Scopus İndeksli Açık & Kapalı Erişimli Yayınlar

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Author: search.filters.author.Alici, Yasemin Hosgorensearch.filters.applied.f.rights: search.filters.rights.info:eu-repo/semantics/closedAccess

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    Deep neural network to differentiate brain activity between patients with euthymic bipolar disorders and healthy controls during verbal fluency performance: A multichannel near-infrared spectroscopy study
    (2022) Alici, Yasemin Hosgoren; Oztoprak, Huseyin; Rizaner, Nahit; Baskak, Bora; Ozguven, Halise Devrimci; 0000-0003-3384-8131; 36088826
    In this study, we aimed to differentiate between euthymic bipolar disorder (BD) patients and healthy controls (HC) based on frontal activity measured by fNIRS that were converted to spectrograms with Convolutional Neural Networks (CNN). And also, we investigated brain regions that cause this distinction. In total, 29 BD patients and 28 HCs were recruited. Their brain cortical activities were measured using fNIRS while performing letter versions of VFT. Each one of the 24 fNIRS channels was converted to a 2D spectrogram on which a CNN architecture was designed and utilized for classification. We found that our CNN algorithm using fNIRS activity during a VFT is able to differentiate subjects with BD from healthy controls with 90% accuracy, 80% sensitivity, and 100% specificity. Moreover, validation performance reached an AUC of 94%. From our individual channel analyses, we observed channels corresponding to the left inferior frontal gyrus (left-IFC), medial frontal cortex (MFC), right dorsolateral prefrontal cortex (DLPFC), Broca area, and right premotor have considerable activity variation to distinguish patients from HC. fNIRS activity during VFT can be used as a potential marker to classify euthymic BD patients from HCs. Activity particularly in the MFC, left-IFC, Broca's area, and DLPFC have a considerable variation to distinguish patients from healthy controls.
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    The İmportance of Mentalization, Coping Mechanisms, and Perceived Stress in The Prediction of Resilience of Healthcare Workers
    (2023) Alici, Yasemin Hosgoren; Hasanli, Jamal; Saygili, Gorkem; Kocak, Orhan Murat; 36217606
    Resilience is the process of overcoming stressors. Being able to examine the effect of the Covid epidemic on healthcare workers (HCWs) has provided us a unique opportunity to understand the impact of trauma on resilience. We aimed to investigate the relationship between stress, mentalization, and an individual's coping capacity against a real risk (Covid-19) and evaluate the predictors of resilience. 302 HCWs have enrolled in the study and completed an online questionnaire assessing demographics, perceived stress, resilience, coping, and mentalization. We utilized statistical analysis together with a Random Forest classifier to analyze the interaction between these factors extensively. We applied ten times ten-fold cross-validation and plotted Receiver Operator Characteristic (ROC) with the calculated Area Under the Curve(AUC) score and identify the most important features. Our experiments showed that the Perceived stress scale has the strongest relationship with resilience. The subject's awareness level of emotional states is an important factor that determines the level of resilience. Coping styles such as the decision of giving up is also a crucial indicator. We conclude that being aware of the risks and the mental states are the dominant factors behind the resilience levels of healthcare workers under pandemic conditions.