Hemodynamic and electrophysiological responses of the human amygdala during face imitation-a study using functional MRI and intracranial EEG.

The involvement of the human amygdala in facial mimicry remains a matter of debate. We investigated neural activity in the human amygdala during a task in which an imitation task was separated in time from an observation task involving facial expressions. Neural activity in the amygdala was measured using functional magnetic resonance imaging in 18 healthy individuals and using intracranial electroencephalogram in six medically refractory patients with epilepsy. The results of functional magnetic resonance imaging experiment showed that mimicry of negative and positive expressions activated the amygdala more than mimicry of non-emotional facial movements. In intracranial electroencephalogram experiment and time-frequency analysis, emotion-related activity of the amygdala during mimicry was observed as a significant neural oscillation in the high gamma band range. Furthermore, spectral event analysis of individual trial intracranial electroencephalogram data revealed that sustained oscillation of gamma band activity originated from an increased number and longer duration of neural events in the amygdala. Based on these findings, we conclude that during facial mimicry, visual information of expressions and feedback from facial movements are combined in the amygdalar nuclei. Considering the time difference of information approaching the amygdala, responses to facial movements are likely to modulate rather than initiate affective processing in human participants.

One out of ten independent components shows flipped polarity with poorer data quality: EEG database study.

Independent component analysis (ICA) is widely used today for scalp-recorded EEG analysis. One of the limitations of ICA-based analysis is polarity indeterminacy. It is not easy to find detailed documentations that explains engineering solutions of how the polarity indeterminacy is addressed in a given implementation. We investigated how it is implemented in the case of EEGLAB and also the relation between the outcome of the polarity determination and classification of independent components (ICs) in terms of the estimated nature of the sources (brain, muscle, eye, etc.) using an open database of n = 212 EEG dataset of resting state recordings. We found that (1) about 91% of ICs showed positive-dominant IC scalp topographies; (2) positive-dominant ICs were more associated with brain-originated signals; (3) positive-dominant ICs showed more radial (peaked at 10-30 degrees deviations from the radial axis) dipolar projection pattern with less residual variance from fitting the equivalent current dipole. In conclusion, using the EEGLAB's default ICA algorithm, one out of 10 ICs results in flipping its polarity to negative, which is associated with non-radial dipole orientation with higher residual variance. Thus, we determined EEGLAB biases toward positive polarity in decomposing high-quality brain ICs.

Uncovering spatiotemporal dynamics of the corticothalamic network at ictal onset.

OBJECTIVE: Although the clinical efficacy of deep brain stimulation targeting the anterior nucleus (AN) and centromedian nucleus (CM) of the thalamus has been actively investigated for the treatment of medication-resistant epilepsy, few studies have investigated dynamic ictal changes in corticothalamic connectivity in human electroencephalographic (EEG) recording. This study aims to establish the complex spatiotemporal dynamics of the ictal corticothalamic network associated with various seizure foci. METHODS: We analyzed 10 patients (aged 2.7-28.1 years) with medication-resistant focal epilepsy who underwent stereotactic EEG evaluation with thalamic sampling. We examined both undirected and directed connectivity, incorporating coherence and spectral Granger causality analysis (GCA) between the diverse seizure foci and thalamic nuclei (AN and CM) at ictal onset. RESULTS: In our analysis of 36 seizures, coherence between seizure onset and thalamic nuclei increased across all frequencies, especially in slower bands (delta, theta, alpha). GCA showed increased information flow from seizure onset to the thalamus across all frequency bands, but outflows from the thalamus were mainly in slower frequencies, particularly delta. In the subgroup analysis based on various seizure foci, the delta coherence showed a more pronounced increase at CM than at AN during frontal lobe seizures. Conversely, in limbic seizures, the delta coherence increase was greater at AN compared to CM. SIGNIFICANCE: It appears that the delta frequency plays a pivotal role in modulating the corticothalamic network during seizures. Our results underscore the significance of comprehending the spatiotemporal dynamics of the corticothalamic network at ictal onset, and this knowledge could guide personalized responsive neuromodulation treatment strategies.

Revisiting Polarity Indeterminacy of ICA-Decomposed ERPs and Scalp Topographies.

We propose an alternative method to align the polarities of independent components (ICs) for group-level IC cluster analysis. Current methods are presently limited in how indeterminacy of IC polarities is handled, as when multiplying a weight matrix to a time-series IC activation, the result from 1 × 1 and - 1 × - 1 are indistinguishable. We first clarify the EEGLAB's default solution and define it as the iterative correlation maximization as it maximizes the within-cluster correlations of the IC scalp topographies to the cluster mean. We then propose the covariance maximization method, which determines the polarity of ICs based on the sign of the largest eigenvalue of covariance matrix. We compared the two methods on datasets from a published visual event-related potential (ERP) study. The results were similar when both methods were applied to the IC scalp topographies. However, when the proposed method was applied to IC ERPs, the number of clusters that showed significant ERP amplitudes increased from 5 to 9 out of 9 due to minimization of within-cluster ERP amplitude cancellation. Our study confirm covariance maximization provides an alternative solution to post-ICA group-level analysis that can maximize sensitivity of IC ERPs.

Approaches for Hybrid Coregistration of Marker-Based and Markerless Coordinates Describing Complex Body/Object Interactions.

Full-body motion capture is essential for the study of body movement. Video-based, markerless, mocap systems are, in some cases, replacing marker-based systems, but hybrid systems are less explored. We develop methods for coregistration between 2D video and 3D marker positions when precise spatial relationships are not known a priori. We illustrate these methods on three-ball cascade juggling in which it was not possible to use marker-based tracking of the balls, and no tracking of the hands was possible due to occlusion. Using recorded video and motion capture, we aimed to transform 2D ball coordinates into 3D body space as well as recover details of hand motion. We proposed four linear coregistration methods that differ in how they optimize ball-motion constraints during hold and flight phases, using an initial estimate of hand position based on arm and wrist markers. We found that minimizing the error between ball and hand estimate was globally suboptimal, distorting ball flight trajectories. The best-performing method used gravitational constraints to transform vertical coordinates and ball-hold constraints to transform lateral coordinates. This method enabled an accurate description of ball flight as well as a reconstruction of wrist movements. We discuss these findings in the broader context of video/motion capture coregistration.

Age-Related Changes in Episodic Processing of Scenes: A Functional Activation and Connectivity Study.

The posterior-to-anterior shift in aging (PASA) effect is seen as a compensatory model that enables older adults to meet increased cognitive demands to perform comparably as their young counterparts. However, empirical support for the PASA effect investigating age-related changes in the inferior frontal gyrus (IFG), hippocampus, and parahippocampus has yet to be established. 33 older adults and 48 young adults were administered tasks sensitive to novelty and relational processing of indoor/outdoor scenes in a 3-Tesla MRI scanner. Functional activation and connectivity analyses were applied to examine the age-related changes on the IFG, hippocampus, and parahippocampus among low/high-performing older adults and young adults. Significant parahippocampal activation was generally found in both older (high-performing) and young adults for novelty and relational processing of scenes. Younger adults had significantly greater IFG and parahippocampal activation than older adults, and greater parahippocampal activation compared to low-performing older adults for relational processing-providing partial support for the PASA model. Observations of significant functional connectivity within the medial temporal lobe and greater negative left IFG-right hippocampus/parahippocampus functional connectivity for young compared to low-performing older adults for relational processing also supports the PASA effect partially.

High-power gamma-related delta phase alteration in schizophrenia patients at rest.

AIM: Information processing is supported by the cortico-cortical transmission of neural oscillations across brain regions. Recent studies have demonstrated that the rhythmic firing of neural populations is not random but is governed by interactions with other frequency bands. Specifically, the amplitude of gamma-band oscillations is associated with the phase of lower frequency oscillations in support of short and long-range communications among networks. This cross-frequency relation is thought to reflect the temporal coordination of neural communication. While schizophrenia patients show abnormal oscillatory responses across multiple frequencies at rest, it is unclear whether the functional relationships among frequency bands are intact. This study aimed to characterize the lower frequency (delta/theta, 1-8 Hz) phase and the amplitude of gamma oscillations in healthy subjects and schizophrenia patients at rest. METHODS: Low frequency-phase (delta- and theta- band) angles and gamma-band amplitude relationships were assessed in 142 schizophrenia patients and 128 healthy subjects. RESULTS: Significant low-frequency phase alteration related to high-power gamma was detected across broadly distributed scalp regions in both healthy subjects and patients. In patients, delta phase synchronization related to high-power gamma was significantly decreased at the frontocentral, right middle temporal, and left temporoparietal electrodes but significantly increased at the left parietal electrode. CONCLUSIONS: High-power gamma-related delta phase alteration may reflect a core pathophysiologic abnormality in schizophrenia. Data-driven measures of functional relationships among frequency bands may prove useful in the development of novel therapeutics. Future studies are needed to determine whether these alterations are specific to schizophrenia or appear in other neuropsychiatric patient populations.

Electroencephalography Reflects User Satisfaction in Controlling Robot Hand through Electromyographic Signals.

This study addresses time intervals during robot control that dominate user satisfaction and factors of robot movement that induce satisfaction. We designed a robot control system using electromyography signals. In each trial, participants were exposed to different experiences as the cutoff frequencies of a low-pass filter were changed. The participants attempted to grab a bottle by controlling a robot. They were asked to evaluate four indicators (stability, imitation, response time, and movement speed) and indicate their satisfaction at the end of each trial by completing a questionnaire. The electroencephalography signals of the participants were recorded while they controlled the robot and responded to the questionnaire. Two independent component clusters in the precuneus and postcentral gyrus were the most sensitive to subjective evaluations. For the moment that dominated satisfaction, we observed that brain activity exhibited significant differences in satisfaction not immediately after feeding an input but during the later stage. The other indicators exhibited independently significant patterns in event-related spectral perturbations. Comparing these indicators in a low-frequency band related to the satisfaction with imitation and movement speed, which had significant differences, revealed that imitation covered significant intervals in satisfaction. This implies that imitation was the most important contributing factor among the four indicators. Our results reveal that regardless of subjective satisfaction, objective performance evaluation might more fully reflect user satisfaction.

Electroencephalographic Study on Sensory Integration in Visually Induced Postural Sway.

A periodically reversing optic flow animation, experienced while standing, induces an involuntary sway termed visually induced postural sway (VIPS). Interestingly, VIPS is suppressed during light finger touch to a stationary object. Here, we explored whether VIPS is mediated by parietal field activity in the dorsal visual stream as well as by activity in early visual areas, as has been suggested. We performed a mobile brain/body imaging study using high-density electroencephalographic recording from human participants (11 men and 3 women) standing during exposure to periodically reversing optic flow with and without light finger touch to a stable surface. We also performed recording their visuo-postural tracking movements as a typical visually guided movement to explore differences of cortical process of VIPS from the voluntary visuomotor process involving the dorsal stream. In the visuo-postural tracking condition, the participants moved their center of pressure in time with a slowly oscillating (expanding, shrinking) target rectangle. Source-resolved results showed that alpha band (8-13 Hz) activity in the medial and right occipital cortex during VIPS was modulated by the direction and velocity of optic flow and increased significantly during light finger touch. However, source-resolved potentials from the parietal association cortex showed no such modulation. During voluntary postural sway with feedback (but no visual flow) in which the dorsal stream is involved, sensorimotor areas produced more theta band (4-7 Hz) and less beta band (14-35 Hz) activity than during involuntary VIPS. These results suggest that VIPS involves cortical field dynamic changes in the early visual cortex rather than in the posterior parietal cortex of the visual dorsal stream.

Correcting whole-body motion capture data using rigid body transformation.

Using motion capture data as a part of mobile brain-body imaging (MoBI) recording has been increasing. With minimal linear algebra background, this paper explains how the rigid body transformation can be a useful preprocessing step for denoising and missing marker recovery. Such a transformation can provide insight and necessary-and-sufficient solutions requiring no assumption other than the minimum number of markers present. First, a simulation test using the empirical datasets from the AudioMaze project published from this journal's same volume demonstrates theoretical accuracy. The simulation results show that the rigid-body method perfectly recovers missing markers on a rigid body if a minimum of three marker positions is available. Second, the same transformation is applied to the empirical dataset. Before preprocessing, the raw data show that 15-80% of data frames had all markers present for rigid-body defined body parts. After using the rigid-body correction, most body parts recovered full markers in 90-95% of the data frames. The result also suggests the necessity for performing across-trial corrections for within-participant (42% missing detected in one of the body parts) and across-participants (11% missing). The discussion section introduces a solution and a performance summary for non-rigid-body marker correction using a neural network. Data support that the rigid body transformation is an intuitive and powerful correction method necessary for preprocessing motion capture data for neurocognitive experiments. The supporting information section contains a URL link to Matlab code and example data.

Bayesian models of human navigation behaviour in an augmented reality audiomaze.

We investigated Bayesian modelling of human whole-body motion capture data recorded during an exploratory real-space navigation task in an "Audiomaze" environment (see the companion paper by Miyakoshi et al. in the same volume) to study the effect of map learning on navigation behaviour. There were three models, a feedback-only model (no map learning), a map resetting model (single-trial limited map learning), and a map updating model (map learning accumulated across three trials). The estimated behavioural variables included step sizes and turning angles. Results showed that the estimated step sizes were constantly more accurate using the map learning models than the feedback-only model. The same effect was confirmed for turning angle estimates, but only for data from the third trial. We interpreted these results as Bayesian evidence of human map learning on navigation behaviour. Furthermore, separating the participants into groups of egocentric and allocentric navigators revealed an advantage for the map updating model in estimating step sizes, but only for the allocentric navigators. This interaction indicated that the allocentric navigators may take more advantage of map learning than do egocentric navigators. We discuss relationships of these results to simultaneous localization and mapping (SLAM) problem.

The AudioMaze: An EEG and motion capture study of human spatial navigation in sparse augmented reality.

Spatial navigation is one of the fundamental cognitive functions central to survival in most animals. Studies in humans investigating the neural foundations of spatial navigation traditionally use stationary, desk-top protocols revealing the hippocampus, parahippocampal place area (PPA), and retrosplenial complex to be involved in navigation. However, brain dynamics, while freely navigating the real world remain poorly understood. To address this issue, we developed a novel paradigm, the AudioMaze, in which participants freely explore a room-sized virtual maze, while EEG is recorded synchronized to motion capture. Participants (n = 16) were blindfolded and explored different mazes, each in three successive trials, using their right hand as a probe to "feel" for virtual maze walls. When their hand "neared" a virtual wall, they received directional noise feedback. Evidence for spatial learning include shortening of time spent and an increase of movement velocity as the same maze was repeatedly explored. Theta-band EEG power in or near the right lingual gyrus, the posterior portion of the PPA, decreased across trials, potentially reflecting the spatial learning. Effective connectivity analysis revealed directed information flow from the lingual gyrus to the midcingulate cortex, which may indicate an updating process that integrates spatial information with future action. To conclude, we found behavioral evidence of navigational learning in a sparse-AR environment, and a neural correlate of navigational learning was found near the lingual gyrus.

Source decomposition of the frontocentral auditory steady-state gamma band response in schizophrenia patients and healthy subjects.

AIM: Gamma-band auditory steady-state response (ASSR) is a neurophysiologic index that is increasingly used as a translational biomarker in the development of treatments of neuropsychiatric disorders. While gamma-band ASSR is generated by distributed networks of highly interactive temporal and frontal cortical sources, the majority of human gamma-band ASSR studies using electroencephalography (EEG) highlight activity from only a single frontocentral scalp site, Fz, where responses tend to be largest and reductions in schizophrenia patients are most evident. However, no previous study has characterized the relative source contributions to Fz, which is a necessary step to improve the concordance of preclinical and clinical EEG studies. METHODS: A novel method to back-project the contributions of independent cortical source components was applied to assess the independent sources and their proportional contributions to Fz as well as source-resolved responses in 432 schizophrenia patients and 294 healthy subjects. RESULTS: Independent contributions of gamma-band ASSR to Fz were detected from orbitofrontal, bilateral superior/middle/inferior temporal, bilateral middle frontal, and posterior cingulate gyri in both groups. In contrast to expectations, the groups showed comparable source contribution weight to gamma-band ASSR at Fz. While gamma-band ASSR reductions at Fz were present in schizophrenia patients consistent with previous studies, no group differences in individual source-level responses to Fz were detected. CONCLUSION: Small differences in multiple independent sources summate to produce scalp-level differences at Fz. The identification of independent source contributions to a single scalp sensor represents a promising methodology for measuring dissociable and homologous biomarker targets in future translational studies.

Cross-Frequency Power-Power Coupling Analysis: A Useful Cross-Frequency Measure to Classify ICA-Decomposed EEG.

Magneto-/Electro-encephalography (M/EEG) commonly uses (fast) Fourier transformation to compute power spectral density (PSD). However, the resulting PSD plot lacks temporal information, making interpretation sometimes equivocal. For example, consider two different PSDs: a central parietal EEG PSD with twin peaks at 10 Hz and 20 Hz and a central parietal PSD with twin peaks at 10 Hz and 50 Hz. We can assume the first PSD shows a mu rhythm and the second harmonic; however, the latter PSD likely shows an alpha peak and an independent line noise. Without prior knowledge, however, the PSD alone cannot distinguish between the two cases. To address this limitation of PSD, we propose using cross-frequency power-power coupling (PPC) as a post-processing of independent component (IC) analysis (ICA) to distinguish brain components from muscle and environmental artifact sources. We conclude that post-ICA PPC analysis could serve as a new data-driven EEG classifier in M/EEG studies. For the reader's convenience, we offer a brief literature overview on the disparate use of PPC. The proposed cross-frequency power-power coupling analysis toolbox (PowPowCAT) is a free, open-source toolbox, which works as an EEGLAB extension.

Cross-correlation task-related component analysis (xTRCA) for enhancing evoked and induced responses of event-related potentials.

We propose an analysis method that extracts trial-reproducible (i.e., recurring) event-related spatiotemporal EEG patterns by optimizing a spatial filter as well as trial timings of task-related components in the time domain simultaneously in a unified manner. Event-related responses are broadly categorized into evoked and induced responses, but those are analyzed commonly in the time and the time-frequency domain, respectively. To facilitate a comparison of evoked and induced responses, a unified method for analyzing both evoked and induced responses is desired. Here we propose a method of cross-correlation task-related component analysis (xTRCA) as an extension of our previous method. xTRCA constructs a linear spatial filter and then optimizes trial timings of single trials based on trial reproducibility as an objective function. The spatial filter enhances event-related responses, and the temporal optimization compensates trial-by-trial latencies that are inherent to ERPs. We first applied xTRCA to synthetic data of induced responses whose phases varied from trial to trial, and found that xTRCA could realign the induced responses by compensating the phase differences. We then demonstrated with mismatch negativity data that xTRCA enhanced the event-related-potential waveform observed at a single channel. Finally, a classification accuracy was improved when trial timings were optimized by xTRCA, suggesting a practical application of the method for a brain computer interface. We conclude that xTRCA provides a unified framework to analyze and enhance event-related evoked and induced responses in the time domain by objectively maximizing trial reproducibility.

Modeling brain dynamic state changes with adaptive mixture independent component analysis.

There is a growing interest in neuroscience in assessing the continuous, endogenous, and nonstationary dynamics of brain network activity supporting the fluidity of human cognition and behavior. This non-stationarity may involve ever-changing formation and dissolution of active cortical sources and brain networks. However, unsupervised approaches to identify and model these changes in brain dynamics as continuous transitions between quasi-stable brain states using unlabeled, noninvasive recordings of brain activity have been limited. This study explores the use of adaptive mixture independent component analysis (AMICA) to model multichannel electroencephalographic (EEG) data with a set of ICA models, each of which decomposes an adaptively learned portion of the data into statistically independent sources. We first show that AMICA can segment simulated quasi-stationary EEG data and accurately identify ground-truth sources and source model transitions. Next, we demonstrate that AMICA decomposition, applied to 6-13 channel scalp recordings from the CAP Sleep Database, can characterize sleep stage dynamics, allowing 75% accuracy in identifying transitions between six sleep stages without use of EEG power spectra. Finally, applied to 30-channel data from subjects in a driving simulator, AMICA identifies models that account for EEG during faster and slower response to driving challenges, respectively. We show changes in relative probabilities of these models allow effective prediction of subject response speed and moment-by-moment characterization of state changes within single trials. AMICA thus provides a generic unsupervised approach to identifying and modeling changes in EEG dynamics. Applied to continuous, unlabeled multichannel data, AMICA may likely be used to detect and study any changes in cognitive states.

Phase-amplitude coupling between interictal high-frequency activity and slow waves in epilepsy surgery.

OBJECTIVE: We hypothesized that the modulation index (MI), a summary measure of the strength of phase-amplitude coupling between high-frequency activity (>150 Hz) and the phase of slow waves (3-4 Hz), would serve as a useful interictal biomarker for epilepsy presurgical evaluation. METHODS: We investigated 123 patients who underwent focal cortical resection following extraoperative electrocorticography recording and had at least 1 year of postoperative follow-up. We examined whether consideration of MI would improve the prediction of postoperative seizure outcome. MI was measured at each intracranial electrode site during interictal slow-wave sleep. We compared the accuracy of prediction of patients achieving International League Against Epilepsy class 1 outcome between the full multivariate logistic regression model incorporating MI in addition to conventional clinical, seizure onset zone (SOZ), and neuroimaging variables, and the reduced logistic regression model incorporating all variables other than MI. RESULTS: Ninety patients had class 1 outcome at the time of most recent follow-up (mean follow-up = 5.7 years). The full model had a noteworthy outcome predictive ability, as reflected by regression model fit R2 of 0.409 and area under the curve (AUC) of receiver operating characteristic plot of 0.838. Incomplete resection of SOZ (P < 0.001), larger number of antiepileptic drugs at the time of surgery (P = 0.007), and larger MI in nonresected tissues relative to that in resected tissue (P = 0.020) were independently associated with a reduced probability of class 1 outcome. The reduced model had a lower predictive ability as reflected by R2 of 0.266 and AUC of 0.767. Anatomical variability in MI existed among nonepileptic electrode sites, defined as those unaffected by magnetic resonance imaging lesion, SOZ, or interictal spike discharges. With MI adjusted for anatomical variability, the full model yielded the outcome predictive ability of R2 of 0.422, AUC of 0.844, and sensitivity/specificity of 0.86/0.76. SIGNIFICANCE: MI during interictal recording may provide useful information for the prediction of postoperative seizure outcome.

How the Non-attending Brain Hears Its Owner's Name.

We used functional magnetic resonance imaging to investigate how attended and non-attended hearing of a subject's own name (SON) captures his or her attention. It has been reported that SON presentation activates the medial prefrontal cortex (mPFC), which is considered to be the key region for self-recognition. However, it remains unclear whether non-attended SON presentation also activates the mPFC. We hypothesized that an attended SON should activate mPFC more than a non-attended SON. To test this hypothesis, we designed an experiment in which we manipulated the task-relevance of SON; in a name-detection task, SON was a target stimulus, whereas in a tone-judgment task, SON was unrelated to the task. In each condition, identical sets of sound stimuli were presented. SON activated mPFC in the name-detection task but not in the tone-judgment task, supporting our hypothesis. In contrast, non-attended SON activated midbrain reticular formation, thalamus, insula, auditory cortex, and precuneus. We interpreted these to be related to low-level, automatic SON detection. Thus, hearing one's own name in a non-attended condition does not primarily engage the mPFC, but recruits a cortico-subcortical auditory attention network; this may account for the oft-observed salience of SON.

ICA-derived cortical responses indexing rapid multi-feature auditory processing in six-month-old infants.

The abilities of infants to perceive basic acoustic differences, essential for language development, can be studied using auditory event-related potentials (ERPs). However, scalp-channel averaged ERPs sum volume-conducted contributions from many cortical areas, reducing the functional specificity and interpretability of channel-based ERP measures. This study represents the first attempt to investigate rapid auditory processing in infancy using independent component analysis (ICA), allowing exploration of source-resolved ERP dynamics and identification of ERP cortical generators. Here, we recorded 60-channel EEG data in 34 typically developing 6-month-old infants during a passive acoustic oddball paradigm presenting 'standard' tones interspersed with frequency- or duration-deviant tones. ICA decomposition was applied to single-subject EEG data. The best-fitting equivalent dipole or bilaterally symmetric dipole pair was then estimated for each resulting independent component (IC) process using a four-layer infant head model. Similar brain-source ICs were clustered across subjects. Results showed ERP contributions from auditory cortex and multiple extra-auditory cortical areas (often, bilaterally paired). Different cortical source combinations contributed to the frequency- and duration-deviant ERP peak sequences. For ICs in an ERP-dominant source cluster located in or near the mid-cingulate cortex, source-resolved frequency-deviant response N2 latency and P3 amplitude at 6 months-of-age predicted vocabulary size at 20 months-of-age. The same measures for scalp channel F6 (though not for other frontal channels) showed similar but weaker correlations. These results demonstrate the significant potential of ICA analyses to facilitate a deeper understanding of the neural substrates of infant sensory processing.

Neural Network Dynamics and Brain Oscillations Underlying Aberrant Inhibitory Control in Internet Addiction.

Previous studies have reported a role of alterations in the brain's inhibitory control mechanism in addiction. Mounting evidence from neuroimaging studies indicates that its key components can be evaluated with brain oscillations and connectivity during inhibitory control. In this study, we developed an internet-related stop-signal task with electroencephalography (EEG) signal recorded to investigate inhibitory control. Healthy controls and participants with Internet addiction were recruited to participate in the internet-related stop-signal task with 19-channel EEG signal recording, and the corresponding event-related potentials and spectral perturbations were analyzed. Brain effective connections were also evaluated using direct directed transfer function. The results showed that, relative to the healthy controls, participants with Internet addiction had increased Stop-P3 during inhibitory control, suggesting that they have an altered neural mechanism in impulsive control. Furthermore, participants with Internet addiction showed increased low-frequency synchronization and decreased alpha and beta desynchronization in the middle and right frontal regions compared to healthy controls. Aberrant brain effective connectivity was also observed, with increased occipital-parietal and intra-occipital connections, as well as decreased frontal-paracentral connection in participants with Internet addiction. These results suggest that physiological signals are essential in future implementations of cognitive assessment of Internet addiction to further investigate the underlying mechanisms and effective biomarkers.