Browsing by Author "Tarlaci, Sultan"

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Attenuated Rightward Hemispheric Asymmetry in ADHD: Structural MRI Evidence from a Normalized Asymmetry Index and Its Association with Cognitive Performance
(Frontiers Media SA, 2026) Tarlaci, Sultan; Cinaroglu, Metin; Yilmazer, Eda; Ulker, Selami Varol
Background Altered hemispheric asymmetry has been proposed as a potential neurodevelopmental feature of Attention-Deficit/Hyperactivity Disorder (ADHD). However, findings remain inconsistent, and the functional relevance of structural asymmetry patterns is not well established. This study examines volumetric and cortical-thickness asymmetries across cortical and subcortical regions in children and adolescents with ADHD compared to typically developing controls and evaluates their association with objective cognitive performance. Methods Forty participants with ADHD and 30 age- and sex-matched controls underwent high-resolution T1-weighted MRI. Bilateral regional volumes and cortical thickness were quantified using the volBrain pipeline, and asymmetry indices (AI = [R-L]/[(R + L)/2]) were computed for lobar and subcortical structures. Group differences were assessed using independent t-tests. Within the ADHD group, associations between asymmetry indices and MOXO-d-CPT performance (Attention, Timing, Impulsivity, Hyperactivity) were examined using Pearson correlations with correction for multiple comparisons. Results ADHD participants showed significantly reduced rightward asymmetry in frontal lobe volume, cerebellar hemispheres, caudate, putamen, and amygdala (ps < 0.05). Cortical-thickness asymmetry was also diminished in the frontal and parietal lobes and the anterior cingulate cortex. Temporal and occipital asymmetries were preserved. Within the ADHD group, greater rightward frontal and ACC thickness asymmetry correlated with better attention performance (r = 0.45 and 0.40), rightward parietal asymmetry associated with more accurate timing (r = 0.38), reduced rightward IFG asymmetry related to greater impulsivity (r = -0.42), and amygdala asymmetry correlated with lower hyperactivity (r = 0.36). Conclusion Children with ADHD exhibit a consistent attenuation of typical right-hemisphere dominance across frontal, striatal, cerebellar, and limbic systems. These altered asymmetry patterns are meaningfully associated with attentional control, timing accuracy, impulsivity, and hyperactivity, suggesting that hemispheric imbalance may serve as a structural may represent a neurodevelopmental characteristic associated with ADHD. Findings support models emphasizing right-hemisphere developmental lag and highlight hemispheric asymmetry as a clinically relevant dimension of ADHD neurobiology.
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.
Redefinition of EEG Frequency Bands: A Fractal Model Inspired by Blagg’s Titius-Bode Law
(Frontiers Media SA, 2026) Tarlaci, Sultan; Cinaroglu, Metin; Yilmazer, Eda; Ulker, Selami Varol
The canonical frequency bands used to categorize human electroencephalographic (EEG) activity-delta, theta, alpha, beta, and gamma-have historically been defined using pragmatic and variably applied thresholds rather than a unifying mathematical principle. In this theoretical study, we propose a geometric framework for redefining EEG frequency bands based on logarithmic scaling, drawing on the exponential formulation introduced in Mary Blagg's refinement of the Titius-Bode law. Using the mean adult alpha rhythm as a reference frequency and applying a constant scaling ratio (R = 1.7275), we derive a mathematically ordered hierarchy of EEG band centers and boundaries within a continuous log-spaced spectrum. Unlike descriptive models of spectral 1/f scaling, the present framework provides an explicit generative rule for discrete band centers and transition frequencies. The resulting segmentation produces band definitions numerically consistent with commonly reported EEG frequency ranges while offering a fully proportional, non-overlapping structure. The model further introduces principled subdivisions within the alpha and gamma ranges and redefines the beta-gamma transition using geometric rather than conventional criteria. As a descriptive quantitative observation, the model-derived theta-alpha transition (similar to 7.98 Hz) lies in numerical proximity to the Earth's fundamental Schumann resonance (similar to 7.83 Hz); this correspondence arises from the predefined geometric rule and does not imply causal interaction. Overall, the proposed framework reframes EEG band organization as a mathematically explicit, scale-invariant system and provides a hypothesis-generating basis for future empirical evaluation of oscillatory structure.
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.