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Dergiler / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 4

Unveiling Depression-Related Network Changes through EEG Coherence Analysis with Volume Conduction Mitigation

Mehmet Akif Özçoban(GAZİANTEP ÜNİVERSİTESİ)0000-0002-1127-0490
Sayfa
1682–1695
Yayın Tarihi
2026-07-15
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Overall, the results highlight EEG coherence as a promising objective biomarker for characterizing functional dysconnectivity in MDD.

📌 3 Maddede Bu Makale

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Amaç & Hipotez:Recently, a model has been proposed that defines depression as the disconnection of extensive neural networks affecting the functioning of cognitive and emotion-related systems.
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Temel Bulgular & Veri:The present study conducted a secondary analysis on inter- and intrahemispheric coherence measures in individuals belonging to a high-depression group, defined via the Beck Depression Inventory (BDI), and compared them against those of the healthy subjects, using 121 individuals with high-depression BDI scores (n = 46) and normal (control) individuals (n = 75).
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Sonuç & Katkı:Overall, the results highlight EEG coherence as a promising objective biomarker for characterizing functional dysconnectivity in MDD.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Coherence · Spatial filtering · Electroencephalography

Özet

Recently, a model has been proposed that defines depression as the disconnection of extensive neural networks affecting the functioning of cognitive and emotion-related systems. The present study conducted a secondary analysis on inter- and intrahemispheric coherence measures in individuals belonging to a high-depression group, defined via the Beck Depression Inventory (BDI), and compared them against those of the healthy subjects, using 121 individuals with high-depression BDI scores (n = 46) and normal (control) individuals (n = 75). Due to the use of threshold-based BDI score cut-off rather than a structured diagnosis of MDD, the conclusions regarding network dysconnectivity in depressed patients would be regarded as relating to depression symptoms alone. To increase spatial resolution and minimize the impact of volume conduction, a surface Laplacian filter was applied before estimating the coherence magnitude across five frequency bands. Coherence analysis showed significant coherence reductions in the high-depression group at several inter- (frontal – F3-F4, F7-F8, fronto-temporal – FT7-FT8, central – C3-C4, parietal – P3-P4, occipital - O1-O2) and intra-hemispheric electrode pairs (fronto-temporal – Fp1-T7, Fp2-T8; temporo-occipital – T7-O1, T8-O2), except the interhemispheric alpha coherence measures, which did not reach statistical significance. Both delta and theta bands showed inter-hemispheric coherence reduction, while gamma coherence decreased in fronto-temporal and frontal networks; the latter findings should be interpreted with caution due to possible contamination by high-frequency muscle artifacts in the EEG. Moderate machine learning performance was achieved, with accuracies ranging from 70% to 74% and an area under the curve between 0.60 and 0.76. These findings indicate large-scale impairments in neural synchronization across multiple frequency bands in MDD and support the hypothesis that depressive pathology involves disrupted integration within cognitive-emotional regulatory networks. Overall, the results highlight EEG coherence as a promising objective biomarker for characterizing functional dysconnectivity in MDD.

Abstract

Recently, a model has been proposed that defines depression as the disconnection of extensive neural networks affecting the functioning of cognitive and emotion-related systems. The present study conducted a secondary analysis on inter- and intrahemispheric coherence measures in individuals belonging to a high-depression group, defined via the Beck Depression Inventory (BDI), and compared them against those of the healthy subjects, using 121 individuals with high-depression BDI scores (n = 46) and normal (control) individuals (n = 75). Due to the use of threshold-based BDI score cut-off rather than a structured diagnosis of MDD, the conclusions regarding network dysconnectivity in depressed patients would be regarded as relating to depression symptoms alone. To increase spatial resolution and minimize the impact of volume conduction, a surface Laplacian filter was applied before estimating the coherence magnitude across five frequency bands. Coherence analysis showed significant coherence reductions in the high-depression group at several inter- (frontal – F3-F4, F7-F8, fronto-temporal – FT7-FT8, central – C3-C4, parietal – P3-P4, occipital - O1-O2) and intra-hemispheric electrode pairs (fronto-temporal – Fp1-T7, Fp2-T8; temporo-occipital – T7-O1, T8-O2), except the interhemispheric alpha coherence measures, which did not reach statistical significance. Both delta and theta bands showed inter-hemispheric coherence reduction, while gamma coherence decreased in fronto-temporal and frontal networks; the latter findings should be interpreted with caution due to possible contamination by high-frequency muscle artifacts in the EEG. Moderate machine learning performance was achieved, with accuracies ranging from 70% to 74% and an area under the curve between 0.60 and 0.76. These findings indicate large-scale impairments in neural synchronization across multiple frequency bands in MDD and support the hypothesis that depressive pathology involves disrupted integration within cognitive-emotional regulatory networks. Overall, the results highlight EEG coherence as a promising objective biomarker for characterizing functional dysconnectivity in MDD.

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Bu Makaleye Atıf Yap

Özçoban, M. A. (2026). Unveiling Depression-Related Network Changes through EEG Coherence Analysis with Volume Conduction Mitigation. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 4, 1682–1695. https://doi.org/10.34248/bsengineering.1936174

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