Brain Sciences最新文献

This year, the selection criteria for highly cited articles in the 'Molecular and Cellular Neuroscience' section of Brain Sciences were focused on publications that achieved a citation count of 10 or more during 2024. Applying this metric, the Editorial Office, in collaboration with myself as Associate Editor of the 'Molecular and Cellular Neuroscience' section of the journal, identified eight articles that not only exemplified the mission of this section but also made significant scientific contributions by advancing our current understanding of the molecular and cellular mechanisms underlying major and rare neurological disorders. These articles encompass miscellaneous topics, including Alzheimer's disease (AD), chronic alcoholism, glioblastoma multiforme (GBM), amyotrophic lateral sclerosis (ALS), cognitive impairment, cerebrovascular disease, and Rett syndrome (RTT). Importantly, several contributions highlight experimental therapeutic strategies aimed at mitigating pathogenic mechanisms, offering promising avenues for translational research and future clinical applications.

Background: Ketamine and magnesium sulfate are commonly used perioperatively to prevent shivering, a frequent and clinically relevant complication of spinal and general anesthesia. Although their hypothermic effects are well documented, the neurotransmitter mechanisms underlying these effects remain insufficiently understood. This study examines whether serotonergic, adrenergic (α₂), and GABAergic (GABA_A) systems contribute to hypothermia induced by ketamine and a ketamine-magnesium sulfate combination. Methods: Body temperature was measured in Wistar rats after administration of ketamine (10 mg/kg) or the ketamine (5 mg/kg)-magnesium sulfate (5 mg/kg) combination. To assess neurotransmitter involvement, animals received yohimbine (α₂ antagonist), methysergide (non-selective 5-HT antagonist), or bicuculline (GABA_A antagonist) prior to ketamine or the drug combination. Data were analyzed using two-way repeated measures ANOVA followed by Tukey's post hoc test. Results: Yohimbine at 0.5 and 1 mg/kg significantly potentiated ketamine-induced hypothermia, while only 3 mg/kg enhanced the effect of the ketamine-magnesium sulfate combination. Methysergide had a bidirectional influence: 1 mg/kg methysergide deepened ketamine-induced hypothermia, whereas 0.5 mg/kg methysergide attenuated the hypothermic effect of the ketamine-magnesium sulfate combination. Bicuculline (1-2 mg/kg) did not alter the hypothermic responses to ketamine or the combination. Conclusions: These findings indicate that ketamine- and ketamine-magnesium sulfate-induced hypothermia is primarily modulated by serotonergic and adrenergic mechanisms, whereas GABA_A receptor-dependent pathways do not appear to play a major role under the experimental conditions used. These results provide new mechanistic insights into NMDA antagonist-related thermoregulation and may help inform anesthetic strategies for shivering prevention and maintenance of perioperative thermal stability.

Background: Transcranial Direct Current Stimulation (tDCS) has shown potential in improving negative symptoms (NS) and Cognitive Impairment Associated with Schizophrenia (CIAS). However, heterogeneity in stimulation protocols and sample characteristics limit definitive conclusions regarding tDCS effectiveness in schizophrenia. Given the detrimental effects of cigarette smoking, particularly on cognition, this study explored the role of cigarette smoking as a modifiable individual factor potentially contributing to methodological heterogeneity by evaluating tDCS effects on NS and CIAS in Smoker (SM) and Non-Smoker (NoSM) patients. Methods: Post hoc analyses of a double-blind RCT were performed on 50 patients, randomized to 2 mA active or sham-tDCS (15 weekday sessions) with bilateral bipolar-nonbalanced prefrontal placement. The sample was divided according to the smoking status, consisting of 28 SM and 22 NoSM. Separate one-way analyses of covariance (ANCOVA) were performed within each subgroup to assess changes over time between treatment conditions. Clinical outcomes included Positive and Negative Symptoms Scale (PANSS), Brief Assessment of Cognition in Schizophrenia (BACS), Clinical Global Impression (CGI) and Calgary Depression Scale for Schizophrenia (CDSS) total scores. Results: SM exhibited baseline lower cognitive scores in verbal memory, motor speed and working memory domains. NS improved in both SM and NoSM with large effect size. Significant improvement in CIAS, specifically in working memory and verbal fluency, were found exclusively in NoSM. Conclusions: Cigarette smoking appeared to limit tDCS effectiveness in improving CIAS but not NS in schizophrenia. We suggested that the neurotoxic milieu linked to chronic exposure to neurotoxins of cigarette smoking could be responsible for these effects, counterbalancing the neuroprotective effects of tDCS. Further studies are warranted to replicate these findings.

Background/Objectives: Concussions produce a wide array of symptoms that are often subtle and difficult to quantify. One such symptom involves reaction or response time (RT), consisting of perceptual latency time (LT) and movement time (MT). This pilot study examined the relationship between concussion history, mental health, and perceptual-motor performance among military veterans using a virtual reality (VR)-based assessment. The primary outcome was intraindividual variability (IIV), defined as the standard deviation of an individual's responses across repeated trials. Methods: Of 78 veterans who volunteered, 29 (22 males, 7 females) provided complete VR data. Participants completed surveys assessing concussion and combat history, mental health issues, and suicide ideation. During VR testing, participants responded to 40 trials requiring neck rotation, arm reach, and a step toward left or right virtual targets. Associations between predictors (e.g., concussion, mental health) and VR outcomes (RT, LT, IIV) were evaluated using Receiver Operating Characteristic (ROC) Area Under the Curve (AUC) values. Results: Concussion history was the strongest predictor of performance deficits. IIV metrics were sensitive indicators of both concussion and mental health issues. Univariable analyses yielded AUC values of 0.944-0.806 all of which were statistically significant (p ≤ 0.001), and multivariable analyses produced AUCs of 0.950-0.870 all of which were also statistically significant (p ≤ 0.001). Incongruent movements and longer LT values were especially discriminative. Conclusions: Veterans with concussion and mental health histories demonstrated quantifiable perceptual-motor impairments in VR environments. Findings support VR assessment as a feasible, sensitive tool for detecting subtle residual effects of concussion.

Objectives: Addiction disorders remain a major challenge in contemporary psychiatry due to high relapse rates and significant individual and societal burden. Despite advances in addiction neurobiology, current diagnostic frameworks and dominant models offer limited tools for early risk identification and dynamic support of clinical decision-making across the course of treatment. The aim of this narrative review is to introduce the MAC/MAB-RCS model as an integrated conceptual framework for risk stratification and personalized intervention in addiction psychiatry.

Methods: The proposed model integrates evidence from four complementary domains: genetic, epigenetic, and stress-axis biomarkers; functional brain network organization; and psychological/psychiatric dimensions relevant to addictive behaviors. These domains are synthesized into a unified conceptual structure designed to capture dynamic regulatory processes underlying addiction vulnerability.

Results: At the core of the model lies the Regulatory Control State (RCS), a latent higher-order construct representing an individual's dynamic regulatory capacity through the integration of cognitive control, emotional regulation, and motivational drive modulation. Disruption of the RCS is conceptualized as a shared transdiagnostic mechanism driving craving escalation, compulsive behavior, and relapse vulnerability, independent of substance class or specific addictive behavior.

Conclusions: The MAC/MAB-RCS model aligns with the principles of precision psychiatry by offering a pragmatic, clinically oriented translational framework with potential applicability across clinical settings, bridging neurobiological research and clinical practice. The review discusses its relationship to existing models, potential clinical and systemic applications, key limitations, and priorities for future validation studies.

Anosognosia (from the ancient Greek-a-, "without," nosos, "disease," and gnōsis, "knowledge") has been recognised as one of the most complex syndromes investigated from different theoretical and clinical perspectives in patients with brain damage since the beginning of the last century (see [...].

Background/Objectives: Adult degenerative scoliosis (ADS) is a spinal disease causing pain and reduced mobility, often occurring with degenerative lumbar spinal stenosis (DLSS). While fusion stabilizes the spine, it has drawbacks like loss of motion and adjacent segment degeneration. Minimally invasive techniques, such as tubular unilateral laminotomy for bilateral decompression (tULBD), provide a less invasive alternative, but their impact on ADS with DLSS is underexplored. This study examines the short-term effects of navigated tULBD on radiological and clinical outcomes in this patient population. Methods: This retrospective single-center study analyzed patients aged ≥18 years with DLSS and ADS (Cobb angle ≥ 10°), with or without grade I spondylolisthesis, who underwent navigated tULBD between June 2019 and October 2022. Radiological parameters were assessed pre- and post-operatively using AI-powered FXA™ Version 1.33, Raylytic Software GmbH, Leipzig, Germany, while clinical outcomes were evaluated using the Oswestry Disability Index (ODI) and Numeric Rating Scale (NRS) for back and leg pain. Statistical analyses were conducted with R Studio. Results: This study included 20 patients (mean age 74.6 ± 7.6 years, body mass index [BMI] 26.08 ± 3.7 kg/m²), with a median follow-up of 2 months. Most underwent single-level decompression (45%), with a median of 2 surgical levels (IQR: 1-3). Radiological parameters showed no significant changes (p > 0.05). Clinically, the median NRS back improved from 5 (IQR: 3-9) preoperatively to 2 (IQR: 0-2) postoperatively (p = 0.009) and 1 (IQR: 0-4.5) at follow-up (p = 0.004). NRS leg scores dropped from 3.5 (IQR: 0-5) to 0 postoperatively and at follow-up (p = 0.02, p = 0.04). ODI improved from 37.8 (IQR: 29-42.5) preoperatively to 17.5 (IQR: 5-24) at follow-up (p = 0.04). There were no neurological complications. Conclusions: Navigated tULBD is a promising, minimally invasive option for mild ADS and DLSS. It provides significant pain and disability relief without adversely affecting stability and alignment. Long-term studies are needed to confirm durability and efficacy, particularly in severe cases.

Background: Artificial intelligence (AI) has emerged as a transformative tool in medicine, leveraging rapid analysis of large datasets to accelerate diagnosis, enhance clinical decision-making, and improve clinical workflows. This is highly relevant in stroke care given the time-sensitive nature of the disease process. This review evaluates the current landscape of evidence-based medicine utilizing AI in stroke, with emphasis on its use in phases of clinical care across the stroke continuum, including pre-hospital, acute, and recovery phases. This offers a comprehensive understanding of the current state of AI in both practice and literature.

Methods: A review of major databases was conducted, identifying peer-reviewed literature evaluating the use of AI and its level of evidence across the stroke continuum. Given the heterogeneity of study designs, interventions, and outcome metrics spanning multiple disciplines, findings were synthesized narratively.

Results: Across all phases of care, there remain no randomized controlled trials (RCTs) evaluating patient-level outcome data using AI (Level A). In the pre-hospital phase of care, AI has been used to identify stroke symptoms and assist EMS routing/training but presently remains limited to research. AI is most studied in the acute phase of care, representing the only phase to achieve commercial application in imaging detection and telestroke assistance, supported by non-randomized evidence (Level B-NR). In the recovery phase, AI may enhance wearable technologies, tele-rehabilitation, and robotics/brain-computer interfaces, with early RCTs (Level B-R) supporting the latter two, representing the strongest evidence for AI in stroke care to date.

Conclusions: Despite the potential for AI to transform all phases of care across the stroke continuum, major challenges remain, including transparency, generalizability, equity, and the need for externally validated clinical studies.

The clinical management of neurological and psychiatric disorders is currently witnessing a paradigm shift [...].

Parkinson's disease (PD) is a multifactorial neurodegenerative disorder characterized by complex interactions across molecular, cellular, circuit, and behavioral scales. While experimental and clinical studies have provided critical insights into PD pathology, integrating these heterogeneous data into coherent mechanistic frameworks and translational strategies remains a major challenge. Computational modeling offers a powerful approach to bridge these scales, enabling the systematic investigation of disease mechanisms, candidate biomarkers, and therapeutic strategies. In this review, we survey state-of-the-art computational approaches applied to PD, spanning molecular dynamics and biophysical models, cellular- and circuit-level network models, systems and abstract-level simulations of basal ganglia function, and whole-brain and data-driven models linked to clinical phenotypes. We highlight how multiscale and hybrid modeling strategies connect α-synuclein pathology, mitochondrial dysfunction, oxidative stress, and dopaminergic degeneration to alterations in neural dynamics and motor and non-motor symptoms. We further discuss the role of computational models in biomarker discovery, including imaging, electrophysiological, and digital biomarkers. In particular, eye-movement-based measures are highlighted as quantitative, reproducible behavioral signals that provide principled constraints for individualized computational modeling. We also review the emerging impact of computational approaches on drug discovery, target prioritization, and in silico clinical trials. Finally, we examine future directions toward personalized and precision medicine in PD, emphasizing digital twin frameworks, longitudinal validation, and the integration of patient-specific data with mechanistic and data-driven models. Together, these advances underscore the growing role of computational modeling as an integrative and hypothesis-generating framework, with the long-term goal of supporting data-constrained predictive approaches for biomarker development and translational applications.