Guofa Shou

Lasting Modulation Effects of rTMS on Neural Activity and Connectivity as Revealed by Resting-State EEG

The long-lasting neuromodulatory effects of repetitive transcranial magnetic stimulation (rTMS) are of great interest for therapeutic applications in various neurological and psychiatric disorders, due to which functional connectivity among brain regions is profoundly disturbed. Classic TMS studies selectively alter neural activity in specific brain regions and observe neural activity changes on nonperturbed areas to infer underlying connectivity and its changes. Less has been indicated in direct measures of functional connectivity and/or neural network and on how connectivity/network alterations occur. Here, we developed a novel analysis framework to directly investigate both neural activity and connectivity changes induced by rTMS from resting-state EEG (rsEEG) acquired in a group of subjects with a chronic disorder of imbalance, known as the mal de debarquement syndrome (MdDS). Resting-state activity in multiple functional brain areas was identified through a data-driven blind source separation analysis on rsEEG data, and the connectivity among them was characterized using a phase synchronization measure. Our study revealed that there were significant long-lasting changes in resting-state neural activity, in theta, low alpha, and high alpha bands and neural networks in theta, low alpha, high alpha and beta bands, over broad cortical areas 4 to 5 h after the last application of rTMS in a consecutive five-day protocol. Our results of rsEEG connectivity further indicated that the changes, mainly in the alpha band, over the parietal and occipital cortices from pre- to post-TMS sessions were significantly correlated, in both magnitude and direction, to symptom changes in this group of subjects with MdDS. This connectivity measure not only suggested that rTMS can generate positive treatment effects in MdDS patients, but also revealed new potential targets for future therapeutic trials to improve treatment effects. It is promising that the new connectivity measure from rsEEG can be used to understand the variability in treatment response to rTMS in brain disorders with impaired functional connectivity and, eventually, to determine individually tailored stimulation parameters and treatment procedures in rTMS.

Probing neural activations from continuous EEG in a real-world task: Time-frequency independent component analysis

It is of fundamental significance to study human brain functions using neuroimaging technologies, such as electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI), in real-world tasks. The present study explores the feasibility of using EEG to identify networked brain activations when subjects perform a realistic task. To robustly identify physiologically plausible EEG patterns related to brain activations involved in the task, a novel data-driven method, i.e., time-frequency independent component analysis (tfICA), is developed to analyze high-density EEG data, which combines the time-frequency analysis and complex-valued ICA method. Six classes of independent components (ICs) of various spatio-temporal-spectral patterns were identified across subjects, relating to frontal, motor, premotor, supplementary motor, secondary somatosensory, and occipital cortices, which suggest a networked brain activation involving visual perception and processing, movement planning and execution, working memory, performance monitoring, and decision making to accomplish the task. Our results indicate that temporal patterns of these ICs are consistent, show causal relationship among them, and of significant correlation to behavioral performance data recorded in same task sessions. Furthermore, the time-on-task effect that indicates the phenomenon of mental fatigue in sustained tasks for a long duration (i.e., 1h) was observed. The present study demonstrates the capability of the tfICA method in distinguishing various brain processes from continuous EEG data obtained in a realistic task and it is thus promising to address real-world problems, such as time-on-task fatigue.

Frontal theta EEG dynamics in a real-world air traffic control task

Mental workload and time-on-task effect are two major factors expediting fatigue progress, which leads to performance decline and/or failure in real-world tasks. In the present study, electroencephalography (EEG) is applied to study mental fatigue development during an air traffic control (ATC) task. Specifically, the frontal theta EEG dynamics are firstly dissolved into a unique frontal independent component (IC) through a novel time-frequency independent component analysis (tfICA) method. Then the temporal fluctuations of the identified frontal ICs every minute are compared to workload (reflected by number of clicks per minute) and time-on-task effect by correlational analysis and linear regression analysis. It is observed that the frontal theta activity significantly increase with workload augment and time-on-task. The present study demonstrates that the frontal theta EEG activity identified by tfICA method is a sensitive and reliable metric to assess mental workload and time-on-task effect in a real-world task, i.e., ATC task, at the resolution of minute(s).

Changes of symptom and EEG in mal de debarquement syndrome patients after repetitive transcranial magnetic stimulation over bilateral prefrontal cortex: A pilot study

Mal de debarquement syndrome (MdDS) is a chronic disorder of imbalance characterized by a feeling of rocking and swaying. The medical treatment for MdDS is still limited. Motivated by our previous pilot study that demonstrates the promising clinical efficacy of repetitive transcranial stimulation (rTMS) in MdDS patients, a novel rTMS paradigm, i.e., 1 Hz stimulation over ipsilateral dorsal lateral prefrontal cortex (DLPFC) with respect to the dominant hand followed by 10 Hz stimulation over contralateral DLPFC, was proposed and conducted in MdDS in the present study. To evaluate the potential efficacy, we examined the changes before and after rTMS in both subjective reported symptom using visual analogue scale (VAS) and direct brain activity in resting state electroencephalography (rsEEG). To disentangle activity from distinct brain substrates and/or local networks in rsEEG signals, a group-wise independent component analysis was employed and the corresponding spectral power changes were examined in the identified components. In general, reduction in rocking sensation was reported in five of ten subjects (with dramatic reductions (changes > 30) in three subjects) after rTMS using the present paradigm, while no changes and slight increases in rocking sensation were reported in the remaining subjects. In rsEEG, significant elevated spectral powers in low frequency bands (i.e., theta and alpha) over broad areas of occipital, parietal, motor, and prefrontal cortices were induced by rTMS, reflecting the enhancement of cortical inhibition over these areas. Meanwhile, the significant correlations between changes in rsEEG and VAS scores were detected in the high frequency bands (i.e., high alpha and beta) over posterior parietal and left visual areas, reflecting the suppression of spatial information processing. Therefore, the present findings demonstrate the promising clinical efficacy of a new rTMS paradigm for MdDS, and suggest its merit for further studies in more patients.

Ongoing EEG oscillatory dynamics suggesting evolution of mental fatigue in a color-word matching stroop task

Mental fatigue would develop when performing a tedious cognitive task for a long time. The present study evaluated the evolution of mental fatigue in a Stroop task using electroencephalography (EEG) with independent component analysis (ICA) method. Specifically, two aspects of mental fatigue, i.e., mental effort and mental engagement, were tracked by the ongoing oscillatory dynamics from frontal independent component (IC) related to cognitive control and posterior ICs related to attention. While behavioral data, i.e., number of errors and response times, indicated complicated patterns along the time, increasing patterns were consistently observed in the theta band activity of the frontal IC and in the alpha band activity of the posterior ICs as the time on task. These patterns are indicative of a gradual impairment of mental effort and mental engagement (or sustained attention), which can be explained by the evolution of mental fatigue. The present results demonstrate that ongoing EEG obtained from ICA is a sensitive and reliable mean to measure mental fatigue.

Neural markers for immediate performance accuracy in a Stroop color-word matching task: An event-related potentials analysis

The present study examined the neural markers measured in event-related potentials (ERPs) for immediate performance accuracy during a cognitive task with less conflict, i.e., a Stroop color-word matching task, in which participants were required to judge the congruency of two feature dimensions of a stimulus. In an effort to make ERP components more specific to distinct underlying neural substrates, recorded EEG signals were firstly dissolved into multiple independent components (ICs) using independent component analysis (ICA). Thereafter, individual ICs with prominent sensory- or cognitive-related ERP components were selected to separately reconstruct scalp EEG signals at representative channels, from which ERP waveforms were built, respectively. Statistical comparisons on amplitudes of stimulus-locked ERP components, i.e., prefrontal P2 and N2, parietal P3, bilateral occipital P1 and N1, revealed significant reduced P3 amplitude in error trials than in correct trials. In addition, significant evident ERN was also observed in error trials but not in correct trials. Considering the temporal locus of semantic conflict in the present task, we concluded that reduced P3 amplitude in error trials reflect impaired resolving process of semantic conflict, which further lead to a performance error in the Stroop color-word matching task.

Investigation of independent components based EEG metrics for mental fatigue in simulated ATC task

Changes in the cortical brain activity due to mental fatigue have to be assessed in task demanding operating environments for the purpose of safety. In this investigation, ten subjects performed a computer-simulated air traffic control (ATC) task, while electroencephalogram (EEG) data were collected. EEG data were decomposed into brain activity related independent components (ICs) using a group-level independent component analysis (gICA). Development of mental fatigue was examined at two different time windows by examining spectral powers from identified ICs and was evaluated against behavioral data. The present results suggest that, as the time on the task increases, significant changes in EEG spectral powers can be observed in identified ICs, which indicates the evolution of mental fatigue. Potential applications of such research include assessment of mental effectiveness of ATC operators who are involved in prolonged and demanding task assignments.

A comparison study of nonlinear and linear metrics in probing intrinsic brain networks from EEG data

Functional intrinsic brain networks (IBNs) has been widely studied due to its close relationship to different brain functions and diseases. In these studies, linear metrics, e.g., correlation, have been commonly used in identifying brain networks, especially on functional magnetic resonance imaging (fMRI) data. However, nonlinear mechanism is believed to exist in forming brain networks. In the present study, we investigated the performance of a nonlinear metric, i.e., phase coherence, in probing brain networks, as compared with a linear metric, i.e., power correlation. Specifically, individual IBNs were firstly obtained by a time-frequency independent component analysis (tfICA), and then the interaction among them were probed using either phase coherence (inter-component phase coherence, ICPC) or power correlation coefficient (PCC). We examined them using high-density resting-state electroencephalography (EEG) data from a group of patients with a balance disorder who received repetitive transcranial magnetic stimulation (rTMS) treatments. The results indicated that the use of ICPC indicated more detections of significant connectivity crossing multiple brain regions in various frequency bands than PCC. Moreover, consistent treatment-related network changes, as compared with previous neuroimaging findings, in this brain disorder were more successfully detected with ICPC. Therefore, it is important to use nonlinear metric in characterizing interactions between different brain regions and IBNs.

ICA-Derived EEG Correlates to Mental Fatigue, Effort, and Workload in a Realistically Simulated Air Traffic Control Task

Electroencephalograph (EEG) has been increasingly studied to identify distinct mental factors when persons perform cognitively demanding tasks. However, most of these studies examined EEG correlates at channel domain, which suffers the limitation that EEG signals are the mixture of multiple underlying neuronal sources due to the volume conduction effect. Moreover, few studies have been conducted in real-world tasks. To precisely probe EEG correlates with specific neural substrates to mental factors in real-world tasks, the present study examined EEG correlates to three mental factors, i.e., mental fatigue [also known as time-on-task (TOT) effect], workload and effort, in EEG component signals, which were obtained using an independent component analysis (ICA) on high-density EEG data. EEG data were recorded when subjects performed a realistically simulated air traffic control (ATC) task for 2 h. Five EEG independent component (IC) signals that were associated with specific neural substrates (i.e., the frontal, central medial, motor, parietal, occipital areas) were identified. Their spectral powers at their corresponding dominant bands, i.e., the theta power of the frontal IC and the alpha power of the other four ICs, were detected to be correlated to mental workload and effort levels, measured by behavioral metrics. Meanwhile, a linear regression analysis indicated that spectral powers at five ICs significantly increased with TOT. These findings indicated that different levels of mental factors can be sensitively reflected in EEG signals associated with various brain functions, including visual perception, cognitive processing, and motor outputs, in real-world tasks. These results can potentially aid in the development of efficient operational interfaces to ensure productivity and safety in ATC and beyond.

Optimizing rTMS treatment of a balance disorder with EEG neural synchrony and functional connectivity

Repetitive transcranial magnetic stimulation (rTMS) has been increasingly used for its potential treatment effects across diverse mental disorders. However, the treatment effect is elusive and the rate of positive responders is not high, which make it in great demand of optimizing rTMS protocols to improve the treatment effects and the rate. In this regard, neural activity guided optimization has indicated great potential in several neuroimaging studies. In this paper, we present our ongoing work on optimizing rTMS treatment of a balance disorder, i.e., Mal de Debarquement syndrome (MdDS), by investigating treatment-related EEG neural synchrony and functional connectivity changes. Motivated by our previous pilot study of rTMS on MdDS, we firstly applied a bilateral dorsolateral prefrontal cortex (DLPFC) rTMS protocol to evaluate its efficacy and the treatment-related neural responses via an independent component analysis (ICA)-based framework. Thereafter, guided by identified EEG neural synchrony and functional connectivity patterns, we proposed three potential stimulation targets covering posterior nodes of the default mode network (DMN), and implemented a new rTMS protocol by stimulating the target with the great symptoms relief. The preliminary clinical response data has indicated that the new rTMS protocol significantly increase the rate of positive responders and the degrees of the improvement. The present study demonstrates that it is promising to integrate EEG neural synchrony and functional connectivity into the optimization of rTMS protocols for different mental disorders.