detected.ConclusionThe affective-motivational dimension of pain and mood disorders may be related to abnormal patterns of electrical activity in patients with FM.Trial registrationRetrospectively registered on ClinicalTrials.gov (NCT05962658).

Understanding responses to multi-electrode epiretinal stimulation using a biophysical model.

Neural interfaces are designed to evoke specific patterns of electrical activity in populations of neurons by stimulating with many electrodes. However, currents passed simultaneously through multiple electrodes often combine nonlinearly to drive neural responses, making evoked responses difficult to predict and control. This response nonlinearity could arise from the interaction of many excitable sites in each cell, any of which can produce a spike. However, this multi-site activation hypothesis is difficult to verify experimentally. We developed a biophysical model to study retinal ganglion cell (RGC) responses to multi-electrode stimulation and validated it using data collected from ex vivo preparations of the macaque retina using a microelectrode array (512 electrodes; 30µm pitch; 10µm diameter). First, the model was validated by reproducing essential empirical findings from single-electrode stimulation and recording, including spike waveforms over the electrode array and sigmoidal responses to injected current. Then, stimulation with two electrodes was modeled to test how the positioning of the electrodes relative to the cell affected the degree of response nonlinearity. Currents passed through pairs of electrodes positioned near the cell body or far from the axon (>40 µm) exhibited linear summation. Currents passed through pairs of electrodes close to the axon summed linearly when their locations along the axon were similar, and nonlinearly otherwise. Over a range of electrode placements, several distinct, localized spike initiation sites were observed, and the number of these sites covaried with the degree of response nonlinearity. Similar trends were observed for three-electrode stimuli. All of these trends were consistent with experimental observations. These findings support the multi-site activation hypothesis for nonlinear activation of neurons, providing a biophysical interpretation of previous experimental results and potentially enabling more efficient use of multi-electrode stimuli in future neural implants.

Effects of Exercise Training on EEG Activity Patterns during Cognitive Tasks

To explore the effective method to detect the effect of exercise training on EEG activity, this paper compared the distinguishability of EEG changes relevanted to exercise training in different cognitive states. EEG signals of college students who have undergone long-term exercise training (exercise training group) and college students who have not undergone such training (control group) were collected under specific cognitive tasks (Motor imagination tasks). Then the power characteristics of alpha wave and beta wave were obtained by wavelet transform method. The machine learning method was used to distinguish the difference of brain electrical activity between the exercise-trained subjects and the control group during the exercise-related cognitive tasks. Results showed that EEG patterns between two groups were not distinguished in rest status (the accuracy is lower than 59%), but they were separable in the completion of the motor cognitive tasks. The accuracy was over 85%. The pattern of brain electrical activity during cognitive tasks can better reflect the effect of exercise training.

Exploring discrete space–time models for information transfer: Analogies from mycelial networks to the cosmic web

Fungal mycelium networks are large scale biological networks along which nutrients, metabolites flow. Recently, we discovered a rich spectrum of electrical activity in mycelium networks, including action-potential spikes and trains of spikes. Reasoning by analogy with animals and plants, where travelling patterns of electrical activity perform integrative and communicative mechanisms, we speculated that waves of electrical activity transfer information in mycelium networks. Using a new discrete space–time model with emergent radial spanning-tree topology, hypothetically comparable mycelial morphology and physically comparable information transfer, we provide physical arguments for the use of such a model, and by considering growing mycelium network by analogy with growing network of matter in the cosmic web, we develop mathematical models and theoretical concepts to characterise the parameters of the information transfer.

Reconstruction of excitation waves from mechanical deformation using physics-informed neural networks

Non-invasive assessment of the electrical activation pattern can significantly contribute to the diagnosis and treatment of cardiac arrhythmias, due to faster and safer diagnosis, improved surgical planning and easier follow-up. One promising path is to measure the mechanical contraction via echocardiography and utilize this as an indirect way of measuring the original activation pattern. To solve this demanding inversion task, we make use of physics-informed neural networks, an upcoming methodology to solve forward and inverse physical problems governed by partial differential equations. In this study, synthetic data sets were created, consisting of 2D excitation waves coupled to an isotropic and linearly deforming elastic medium. We show that for both focal and spiral patterns, the underlying excitation waves can be reconstructed accurately. We test the robustness of the method against Gaussian noise, reduced spatial resolution and projected tri-planar data. In situations where the data quality is heavily reduced, we show how to improve the reconstruction by additional regularization on the wave speed. Our findings suggest that physics-informed neural networks hold the potential to solve sparse and noisy bio-mechanical inversion problems and may offer a pathway to non-invasive assessment of certain cardiac arrhythmias.

Ascorbate insufficiency disrupts glutamatergic signaling and alters electroencephalogram phenotypes in a mouse model of Alzheimer's disease

Clinical studies have reported that increased epileptiform and subclinical epileptiform activity can be detected in many patients with an Alzheimer's disease (AD) diagnosis using electroencephalogram (EEG) and this may correlate with poorer cognition. Ascorbate may have a specific role as a neuromodulator in AD as it is released concomitantly with glutamate reuptake following excitatory neurotransmission. Insufficiency may therefore result in an exacerbated excitatory/inhibitory imbalance in neuronal signaling. Using a mouse model of AD that requires dietary ascorbate (Gulo−/-APPswe/PSEN1dE9), EEG was recorded at baseline and during 4 weeks of ascorbate depletion in young (5-month-old) and aged (20-month-old) animals. Data were scored for changes in quantity of spike trains, individual spikes, sleep-wake rhythms, sleep fragmentation, and brainwave power bands during light periods each week. We found an early increase in neuronal spike discharges with age and following ascorbate depletion in AD model mice and not controls, which did not correlate with brain amyloid load. Our data also show more sleep fragmentation with age and with ascorbate depletion. Additionally, changes in brain wave activity were observed within different vigilance states in both young and aged mice, where Gulo−/-APPswe/PSEN1dE9 mice had shifts towards higher frequency bands (alpha, beta, and gamma) and ascorbate depletion resulted in shifts towards lower frequency bands (delta and theta). Microarray data supported ascorbate insufficiency altering glutamatergic transmission through the decreased expression of glutamate related genes, however no changes in protein expression of glutamate reuptake transporters were observed. These data suggest that maintaining optimal brain ascorbate levels may support normal brain electrical activity and sleep patterns, particularly in AD patient populations where disruptions are observed.

Hippocampal network activity forecasts epileptic seizures.

Seizures in people with epilepsy were long thought to occur at random, but recent methods for seizure forecasting enable estimation of the likelihood of seizure occurrence over short horizons. These methods rely on days-long cyclical patterns of brain electrical activity and other physiological variables that determine seizure likelihood and that require measurement through long-term, multimodal recordings. In this retrospective cohort study of 15 adults with bitemporal epilepsy who had a device that provides chronic intracranial recordings, functional connectivity of hippocampal networks fluctuated in multiday cycles with patterns that mirrored cycles of seizure likelihood. A functional connectivity biomarker of seizure likelihood derived from 90-s recordings of background hippocampal activity generalized across individuals and forecasted 24-h seizure likelihood as accurately as cycle-based models requiring months-long baseline recordings. Larger, prospective studies are needed to validate this approach, but our results have the potential to make reliable seizure forecasts accessible to more people with epilepsy.

Variant patterns of electrical activation and recovery in normal human hearts revealed by noninvasive electrocardiographic imaging.

Although electrical activity of the normal human heart is well characterized by the electrocardiogram, detailed insights into within-subject and between-subject variations of ventricular activation and recovery by noninvasive electroanatomic mapping are lacking. We characterized human epicardial activation and recovery within and between normal subjects using non-invasive electrocardiographic imaging (ECGI) as a basis to better understand pathology. Epicardial activation and recovery were assessed by ECGI in 22 normal subjects, 4 subjects with bundle branch block (BBB) and 4 with long-QT syndrome (LQTS). We compared characteristics between the ventricles [left ventricle (LV) and right ventricle (RV)], sexes, and age groups (<50/≥50years). Pearson's correlation coefficient (CC) was used for within-subject and between-subject comparisons. Age of normal subjects averaged 49 ± 14 years, 6/22 were male, and no structural/electrical heart disease was present. The average activation time was longer in LV than in RV, but not different by sex or age. Electrical recovery was similar for the ventricles, but started earlier and was on average shorter in males. Median CCs of between-subject comparisons of the ECG signals, activation, and recovery patterns were 0.61, 0.32, and 0.19, respectively. Within-subject beat-to-beat comparisons yielded higher CCs (0.98, 0.89, and 0.82, respectively). Activation and/or recovery patterns of patients with BBB or LQTS contrasted significantly with those found in the normal population. Activation and recovery patterns vary profoundly between normal subjects, but are stable individually beat to beat, with a male preponderance to shorter recovery. Individual characterization by ECGI at baseline serves as reference to better understand the emergence, progression, and treatment of electrical heart disease.

On Effect of Chloroform on Electrical Activity of Proteinoids.

Proteinoids, or thermal proteins, produce hollow microspheres in aqueous solutions. Ensembles of the microspheres produce endogenous spikes of electrical activity, similar to that of neurons. To make the first step toward the evaluation of the mechanisms of such electrical behaviour, we decided to expose proteinoids to chloroform. We found that while chloroform does not inhibit the electrical oscillations of proteinoids, it causes substantial changes in the patterns of electrical activity. Namely, incremental chloroform exposure strongly affects proteinoid microsphere electrical activity across multiple metrics. As chloroform levels rise, the spike potential drops from 0.9 mV under control conditions to 0.1 mV at 25 mg/mL. This progressive spike potential decrease suggests chloroform suppresses proteinoid electrical activity. The time between spikes, the interspike period, follows a similar pattern. Minimal chloroform exposure does not change the average interspike period, while higher exposures do. It drops from 23.2 min under control experiments to 3.8 min at 25 mg/mL chloroform, indicating increased frequency of the electrical activity. These findings might lead to a deeper understanding of the electrical activity of proteinoids and their potential application in the domain of bioelectronics.

CineECG demonstrates abnormal electrical activity in preclinical plakophilin-2-pathogenic variant carriers with a normal 12-lead ECG

Abstract Background/introduction Arrhythmogenic cardiomyopathy (ACM) is an inherited cardiac disease. The presence of a pathogenic variant (PV) of the plakophilin-2 (PKP2) gene increases the likelihood of developing this disease. Although cardiac screening is routinely performed, early detection of ACM remains challenging. CineECG as a tool for analysis of the 12-lead electrocardiogram (ECG) can provide a 3D-reconstruction of the average pathway of electrical activation. With the emergence of this technique, a new insight in ventricular activation patterns can be obtained. CineECG might therefore be of aid in identifying early signs of disease in preclinical PKP2-PV carriers with a normal 12-lead ECG. Purpose This study aimed to analyze the ECG’s of preclinical PKP2-PV carriers with CineECG to determine the ventricular electrical activation patterns and to compare these patterns to the normal CineECG trajectory. Methods The 12-lead ECG’s of 19 preclinical PKP2-PV carriers were selected and evaluated by three experts on depolarization abnormalities. CineECG analysis was conducted in all preclinical PKP2-PV carriers to ascertain the average location and direction of the ventricular electrical activation. The resulting CineECG trajectory was then compared to the normal CineECG, which was previously determined from normal ECG’s from the PTB-XL database. The CineECG was considered abnormal if more than 15% of the CineECG trajectory was outside the normal distribution. Results Expert analysis of the ECG’s revealed no depolarization abnormalities (e.g. abnormal electrical heart axis, pathological Q-waves or low voltage, and ECG Task Force Criteria were absent). The CineECG trajectory was abnormal in most cases (11 out of 19 PKP2-PV carriers), while the other eight carriers exhibited normal CineECG trajectories. Figure 1 shows three examples of A) a normal case, B) a PKP2-PV carrier with a normal CineECG and C) a PKP2-PV carrier with an abnormal CineECG. In example C, the activation pattern is abnormal as the latest activation is aimed towards inferior and far to the left basal area of the heart. Conclusions CineECG analysis of the normal 12-lead ECG’s of preclinical PKP2-PV carriers resulted in the finding of abnormal electrical activation patterns in the majority of carriers. CineECG could therefore be a sensitive tool to unveil subtle and early abnormalities in the depolarization that would otherwise not be easily detected.Figure 1

Brain electrical patterns associated with pleasure and emotion induced by tonal and atonal music.

Several studies in the last 40 years have used electroencephalography (EEG) to recognize patterns of brain electrical activity correlated with emotions evoked by various stimuli. For example, the frontal alpha and theta asymmetry models to distinguish musical emotions and musical pleasure, respectively. Since these studies have used mainly tonal music, in this study, we decided to incorporate both tonal (n = 8) and atonal (n = 8) musical stimuli to observe the subjective and electrophysiological responses associated with valence, arousal, pleasure, and familiarity, from 25 nonmusician Mexican adults (10 females, 15 males; M = 37.8 years old, SD = 15.1). Our results showed that atonal music was perceived as less familiar and pleasurable than tonal music, according to the average subjective ratings. Interestingly, greater right hemispheric activity (alpha suppression) was associated with atonal music listening. Additionally, there was an increase of theta power at the right frontal cortex (F4) correlated with a decrease of pleasure ratings, in line with the frontal theta asymmetry (FTA) model. Finally, according to the model of frontal alpha asymmetry (FAA) to distinguish musical emotions, activation (alpha suppression) of the left frontal cortex (F3) was correlated with greater valence and arousal-that is, joyful music. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Explaining the predictions of kernel SVM models for neuroimaging data analysis

Machine learning methods have shown great performance in many areas, including neuroimaging data analysis. However, model performance is only one objective in neuroimaging analysis. Gaining insight from the data is also critical in this field, such as identifying regions where detected signals are relevant to cognitive and diagnostic tasks. To fulfill this need, enabling the explainability of a model’s decision-making process is critical. Predictions of complex machine learning models are notoriously difficult to explain. This limits the use of complex models like kernel support vector machines (SVM) in neuroimaging analysis. Recently, several permutation-based methods have been developed to explain these complex models. However, the explanation results are affected by class-irrelevant features like suppressor variables and high background noise variables. This problem may also happen when explaining linear models. One possible reason is that the permutation process will produce unrealistic data instances when features are not independent, e.g. correlated. These unrealistic data instances will influence the explanation results. In neuroimaging analysis, the activation pattern, the estimated weight of the assumed generative model corresponding to the current classifier, is used to deal with this problem for linear models. This method does not rely on a permutation process but rather on the available data information. In this paper, we propose a novel method of Explanation through Activation Pattern (EAP) to explain the SVM models with different types of kernels for neuroimaging data analysis. Our method can generate a global feature importance score by estimating the activation pattern of kernel SVM models. We evaluate our method against three popular methods on both simulation datasets and a publicly available EEG/MEG dataset on visual tasks. The experimental results demonstrate that the proposed EAP method can provide explanations with low computational cost and is less affected by class-irrelevant features than the other three methods. In the experiment using the MEG/EEG dataset of visual tasks, the proposed EAP method provides agreement results with the brain’s electrical activity patterns reported in the literature on the visual tasks EEG/MEG data and is significantly faster than the other explanation methods.

Analysis of the relationship of various pathologies with the degree of multifractality of electrical activity of the brain

The review is devoted to the analysis of the relationship between dynamic changes in patterns of electrical activity of the brain during the occurrence of mental disorders in the form of paranoid schizophrenia and depression and in patterns of brain activity in cardiovascular pathology associated with permanent atrial fibrillation, as well as indicators of multifractality of the studied patterns. To assess these indicators of electroencephalographic patterns, we describe a method of multifractal analysis based on the search for maxima of wavelet coefficient modules, and to isolate the fractal component of the signal in the power spectrum we describe a method of autospectral analysis with irregular resampling. It has been shown that the main differences between the multifractal properties of the electrical activity of the brain in health and in pathology are the different widths of the multifractality spectrum and its location, associated with different types of sequential pattern values. In this regard, the multifractality indicators can serve as informative markers of neuronal disorders and can be included in a set of tests for studying various pathologies.

Ultra-high-frequency ECG volumetric and negative derivative epicardial ventricular electrical activation pattern

From precordial ECG leads, the conventional determination of the negative derivative of the QRS complex (ND-ECG) assesses epicardial activation. Recently we showed that ultra-high-frequency electrocardiography (UHF-ECG) determines the activation of a larger volume of the ventricular wall. We aimed to combine these two methods to investigate the potential of volumetric and epicardial ventricular activation assessment and thereby determine the transmural activation sequence. We retrospectively analyzed 390 ECG records divided into three groups-healthy subjects with normal ECG, left bundle branch block (LBBB), and right bundle branch block (RBBB) patients. Then we created UHF-ECG and ND-ECG-derived depolarization maps and computed interventricular electrical dyssynchrony. Characteristic spatio-temporal differences were found between the volumetric UHF-ECG activation patterns and epicardial ND-ECG in the Normal, LBBB, and RBBB groups, despite the overall high correlations between both methods. Interventricular electrical dyssynchrony values assessed by the ND-ECG were consistently larger than values computed by the UHF-ECG method. Noninvasively obtained UHF-ECG and ND-ECG analyses describe different ventricular dyssynchrony and the general course of ventricular depolarization. Combining both methods based on standard 12-lead ECG electrode positions allows for a more detailed analysis of volumetric and epicardial ventricular electrical activation, including the assessment of the depolarization wave direction propagation in ventricles.

Temporal Coherence in the Synchronization of Brain Electrical Activity Patterns: An Application with the RMS Fluctuation Function

In this article, we performed a preliminary test with the mean square fluctuation (RMS) function, using EEG data, in which the task consisted of the response given to the real/imagined motor stimulus. All steps, up to the configuration of the RMS function, relied on information from subject S010, available in the Physionet database. We analyzed 12 of the 64 channels and four stimuli. We evaluated the amplitude of channels 32 and 37 in relation to the others (11, 22, 24, 43, 44, 49, 54, 61, 63 and 64). We quantitatively observed similarities when the brain perfoRMS the same real/imagined motor task, and that the timing of the amplitude changes with increasing n scale (time scales). In all experiments (S010R03, S010I03, S010R04, S010I04, S010R05, S010I05, S010R06 and S010I06), channels 32 x 22(∆logF32,22) and 37 x 24 (∆logF37,24), for n &gt; 100 (60 seconds), were smaller than the others, showing so channels 32 and 37 (left/right hemisphere) have the greatest fluctuation and differences between them. From data processing (.EDF) to visualization of the curves (FDFA / ∆log), we concluded that it is possible to replicate the test with more channels and subjects, as well as investigate other types of activities of the human brain with signals extracted from the EEG.

Current Role of Electrocardiographic Imaging in Patient Selection for Cardiac Resynchronization Therapy.

Cardiac resynchronization therapy (CRT) is a recognized therapy for heart failure with altered ejection fraction and abnormal left ventricular activation time. Since the introduction of the therapy, a 30% rate of non-responders is observed and unchanged. The 12-lead ECG remains the only recommended tool for patient selection to CRT. The 12-lead ECG is, however, limited in its inability to provide a precise pattern of regional electrical activity. Electrocardiographic imaging (ECGi) provides a non-invasive detailed mapping of cardiac activation and therefore appears as a promising tool for CRT candidates. The non-invasive ventricular activation maps acquired by ECGi have been primarily explored for the diagnosis and guidance of therapy in patients with atrial or ventricular tachyarrhythmia. However, the accuracy of the system in this field is lacking and needs further improvement before considering a clinical application. On the other hand, its use for patient selection for CRT is encouraging. In this review, we introduce the technical considerations and we describe how ECGi can precisely characterize ventricular activation, especially in patients with left bundle branch block, thus identifying the electrical substrate responsive to CRT.

Noninvasive imaging of 4D electrical activation patterns of uterine peristalsis during normal menstrual cycles

Uterine peristalsis, characterized by spontaneous slow-wave contractions of the subendometrial layer of the uterine myometrium, occurs throughout the menstrual cycle. Disruptions in peristalsis patterns may occur in women experiencing abnormal uterine bleeding, endometriosis, and infertility. Current tools to measure uterine peristalsis in humans have limitations that hamper their research or clinical utility. Here, we developed an electrophysiological imaging system with wearable electrical sensors to noninvasively quantify the four-dimensional electrical activation pattern during human uterine peristalsis with high spatial and temporal resolution and coverage. To demonstrate capabilities of this new imaging system, we enrolled 26 pre-menopausal participants ages 18–40 years old with regular menstrual cycles (25–34 days in length) and normal gynecologic anatomy. Participants initially underwent magnetic resonance imaging to obtain the body-uterus geometry. Then, during each of the four phases of a single menstrual cycle, patients underwent noninvasive electrical recording for 30 min. We measured uterine peristalsis frequency, direction, duration, magnitude, and power and found that these parameters differed in different phases of the menstrual cycle. Future use of this uterine peristalsis imaging system may advance our understanding of the role of uterine peristalsis in maintaining normal uterine function and contributing to gynecological pathophysiology.

Body surface gastric mapping to determine patterns of gastric electrical activity associated with delayed gastric emptying after pancreaticoduodenectomy: study protocol and preliminary results of the gemap study

Body surface gastric mapping to determine patterns of gastric electrical activity associated with delayed gastric emptying after pancreaticoduodenectomy: study protocol and preliminary results of the gemap study

Improvements for recording retinal function with Microelectrode Arrays

A microelectrode array (MEA) is a configuration of multiple electrodes that enables the concurrent targeting of multiple sites for extracellular recording and stimulation. By utilizing light pulses or electrical stimulations, MEA recordings unveil the complex patterns of electrical activities that arise from the signaling processes within the retinal network. Here, we present a stepwise approach for using microelectrode arrays (MEAs) for recording action potentials from the mouse retina in response to electrical and light stimuli. We provide detailed techniques accompanied by description of a custom optical system, example recordings, troubleshooting guidelines, and data processing methods including spike sorting and code resources for analyzing light and electrical responses. The comprehensive nature of this paper aims to guide researchers in utilizing MEAs effectively for investigating retinal functionality. In particular, it can be easy to have a MEA experiment fail, but hard to identify the source of the failure. This paper is meant to demystify that process. It includes:•A description of MEA setup, recording, and spike data validation.•A troubleshooting guide for common failure modes in MEA recordings from mouse retina.•Spike detection and sorting to precisely extract distinctive action potential.

Placing willed attention in context: a review of attention and free will

Attention is the ability to focus one's awareness on relevant events and objects while ignoring distracting ones. Laboratory studies of top-down voluntary attention commonly use predictive or instructional cues to direct attention. However, in real world scenarios, voluntary attention is not necessarily externally cued, but may be focused by internal, self-generated processes. The voluntary focusing of attention in the absence of external guidance has been referred to as “willed attention,” a term borrowed from the literature on willed motor actions. In a fashion similar to studies of willed (self-initiated) actions, during willed attention, participants are given the freedom to deploy attention based on their own free choices. Electrophysiological studies have shown that during willed attention, ongoing neural activity biases willed attention decisions on a moment-to-moment basis as reflected in transient patterns of brain electrical activity that predict where participants will later choose to focus their attention. Brain imaging studies have revealed that compared to cued attention, willed attention involves additional frontal cortical structures, which interact with the classic attentional control networks of the human brain to produce a modified network organization for willed attention control. In this introduction to willed attention, we briefly review the fields of voluntary attention and self-initiated motor actions, in order to describe willed attention and its neural correlates as they relate to the broader concepts of attention and volition.