Alpha-band functional connectivity during cued versus implicit modality-specific anticipatory attention: EEG-source coherence analysis

2018 ◽  
Vol 55 (12) ◽  
pp. e13269 ◽  
Author(s):  
I. V. Talalay ◽  
A. V. Kurgansky ◽  
R. I. Machinskaya
2015 ◽  
Vol 27 (3) ◽  
pp. 492-508 ◽  
Author(s):  
Nicholas E. Myers ◽  
Lena Walther ◽  
George Wallis ◽  
Mark G. Stokes ◽  
Anna C. Nobre

Working memory (WM) is strongly influenced by attention. In visual WM tasks, recall performance can be improved by an attention-guiding cue presented before encoding (precue) or during maintenance (retrocue). Although precues and retrocues recruit a similar frontoparietal control network, the two are likely to exhibit some processing differences, because precues invite anticipation of upcoming information whereas retrocues may guide prioritization, protection, and selection of information already in mind. Here we explored the behavioral and electrophysiological differences between precueing and retrocueing in a new visual WM task designed to permit a direct comparison between cueing conditions. We found marked differences in ERP profiles between the precue and retrocue conditions. In line with precues primarily generating an anticipatory shift of attention toward the location of an upcoming item, we found a robust lateralization in late cue-evoked potentials associated with target anticipation. Retrocues elicited a different pattern of ERPs that was compatible with an early selection mechanism, but not with stimulus anticipation. In contrast to the distinct ERP patterns, alpha-band (8–14 Hz) lateralization was indistinguishable between cue types (reflecting, in both conditions, the location of the cued item). We speculate that, whereas alpha-band lateralization after a precue is likely to enable anticipatory attention, lateralization after a retrocue may instead enable the controlled spatiotopic access to recently encoded visual information.


2020 ◽  
Vol 52 (1) ◽  
pp. 52-60
Author(s):  
Yousef Mohammadi ◽  
Mohammad Hassan Moradi

Background Depression is one of the most common mental disorders and the leading cause of functional disabilities. This study aims to specify whether functional connectivity and complexity of brain activity can predict the severity of depression (Beck Depression Inventory–II scores). Methods Resting-state, eyes-closed EEG data were recorded from 60 depressed patients. A phase synchronization measure was used to estimate functional connectivity between all pairs of the EEG channels in the delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) frequency bands. To quantify the local value of functional connectivity, 2 graph theory metrics, degree, and clustering coefficient (CC), were measured. Moreover, Lempel-Ziv complexity (LZC) and fuzzy entropy (FuzzyEn) were used to measure the complexity of the EEG signal. Results Through correlation analysis, a significant negative relationship was found between graph metrics and depression severity in the alpha band. This association was strongly positive for the complexity measures in alpha and delta bands. Also, the linear regression model represented a substantial performance of depression severity prediction based on EEG features of the alpha band ( r = 0.839; P < .0001, root mean square error score of 7.69). Conclusion We found that the brain activity of patients with depression was related to depression severity. Abnormal brain activity reflects an increase in the severity of depression. The presented regression model provides a quantitative depression severity prediction, which can inform the development of EEG state and exhibit potential desirable application for the medical treatment of the depressive disorder.


2020 ◽  
Author(s):  
Jintao Wu ◽  
Qianxiang Zhou ◽  
Jiaxuan Li ◽  
Yang Chen ◽  
Shuyu Shao ◽  
...  

Abstract BackgroundCognitive abilities are impaired by sleep deprivation and can be recovered when sufficient sleep is obtained. Changes in alpha-band oscillations are considered to be highly related to sleep deprivation. The effect of sleep deprivation on brain activation and functional connectivity in the resting-state alpha band remains unclear. The purpose of this study was to investigate how sleep deprivation and recovery sleep could change resting-state alpha-band neural oscillations.MethodsIn this study, thirty young, healthy participants obtained approximately 8 h of normal sleep, followed by 36 h of sleep deprivation. On the following recovery night, subjects underwent recovery sleep. Resting-state EEG after normal sleep, sleep deprivation and recovery sleep was recorded. Power spectrum, source localization and functional connectivity analyses were used to investigate the changes in resting-state alpha-band activity after normal sleep, sleep deprivation and recovery sleep.ResultsThe results showed that the global alpha power spectrum decreased and source activation was notably reduced in the precuneus, posterior cingulate cortex, cingulate gyrus, and paracentral lobule after sleep deprivation. Functional connectivity analysis after sleep deprivation showed a weakened functional connectivity pattern in a widespread network with the precuneus and posterior cingulate cortex as the key nodes. Furthermore, the changes caused by sleep deprivation were reversed to a certain extent but not significantly after one night of sleep recovery, which may be due to inadequate time for recovery sleep.ConclusionsIn conclusion, large-scale resting-state alpha-band activation and functional connectivity were weakened after sleep deprivation, and the inhibition of default mode network function with the precuneus and posterior cingulate cortex as the pivotal nodes may be an important cause of cognitive impairment. These findings provide new insight into the physiological response of sleep deprivation and determine how sleep deprivation disrupts brain alpha-band oscillations.


2019 ◽  
Author(s):  
Pablo Ortega-Auriol ◽  
Winston D Byblow ◽  
Angus JC McMorland

AbstractTo elucidate the underlying physiological mechanism of muscle synergies, we investigated the functional corticomuscular and intermuscular binding during an isometric upper limb task in 14 healthy participants. Cortical activity was recorded using 32-channel encephalography (EEG) and muscle activity using 16-channel electromyography (EMG). Using non-negative matrix factorization (NMF), we calculated muscle synergies from two different tasks. A preliminary multidirectional task was used to identify synergy preferred directions. A subsequent coherence task, consisting of generating forces isometrically in the synergy PDs, was used to assess the functional connectivity properties of synergies. Functional connectivity was estimated using corticomuscular coherence (CMC) and intermuscular coherence (IMC). Overall, we were able to extract four different synergies from the multidirectional task. A significant alpha band IMC was present consistently in all extracted synergies. Moreover, alpha band IMC was higher between muscles with higher weights within a synergy. In contrast, no significant CMC was found between the motor cortex area and synergy muscles. In addition, there is a relationship between a synergy muscle weight and the level of IMC. Our findings suggest the existence of a consistent shared input between muscles of each synergy. Finally, the existence of a shared input onto synergistic muscles within a synergy supports the idea of neurally-derived muscle synergies that build human movement.


2021 ◽  
Vol 12 ◽  
Author(s):  
Jiahua Xu ◽  
Zheng Wu ◽  
Andreas Nürnberger ◽  
Bernhard A. Sabel

Objective: Non-invasive brain stimulation (NIBS) is already known to improve visual field functions in patients with optic nerve damage and partially restores the organization of brain functional connectivity networks (FCNs). However, because little is known if NIBS is effective also following brain damage, we now studied the correlation between visual field recovery and FCN reorganization in patients with stroke of the central visual pathway.Method: In a controlled, exploratory trial, 24 patients with hemianopia were randomly assigned to one of three brain stimulation groups: transcranial direct current stimulation (tDCS)/transcranial alternating current stimulation (tACS) (ACDC); sham tDCS/tACS (AC); sham tDCS/sham tACS (Sham), which were compared to age-matched controls (n = 24). Resting-state electroencephalogram (EEG) was collected at baseline, after 10 days stimulation and at 2 months follow-up. EEG recordings were analyzed for FCN measures using graph theory parameters, and FCN small worldness of the network and long pairwise coherence parameter alterations were then correlated with visual field performance.Result: ACDC enhanced alpha-band FCN strength in the superior occipital lobe of the lesioned hemisphere at follow-up. A negative correlation (r = −0.80) was found between the intact visual field size and characteristic path length (CPL) after ACDC with a trend of decreased alpha-band centrality of the intact middle occipital cortex. ACDC also significantly decreased delta band coherence between the lesion and the intact occipital lobe, and coherence was enhanced between occipital and temporal lobe of the intact hemisphere in the low beta band. Responders showed significantly higher strength in the low alpha band at follow-up in the intact lingual and calcarine cortex and in the superior occipital region of the lesioned hemisphere.Conclusion: While ACDC decreases delta band coherence between intact and damaged occipital brain areas indicating inhibition of low-frequency neural oscillations, ACDC increases FCN connectivity between the occipital and temporal lobe in the intact hemisphere. When taken together with the lower global clustering coefficient in responders, these findings suggest that FCN reorganization (here induced by NIBS) is adaptive in stroke. It leads to greater efficiency of neural processing, where the FCN requires fewer connections for visual processing.


NeuroImage ◽  
2020 ◽  
Vol 207 ◽  
pp. 116376 ◽  
Author(s):  
Yingying Shang ◽  
Leighton B. Hinkley ◽  
Chang Cai ◽  
Danielle Mizuiri ◽  
Steven W. Cheung ◽  
...  

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