Evidence map›Paper›PMID 37962638›Full record

ArticleExperimental brain research2024

The effect and efficiency of attentional networks and the brain electrophysiology.

Hossein Akbari-Lalimi, Shahrokh Naseri, Mahdi Momennezhad, Hoda Zare, Ali Talaei, Seyed Ali Shafiei

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Article in Experimental brain research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors at 2 institutions in 1 country.

Hossein Akbari-LalimiDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Shahrokh NaseriDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Mahdi MomennezhadNuclear Medicine Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Hoda ZareDepartment of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
Ali TalaeiPsychiatry and Behavioral Sciences Research Center, Mashhad University of Medical Sciences, Mashhad, Iran.
Seyed Ali ShafieiNeuroscience Research Center, Qom University of Medical Sciences, Qom, Iran. sashafiei@muq.ac.ir.
Mashhad University of Medical Sciences · IRQom University of Medical Science and Health Services · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The attention networks test (ANT) is frequently utilized to evaluate executive, alerting, and orienting attentional components. Additionally, it serves as an activation task in neuroimaging studies. This study aimed to examine the relationship between attention networks and brain electrophysiology. The study enrolled 40 right-handed male students (age = 20.8 ± 1.3 years) who underwent the revised attention network test, while their electroencephalogram signals were recorded. The study aimed to explore the effects of attention networks and their efficiencies on brain electrophysiology. The results indicated that the P3 amplitude was modulated by the conflict effect in the central (p-value = 0.014) and parietal (p-value = 0.002) regions. The orienting component significantly influenced P1 and N1 latencies in the parietal and parieto-occipital regions (p-values < 0.006), as well as P1 and N1 amplitude in the parieto-occipital region (p-values = 0.017 and 0.011). The alerting component significantly affected P1 latency and amplitude in the parietal and parieto-occipital regions, respectively (p-value = 0.02). Furthermore, N1 amplitude and the time interval between P1 and N1 were significantly correlated with the efficiency of alerting and orienting networks. In terms of connectivity, the coherence of theta and alpha bands significantly decreased in the incongruent condition compared to the congruent condition. Additionally, the effects of attention networks on event-related spectral perturbation were observed. The study revealed the influence of attention networks on various aspects of brain electrophysiology. Specifically, the alerting score correlated with the amplitude of the N1 component in the double-cue and no-cue conditions in the parieto-occipital region, while the orienting score in the same region correlated with the N1 amplitude in the valid cue condition and the difference in N1 amplitude between the valid cue and double-cue conditions. Overall, empirical evidence suggests that attention networks not only impact the amplitudes of electrophysiological activities but also influence their time course.

Indexed as

BrainOrientationAdultElectroencephalographyElectrophysiologyHumansMaleOccipital LobeReaction TimeYoung AdultAlertingAttentionEvent-related potentialExecutiveOrienting

Identifiers

PMID37962638
OpenAlexW4388654152

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.