Browsing by Author "Paksoy, Yahya"

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Citation - WoS: 1
Citation - Scopus: 2
Machine Learning Based Detection of Depression From Task-Based Fmri Using Weighted-3d Denoising Method
(Springer, 2023) Özmen, Güzin; Özsen, Seral; Paksoy, Yahya; Güler, Özkan; Tekdemir, Rukiye
Depression has become an important public health problem in recent years because the probability of a depressive episode in a person's entire life is generally between 18-20%. Neuroimaging techniques investigate diagnostic biomarkers in depression disorders and support traditional communication-based diagnosis in psychiatry. The quality of the brain images used in functional MRI (fMRI), and the design of decision support systems using these images are essential for accurate diagnosis. The Gaussian smoothing for fMRI preprocessing blurs the image for statistical analysis but is inadequate because image detail is lost during filtering, leading to poor classification results. We argue that the weighted-3 Dimensional-Discrete Wavelet Transform (weighted-3D-DWT) denoising approach instead of Gaussian smoothing for task-based fMRI. The activation maps show differences in intensity values in the cluster size of voxels in the mood-related regions between patients and control subjects (p<0.05). Thus, we classify depression disorders using a machine learning approach and improve the classification accuracy using weighted-3D-DWT. The classification results show that weighted-3D- DWT with Random Forest and 10-fold cross-validation achieves 96.4% accuracy, while Gaussian Smoothing with a Support Vector Machine achieves 83.9% classification accuracy. Classification accuracy increases to 97.3% when 30 components are used with principal component analysis. Our results show that an fMRI experiment with visual stimuli that can aid the diagnosis of depression provides significant classification accuracy using weighted-3D-DWT.
Quantitative Evaluation of the Cerebellum in Patients With Depression and Healthy Adults by Volbrain Method
(2021) Özmen, Güzin; Saygın, Duygu Akın; Uysal, İsmihan İlknur; Özşen, Seral; Paksoy, Yahya; Güler, Özkan
Objectives: Besides the well-known sensorimotor control function, the cerebellum is also associated with cognitive functions and mood via the cerebral-cerebellar circuit. This study aimed to investigate possible cerebellar morphometric changes in untreated patients with depression. Methods: Brain magnetic resonance (MR) images of 40 adults (age: 18–50 years), including 20 untreated depression patients and 20 healthy controls were analysed prospectively. Intracranial cavity and total cerebellar volumes were measured by using VolBrain. The cerebellum segmentation was performed with CERES to obtain the total gray matter volumes and cortical thickness of the lobules. Results: Total cerebellar volume was 141.27±13.12 cm3 in the depressed group and 142.63±8.01 cm3 in the control group (p>0.05). The difference between males and females in the depressed group was not statistically significant (p>0.05). Total cerebellar volume was approximately 11% of total intracranial volume in both groups. The cortical thickness of lobule V (right-total), lobule VIIIB (right), and lobule IX (right) was smaller in the depressed group, independent of sex (p<0.05). Lobule V, VIIIB and IX volume was smaller and Crus-I cortical thickness was increased in depressed females (p<0.05). Conclusion: The cerebellar volume and cortical thickness of cerebellar lobules in patients with depression show significant differences compared to healthy subjects.