Browsing by Author "Güntekin, B."

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Basic Science and Pathogenesis
(2024) Noce, G.; Percio, C.D.; Lizio, R.; Lopez, S.; Jakhar, D.; Güntekin, B.; Babiloni, C.
BACKGROUND: Parkinson's disease and Huntington's disease are both neurodegenerative conditions involving the basal ganglia area of the brain. Both conditions can cause symptoms that affect movement. Cognitive decline or dementia can also occur in both. Resting state EEG (rsEEG) rhythms reflect neurophysiological mechanisms and operational functions related to the fluctuation of brain arousal and quiet vigilance in humans. The hypothesis was that rsEEG sources may be more abnormal in Huntington's disease patients in symptomatic stage (S-HD) than patients with dementia due to Parkinson's disease. METHOD: Clinical and rsEEG datasets in 16 PDD, 18 S-HD, and 25 matched cognitively unimpaired (Nold) participants - matched as demography, education, and gender - were taken from an international archive. The eLORETA freeware was used to estimate cortical rsEEG sources at delta, theta, alpha1, alpha2, alpha3, beta1, beta2, and gamma frequency bands. RESULT: Results showed lower amplitude of the posterior alpha activities and higher amplitude of widespread low frequencies bands (i.e., delta and theta) in the PDD and S-HD groups than in the Healthy group. As compared to the PDD group, the S-HD showed greater reductions in the rsEEG alpha 2 rhythms in the frontal and temporal regions (see Figure 1). CONCLUSION: These results suggest that cortical sources of rsEEG rhythms might reflect different abnormalities of the core neurophysiological mechanisms underlying brain arousal in quiet wakefulness and low vigilance in PDD, and S-HD patients. The mentioned rsEEG markers might be clinically useful in the disease staging, monitoring over time, and drug discovery. © 2024 The Alzheimer's Association. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.
Citation - WoS: 7
Citation - Scopus: 7
Lower Oddball Event-Related Eeg Delta and Theta Responses in Patients With Dementia Due To Parkinson's and Lewy Body Than Alzheimer's Disease
(Elsevier Inc., 2024) Yıldırım, E.; Aktürk, T.; Hanoğlu, L.; Yener, Görsev; Babiloni, C.; Güntekin, B.
Oddball task-related EEG delta and theta responses are associated with frontal executive functions, which are significantly impaired in patients with dementia due to Parkinson's disease (PDD) and Lewy bodies (DLB). The present study investigated the oddball task-related EEG delta and theta responses in patients with PDD, DLB, and Alzheimer's disease dementia (ADD). During visual and auditory oddball paradigms, EEG activity was recorded in 20 ADD, 17 DLB, 20 PDD, and 20 healthy (HC) older adults. Event-related EEG power spectrum and phase-locking analysis were performed at the delta (1–4 Hz) and theta (4–7 Hz) frequency bands for target and nontarget stimuli. Compared to the HC persons, dementia groups showed lower frontal and central delta and theta power and phase-locking associated with task performance and neuropsychological test scores. Notably, this effect was more significant in the PDD and DLB than in the ADD. In conclusion, oddball task-related frontal and central EEG delta and theta responses may reflect frontal supramodal executive dysfunctions in PDD and DLB patients. © 2024 Elsevier Inc.
Citation - Scopus: 1
Qeeg Methods To Probe Abnormal Brain Rhythms Related To Quiet Vigilance in Patients With Dementia Due To Alzheimer’s, Parkinson’s, and Lewy Body Diseases
(Humana Press Inc., 2024) Babiloni, C.; Güntekin, B.; Yener, Görsev; Del, Percio, C.
Here, we discuss relevant literature findings on abnormal resting-state scalp-recorded electroencephalographic (rsEEG) rhythms in old patients with severe cognitive deficits and disabilities in activities of daily living (i.e., dementia) due to Alzheimer’s (ADD), Parkinson’s (PDD), and Lewy body (DLB) neurodegenerative diseases. Furthermore, we described a modern quantitative EEG (qEEG) methodology to explore those rhythms and related vigilance disorders. The reviewed findings unveil consistent abnormalities in topographic and frequency (most in <12 Hz) features of the rsEEG rhythms recorded in ADD, PDD, and DLB patients, probably reflecting altered neurophysiological oscillatory mechanisms of synchronization and functional connectivity in neural brain populations underpinning the regulation and maintenance of the quiet vigilance. The proposed qEEG methodology showed significant differences in the posterior cortical sources of rsEEG alpha rhythms at individual frequencies among small groups of ADD, PDD, and DLB patients. Although the above abnormalities may have a limited diagnostic value at the individual level, not specifically reflecting the neuropathological processes underlying ADD, PDD, and DLB, they have significant heuristic and clinical relevance. Namely, the rsEEG readouts at the alpha frequencies unveiled the altered neurophysiological oscillatory mechanisms responsible for vigilance disorders in ADD, PDD, and DLB patients and may be used as pathophysiological biomarkers to evaluate the efficacy of (non)pharmacological interventions to treat those disorders. We recommend using the present qEEG methodology in longitudinal rsEEG studies carried out in ADD, PDD, and DLB patients to explore the abnormalities in the rsEEG biomarkers of vigilance dysregulations during the disease progression. © The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2024.
Citation - WoS: 1
Citation - Scopus: 2
Reconstructing Brain Functional Networks Through Identifiability and Deep Learning
(MIT Press Journals, 2024) Zanin, M.; Aktürk, T.; Yıldırım, E.; Yerlikaya, D.; Yener, Görsev; Güntekin, B.
We propose a novel approach for the reconstruction of functional networks representing brain dynamics based on the idea that the coparticipation of two brain regions in a common cognitive task should result in a drop in their identifiability, or in the uniqueness of their dynamics. This identifiability is estimated through the score obtained by deep learning models in supervised classification tasks and therefore requires no a priori assumptions about the nature of such coparticipation. The method is tested on EEG recordings obtained from Alzheimer’s and Parkinson’s disease patients, and matched healthy volunteers, for eyes-open and eyes-closed resting–state conditions, and the resulting functional networks are analysed through standard topological metrics. Both groups of patients are characterised by a reduction in the identifiability of the corresponding EEG signals, and by differences in the patterns that support such identifiability. Resulting functional networks are similar, but not identical to those reconstructed by using a correlation metric. Differences between control subjects and patients can be observed in network metrics like the clustering coefficient and the assortativity in different frequency bands. Differences are also observed between eyes open and closed conditions, especially for Parkinson’s disease patients. © 2024 Massachusetts Institute of Technology.