Browsing by Author "Candemir, Cemre"

Now showing 1 - 4 of 4

Change Point Detection Methods for Locating Activations in Functional Neuronal Images
(2022) Candemir, Cemre; Oğuz, Kaya
The most common analysis for fMRI images is activation detection, in which the purpose is to find the locations in the brain that respond to specific functions, such as visual processing or motor functions by providing related stimuli as tasks in the experiment. On the other hand, it is also important to detect the instance the activation is triggered. One of the powerful techniques that can analyze the abnormal behavior of any data is change point (CP) analysis. We suggest that CP detection algorithms also can be used to locate the activations in functional magnetic resonance imaging (fMRI) sequences, as well. Our paper presents a two-fold innovative study in that respect. First, we propose to use CP detection algorithms to locate the activations in fMRI signals as a state-of-art topic. Furthermore, we propose and compare a set of change point analysis methods, a regression-based method (RBM), a statistical method (SM), and a mean difference of double sliding windows method (MDSW)) to locate such points. Second, we apply these methods to the fMRI signals, which are acquired from the real subjects, while they were performing fMRI tasks. Proposed methods were applied to three different fMRI experiments with a motor task, a visual task, and a linguistic task. The analysis shows that the methods find activations in accordance with established methods such as statistical parametric maps (SPM). The acquired up to 94 % results also show that the proposed methods can be used effectively to locate the activation times on fMRI time series.
Citation - Scopus: 2
A Comparative Study on Parameter Selection and Outlier Removal for Change Point Detection in Time Series
(IEEE, 2017) Candemir, Cemre; Oguz, Kaya
Change point analysis is an efficient method for understanding the unexpected behaviour of the data used in many different disciplines. Although the literature contains a variety of change point analysis methods, there are relatively fewer studies that focus on the performance of parameter selection and outlier removal that are applied on real data sets. In this study two methods based on regression and statistical properties are proposed and compared with a method using Bayesian approach to evaluate their performance on the selection of parameters and removal of outliers. The methods are executed using different parameters on the well-log data set with and without outliers that are removed either manually or automatically. The results show that different data sets require different parameters to locate their change points. The proposed methods have intuitive parameters to control the algorithm, run faster, and do not require any assumptions to be made such as maximum number of change points. These properties also make them good candidates for online change point analysis.
Citation - WoS: 1
Gradual Loss of Social Group Support During Competition Activates Anterior Tpj and Insula but Deactivates Default Mode Network
(MDPI, 2023) Özkul, Burcu; Candemir, Cemre; Oğuz, Kaya; Eroğlu-Koç, Seda; Kizilates-Evin, Gozde; Ugurlu, Onur; Erdoğan, Yiğit
Group forming behaviors are common in many species to overcome environmental challenges. In humans, bonding, trust, group norms, and a shared past increase consolidation of social groups. Being a part of a social group increases resilience to mental stress; conversely, its loss increases vulnerability to depression. However, our knowledge on how social group support affects brain functions is limited. This study observed that default mode network (DMN) activity reduced with the loss of social group support from real-life friends in a challenging social competition. The loss of support induced anterior temporoparietal activity followed by anterior insula and the dorsal attentional network activity. Being a part of a social group and having support provides an environment for high cognitive functioning of the DMN, while the loss of group support acts as a threat signal and activates the anterior temporoparietal junction (TPJ) and insula regions of salience and attentional networks for individual survival.
Citation - WoS: 1
Citation - Scopus: 1
Task-Dependent Functional Connectivity Changes in Response To Varying Levels of Social Support
(Cambridge Univ Press, 2024) Burhanoglu, Birce Begüm; Uslu, Özgül; Özkul, Burcu; Oğuz, Kaya; Eroğlu-Koç, Seda; Kizilates-Evin, Gozde; Candemir, Cemre
Background Having social support improves one's health outcomes and self-esteem, and buffers the negative impact of stressors. Previous studies have explored the association between social support and brain activity, but evidence from task-dependent functional connectivity is still limited.Aims We aimed to explore how gradually decreasing levels of social support influence task-dependent functional connectivity across several major neural networks.Method We designed a social support task and recruited 72 young adults from real-life social groups. Of the four members in each group, one healthy participant (18 participants in total) completed the functional magnetic resonance imaging (fMRI) scan. The fMRI task included three phases with varying levels of social support: high-support phase, fair phase and low-support phase. Functional connectivity changes according to three phases were examined by generalised psychophysiological interaction analysis.Results The results of the analysis demonstrated that participants losing expected support showed increased connectivity among salience network, default mood network and frontoparietal network nodes during the fair phase compared with the high-support phase. During the low-support phase, participants showed increased connectivity among only salience network nodes compared with the high-support phase.Conclusions The results indicate that the loss of support was perceived as a threat signal and induced widespread increased functional connectivity within brain networks. The observation of significant functional connectivity changes between fair and high-support phases suggests that even a small loss of social support from close ones leads to major changes in brain function.