Evidence map›Paper›PMID 39821106›Full record

ArticleScientific reports2025

EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis.

Majid Roshanaei, Hamzeh Norouzi, Julie Onton, Scott Makeig, Alireza Mohammadi

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed.

  1. Article
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  3. Physiologically Guided Modeling for EEG Multichannel Signals.Bioengineering (Basel, Switzerland) · 2026
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  7. Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Majid RoshanaeiStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Hamzeh NorouziStudent Research Committee, Baqiyatallah University of Medical Sciences, Tehran, Iran.
Julie OntonSwartz Center for Computational Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Scott MakeigSwartz Center for Computational Neuroscience, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Alireza MohammadiNeuroscience Research Center, Baqiyatallah University of Medical Sciences, Tehran, Iran. ar.mohammadi@bmsu.ac.ir.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding the neural mechanisms underlying emotional processing is critical for advancing neuroscience and mental health interventions. This study examined these mechanisms by analyzing EEG connectivity patterns across different brain regions while participants evoked various emotions. After applying independent component analysis (ICA) to eliminate non-cortical activity, we assessed frequency-specific connectivity patterns using coherence, Granger causality, and graph theoretical measures to evaluate both functional and effective connectivity. Graph theoretical analysis revealed significant differences in connectivity between emotions across multiple frequency bands, particularly in the delta and beta bands. These results indicated modulations in key brain regions, such as the precentral, superior frontal, and temporal areas, suggesting that these regions play a crucial role in emotional processing. Coherence analysis demonstrated predominant alpha band activity across all emotions, with specific emotional states, such as fear, grief, and jealousy, exhibiting enhanced beta band activity. In terms of coherence strength, we observed that the gamma band was largely inactive, except for the emotion of sadness, which displayed significant activity in the right lobe, particularly in regions such as the supplementary motor area and the lingual gyrus. Additionally, Granger causality analysis highlighted that the beta and gamma bands were dominant across all emotional states, with minimal modulation observed in the theta band. Clustering coefficients from the graph analysis further revealed distinct patterns of connectivity in the delta and beta bands, with significant variations across different emotions, particularly in the temporal and frontal regions. These findings enhance our understanding of emotional processing and have practical applications in mental health, biomarker identification, and human-computer interaction.

Indexed as

BrainElectroencephalographyEmotionsNerve NetAdultBrain MappingFemaleHumansMaleYoung AdultBrain networksCoherence analysisEEG connectivityEmotional statesGranger causalityGraph theoretical analysis

Identifiers

PMID39821106
PMCPMC11739399

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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.