Reference electrode standardization technique (REST) has been increasingly acknowledged and applied as a re-reference technique to transform an actual multi-channels recordings to approximately zero reference ones in electroencephalography/event-related potentials (EEG/ERPs) community around the world in recent years. However, a more easy-to-use toolbox for re-referencing scalp EEG data to zero reference is still lacking. Here, we have therefore developed two open-source MATLAB toolboxes for REST of scalp EEG. One version of REST is closely integrated into EEGLAB, which is a popular MATLAB toolbox for processing the EEG data; and another is a batch version to make it more convenient and efficient for experienced users. Both of them are designed to provide an easy-to-use for novice researchers and flexibility for experienced researchers. All versions of the REST toolboxes can be freely downloaded at http://www.neuro.uestc.edu.cn/rest/Down.html, and the detailed information including publications, comments and documents on REST can also be found from this website. An example of usage is given with comparative results of REST and average reference. We hope these user-friendly REST toolboxes could make the relatively novel technique of REST easier to study, especially for applications in various EEG studies.

Recently, scalp electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) multimodal fusion has been pursued in an effort to study human brain function and dysfunction to obtain more comprehensive information on brain activity in which the spatial and temporal resolutions are both satisfactory. However, a more flexible and easy-to-use toolbox for EEG–fMRI multimodal fusion is still lacking. In this study, we therefore developed a freely available and open-source MATLAB graphical user interface toolbox, known as the Neuroscience Information Toolbox (NIT), for EEG–fMRI multimodal fusion analysis. The NIT consists of three modules: (1) the fMRI module, which has batch fMRI preprocessing, nuisance signal removal, bandpass filtering, and calculation of resting-state measures; (2) the EEG module, which includes artifact removal, extracting EEG features (event onset, power, and amplitude), and marking interesting events; and (3) the fusion module, in which fMRI-informed EEG analysis and EEG-informed fMRI analysis are included. The NIT was designed to provide a convenient and easy-to-use toolbox for researchers, especially for novice users. The NIT can be downloaded for free at http://www.neuro.uestc.edu.cn/NIT.html, and detailed information, including the introduction of NIT, user’s manual and example data sets, can also be observed on this website. We hope that the NIT is a promising toolbox for exploring brain information in various EEG and fMRI studies.

Introduction: As aging attracted attention globally, revealing changes in brain function across the lifespan was largely concerned. In this study, we aimed to reveal the changes of functional networks of the brain (via local functional connectivity, local FC) in lifespan and explore the mechanism underlying them.Materials and Methods: A total of 523 healthy participants (258 males and 265 females) aged 18–88 years from part of the Cambridge Center for Ageing and Neuroscience (CamCAN) were involved in this study. Next, two data‐driven measures of local FC, local functional connectivity density (lFCD) and four‐dimensional spatial‐temporal consistency of local neural activity (FOCA), were calculated, and then, general linear models were used to assess the changes of them in lifespan.Results: Local functional connectivity (lFCD and FOCA) within visual networks (VN), sensorimotor network (SMN), and default mode network (DMN) decreased across the lifespan, while within basal ganglia network (BGN), local connectivity was increased across the lifespan. And, the fluid intelligence decreased within BGN while increased within VN, SMN, and DMN.Conclusion: These results might suggest that the decline of executive control and intrinsic cognitive ability in the aging population was related to the decline of functional connectivity in VN, SMN, and DMN. Meanwhile, BGN might play a regulatory role in the aging process to compensate for the dysfunction of other functional systems. Our findings may provide important neuroimaging evidence for exploring the brain functional mechanism in lifespan.

Objective . Despite electroencephalography (EEG) being a widely used neuroimaging technique with an excellent temporal resolution, in practice, the signals are heavily contaminated by artifacts masking responses of interest in an experiment. It is thus essential to guarantee a prompt and effective detection of artifacts that provides quantitative quality assessment (QA) on raw EEG data. This type of pipeline is crucial for large-scale EEG studies. However, current EEG QA studies are still limited. Approach . In this study, combined from a big data perspective, we therefore describe a quantitative signal quality assessment pipeline, a stable and general threshold-based QA pipeline that automatically integrates artifact detection and new QA measures to assess continuous resting-state raw EEG data. One simulation dataset and two resting-state EEG datasets from 42 healthy subjects and 983 clinical patients were utilized to calibrate the QA pipeline. Main Results . The results demonstrate that (1) the QA indices selected are sensitive: they almost strictly and linearly decrease as the noise level increases; (2) stable, replicable QA thresholds are valid for other experimental and clinical EEG datasets; and (3) use of the QA pipeline on these datasets reveals that high-frequency noises are the most common noises in EEG practice. The QA pipeline is also deployed in the WeBrain cloud platform ( https://webrain.uestc.edu.cn/ , the Chinese EEG Brain Consortium portal). Significance . These findings suggest that the proposed QA pipeline may be a stable and promising approach for quantitative EEG signal quality assessment in large-scale EEG studies.

Albeit the few resting-state fMRI neuroimaging studies in frontal lobe epilepsy (FLE) patients, these studies focused on functional connectivity. The aim of this current study was to examine the effective connectivity based on voxel-based morphometry in FLE patients. Resting-state structural and functional magnetic resonance imaging (fMRI) data were acquired from 19 FLE patients and 19 age and gender-matched healthy controls using the 3.0 Tesla magnetic resonance imaging (3.0 T MRI). The investigations were done by acquiring the structural information through voxel-based morphometry, then based on the seed obtained, Granger causality analysis was used to evaluate the causal flow of the designated seed to and from other significant voxels. Our results showed altered structural and effective connectivity. Compared with healthy controls, FLE patients showed reduced grey matter volume in bilateral putamen and right caudate as well as altered causality with increased, and decreased causal outflow from the right caudate (seed region) to inferior frontal gyrus-triangular, from bilateral putamen (seed regions) to right middle frontal gyrus and frontal gyrus medial-orbital representing the frontal executive areas, respectively. Also, significantly increased and decreased inflow from left calcarine to right caudate and from cerebellum_6 and vermis_6 to bilateral putamen, respectively. Moreover, we found that the causal alterations to and from the seed regions (from vermis_6 to right putamen and from left putamen to right middle frontal gyrus) negatively correlated with clinical scores (duration of epilepsy). The findings point to the impairment within the executive and motor-controlled system including the cerebellum, frontal, caudate and putamen regions in FLE patients. These results would therefore enhance our understanding of structural and effective mechanisms in FLE.

Goal: Idiopathic generalized epilepsy (IGE) represents generalized spike-wave discharges (GSWD) and distributed changes in thalamocortical circuit. The purpose of this paper is to investigate how the ongoing alpha oscillation acts upon the local temporal dynamics and spatial hyperconnectivity during epileptic state. Methods: We evaluated the spatiotemporal regulation of alpha oscillations in epileptic state based on simultaneous EEG-fMRI recordings in 45 IGE patients. The alpha-BOLD temporal consistency, as well as the effect of alpha power windows on dynamic functional connectivity strength (dFCS) was analyzed. Then, stable synchronization networks during GSWD were constructed, and the spatial covariation with alpha-based network integration was investigated. Results: Increased temporal covariation was demonstrated between alpha power and BOLD fluctuations in thalamus and distributed cortical regions in IGE. High alpha power had inhibition effect on dFCS in healthy controls, while in epilepsy, high alpha windows arose along with the enhancement of dFCS in thalamus, caudate and some default mode network (DMN) regions. Moreover, synchronization networks in GSWD-before, GSWD-onset and GSWD-after stages were demonstrated, and the connectivity strength in prominent hub nodes (precuneus, thalamus) was associated with the spatially disturbed alpha-based network integration. Conclusion: The results indicated the spatiotemporal regulation of alpha in epilepsy by means of increased power and decreased coherence communication. It provided links between alpha rhythm and the altered temporal dynamics, as well as the hyperconnectivity in thalamus-DMN circuit. Significance: The combination between neural oscillations and epileptic representations may be of clinical importance in terms of seizure prediction and non-invasive interventions.