The ventral tegmental area (VTA) is a central hub in reward and motivation, integrating heterogeneous neuronal populations and projection-defined circuits to regulate learning, decision-making, and addiction. This review synthesizes anatomical organization and cellular diversity (dopaminergic, GABAergic, and glutamatergic neurons, including multitransmitter co-release), and maps major efferent pathways constituting the mesolimbic and mesocortical systems. We highlight computational roles of VTA activity, including reward prediction errors, incentive salience, and context memory integration, and summarize drug class-specific mechanisms that perturb VTA output. We then outline time-dependent neuroadaptations across mesocorticolimbic networks and discuss therapeutic implications spanning pharmacological, behavioral, and emerging circuit-targeted interventions. Together, these perspectives frame the VTA as a projection- and state-dependent controller whose dysfunction underlies core features of substance use disorders and other reward-related psychopathologies, pointing toward precision, circuit-informed treatments.
Objective: To develop a machine learning-based model for predicting epileptic seizures by analyzing electroencephalogram (EEG) signals and classifying the interictal and preictal phases. Methods: The EEG signals were analyzed in both the time and frequency domains using advanced feature extraction techniques. Three different machine learning approaches were employed, with special focus on long short-term memory (LSTM) networks. Features were extracted and classified to differentiate between seizure and non-seizure phases. The proposed models were evaluated using the publicly available CHB-MIT EEG dataset. The study was conducted between September 2022 and September 2024. Results: Among the tested models, the LSTM-based approach using the full-feature extraction pipeline achieved the highest prediction accuracy of 97.73%. This performance demonstrates strong potential for real-time seizure forecasting. Conclusion: Machine learning, particularly LSTM models, can accurately predict epileptic seizures by analyzing EEG signal patterns. These findings support the development of automated seizure prediction systems that could significantly improve the quality of life for epilepsy patients.
Objectives: To define prognostic factors that affect clinical outcome of patients after surgical resection of spinal intramedullary ependymoma. Methods: A retrospective multi-center study, including all cases operated on from 2007 through 2024 at 3 institutes. Data on surgical factors included: resection extent, intraoperative neurophysiology monitoring methods, and intraoperative/postoperative complications. Results: A total of 19 patients were included with a mean age of 37.8 years (SD = 13.2, range 8-57 years). The majority of ependymomas (n = 14, 73.7%) were in the cervical region. The most common presenting symptoms included pain in 14 patients (73.7%), sensory deficits in 11 patients (57.8%), and motor deficits 10 patients (52.6%). As per preoperative McCormick grading, 31.6%, 10.5%, and 31.6% were grade I, II, and III, respectively. Postoperatively, 57.9% of patients improved to McCormick grade I. Total gross tumor resection was achieved in 15 participants (78.9%). Syndromes, specifically neurofibromatosis type II, were significantly associated with a chance of recurrence (p = 0.004) and the need for a second surgery (p = 0.018). Conclusion: The preoperative neurological status correlates significantly with the postoperative outcome of the patient.
Objective: To assess burnout (BU) and sleep deprivation (SD) among neurology residents and their impact on clinical performance (CP). This study is based on a neurology residents from Saudi Arabia. Methods: This multi-centric, cross-sectional study invited 214 registered neurology residents in the country; 179 participated in the study. The Maslach Burnout Inventory (MBI), the Pittsburgh Sleep Quality Index (PSQI), and the Medical Council of Canada's Physicians' Self-Assessment Questionnaire (PSAQ) were used to evaluate burnout (BU) and sleep deprivation (SD) among neurology residents and their impact on clinical performance (CP). Results: Of the total 214 registered neurology residents, 179 participated in the study. About 50% reported irregularity in daily meals, while >50% failed to exercise due to time constraints. More than 80% of residents had moderate-to-high levels of emotional exhaustion (EE) and depersonalisation (DP), and 90% had low personal accomplishment (PA). The global PSQI score of 9.3 resulted from mediocre sleep quality, poor sleep duration, high sleep latency and daytime dysfunction. Moreover, 83.2% of residents complained of SD and opined that it affects their mental capability at work. Regarding residents' CP, 49.44% were high performers, while the proportion of low performers was marginally higher at 50.56%. The rampant prevalence of moderate-to-high EE and DP and poor PSQI scores had a significantly negative correlation with residents' CP (p= 0.0168, 0.0011 and 0.0441, respectively), CP correlated positively with PA levels (p=0.0045). Conclusion: Moderate to high levels of BU prevail among neurology residents in Saudi Arabia. A high number of neurology residents suffer from sleep disturbances. Both BU and SD exert a detrimental impact on neurology residents' CP, which adversely affects the quality of neurological care delivered by the burned-out and sleep-deprived residents.
Objective: To explore current practices and barriers in pediatric neuro-oncology care coordination, and to identify opportunities to optimize care pathways. Timely coordination between pediatric neurosurgery and oncology is critical for optimal outcomes in children with central nervous system (CNS) tumors. However, in many centers, pediatric oncologists are not involved until after surgery, which contributes to treatment delays and repeated procedures. Methods: A cross-sectional survey was conducted among neurosurgeons and pediatric oncologists in Saudi Arabia. The questionnaire addressed the timing of oncological involvement, neuroimaging practices, cerebrospinal fluid (CSF) cytology collection, and opinions on care coordination solutions. Results: A total of 62 responses were obtained. Only 29% of the participants reported presurgical oncology involvement in their typical practice, whereas 58% indicated that involvement occurred only after the final pathology results were obtained. Neuroimaging protocols varied: only 56.5% always performed dedicated CNS tumor magnetic resonance imaging (MRI) protocols before surgery, and spinal MRI was often delayed. CSF cytology practices were inconsistent, and major barriers included the lack of multidisciplinary tumor boards and the belief that oncology input before pathology is unnecessary. Over 88% of the respondents supported the use of presurgical tumor boards as a solution. Conclusion: There is substantial heterogeneity in pediatric neuro-oncology coordination practices. Efforts to promote early oncology consultations, standardized imaging protocols, and integrated care discussions may bridge critical gaps in care delivery.
