Aim: Obsessive Compulsive Disorder (OCD) is a common psychiatric disorder that usually begins in adolescence. The fact that it is frequently seen together with other psychiatric disorders, its symptoms overlap with different mental illnesses, and the diagnosis is primarily based on clinical interviews and psychometric scales makes it difficult to diagnose obsessive-compulsive disorder. In this context, it is aimed to contribute to the objective diagnostic processes of OCD with biomarker and artificial intelligencesupported approaches. Materials and Methods: In this study, individuals diagnosed with OCD were classified from healthy individuals using two different hybrid deep learning models: Gated Recurrent Unit (GRU) and Transformer Encoder (TE) with one-dimensional convolutional neural networks (1DCNN), respectively. Results: In the 1DCNN-TE model, false negatives (11) and false positives (1) remain at low levels, while in the 1DCNN-GRU model, these values are 30 and 95, respectively. While the training and test accuracy of the 1DCNN-TE model is over 95%, the accuracy of the 1DCNN-GRU model has reached over 90%. While the training and test losses tend to decrease in both models, the fluctuations in the test loss are more pronounced in the 1DCNN-TE model. Conclusion: The results indicate that both deep learning models could classify OCD with high accuracy based on EEG signals and successfully learn discriminative features. However, the fluctuations observed in the test data and errors in detecting the control group have indicated limitations regarding the models’ generalizability and reliability on new data.

Aim: This study aimed to investigate mass psychogenic illness (MPI) across various institutional settings worldwide, identifying its triggering factors and symptom profiles. Additionally, it sought to analyze the organizational and psychosocial factors contributing to MPI outbreaks and to propose recommendations for their prevention. Method: The research was based on a dataset comprising peer-reviewed articles published between 2000 and 2025, written in English and documenting MPI cases within specific institutional contexts. A systematic review was conducted using PubMed, Scopus and Web of Science databases. Employing qualitative content analysis, 14 case studies were evaluated through descriptive and thematic approaches, focusing on institutional categories, triggering factors and symptom profiles. Results: The analysis revealed that 85.7% (12/14) of MPI cases occurred in school settings, while 7.1% (1/14) was reported to occur in a hospital and 7.1% (1/14) in an office environment. In schools, emotional vulnerability among adolescents, academic stress, misinformation dissemination and group interactions emerged as primary triggers. In contrast, environmental perceptions and dramatic interventions were prominent in hospital and office cases. The most frequently reported physical symptoms included nausea, headaches and dizziness, while psychical symptoms such as anxiety and panic were less common. Notably, MPI cases were more prevalent among female students. Conclusion: MPI is predominantly observed in school environments, driven by stress, misinformation and social contagion. Being less frequent in adult-oriented settings, its presence reflects distinct dynamics rather than immunity. The study highlights the critical role of organizational culture and leadership in preventing MPI. Strengthening psychosocial support systems, implementing stress management and fostering transparent communication within institutions can mitigate MPI risks.

Aim: The glymphatic system is a recently discovered waste drainage system that facilitates the movement of cerebrospinal fluid through the brain’s perivascular spaces and aids in removing soluble proteins. The Diffusion Tensor Imaging (DTI-ALPS) index analysis is a modern method used to evaluate the movement of water molecules in these spaces by measuring the diffusion coefficient. This study aimed to examine glymphatic system function in Alzheimer’s disease (AD) patients compared to healthy controls (HC) using the DTI-ALPS method and to analyze its relationship with cognitive disorders. Methods:DTI data from 59 AD patients and 59 HC were obtained by downloading medical data from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) platform. Using DSI Studio software, the diffusivities of the DTI data were extracted, and DTI-ALPS indices were calculated. Correlation analysis evaluated the relationship between the DTI-ALPS index and clinical features. Results:The findings indicated that the DTI-ALPS index was significantly lower in AD patients compared to HC (p = 0.042). Furthermore, the DTI-ALPS index showed a significant correlation with the Functional Activities Questionnaire (FAQ) (r = -0.214, p = 0.020) and the Mini-Mental State Examination (MMSE) (r = 0.225, p = 0.014). Conclusions:The study demonstrated that AD individuals have impaired glymphatic system function, as indicated by the DTIALPS index, which correlates with worse cognitive performance. These findings support early diagnosis methods for AD. A better understanding of glymphatic system function may provide new perspectives for monitoring AD progression.

This review aims to systematically examine the structural, functional, and cognitive differences between male and female brains through neuroscientific findings. The process of sexual differentiation begins prenatally under hormonal influences and continues to be shaped by environmental stimuli after birth. Based on an extensive literature review, the study discusses neuroanatomical structures (e.g., corpus callosum, hippocampus, amygdalae), neural connectivity patterns, and cognitive performance variations. It also explores the influence hormones on neurodevelopmental processes and the relationship between sexbased cognitive tendencies and individual differences. The findings indicate that gender-based distinctions should be interpreted within the framework of functional complementarity and neurodiversity, rather than superiority. Differences between male and female brains arise from a complex interplay of biological and environmental factors. These distinctions should be viewed as part of a broader spectrum of neurodiversity.

Neuroplasticity is a fundamental neuroscientific principle demonstrating that the brain can be structurally and functionally reshaped throughout life via experience, learning, and repetition. This concept plays a critical role in understanding the potential for change at the cognitive, emotional, and behavioral levels. Coaching, on the other hand, is a goal-oriented developmental process that enables individuals to question their current thought patterns, enhance self-awareness, and develop cognitive flexibility. This literaturebased review highlights how elements known to facilitate neuroplasticity-such as attention, emotional arousal, relational interaction, and experiential repetition-are inherently present in the coaching process. It is argued that coaching conversations provide a fertile ground for neuroplastic change, facilitating the formation of new synaptic connections, the weakening of outdated neural pathways, and the restructuring of cognitive maps. In this regard, coaching is proposed not only as a psychosocial intervention but also as a practice with the potential to induce change at the neurobiological level. This study aims to establish a theoretical framework for the intersection between coaching and neuroplasticity, while also offering a foundation for future experimental research in this field.