Browsing by Author "Tarlaci, Sultan"

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Interhemispheric EEG Coherence as a Candidate Biomarker in Gambling Disorder: Evidence of Frontal Hyperconnectivity and Posterior Disconnectivity
(Frontiers Media SA, 2025) Yilmazer, Eda; Cinaroglu, Metin; Ulker, Selami Varol; Tarlaci, Sultan
Background Gambling Disorder (GD) is a behavioral addiction marked by impaired decision-making and poor impulse control. We investigated whether resting-state interhemispheric quantitative EEG (qEEG) coherence-a measure of functional connectivity between homologous cortical regions-could serve as a biomarker of GD.Methods Twenty-nine male patients with GD and 45 healthy male controls underwent resting-state qEEG recording. Coherence was computed for homologous electrode pairs across delta, theta, alpha, and beta bands. Group differences were analyzed using independent-samples t-tests; associations with disorder duration were assessed via age-controlled partial correlations.Results Consistent with our hypothesis, GD participants exhibited frontal pole hypercoherence (Fp1-Fp2) across delta, theta, and beta bands, which is likely influenced by prefrontal/orbitofrontal generators. In contrast, GD showed hypocoherence in temporal (T3-T4, T5-T6), central (C3-C4), and parietal (P3-P4) regions across these frequencies. Greater disorder duration was associated with lower beta coherence at F3-F4 and Fp1-Fp2, and higher delta coherence at O1-O2.Conclusions These findings reveal a dual pattern of interhemispheric connectivity disruption in GD-hypercoherence at frontal pole sites and hypocoherence in sensorimotor and attentional posterior networks-supporting theoretical models of addiction neurocircuitry. Resting-state qEEG coherence holds promise as a clinically relevant biomarker for GD and may inform the development of neuromodulatory interventions aimed at network rebalancing.
Resting-State EEG Power and Machine-Learning Classification in Adult Males with Gambling Disorder
(Frontiers Media SA, 2026) Cinaroglu, Metin; Yilmazer, Eda; Ulker, Selami Varol; Tarlaci, Sultan
Background: Gambling disorder (GD) is a behavioral addiction sharing neurobiological features with substance use disorders, yet objective biomarkers remain limited. This study examined resting-state EEG power and applied machine learning to identify potential electrophysiological markers of GD. Methods: Resting eyes-closed Electroencephalography (EEG) was recorded from 47 individuals with GD and 32 healthy controls. Absolute and relative power across delta (1-4 Hz), theta (4-8 Hz), alpha (8-13 Hz), and beta (13-30 Hz) bands were quantified over eight cortical regions. Group differences and correlations with the South Oaks Gambling Screen (SOGS) were analyzed. Multiple comparisons were controlled using the Benjamini-Hochberg False Discovery Rate (FDR) correction. A Linear Discriminant Analysis (LDA) classifier was trained to differentiate GD from controls based on EEG features. Results: Group differences in EEG power were subtle, with GD showing significantly higher delta power in the left temporal region (p = 0.032, d = 0.43). Within the GD group, greater gambling severity was associated with higher absolute beta power across frontal, parietal, temporal, and occipital regions (r approximate to 0.40-0.50, p < 0.01), and these associations remained significant after FDR correction (pFDR < 0.05). The LDA model using absolute power achieved 73.7% classification accuracy (AUC = 0.74), whereas relative power yielded near-chance accuracy (57.9%). Conclusions: GD is characterized by subtle but meaningful EEG alterations, particularly increased beta activity linked to gambling severity. Multivariate EEG patterns can distinguish GD from controls, supporting the potential of resting-state EEG as a biomarker for clinical assessment and severity monitoring in behavioral addiction.
Volumetric and Cortical Thickness Alterations in Alcohol Dependence: Evidence of Accelerated Brain Aging and Clinical Correlations
(Frontiers Media SA, 2025) Cinaroglu, Metin; Yilmazer, Eda; Ulker, Selami Varol; Tacyildiz, Kerime; Tarlaci, Sultan
Background: Chronic alcohol dependence is associated with structural brain changes that resemble premature aging, particularly in frontal, parietal, and subcortical regions. This study examined brain volume, cortical thickness, and brain-predicted age in individuals with alcohol dependence and assessed associations with clinical symptoms. Methods: Thirty-one alcohol-dependent patients (mean age = 37.8 +/- 7.3 years) and 26 age-matched healthy controls (mean age = 35.0 +/- 8.5 years) underwent high-resolution T1-weighted MRI scanning. Brain structural analyses, including regional volumetry and cortical thickness estimation, were conducted using the validated volBrain platform. The system also provided individualized brain-predicted age estimates via its machine learning-based Brain Structure Ages (BSA) pipeline. Clinical assessments included the Michigan Alcoholism Screening Test (MATT), Penn Alcohol Craving Scale (PENN), Beck Depression and Anxiety Inventories (BDI-II, BAI), and detailed alcohol use history. Results: Alcohol-dependent participants showed significant reductions in total white matter, right frontal lobe, inferior frontal gyrus, bilateral postcentral gyri, and left superior occipital gyrus volumes (p < 0.05), along with widespread cortical thinning. Brain-predicted age was on average 11.5 years greater in patients than in controls (p < 0.001), especially in white matter and basal ganglia structures. Higher MATT scores correlated with reduced right precentral gyrus and left caudate volumes. PENN scores were positively associated with occipital volumes; however, this association weakened after controlling for age. Depression was linked to reduced frontal pole and increased amygdala volume, while anxiety was associated with smaller orbitofrontal and angular gyrus volumes. Conclusions: Alcohol dependence is marked by diffuse brain atrophy and accelerated brain aging. Structural alterations correspond to addiction severity, craving, and mood symptoms, highlighting brain-predicted age as a potential biomarker of cumulative alcohol-related neurodegeneration.