Electroencephalographic Complexity and Decreased Randomness in Drug-Naive Obsessive-Compulsive Patients

Çalışmalar birbirinden farklı nöropsikiyatrik hastalıklarda elektroensefalografide (EEG) karmaşıklığın anormal olduğunu göstermiştir. Ancak obsesif kompülsif bozuklukta (OKB) EEG karmaşıklığını araştıran çok az çalışma vardır.Yöntem: OKB'li hastalarda ve sağlıklı kontrollerde gözler kapalı halde 3 dakikalık EEG serileri çekildi. Her bir seri, 10 ve 30 saniyelik pencerelere bölünerek çoklu özdeş epoklara ayrıldı. Kolmogorov karmaşıklığı (KK) ve oto regresif (OR) model kullanılarak segmentlere ayrılmış EEG epoklarının karmaşıklığı hesaplandı. Bulgular: Gerek KK gerekse OR model, OKB'lilerde karmaşıklığın kontrollere göre anlamlı derecede düşük olduğunu gösterdi. Bu düşüklük hem 10 hem de 30 saniyelik pencereler için geçerliydi, ama OR modelde 10 saniyelik pencere hastalarla kontrolleri 30 saniyelik pencereye göre daha iyi ayırt etti.Sonuç: OKB'lilerin EEG'lerinde karmaşıklık ve rastgelelik azalmış, düzenlilik artmıştır. Kantitatif bir belirleme yapabilmek için EEG sinyallerinin segmentasyonu faydalıdır. Daha küçük pencereler EEG karmaşıklığını daha duyarlı biçimde gösterir

Tedavi almamış obsesif-kompulsif hastalarda elektroensefalografik karmaşıklık ve azalmış rasgelelik

Studies investigating the complexity in electroencephalography (EEG) in various neuropsychiatric disorders have yielded abnormal results. However, few studies have examined EEG complexity in obsessive-compulsive disorder (OCD). Methods: An eyes-closed scalp EEG series of 3 minutes was recorded in drug-naive patients with OCD and in healthy controls. Each single trial was segmented into multiple identical epochs using two windows of 10 and 30 seconds. Both Kolmogorov Complexity (KC) values and autoregressive (AR) model orders were estimated to quantify the EEG complexity for segmented EEG epochs. Results: The EEG complexity, measured by both KC and AR model orders and in estimations using window lengths of 10 and 30 seconds, was lower in the patients than in the controls. In the AR model orders, the 10-second window differentiated the patients and controls better than the 30-second window. Conclusion: OCD is characterized by low EEG complexity, increased regularity, or decreased randomness. Segmentation of EEG signals is useful for their quantitative identification, a smaller window providing a more sensitive characterization of EEG

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