Scopus İndeksli Açık & Kapalı Erişimli Yayınlar
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Item Factors associated with progression of depression, anxiety, and stress-related symptoms in outpatients and inpatients with COVID-19: A longitudinal study(2022) Alici, Yasemin Hosgoren; Cinar, Gule; Hasanli, Jamal; Ceran, Selvi; Onar, Deha; Gulten, Ezgi; Kalkan, Irem Akdemir; Memikoglu, Kemal Osman; Celik, Casit Olgun; Devrimci-Ozguven, Halise; 0000-0003-3384-8131; 0000-0003-1364-625X; 0000-0002-7984-2440; 0000-0002-7190-5443; 35593144; AAJ-8113-2021; AAK-3227-2021; AAD-5477-2021It is known that there is an increase in the frequency of psychiatric disturbances in the acute and post-illness phase of coronavirus disease (COVID-19). Comorbid psychiatric symptoms complicate the management of patients and negatively affect the prognosis, but there is no clear evidence of their progress. We aimed to determine psychiatric comorbidity in inpatients and outpatients with COVID-19 and recognize the factors that predict psychiatric comorbidity. For this purpose, we evaluated patients on the first admission and after 4 weeks. We investigated psychiatric symptoms in outpatients (n = 106) and inpatients (n = 128) diagnosed with COVID-19. In the first 7 days after diagnosis (first phase), sociodemographic and clinic data were collected, a symptom checklist was constructed, and the Hospital Anxiety and Depression Scale (HADS) and the Severity of Acute Stress Symptoms Scale (SASSS) were applied. After 30-35 days following the diagnosis, the SASSS and the HADS were repeated. In the first phase, the frequency of depression and anxiety were 55% and 20% in inpatients, and 39% and 18% in outpatients, respectively. In the second phase, depression scores are significantly decreased in both groups whereas anxiety scores were decreased only in inpatients. The frequencies of patients reporting sleep and attention problems, irritability, and suicide ideas decreased after 1 month. Patients with loss of smell and taste exhibit higher anxiety and depression scores in both stages. Our results revealed that the rate of psychiatric symptoms in COVID-19 patients improves within 1 month. Inpatients have a more significant decrease in both depression and anxiety frequency than do outpatients. The main factor affecting anxiety and depression was the treatment modality. Considering that all patients who were hospitalized were discharged at the end of the first month, this difference may be due to the elimination of the stress caused by hospitalization.Item 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; 36088826In 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.Item 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; 36217606Resilience 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.Item An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry(2021) Guney, Gokhan; Yigin, Busra Ozgode; Guven, Necdet; Colak, Burcin; Alici, Yasemin Hosgoren; Erzin, Gamze; Saygili, Gorkem; 0000-0003-3384-8131; 33888650Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.