Browsing by Author "Eker, Cagdas"

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Citation - WoS: 59
Citation - Scopus: 65
Brain Regions Associated With Risk and Resistance for Bipolar I Disorder: a Voxel-Based Mri Study of Patients With Bipolar Disorder and Their Healthy Siblings
(Wiley-Blackwell, 2014) Eker, Cagdas; Simsek, Fatma; Yilmazer, Evrim Ebru; Kitis, Omer; Cinar, Cem; Eker, Ozlem Donat; Coburn, Kerry
ObjectiveBipolar I disorder is a highly heritable disorder but not all siblings manifest with the illness, even though they may share similar genetic and environmental risk factors. Thus, sibling studies may help to identify brain structural endophenotypes associated with risk and resistance for the disorder. MethodsStructural magnetic resonance imaging (MRI) scans were acquired for 28 euthymic patients with bipolar disorder, their healthy siblings, and 30 unrelated healthy controls. Statistical Parametric Mapping 8 (SPM8) was used to identify group differences in regional gray matter volume by voxel-based morphometry (VBM). ResultsUsing analysis of covariance, gray matter analysis of the groups revealed a group effect indicating that the left orbitofrontal cortex [Brodmann area (BA) 11] was smaller in patients with bipolar disorder than in unrelated healthy controls [F=14.83, p<0.05 (family-wise error); 7mm(3)]. Paired t-tests indicated that the orbitofrontal cortex of patients with bipolar disorder [t=5.19, p<0.05 (family-wise error); 37mm(3)] and their healthy siblings [t=3.89, p<0.001 (uncorrected); 63mm(3)] was smaller than in unrelated healthy controls, and that the left dorsolateral prefrontal cortex was larger in healthy siblings than in patients with bipolar disorder [t=4.28, p<0.001 (uncorrected); 323mm(3)] and unrelated healthy controls [t=4.36, p<0.001 (uncorrected); 245mm(3)]. Additional region-of-interest analyses also found volume deficits in the right cerebellum of patients with bipolar disorder [t=3.92, p<0.001 (uncorrected); 178mm(3)] and their healthy siblings [t=4.23, p<0.001 (uncorrected); 489mm(3)], and in the left precentral gyrus of patients with bipolar disorder [t=3.61, p<0.001 (uncorrected); 115mm(3)] compared to unrelated healthy controls. ConclusionsThe results of this study suggest that a reduction in the volume of the orbitofrontal cortex, which plays a role in the automatic regulation of emotions and is a part of the medial prefrontal network, is associated with the heritability of bipolar disorder. Conversely, increased dorsolateral prefrontal cortex volume may be a neural marker of a resistance factor as it is part of a network of voluntary emotion regulation and balances the effects of the disrupted automatic emotion regulation system.
Citation - Scopus: 7
Classification of Healthy Siblings of Bipolar Disorder Patients From Healthy Controls Using Mri
(IEEE, 2019) Cigdem, Ozkan; Soyak, Refik; Aydeniz, Burhan; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas
Three Dimensional magnetic resonance imaging (3D-MRI) has been utilized to classify patients with neuroanatomical abnormalities apart from healthy controls (HCs). The studies on the diagnosis of Bipolar Disorder (BD) focuses also on the unaffected relatives of BD patients in order to examine the heritable resistance factors associated with the disorder. Hence, the comparison of Healthy Siblings of Bipolar Disorder patients (HSBDs) and HCs is also required owing to the high heritability of BD. In this paper, the classification of 27HSBDs from 38HCs has been studied by using 3D-MRI and Computer-Aided Detection (CAD). The pre-processing of 3D-MRI data is performed by taking advantage of Voxel-Based Morphometry (VBM) and the structural deformations in the Gray Matter (GM) and White Matter (WM) are obtained by using a general linear model. The model is configured by using a two sample t-test technique and Total Intracranial Volume (TIV) as a covariate. The altered voxels between data groups are considered as Voxel of Interests (VOIs) and the 3D masks are generated for GM and WM tissue probability maps. The Relief-F algorithm is utilized to rank the features and a Fisher Criterion (FC) method is considered to determine the number of top-ranked discriminative features. The performances of Support Vector Machines (SVM) and the Naive Bayes (NB) algorithms are compared on the classification of HSBD and HC. The experiments are performed for GM-only, WM-only, and their combinations. The experimental results indicate that the changes between the brain regions of HSBD and HC might provide information on the heritable factors associated with the BD. Additionally, it is concluded that using the combination of GM and WM tissue probability map provides better results than considering them, separately. Finally, it is obtained that the classification accuracy of SVM on HSBD and HC comparison is better than that of NB.
Citation - Scopus: 3
Diagnosis of Bipolar Disease Using Correlation-Based Feature Selection With Different Classification Methods
(IEEE, 2019) Cigdem, Ozkan; Sulucay, Aysu; Yilmaz, Arif; Oguz, Kaya; Demirel, Hasan; Kitis, Omer; Eker, Cagdas
Three-Dimensional Magnetic Resonance Imaging (3D-MRI) and Computer-Aided Detection (CAD) have been widely studied in the detection of bipolar disorder (BD). In this study, the structural alterations at the grey matter (GM) and white matter (WM) of BD subjects versus healthy controls (HCs) have been compared using Voxel-Based Morphometry (VBM). In order to obtain 3D GM and WM masks, the two sample t-test method and total intracranial volumes of BD and HC as a covariate have been utilized. In addition to analyzing effects of GM and WM tissue maps separately in the detection of BD, impacts of both GM and WM ones are studied by concatenating them in a matrix. The correlation-based feature selection (CFS) feature ranking method is applied to the obtained 3D masks to rank the features, the number of selected top-ranked features are determined using a Fisher criterion (FC) approach, and different classification algorithms are used to classify BD apart from HCs. In this study, 26 BDs and 38 HCs data are used. The experimental results indicate that the classification accuracy of Naive Bayes outperforms the other four classification algorithms used in this study. Additionally, concatenation of GM and WM tissue maps enhances the classification performances of using GM-only and WM-only ones. The classification accuracies obtained for GM, WM, and their concatenation are 72.92%, 78.33%, and 80.00% respectively.
Citation - WoS: 4
Citation - Scopus: 8
Emotional Context Effect on Recognition of Varying Facial Emotion Expression Intensities in Depression
(Elsevier, 2022) Yildirim-Celik, Hande; Eroglu, Seda; Oguz, Kaya; Karakoc-Tugrul, Gulser; Erdogan, Yigit; Isman-Haznedaroglu, Damla; Eker, Cagdas
Background: Previous research has indicated that Major Depressive Disorder (MDD) patients have deficits in the process of facial emotion recognition. In most of these studies, isolated emotional faces were used, and the effect of the surrounding context of the face was neglected. We aimed to investigate how context emotion (sad or happy) affects facial emotion recognition and whether this effect is different in depressive individuals compared to the control group. Methods: Happy, sad, neutral facial expressions with congruent and incongruent visual contexts were presented to 51 MDD patients and 42 matched healthy controls. Emotional facial expressions are presented as morphs gradually expressing happiness or sadness with 40% and 80% intensity levels. Mean reaction time, mean accuracy rate, and mean emotion intensity rating score was calculated for each condition. Results: The performances on facial emotion intensity rating and accuracy rate were similar between MDD patients and controls. MDD patients were slower to recognize all facial emotions and to recognize facial emotions with emotionally incongruent backgrounds compared to congruent ones. Limitations: Antidepressant therapy of patients might have affected our results. Conclusions: Emotional contextual features have an important role in facial emotion recognition but this effect is independent of depression. Longer reaction time in depression may be related to some cognitive impairments.