Background: By integrating evidence-based pre-, intra-, and postoperative therapies, enhanced recovery after surgery (ERAS) protocols have transformed perioperative care in numerous surgical specialties. Although their use in neurosurgery aims to maximize patient recovery, minimize complications, and reduce hospital stays, the evidence remains heterogeneous, and large randomized controlled trials are scarce. Objectives: This narrative review contrasts the clinical outcomes, advantages, and challenges of ERAS protocols with those of conventional postoperative care in neurosurgery, with a focus on cranial, spinal, pediatric, cancer, and functional procedures. Methods: A thematic synthesis of peer-reviewed clinical evidence was conducted using PubMed, Scopus, and Google Scholar (2000–2025). Studies comparing ERAS and standard care were reviewed for outcomes including length of stay (LOS), complications, pain control, opioid use, and patient satisfaction. Meta-analyses and systematic reviews were considered for context only, while emphasis was placed on primary clinical data. Results: ERAS protocols were associated with reduced LOS (3.9 → 3.4 days in spine; 13 → 10 days in cranial surgery), lower postoperative complications [fever −7.9%, unplanned intensive care unit admissions −34.1%], and decreased opioid and patient-controlled analgesia use (−12.1% and −60.2%, respectively). Patient recovery and functional independence improved, although 30-day readmissions slightly increased (+1.0%) before declining by 90 days (−0.3%). Implementation remains inconsistent due to resource limitations, challenges with multidisciplinary coordination, and protocol variability across institutions. Conclusion: This narrative review demonstrates that ERAS protocols improve recovery, reduce complications, and lower opioid dependence in neurosurgical patients compared with standard care. Despite these advantages, variation in protocol adherence and the limited number of high-quality RCTs restrict universal adoption. Future multicenter trials and standardized frameworks are essential to validate and optimize ERAS integration across neurosurgical practice.

The rapid integration of artificial intelligence (AI) into neurosurgical practices in the United States is transforming how diagnoses are interpreted, how surgical plans are developed, and how guidance is provided during operations as clinical needs continue to grow. Models based on radiomics and the Food and Drug Administration approved tools for tumor segmentation, aneurysm identification, and spinal navigation are showing enhanced accuracy and decreased variability among observers, highlighting AI’s potential for significant change. Nevertheless, there are major concerns about the opacity of black-box models, inaccurate outputs, and the increasing legal uncertainties arising from AI-related errors. Ethical challenges, such as the risk of clinician de-skilling, diminished professional autonomy, and exacerbated inequities in rural areas with limited access to imaging, complicate responsible deployment. This letter emphasizes the necessity for transparent validation processes, oversight led by clinicians, diverse and inclusive datasets, and improved regulatory protections. Requiring AI competency training and nationally reporting adverse events associated with AI are crucial to ensure that AI enhances clinical judgment rather than replacing it, thereby maintaining patient safety, equity, and professional integrity in neurosurgical decision-making.

Connectomics is a comprehensive mapping of structural and functional brain networks. It has introduced a paradigm shift in neurosurgery by redefining brain tumors as network disorders rather than isolated lesions. Unlike traditional surgeries, connectome-guided neurosurgery conceptualizes brain tumors as network disorders and aims to preserve brain function in resection. The objective was to study the role of connectome-guided neurosurgery in the management of Central Nervous System tumors and the use of structural and functional connectomics in tumor resection while preserving neurological function. Articles have been identified by searches of PubMed, Scopus and Google scholar from January 2000 to May 2025, using a combination of search terms like “connectomes,” “neurosurgery,” “central nervous system tumors,” “brain mapping,” “glioma surgery,” “tractography,” “neuroimaging,” “functional MRI,” “brain tumors,” “functional connectomics,” “structural connectomics,” “connectome-guided neurosurgery,” and “artificial intelligence” and addition of relevant references of articles. Included studies addressed both conventional and novel treatments for CNS tumors, including gliomas. Structural (DTI/tractography) and functional (fMRI, MEG, EEG) connectomes support preoperative planning, intraoperative guidance, and postoperative outcomes in CNS tumors. However, to reduce tumor size, a multiphase strategy is proposed by medical professionals, which includes rehabilitation, possible second surgery, and chemotherapy. Limitations of this study include tractography errors, fMRI neurovascular coupling, high costs, limited availability, and barriers to neurosurgeon training. Connectome-guided neurosurgery provides a paradigm shift from lesion-centric to network centric neurosurgery of CNS tumors. Although challenges remain, the use of connectomics with AI, intraoperative, and supramaximal resection strategies provides safe resection, reduces morbidity, and improves quality of life.

Hamayal et al found a significant correlation between metformin exposure and poor sleep quality in patients with diabetes, highlighting a significant clinical issue. However, the study’s validity, reproducibility, and generalizability are limited by methodological limitations. Causal inference is not possible due to the cross-sectional and single-center design, convenience sampling, and selection bias. The use of a self-report instrument, such as the Pittsburgh Sleep Quality Index, increases measurement and recollection bias and risks false classification of sleep disturbance. The relationship between metformin exposure and sleep quality is prone to residual confounding by diabetes duration, glycemic control, comorbidity burden, medication use, depressive symptoms, and socioeconomic factors. Reverse causality is also possible, as disrupted sleep may aggravate glycemic indices. Future research should adopt a prospective, multicentric design, objective measures of sleep, standardized definitions of case and outcome, and examine putative mechanisms involving antidiabetic interventions and sleep physiology.

Background: Meningiomas are the second most prevalent adult central nervous system neoplasm, developing from arachnoid cap cells. While most are benign (WHO Grade I), atypical (Grade II), and anaplastic (Grade III), meningiomas portray aggressive behavior, higher relapse rates, and resistance to standard treatments. Objective: To study the progression of treatment strategies for meningiomas, underscoring emerging treatments and challenges, especially in recurrent and high-grade subtypes. Methods: A narrative review was conducted, identifying studies from the PubMed, Scopus, and Google Scholar databases (2000–2025). Keywords included “meningioma,” “surgery,” “radiotherapy,” “targeted therapy,” “immunotherapy,” and “PRRT.” Included studies addressed conventional or novel treatments for meningiomas. Results: Surgical resection continues as the gold standard, with the extent of removal impacting recurrence. Radiotherapy, comprising fractionated and stereotactic techniques, plays a vital role when surgery is incomplete or not possible. Chemotherapy demonstrates limited benefit. Advances in molecular profiling have shown genetic drivers (e.g., NF2, TRAF7, AKT1) and hormonal receptors as clinical targets. Emerging options include mTOR inhibitors (everolimus), anti-angiogenic agents (bevacizumab), PD-1/PD-L1 inhibitors (pembrolizumab), and peptide receptor radionuclide therapy (PRRT). Drug delivery treatments like convection-improved delivery show promise in improving CNS penetration. Nonetheless, resistance mechanisms, the absence of validated biomarkers, and insufficient trial data pose significant challenges. Conclusion: Management of severe aggressive meningiomas is evolving with novel molecular and immunotherapeutic strategies. A multidisciplinary treatment strategy combining surgery, radiation, and targeted therapies, facilitated by further clinical trials and biomarker research, is necessary to enhance patient outcomes.