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Author: search.filters.author.Kibaroglu, SedaSubject: search.filters.subject.Alzheimer's disease

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    The Relationship Between the Degree of Cognitive Impairment and Retinal Nerve Fiber Layer Thickness
    (2015) Oktem, Ece Ozdemir; Derle, Eda; Kibaroglu, Seda; Oktem, Caglar; Akkoyun, Imren; Can, Ufuk; 0000-0002-2860-7424; 0000-0003-2122-1016; 0000-0001-8689-417X; 0000-0002-3964-268X; 25575807; AAK-7713-2021; AAI-8830-2021; AAJ-2999-2021; AAJ-2956-2021
    The goal of the present study is to investigate the relationship between the degree of cognitive impairment and retinal nerve fiber layer (RNFL) thickness which is measured by the optical coherence tomography (OCT). Thirty-five patients with Alzheimer's disease (AD), 35 patients with mild cognitive impairment (MCI), and 35 healthy volunteers, between the ages of 60-87, who were examined in the neurology outpatient clinic among 2012-2013 were prospectively involved in our study. Mini mental state examination (MMSE) test, montreal cognitive assessment (MOCA), and also neuropsychological test batteries were used for the neurocognitive evaluation. RNFL thickness was measured by the OCT technique and the differences among groups were studied. The relationship between RNFL thickness and MMSE scores with demographic characteristics was investigated. RNFL thickness was significantly lower in AD and MCI groups compared with the control group (p < 0.01). No significant differences of RNFL were found between the MCI and the AD groups (p > 0.05). Significant correlation was found between MMSE scores and the RNFL values (p < 0.05). Significant thinning in RNFL along with age was detected (p < 0.05). In our study, it is thought that retinal nerve fiber degeneration and central nervous system degeneration may be concurrent according to the thinning of RNFL measured by OCT in AD and MCI groups. RNFL measurement may also be useful for early diagnosis and evaluation of the disease progression. Further studies are needed to optimize the utility of this method as an ocular biomarker in AD.
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    A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection
    (2021) Oltu, Burcu; Aksahin, Mehmet Feyzi; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021
    Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals. Methods: This study analyzed the EEGs of 35 subjects (16 MCI, 8 AD, 11 healthy control) with the developed algorithm. The algorithm consists of 3 methods for analysis, discrete wavelet transform(DWT), power spectral density (PSD) and coherence. In the first approach, DWT is applied to the signals to obtain major EEG sub-bands, afterward, PSD of each sub-band is calculated using Burg's method. In the second approach, interhemispheric coherence values are calculated. The variance and amplitude summation of each sub-bands' PSD and the amplitude summation of the coherence values corresponding to the major sub-bands are determined as features. Bagged Trees is selected as a classifier among the other tested classification algorithms. Data set is used to train the classifier with 5-fold cross-validation. Results: As a result, accuracy, sensitivity, and specificity of 96.5%, 96.21%, 97.96% are achieved respectively. Conclusion: In this study, we have investigated whether EEG can provide efficient clues about the neuropathology of Alzheimer's Disease and mild cognitive impairment for early and accurate diagnosis. Accordingly, a decision support system that produces reproducible and objective results with high accuracy is developed.