Quanying Liu

Estimating a neutral reference for electroencephalographic recordings: the importance of using a high-density montage and a realistic head model

Abstract Objective. In electroencephalography (EEG) measurements, the signal of each recording electrode is contrasted with a reference electrode or a combination of electrodes. The estimation of a neutral reference is a long-standing issue in EEG data analysis, which has motivated the proposal of different re-referencing methods, among which linked-mastoid re-referencing (LMR), average re-referencing (AR) and reference electrode standardization technique (REST). In this study we quantitatively assessed the extent to which the use of a high-density montage and a realistic head model can impact on the optimal estimation of a neutral reference for EEG recordings. Approach. Using simulated recordings generated by projecting specific source activity over the sensors, we assessed to what extent AR, REST and LMR may distort the scalp topography. We examined the impact electrode coverage has on AR and REST, and how accurate the REST reconstruction is for realistic and less realistic (three-layer and single-layer spherical) head models, and with possible uncertainty in the electrode positions. We assessed LMR, AR and REST also in the presence of typical EEG artifacts that are mixed in the recordings. Finally, we applied them to real EEG data collected in a target detection experiment to corroborate our findings on simulated data. Main results. Both AR and REST have relatively low reconstruction errors compared to LMR, and that REST is less sensitive than AR and LMR to artifacts mixed in the EEG data. For both AR and REST, high electrode density yields low re-referencing reconstruction errors. A realistic head model is critical for REST, leading to a more accurate estimate of a neutral reference compared to spherical head models. With a low-density montage, REST shows a more reliable reconstruction than AR either with a realistic or a three-layer spherical head model. Conversely, with a high-density montage AR yields better results unless precise information on electrode positions is available. Significance. Our study is the first to quantitatively assess the performance of EEG re-referencing techniques in relation to the use of a high-density montage and a realistic head model. We hope our study will help researchers in the choice of the most effective re-referencing approach for their EEG studies.

Concurrent tACS-fMRI Reveals Causal Influence of Power Synchronized Neural Activity on Resting State fMRI Connectivity

Resting state fMRI (rs-fMRI) is commonly used to study the brains intrinsic neural coupling, which reveals specific spatiotemporal patterns in the form of resting state networks (RSNs). It has been hypothesized that slow rs-fMRI oscillations (<0.1 Hz) are driven by underlying electrophysiological rhythms that typically occur at much faster timescales (>5 Hz); however, causal evidence for this relationship is currently lacking. Here we measured rs-fMRI in humans while applying transcranial alternating current stimulation (tACS) to entrain brain rhythms in left and right sensorimotor cortices. The two driving tACS signals were tailored to the individuals α rhythm (8–12 Hz) and fluctuated in amplitude according to a 1 Hz power envelope. We entrained the left versus right hemisphere in accordance to two different coupling modes where either α oscillations were synchronized between hemispheres (phase-synchronized tACS) or the slower oscillating power envelopes (power-synchronized tACS). Power-synchronized tACS significantly increased rs-fMRI connectivity within the stimulated RSN compared with phase-synchronized or no tACS. This effect outlasted the stimulation period and tended to be more effective in individuals who exhibited a naturally weak interhemispheric coupling. Using this novel approach, our data provide causal evidence that synchronized power fluctuations contribute to the formation of fMRI-based RSNs. Moreover, our findings demonstrate that the brains intrinsic coupling at rest can be selectively modulated by choosing appropriate tACS signals, which could lead to new interventions for patients with altered rs-fMRI connectivity. SIGNIFICANCE STATEMENT Resting state fMRI (rs-fMRI) has become an important tool to estimate brain connectivity. However, relatively little is known about how slow hemodynamic oscillations measured with fMRI relate to electrophysiological processes. It was suggested that slowly fluctuating power envelopes of electrophysiological signals synchronize across brain areas and that the topography of this activity is spatially correlated to resting state networks derived from rs-fMRI. Here we take a novel approach to address this problem and establish a causal link between the power fluctuations of electrophysiological signals and rs-fMRI via a new neuromodulation paradigm, which exploits these power synchronization mechanisms. These novel mechanistic insights bridge different scientific domains and are of broad interest to researchers in the fields of Medical Imaging, Neuroscience, Physiology, and Psychology.

Detecting large-scale networks in the human brain using high-density electroencephalography: Imaging Brain Networks with High Density EEG

High‐density electroencephalography (hdEEG) is an emerging brain imaging technique that can be used to investigate fast dynamics of electrical activity in the healthy and the diseased human brain. Its applications are however currently limited by a number of methodological issues, among which the difficulty in obtaining accurate source localizations. In particular, these issues have so far prevented EEG studies from reporting brain networks similar to those previously detected by functional magnetic resonance imaging (fMRI). Here, we report for the first time a robust detection of brain networks from resting state (256‐channel) hdEEG recordings. Specifically, we obtained 14 networks previously described in fMRI studies by means of realistic 12‐layer head models and exact low‐resolution brain electromagnetic tomography (eLORETA) source localization, together with independent component analysis (ICA) for functional connectivity analysis. Our analyses revealed three important methodological aspects. First, brain network reconstruction can be improved by performing source localization using the gray matter as source space, instead of the whole brain. Second, conducting EEG connectivity analyses in individual space rather than on concatenated datasets may be preferable, as it permits to incorporate realistic information on head modeling and electrode positioning. Third, the use of a wide frequency band leads to an unbiased and generally accurate reconstruction of several network maps, whereas filtering data in a narrow frequency band may enhance the detection of specific networks and penalize that of others. We hope that our methodological work will contribute to rise of hdEEG as a powerful tool for brain research. Hum Brain Mapp 38:4631–4643, 2017.

Neural Correlates of Drug-Related Attentional Bias in Heroin Dependence

The attention of drug-dependent persons tends to be captured by stimuli associated with drug consumption. This involuntary cognitive process is considered as attentional bias (AB). AB has been hypothesized to have causal effects on drug abuse and drug relapse, but its underlying neural mechanisms are still unclear. This study investigated the neural basis of AB in abstinent heroin addicts (AHAs), combining event-related potential (ERP) analysis and source localization techniques. Electroencephalography data were collected in 21 abstinent heroin addicts and 24 age- and gender-matched healthy controls (HCs) during a dot-probe task. In the task, a pair of drug-related image and neutral image was presented randomly in left and right side of the cross fixation, followed by a dot probe replacing one of the images. Behaviorally, AHAs had shorter reaction times (RTs) for the congruent condition compared to the incongruent condition, whereas this was not the case in the HCs. This finding demonstrated the presence of AB towards drug cues in AHAs. Furthermore, the image-evoked ERPs in AHAs had significant shorter P1 latency compared to HCs, as well as larger N1, N2, and P2 amplitude, suggesting that drug-related stimuli might capture attention early and overall require more attentional resources in AHAs. The target-related P3 had significantly shorter latency and lower amplitude in the congruent than incongruent condition in AHAs compared to HCs. Moreover, source localization of ERP components revealed increased activity for AHAs as compared to HCs in the dorsal posterior cingulate cortex (dPCC), superior parietal lobule and inferior frontal gyrus (IFG) for image-elicited responses, and decreased activity in the occipital and the medial parietal lobes for target-elicited responses. Overall, the results of our study confirmed that AHAs may exhibit AB in drug-related contexts, and suggested that the bias might be related to an abnormal neural activity, both in early and late attention processing stages.

Automated detection and labeling of high-density EEG electrodes from structural MR images

OBJECTIVE Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. APPROACH Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. MAIN RESULTS Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. SIGNIFICANCE We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high-density EEG as a brain-imaging tool.

Detecting Large-Scale Brain Networks Using EEG: Impact of Electrode Density, Head Modeling and Source Localization

Resting state networks (RSNs) in the human brain were recently detected using high-density electroencephalography (hdEEG). This was done by using an advanced analysis workflow to estimate neural signals in the cortex and to assess functional connectivity (FC) between distant cortical regions. FC analyses were conducted either using temporal (tICA) or spatial independent component analysis (sICA). Notably, EEG-RSNs obtained with sICA were very similar to RSNs retrieved with sICA from functional magnetic resonance imaging data. It still remains to be clarified, however, what technological aspects of hdEEG acquisition and analysis primarily influence this correspondence. Here we examined to what extent the detection of EEG-RSN maps by sICA depends on the electrode density, the accuracy of the head model, and the source localization algorithm employed. Our analyses revealed that the collection of EEG data using a high-density montage is crucial for RSN detection by sICA, but also the use of appropriate methods for head modeling and source localization have a substantial effect on RSN reconstruction. Overall, our results confirm the potential of hdEEG for mapping the functional architecture of the human brain, and highlight at the same time the interplay between acquisition technology and innovative solutions in data analysis.

Nonlinear Dynamic Complexity and Sources of Resting-state EEG in Abstinent Heroin Addicts

It has been reported that chronic heroin intake induces both structural and functional changes in human brain; however, few studies have investigated the carry-over adverse effects on brain after heroin withdrawal. In this paper, we examined the neurophysiological differences between the abstinent heroin addicts (AHAs) and healthy controls (HCs) using nonlinear dynamic analysis and source localization analysis in resting-state electroencephalogram (EEG) data; 5 min resting EEG data from 20 AHAs and twenty age-, education-, and gender-matched HCs were recorded using 64 electrodes. The results of nonlinear characteristics (e.g., the correlation dimension, Kolmogorov entropy, and Lempel-Ziv complexity) showed that the EEG signals in alpha band from AHAs were significantly more irregular. Moreover, the source localization results confirmed the neuronal activities in alpha band in AHAs were significantly weaker in parietal lobe (BA3 and BA7), frontal lobe (BA4 and BA6), and limbic lobe (BA24). Together, our analysis at both the sensor level and source level suggested the functional abnormalities in the brain during heroin abstinence, in particular for the neuronal oscillations in alpha band.

Heart–Brain Interactions in the MR Environment: Characterization of the Ballistocardiogram in EEG Signals Collected During Simultaneous fMRI

The ballistocardiographic (BCG) artifact is linked to cardiac activity and occurs in electroencephalographic (EEG) recordings acquired inside the magnetic resonance (MR) environment. Its variability in terms of amplitude, waveform shape and spatial distribution over subject’s scalp makes its attenuation a challenging task. In this study, we aimed to provide a detailed characterization of the BCG properties, including its temporal dependency on cardiac events and its spatio-temporal dynamics. To this end, we used high-density EEG data acquired during simultaneous functional MR imaging in six healthy volunteers. First, we investigated the relationship between cardiac activity and BCG occurrences in the EEG recordings. We observed large variability in the delay between ECG and subsequent BCG events (ECG–BCG delay) across subjects and non-negligible epoch-by-epoch variations at the single subject level. The inspection of spatial–temporal variations revealed a prominent non-stationarity of the BCG signal. We identified five main BCG waves, which were common across subjects. Principal component analysis revealed two spatially distinct patterns to explain most of the variance (85% in total). These components are possibly related to head rotation and pulse-driven scalp expansion, respectively. Our results may inspire the development of novel, more effective methods for the removal of the BCG, capable of isolating and attenuating artifact occurrences while preserving true neuronal activity.

Abstinent Heroin Addicts Tend to Take Risks: ERP and Source Localization

Abnormal decision making is a behavioral characteristic of drug addiction. Indeed, drug addicts prefer immediate rewards at the expense of future interests. Assessing the neurocognitive basis of decision-making related to drug dependence, combining event-related potential (ERP) analysis and source localization techniques, may provide new insights into understanding decision-making deficits in drug addicts and further guide withdrawal treatment. In this study, EEG was performed in 20 abstinent heroin addicts (AHAs) and 20 age-, education- and gender-matched healthy controls (HCs) while they participated in a simple two-choice gambling task (99 vs. 9). Our behavioral results showed that AHAs tend to select higher-risk choices compared with HCs (i.e., more “99” choices than “9”). ERP results showed that right hemisphere preponderance of stimulus-preceding negativity was disrupted in AHAs, but not in HCs. Feedback-related negativity of difference wave was higher in AHAs than HCs, with the P300 amplitude associated with risk magnitude and valence. Using source localization that allows identification of abnormal brain activity in consequential cognitive stages, including the reward expectation and outcome evaluation stages, we found abnormalities in both behavioral and neural responses on gambling in AHAs. Taken together, our findings suggest AHAs have risk-prone tendency and dysfunction in adaptive decision making, since they continue to choose risky options even after accruing considerable negative scores, and fail to shift to a safer strategy to avoid risk. Such abnormal decision-making bias to risk and immediate reward seeking may be accompanied by abnormal reward expectation and evaluation in AHAs, which explains their high risk-seeking and impulsivity.

A different state of mind: neural activity related to volitionally up- versus downregulating cortical excitability

To date there exists no reliable method to non-invasively upregulate or downregulate the state of the resting motor system over a large dynamic range. Here we show that an operant conditioning paradigm which provides neurofeedback of the size of motor evoked potentials (MEPs) in response to transcranial magnetic stimulation (TMS), enables participants to self-modulate their own brain state. Following training, participants were able to robustly increase (by 83.8%) and decrease (by 30.6%) their MEP amplitudes. This volitional up- versus downregulation of corticomotor excitability caused an increase of late-cortical disinhibition (LCD), a read-out of presynaptic GABAB disinhibition which was accompanied by an increase of gamma and a decrease of alpha oscillations in the trained hemisphere. This approach paves the way for future investigations into how altered brain state influences motor neurophysiology and recovery of function in a neurorehabilitation context.