Effective brain network analysis with resting-state EEG data: a comparison between heroin abstinent and non-addicted subjects

Abstract

Objective. Neuro-electrophysiological tools have been widely used in heroin addiction studies. Previous studies indicated that chronic heroin abuse would result in abnormal functional organization of the brain, while few heroin addiction studies have applied the effective connectivity tool to analyze the brain functional system (BFS) alterations induced by heroin abuse. The present study aims to identify the abnormality of resting-state heroin abstinent BFS using source decomposition and effective connectivity tools. Approach. The resting-state electroencephalograph (EEG) signals were acquired from 15 male heroin abstinent (HA) subjects and 14 male non-addicted (NA) controls. Multivariate autoregressive models combined independent component analysis (MVARICA) was applied for blind source decomposition. Generalized partial directed coherence (GPDC) was applied for effective brain connectivity analysis. Effective brain networks of both HA and NA groups were constructed. The two groups of effective cortical networks were compared by the bootstrap method. Abnormal causal interactions between decomposed source regions were estimated in the 1–45 Hz frequency domain. Main results. This work suggested: (a) there were clear effective network alterations in heroin abstinent subject groups; (b) the parietal region was a dominant hub of the abnormally weaker causal pathways, and the left occipital region was a dominant hub of the abnormally stronger causal pathways. Significance. These findings provide direct evidence that chronic heroin abuse induces brain functional abnormalities. The potential value of combining effective connectivity analysis and brain source decomposition methods in exploring brain alterations of heroin addicts is also implied.

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

“…Filtering may cause some other aberrant differences between connectivity values of the two groups [72]. To decrease the notch filtering influence at the gamma band, we examined two frequency ranges, 30-45Hz by following previous studies [34, 73] and 30-60Hz similar to the work presented at [13]. The first range that is preserved from notch filtering effect yielded no significant differences by the sensor space analysis, while the second range (30–60 Hz) led to significant results at the gamma band that may be due to notch filtering influence.…”

“…After applying Laplacian filter to EEG data to reduce the volume conduction effect and spatially enhance the data quality [33], functional connectivity was computed in EEG-sensor space among pairwise electrodes. The connectivity values were calculated for five EEG frequency bands: delta (1–4 Hz), theta (4–8 Hz), alpha (8–15 Hz), beta (15–30 Hz), and gamma (30–45 Hz) according to previous addiction studies [13, 34]. Accordingly, we obtained a functional network with 61 nodes in five bands (5×61×61 connectivity matrix) for each subject, where the nodes were considered the sensors and the link between them were acquired using the absolute value of the WPLI matrix.…”

Neural Basis of the Emotional Conflict Processing in Major Depression: ERPs and Source Localization Analysis on the N450 and P300 Components

“…As emphasized in the previous study ( Liu et al, 2017 , 2018 ), high-density EEG can furnish both high spatial sampling density and large head coverage. Furthermore, there are many advanced tools for an effective network study ( Yao et al, 2016 ; Hu et al, 2017 ), the introduction of which to them into major depression research would be meaningful. Finally, the research of functional and structural integrity of frontal pathways which are closely connected with emotional conflict among MDD patients should remain an important area for future study using high-density EEG.…”

Functional connectivity corresponding to the tonotopic differentiation of the human auditory cortex

“…To improve the statistical effectiveness of each single subject, multiple task‐based fMRI runs were used to increase the total number of trials. During the auditory task, participants were only asked to stay awake and passively listen to the stimuli (Cha et al, ; Hu et al, ). The stimulus sequence was played via MR‐compatible high‐fidelity headphones (Optoacoustics) that used an adaptive DSP‐based noise‐reduction filtering algorithm.…”