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Journals / Karaelmas Fen ve Mühendislik Dergisi / 2026 / Cilt: 16 Sayı: 1

Graph Theory Metrics-based Functional Brain Network Analysis in Dyslexia

Disleksi'de Graf Teorisi Metriklerine Dayalı Fonksiyonel Beyin Ağı Analizi

Özlem Karabiber Cura(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0000-0001-8650-1137
Sevde Dervisoglu(İZMİR KATİP ÇELEBİ ÜNİVERSİTESİ)0009-0004-6932-107X
Gunet Eroglu(Bahçeşehir Üniversitesi)0000-0001-8382-8417
Pages
103–117
Publication Date
2026-04-21
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

EEG-based graph metrics are also highlighted in the paper as possible indicators for tracking dyslexic children’s cognitive development and the effectiveness of their treatment.

📌 3 Key Takeaways

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Objective & Hypothesis:Developmental dyslexia, a neurodevelopmental disorder affecting 5-10% of school-aged children, involves persistent reading, decoding, and spelling difficulties.
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Key Findings & Data:This study examines neuroplastic changes in 14 subjects with dyslexia during a 60-day cognitive training protocol, utilizing EEG-based functional connectivity and brain network analysis.
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Conclusion & Impact:EEG-based graph metrics are also highlighted in the paper as possible indicators for tracking dyslexic children’s cognitive development and the effectiveness of their treatment.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Beyin ağı ölçümleri · bilişsel eğitim · EEG

Abstract

Developmental dyslexia, a neurodevelopmental disorder affecting 5-10% of school-aged children, involves persistent reading, decoding, and spelling difficulties. Electroencephalography (EEG), especially when combined with graph theory, offers a powerful, non-invasive method for exploring the neural basis of dyslexia. This study examines neuroplastic changes in 14 subjects with dyslexia during a 60-day cognitive training protocol, utilizing EEG-based functional connectivity and brain network analysis. The international 10–20 system is used to capture EEG signals from 14 scalp locations that encompass the frontal, temporal, parietal, and occipital lobes. The EEG recordings from Days 1 and 60 are used to extract a variety of connectivity metrics, including correlation coefficients, covariances, cross-power spectral densities, magnitude-squared coherence, and entropy-based metrics. Additionally, seven graph theory metrics (assortativity, transitivity, clustering coefficient, global efficiency, local efficiency, modularity, and characteristic path length) are calculated using connectivity measures to assess changes in large-scale brain networks. In order to evaluate the impact of cognitive training, EEG-derived features are classified using machine learning models such as logistic regression, decision trees, bagging trees, k-nearest neighbor, and support vector machines. By Day 60, graph-based connectivity tests revealed enhanced long-range functional connectivity and better brain network integration. These results imply that children with dyslexia may have quantifiable neuroplasticity as a result of cognitive instruction. EEG-based graph metrics are also highlighted in the paper as possible indicators for tracking dyslexic children’s cognitive development and the effectiveness of their treatment.

Özet

Okul çağındaki çocukların %5-10’unu etkileyen nörogelişimsel bir bozukluk olan gelişimsel disleksi, okuma, kod çözme ve yazımda kalıcı zorluklarla karakterizedir. Elektroensefalografi (EEG), özellikle graf teori ile birleştirildiğinde, disleksinin nöral temelini keşfetmek için güçlü ve invaziv olmayan bir yöntem sunar. Bu çalışma, EEG tabanlı işlevsel bağlantı ve beyin ağı analizi kullanılarak 60 günlük bir bilişsel eğitim protokolü boyunca disleksili 14 denekte nöroplastik değişiklikleri araştırmaktadır. EEG sinyalleri, uluslararası 10-20 sistemini izleyerek frontal, temporal, parietal ve oksipital lobları kapsayan 14 kafa derisi bölgesinden kaydedilmiştir. 1. ve 60. günlere ait EEG kayıtları, korelasyon katsayısı, kovaryans, çapraz güç spektral yoğunluğu, büyüklük kareli koherans ve entropi tabanlı metrikler gibi çeşitli bağlantı metriklerini çıkarmak için kullanılmıştır. Ek olarak, büyük ölçekli beyin ağı değişikliklerini değerlendirmek için hesaplanan bağlantı ölçümleri kullanılarak yedi graf teori metriği (asortivite, geçişlilik, kümeleme katsayısı, küresel verimlilik, yerel verimlilik, modülerlik ve karakteristik yol uzunluğu) hesaplanmıştır. Bilişsel eğitimin etkisini değerlendirmek için, EEG’den türetilen özellikler lojistik regresyon, karar ağaçları, torbalama ağaçları, k-en yakın komşu ve destek vektör makineleri gibi makine Computöğrenimi modelleri kullanılarak sınıflandırılmıştır. 60. güne gelindiğinde, grafik tabanlı bağlantı testleri, gelişmiş uzun menzilli işlevsel bağlantı ve daha iyi beyin ağı entegrasyonunu ortaya koymuştur. Bu bulgular, disleksili çocukların bilişsel eğitim sonucunda ölçülebilir nöroplastisiteye sahip olabileceğini göstermektedir. EEG tabanlı grafik metrikler de makalede; disleksili çocukların bilişsel gelişimlerini ve tedavilerinin etkinliğini izlemek için olası göstergeler olarak vurgulanmıştır.

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Cite This Article

Cura, Ö. K., Dervisoglu, S., Eroglu, G. (2026). Graph Theory Metrics-based Functional Brain Network Analysis in Dyslexia. Karaelmas Fen ve Mühendislik Dergisi, Cilt: 16 Sayı: 1, 103–117. https://doi.org/10.7212/karaelmasfen.1796249

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