EEG Source Analysis Research Articles

Disrupted Functional Rich-Club Organization of the Brain Networks in Children with Attention-Deficit/Hyperactivity Disorder, a Resting-State EEG Study.

Growing evidence indicates that disruptions in the brain’s functional connectivity play an important role in the pathophysiology of ADHD. The present study investigates alterations in resting-state EEG source connectivity and rich-club organization in children with inattentive (ADHDI) and combined (ADHDC) ADHD compared with typically developing children (TD) under the eyes-closed condition. EEG source analysis was performed by eLORETA in different frequency bands. The lagged phase synchronization (LPS) and graph theoretical metrics were then used to examine group differences in the topological properties and rich-club organization of functional networks. Compared with the TD children, the ADHDI children were characterized by a widespread significant decrease in delta and beta LPS, as well as increased theta and alpha LPS in the left frontal and right occipital regions. The ADHDC children displayed significant increases in LPS in the central, temporal and posterior areas. Both ADHD groups showed small-worldness properties with significant increases and decreases in the network degree in the θ and β bands, respectively. Both subtypes also displayed reduced levels of network segregation. Group differences in rich-club distribution were found in the central and posterior areas. Our findings suggest that resting-state EEG source connectivity analysis can better characterize alterations in the rich-club organization of functional brain networks in ADHD patients.

Depression of auditory cortex excitability by transcranial alternating current stimulation

Transcranial alternating current stimulation (tACS) is a type of noninvasive brain stimulation technique that has been shown to modulate motor, cognitive and memory function. Direct electrophysiological evidence of an interaction between tACS and the auditory cortex excitability has rarely been reported. Different stimulation parameters and areas of tACS may have different influence on the regulatory results. In this study, 11-Hz tACS was applied to the auditory cortex of 12 subjects with normal hearing in order to explore its effects on the auditory steady-state response (ASSR). The results indicate that tACS has an inhibitory effect on 40-Hz ASSR. In addition, EEG source analysis shows that 11-Hz tACS may enhance the activity of the middle temporal gyrus under both sham and real conditions, while the estimated source activity of the posterior cingulate gyrus may be reduced under real condition. The results reveal that tACS applied to the temporal lobe of humans will make the 40-Hz ASSR a tendency to decrease, and help improve the understanding of modulation of tACS-induced auditory cortex excitability changes in humans.

Phase dependent modulation of cortical activity during action observation and motor imagery of walking: An EEG study

Action observation (AO) and motor imagery (MI) are motor simulations which induce cortical activity related to execution of observed and imagined movements. Neuroimaging studies have mainly investigated where the cortical activities during AO and MI of movements are activated and if they match those activated during execution of the movements. However, it remains unclear how cortical activity is modulated; in particular, whether activity depends on observed or imagined phases of movements. We have previously examined the neural mechanisms underlying AO and MI of walking, focusing on the combined effect of AO with MI (AO+MI) and phase dependent modulation of corticospinal and spinal reflex excitability. Here, as a continuation of our previous studies, we investigated cortical activity depending on gait phases during AO and AO+MI of walking by using electroencephalography (EEG); 64-channel EEG signals were recorded in which participants observed walking with or without imagining it, respectively. EEG source and spectral analyses showed that, in the sensorimotor cortex during AO+MI and AO, the alpha and beta power were decreased, and power spectral modulations depended on walking phases. The phase dependent modulations during AO+MI, but not during AO, were like those which occur during actual walking as reported by previous walking studies. These results suggest that combinatory effects of AO+MI could induce parts of the phase dependent activation of the sensorimotor cortex during walking even without any movements. These findings would extend understanding of the neural mechanisms underlying walking and cognitive motor processes and provide clinically beneficial information towards rehabilitation for patients with neurological gait dysfunctions.

Inter-Subject Variability of Skull Conductivity and Thickness in Calibrated Realistic Head Models

Skull conductivity has a substantial influence on EEG and combined EEG and MEG source analysis as well as on optimized transcranial electric stimulation. To overcome the use of standard literature values, we propose a non-invasive two-level calibration procedure to estimate skull conductivity individually in a group study with twenty healthy adults. Our procedure requires only an additional run of combined somatosensory evoked potential and field data, which can be easily integrated in EEG/MEG experiments. The calibration procedure uses the P20/N20 topographies and subject-specific realistic head models from MRI. We investigate the inter-subject variability of skull conductivity and relate it to skull thickness, age and gender of the subjects, to the individual scalp P20/N20 surface distance between the P20 potential peak and the N20 potential trough as well as to the individual source depth of the P20/N20 source. We found a considerable inter-subject variability for (calibrated) skull conductivity (8.44 ± 4.84 mS/m) and skull thickness (5.97 ± 1.19 mm) with a statistically significant correlation between them (rho = 0.52). Age showed a statistically significant negative correlation with skull conductivity (rho = -0.5). Furthermore, P20/N20 surface distance and source depth showed large inter-subject variability of 12.08 ± 3.21 cm and 15.45 ± 4.54 mm, respectively, but there was no significant correlation between them. We also found no significant differences among gender subgroups for the investigated measures. It is thus important to take the inter-subject variability of skull conductivity and thickness into account by means of using subject-specific calibrated realistic head modeling.

Nested oscillations and brain connectivity during sequential stages of feature-based attention

Brain mechanisms of visual selective attention involve both local and network-level activity changes at specific oscillatory rhythms, but their interplay remains poorly explored. Here, we investigate anticipatory and reactive effects of feature-based attention using separate fMRI and EEG recordings, while participants attended to one of two spatially overlapping visual features (motion and orientation). We focused on EEG source analysis of local neuronal rhythms and nested oscillations and on graph analysis of connectivity changes in a network of fMRI-defined regions of interest, and characterized a cascade of attentional effects at multiple spatial scales. We discuss how the results may reconcile several theories of selective attention, by showing how β rhythms support anticipatory information routing through increased network efficiency, while reactive α-band desynchronization patterns and increased α-γ coupling in task-specific sensory areas mediate stimulus-evoked processing of task-relevant signals.

Electroencephalographic Source Reconstruction by the Finite-Element Approximation of the Elliptic Cauchy Problem.

This paper develops a novel approach for fast and reliable reconstruction of EEG sources in MRI-based head models. The inverse EEG problem is reduced to the Cauchy problem for an elliptic partial-derivative equation. The problem is transformed into a regularized minimax problem, which is directly approximated in a finite-element space. The resulting numerical method is efficient and easy to program. It eliminates the need to solve forward problems, which can be a tedious task. The method applies to complex anatomical head models, possibly containing holes in surfaces, anisotropic conductivity, and conductivity variations inside each tissue. The method has been verified on a spherical shell model and an MRI-based head. Numerical experiments indicate high accuracy of localization of brain activations (both cortical potential and current) and rapid execution time. This study demonstrates that the proposed approach is feasible for EEG source analysis and can serve as a rapid and reliable tool for EEG source analysis. The significance of this study is that it develops a fast, accurate, and simple numerical method of EEG source analysis, applicable to almost arbitrary complex head models.

Modulation of cortical activity in response to learning and long-term memory retrieval of 2D verses stereoscopic 3D educational contents: Evidence from an EEG study

Stereoscopic 3D (S3D) displays provide a very realistic visualization experience to the viewers through a sensation of depth perception. Despite the rapid growth of S3D technology (S3D) in education, very little has been known about the effects of S3D on human behavior and the corresponding brain's responses over traditional 2D display during conscious learning and Long-Term Memory (LTM) recall process. The present study aims to explore the effects of S3D technology on human behavior and the brain responses during learning and memory using an electroencephalography (EEG) technique. A sample of 68 participants between 18 and 30 years of age was recruited to perform three tasks: (1) Raven's Advanced Progressive Matrices (RAPM) test to assess the fluid intelligence ability; (2) learning task to study the S3D or 2D based contents for learning; and (3) memory recall task to assess the memory recall and retention performance. The participants were randomly assigned to two groups―2D group and S3D group, in such a way that their fluid intelligence ability, age, and background knowledge about the learning material are controlled. The analysis of behavioral data suggested that responses of both groups in terms of reaction time after two months of retention were statistically significantly different, F(2.66,125.09) = 4.47, p = .007, ηp2 = 0.087. The differences between the S3D and 2D groups were identified by EEG analysis, i.e., the classifier correctly discriminated the EEG signals of both groups with more than 90% accuracy rate. The EEG source analysis showed statistically significant differences between the groups in the brain regions of BA 7, BA 10, BA 11, and BA 25, reflecting widespread neuronal networks involved in the S3D group as compared to the 2D group in the LTM recollection. In conclusion, it was experimentally showed that the human brain processed the S3D contents differently by utilizing more cortical regions and neuronal networks than the traditional 2D contents, which modulates the behavior of the participants by recollecting the LTM faster in the recall task.

Analysis of Neuropsychiatric Symptoms in Patients with Alzheimer’s Disease Using Quantitative EEG and sLORETA

Objective: The electrocortical activities associated with the neuropsychiatric symptoms (NPSs) of Alzheimer’s disease (AD) were investigated using frequency-domain electroencephalography (EEG) spectral source analysis, and the potential electrocortical indices identified. Materials and Methods: Scalp EEG data were obtained from 51 patients with AD to investigate the presence of four NPS subdomains, hyperactivity, psychosis, affective symptoms, and apathy. EEG power spectra and the standardized low-resolution brain electromagnetic tomography (sLORETA)-localized EEG cortical sources were compared between the groups with and without the four NPS subdomains in eight frequency bands: 1–4, 4–8, 8–10, 10–12, 12–18, 18–20, 20–30, and 30–45 Hz. Results: The power spectral analysis of EEG data showed that AD patients with psychosis had lower values at the α₂-band in most areas. In patients with apathy, the θ-to-β power ratio showed a greater activity over the frontal and central regions. The cortical source analysis using sLORETA revealed that patients with psychosis showed decreased values in the α₂-band and patients with apathy showed higher δ-values, especially in the right frontal and temporal regions. Conclusion: The results of the present study showed that both classical EEG spectral and EEG source analysis could differentiate patients with and without NPSs, especially psychosis and apathy subdomains. Spectral and sLORETA analyses provided information helpful for a better characterization in patients with NPSs.

Distinguishing vigilance decrement and low task demands from mind-wandering: A machine learning analysis of EEG.

Mind‐wandering is a ubiquitous mental phenomenon that is defined as self‐generated thought irrelevant to the ongoing task. Mind‐wandering tends to occur when people are in a low‐vigilance state or when they are performing a very easy task. In the current study, we investigated whether mind‐wandering is completely dependent on vigilance and current task demands, or whether it is an independent phenomenon. To this end, we trained support vector machine (SVM) classifiers on EEG data in conditions of low and high vigilance, as well as under conditions of low and high task demands, and subsequently tested those classifiers on participants' self‐reported mind‐wandering. Participants' momentary mental state was measured by means of intermittent thought probes in which they reported on their current mental state. The results showed that neither the vigilance classifier nor the task demands classifier could predict mind‐wandering above‐chance level, while a classifier trained on self‐reports of mind‐wandering was able to do so. This suggests that mind‐wandering is a mental state different from low vigilance or performing tasks with low demands—both which could be discriminated from the EEG above chance. Furthermore, we used dipole fitting to source‐localize the neural correlates of the most import features in each of the three classifiers, indeed finding a few distinct neural structures between the three phenomena. Our study demonstrates the value of machine‐learning classifiers in unveiling patterns in neural data and uncovering the associated neural structures by combining it with an EEG source analysis technique.

Flanker Task-Elicited Event-Related Potential Sources Reflect Human Recombinant Erythropoietin Differential Effects on Parkinson’s Patients

We used EEG source analysis to identify which cortical areas were involved in the automatic and controlled processes of inhibitory control on a flanker task and compared the potential efficacy of recombinant-human erythropoietin (rHuEPO) on the performance of Parkinson's Disease patients. The samples were 18 medicated PD patients (nine of them received rHuEPO in addition to their usual anti-PD medication through random allocation and the other nine patients were on their regular anti-PD medication only) and 9 age and education-matched healthy controls (HCs) who completed the flanker task with simultaneous EEG recordings. N1 and N2 event-related potential (ERP) components were identified and a low resolution tomography (LORETA) inverse solution was employed to localize the neural generators. Reaction times and errors were increased for the incongruent flankers for PD patients compared to controls. EEG source analysis identified an effect of rHuEPO on the lingual gyri for the early N1 component. N2-related sources in middle cingulate and precuneus were associated with the inhibition of automatic responses evoked by incongruent stimuli differentiated PD and HCs. From our results rHuEPO seems to mediate an effect on N1 sources in lingual gyri but not on behavioural performance. N2-related sources in middle cingulate and precuneus were evoked by incongruent stimuli differentiated PD and HCs.

P69 Combined EEG & MEG Source Analysis coupled with a Two - Phase Spike Clustering Approach: A multifocal epilepsy case

P69 Combined EEG & MEG Source Analysis coupled with a Two - Phase Spike Clustering Approach: A multifocal epilepsy case

On the Quality, Statistical Efficiency, and Safety of Simultaneously Recorded Multiband fMRI/EEG

The recent development of multiband functional magnetic resonance imaging (MB-fMRI) allows for the reduction of sampling period by simultaneously exciting multiple slices-the number of which is referred to as the multiband factor. Simultaneously recorded electroencephalography (EEG)/MB-fMRI has yet to be validated for data quality against conventional single band (SB)-fMRI. Pilot scans were conducted on phantoms twice and on a healthy volunteer to ensure no heating effects. In the main study, two thermometer probes were attached to 16 healthy individuals (ages 20-39, 9 females) whilst they completed two sets of 16-min resting-state and two sets of 9-min n-back task scans-each set consisting of one MB4 and one SB pulse sequence. No heating effects were reported and thermometer data showed mean increases of < 1.0°C. Minimal differences between the two scan types were found in EEG channel variance and spectra. Expected decreases in MB4-fMRI tSNR were observed. In n-back task scans, little to no differences were detected in both EEG source analyses and fMRI local analyses for mixed effects. Resting-state posterior cingulate cortex seed-based analyses of the default mode network along with EEG-informed fMRI analysis of the occipital alpha anticorrelation effect showed improved statistical and spatial sensitivity at lower scan durations. Using EEG/MB4-fMRI for n-back tasks provided no statistical advantages nor disadvantages. However, for studying the resting-state, MB4-fMRI potentially allows for reduced scanning durations for equivalent statistical significance to be obtained or alternatively, larger effect sizes for the same scanning duration. As such, simultaneous EEG/MB4-fMRI is a viable alternative to EEG/SB-fMRI.

Using structural connectivity to augment community structure in EEG functional connectivity.

Recently, EEG recording techniques and source analysis have improved, making it feasible to tap into fast network dynamics. Yet, analyzing whole-cortex EEG signals in source space is not standard, partly because EEG suffers from volume conduction: Functional connectivity (FC) reflecting genuine functional relationships is impossible to disentangle from spurious FC introduced by volume conduction. Here, we investigate the relationship between white matter structural connectivity (SC) and large-scale network structure encoded in EEG-FC. We start by confirming that FC (power envelope correlations) is predicted by SC beyond the impact of Euclidean distance, in line with the assumption that SC mediates genuine FC. We then use information from white matter structural connectivity in order to smooth the EEG signal in the space spanned by graphs derived from SC. Thereby, FC between nearby, structurally connected brain regions increases while FC between nonconnected regions remains unchanged, resulting in an increase in genuine, SC-mediated FC. We analyze the induced changes in FC, assessing the resemblance between EEG-FC and volume-conduction- free fMRI-FC, and find that smoothing increases resemblance in terms of overall correlation and community structure. This result suggests that our method boosts genuine FC, an outcome that is of interest for many EEG network neuroscience questions.

Neural Activity and Decoding of Action Observation Using Combined EEG and fNIRS Measurement.

In a social world, observing the actions of others is fundamental to understanding what they are doing, as well as their intentions and feelings. Studies of the neural basis and decoding of action observation are important for understanding action-related processes and have implications for cognitive, social neuroscience, and human-machine interaction (HMI). In the current study, we first investigated temporal-spatial dynamics during action observation using a combined 64-channel electroencephalography (EEG) and 48-channel functional near-infrared spectroscopy (fNIRS) system. We measured brain activation while 16 healthy participants observed three action tasks: (1) grasping a cup with the intention of drinking; (2) grasping a cup with the intention of moving it; and (3) touching a cup with an unclear intention. The EEG and fNIRS source analysis results revealed the dynamic involvement of both the mirror neuron system (MNS) and the theory of mind (ToM)/mentalizing network during action observation. The source analysis results suggested that the extent to which these two systems were engaged was determined by the clarity of the intention of the observed action. Based on the difference in neural activity observed among different action-observation tasks in the first experiment, we conducted a second experiment to classify the neural processes underlying action observation using a feature classification method. We constructed complex brain networks based on the EEG and fNIRS data. Fusing features from both EEG and fNIRS complex brain networks resulted in a classification accuracy of 72.7% for the three action observation tasks. This study provides a theoretical and empirical basis for elucidating the neural mechanisms of action observation and intention understanding, and a feasible method for decoding the underlying neural processes.

Frontal cortex selectively overrides auditory processing to bias perception for looming sonic motion

Rising intensity sounds signal approaching objects traveling toward an observer. A variety of species preferentially respond to looming over receding auditory motion, reflecting an evolutionary perceptual bias for recognizing approaching threats. We probed the neural origins of this stark perceptual anisotropy to reveal how the brain creates privilege for auditory looming events. While recording neural activity via electroencephalography (EEG), human listeners rapidly judged whether dynamic (intensity varying) tones were looming or receding in percept. Behaviorally, listeners responded faster to auditory looms confirming a perceptual bias for approaching signals. EEG source analysis revealed sensory activation localized to primary auditory cortex (PAC) and decision-related activity in prefrontal cortex (PFC) within 200 ms after sound onset followed by additional expansive PFC activation by 500 ms. Notably, early PFC (but not PAC) activity rapidly differentiated looming and receding stimuli and this effect roughly co-occurred with sound arrival in auditory cortex. Brain-behavior correlations revealed an association between PFC neural latencies and listeners’ speed of sonic motion judgments. Directed functional connectivity revealed stronger information flow from PFC → PAC during looming vs. receding sounds. Our electrophysiological data reveal a critical, previously undocumented role of prefrontal cortex in judging dynamic sonic motion. Both faster neural bias and a functional override of obligatory sensory processing via selective, directional PFC signaling toward auditory system establish the perceptual privilege for approaching looming sounds.

Influence of Head Tissue Conductivity Uncertainties on EEG Dipole Reconstruction.

Reliable EEG source analysis depends on sufficiently detailed and accurate head models. In this study, we investigate how uncertainties inherent to the experimentally determined conductivity values of the different conductive compartments influence the results of EEG source analysis. In a single source scenario, the superficial and focal somatosensory P20/N20 component, we analyze the influence of varying conductivities on dipole reconstructions using a generalized polynomial chaos (gPC) approach. We find that in particular the conductivity uncertainties for skin and skull have a significant influence on the EEG inverse solution, leading to variations in source localization by several centimeters. The conductivity uncertainties for gray and white matter were found to have little influence on the source localization, but a strong influence on the strength and orientation of the reconstructed source, respectively. As the CSF conductivity is most accurately determined of all conductivities in a realistic head model, CSF conductivity uncertainties had a negligible influence on the source reconstruction. This small uncertainty is a further benefit of distinguishing the CSF in realistic volume conductor models.

Capturing the spatiotemporal dynamics of self-generated, task-initiated thoughts with EEG and fMRI

The temporal structure of self-generated cognition is a key attribute to the formation of a meaningful stream of consciousness. When at rest, our mind wanders from thought to thought in distinct mental states. Despite the marked importance of ongoing mental processes, it is challenging to capture and relate these states to specific cognitive contents. In this work, we employed ultra-high field functional magnetic resonance imaging (fMRI) and high-density electroencephalography (EEG) to study the ongoing thoughts of participants instructed to retrieve self-relevant past episodes for periods of 22sec. These task-initiated, participant-driven activity patterns were compared to a distinct condition where participants performed serial mental arithmetic operations, thereby shifting from self-related to self-unrelated thoughts. BOLD activity mapping revealed selective enhanced activity in temporal, parietal and occipital areas during the memory compared to the mental arithmetic condition, evincing their role in integrating the re-experienced past events into conscious representations during memory retrieval. Functional connectivity analysis showed that these regions were organized in two major subparts, previously associated to “scene-reconstruction” and “self-experience” subsystems. EEG microstate analysis allowed studying these participant-driven thoughts in the millisecond range by determining the temporal dynamics of brief periods of stable scalp potential fields. This analysis revealed selective modulation of occurrence and duration of specific microstates in the memory and in the mental arithmetic condition, respectively. EEG source analysis revealed similar spatial distributions of the sources of these microstates and the regions identified with fMRI. These findings imply a functional link between BOLD activity changes in regions related to a certain mental activity and the temporal dynamics of mentation, and support growing evidence that specific fMRI networks can be captured with EEG as repeatedly occurring brief periods of integrated coherent neuronal activity, lasting only fractions of seconds.

Handling anisotropic conductivities in the EEG forward problem with a symmetric formulation

The electroencephalography (EEG) forward problem, the computation of the electric potential generated by a known electric current source configuration in the brain, is a key step of EEG source analysis. In this problem, it is often desired to model the anisotropic conductivity profiles of the skull and of the white matter. These profiles, however, cannot be handled by standard surface integral formulations and the use of volume finite elements is required. Leveraging on the representation theorem using an anisotropic fundamental solution, this paper proposes a modified symmetric formulation for solving the EEG forward problem by a surface integral equation which can take into account anisotropic conductivity profiles. A set of numerical results is presented to corroborate theoretical treatments and to show the impact of the proposed approach on both canonical and real case scenarios.

T155. Brain reactions following the use of robotic hand prosthesis in human amputees

Introduction The amputation of a hand is followed by a cascade of plastic changes into the motor and somatosensory pathways in the CNS; such changes are probably contributing to the Phantom limb syndrome (PLS), a distressing situation affecting the majority of amputees, and could be modulated providing sensory input to the stump or amputation zone. Very few information is reported in literature explaining how plastic changes evolve during the use of prosthesis, particularly the new sensorized hand prosthesis, which provide the use of intraneural electrodes able to restore a “natural” somatosensory feedback. In this line, we performed a clinical study with four trans-radial amputees, who received an implant of four transversal intrafascicular electrodes in median and ulnar nerves controlling hand prosthesis, with the aim to investigate cortical changes occurring during trainings of different durations (4–24 weeks). Methods All patients underwent to a multimodal evaluation performed before and after the use of neural-interfaced hand prosthesis, including TMS examination with motor maps production, SEPs recording, EEG source analysis, connectivity analysis and fMRI. Standard pain questionnaires (VAS, McGill and PPI scores) were used to assess the course of PLS. Results After the interventions, the following findings were observed: – TMS motor maps showed a reduction and a partial reversal of an initial inter-hemispheric asymmetry (because of an enlargement of the excitable area on the hemisphere contralateral to the stump); – SEPs based on N20 component showed a modification of the cortical topography in the central-parietal areas contralateral to the amputation site; – EEG source analysis showed a widespread reduction of delta activity and a significant increase of alpha activity in the central-parietal areas in both hemispheres; – According to the graph theory, the analysis of the sensory-motor network showed in both hemispheres a significant increase of the Characteristic Path Length and a significant decrease of Small World Index in the alpha band; – fMRI showed a decrease in activation of supplementary and premotor areas with a prominent and more selective activation of the M1 area; – All the patients experiencend a decrease of PLP. Conclusion Our data provides a direct and unique evidence of brain plastic changes following the restoration of somatosensory feedback from a missing limb. We observed changes in the cortical organization towards a more physiological state in all the four patients during an extensive use of robotic hand. We associated these brain reactions to a decrease in pain intensity and an improvement of prosthesis control performance. The prosthetic system could induce reduction in aberrant plasticity and promote ‘good’ “training-induced” plasticity. Neurophysiological and neuroimaging analysis could be seen as potential biomarker of the level of CNS reorganization and therefore used as parameter of the effectiveness achieved by the prosthetic device and its interfaces.

Time Course of Brain Network Reconfiguration Supporting Inhibitory Control.

Hemodynamic research has recently clarified key nodes and links in brain networks implementing inhibitory control. Although fMRI methods are optimized for identifying the structure of brain networks, the relatively slow temporal course of fMRI limits the ability to characterize network operation. The latter is crucial for developing a mechanistic understanding of how brain networks shift dynamically to support inhibitory control. To address this critical gap, we applied spectrally resolved Granger causality (GC) and random forest machine learning tools to human EEG data in two large samples of adults (test sample n = 96, replication sample n = 237, total N = 333, both sexes) who performed a color-word Stroop task. Time-frequency analysis confirmed that recruitment of inhibitory control accompanied by slower behavioral responses was related to changes in theta and alpha/beta power. GC analyses revealed directionally asymmetric exchanges within frontal and between frontal and parietal brain areas: top-down influence of superior frontal gyrus (SFG) over both dorsal ACC (dACC) and inferior frontal gyrus (IFG), dACC control over middle frontal gyrus (MFG), and frontal-parietal exchanges (IFG, precuneus, MFG). Predictive analytics confirmed a combination of behavioral and brain-derived variables as the best set of predictors of inhibitory control demands, with SFG theta bearing higher classification importance than dACC theta and posterior beta tracking the onset of behavioral response. The present results provide mechanistic insight into the biological implementation of a psychological phenomenon: inhibitory control is implemented by dynamic routing processes during which the target response is upregulated via theta-mediated effective connectivity within key PFC nodes and via beta-mediated motor preparation.SIGNIFICANCE STATEMENT Hemodynamic neuroimaging research has recently clarified regional structures in brain networks supporting inhibitory control. However, due to inherent methodological constraints, much of this research has been unable to characterize the temporal dynamics of such networks (e.g., direction of information flow between nodes). Guided by fMRI research identifying the structure of brain networks supporting inhibitory control, results of EEG source analysis in a test sample (n = 96) and replication sample (n = 237) using effective connectivity and predictive analytics strategies advance a model of inhibitory control by characterizing the precise temporal dynamics by which this network operates and exemplify an approach by which mechanistic models can be developed for other key psychological processes.