International Journal of Neuroscience最新文献

Emotional states are fundamental attributes distinguishing humans from machines, and productivity represents one of the primary life objectives for this emotionally driven being. However, existing research on productivity and job performance frequently underestimates the impact of underlying emotional mechanisms. Thus, a systematic examination of the emotion-productivity interface is essential to clarify the psychophysiological processes that regulate work efficiency. In this study, emotional induction was achieved through a curated video stimulus set designed to evoke positive (happiness) and negative (sadness) responses in 39 participants, followed by a computer-based Stroop task. Electroencephalography (EEG) was employed to capture emotional states within the two-dimensional valence-arousal framework. During task performance, parameters related to productivity metrics were recorded. Three machine learning models - Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Random Forest (RF) - were implemented to predict productivity levels. For positive emotions, mean absolute error (MAE) values were 0.1031 (ANN), 0.0760 (SVM), and 0.0721 (RF). For negative emotions, the values were 0.1165, 0.0902, and 0.0659, respectively. Results demonstrated that productivity levels increased during tasks performed after the induction of positive emotions. Overall, this study provides empirical evidence that productivity can be predicted from emotional states, emphasizing their integral role in cognitive processes and their potential utility for optimizing workplace performance.

Objective: We investigated the association between middle cerebral artery atherosclerotic plaque features and cerebral small vessel disease (CSVD) imaging markers as well as the total CSVD burden, in patients with branch atheromatous disease (BAD).

Methods: Plaque parameters were quantified using high-resolution magnetic resonance imaging (HR-MRI) with ImageJ software, to characterize distribution, lumen stenosis, remodeling patterns, and other relevant features. Conventional MRI assessed CSVD imaging markers and total CSVD burden. Multivariate logistic regression analysis was performed following adjustment for potential confounders. Receiver operating characteristic (ROC) curve analysis with the DeLong test assessed the predictive value of plaque features for total CSVD burden.

Results: Compared with the non-plaque group, the plaque group showed significantly higher proportions of severe white matter hyperintensities (WMHs), cerebral microbleeds (CMBs), and moderate-to-severe CSVD burden (p < 0.05). In multivariate analysis, the presence of plaque was an independent risk factor for WMHs (OR = 2.920), CMBs (OR = 1.995), and moderate-to-severe CSVD burden (OR = 2.853); plaque distribution was an independent risk factor for WMHs (OR = 3.367); eccentric plaques were independent risk factors for lacunar infarction (OR = 8.670) and CMBs (OR = 7.891); positive remodeling (OR = 9.285) and eccentric plaques (OR = 10.355) were independent risk factors for moderate-to-severe CSVD burden. ROC analysis demonstrated plaque vulnerability effectively predicted moderate-to-severe CSVD burden (AUC = 0.8808, p < 0.05).

Conclusion: In ischemic stroke patients, distinct intracranial atherosclerotic stenosis (ICAS) plaque features correlate with specific CSVD phenotypes. Vulnerable plaques not only significantly increase total CSVD burden but also effectively predict CSVD severity. These findings elucidate how ICAS influences CSVD burden progression from an HR-MRI perspective and facilitate clinical risk stratification.

Purpose: Explore the function and dose calculation accuracy of MRI images in radiotherapy planning through deep learning methods.

Methods: 131 brain tumor patients undergoing radiotherapy with previous MR and CT images were recruited for this study. A new series of MRI from the aligned MR was firstly registered to CT images strictly using MIM software and then resampled. A deep learning method (U-NET) was used to establish a MRI-to-CT conversion model, for which 105 patient images were used as the training set and 26 patient images were used as the tuning set. Data from additional 8 patients were collected as the test set, and the accuracy of the model was evaluated from a dosimetric standpoint.

Results: Comparing the synthetic CT images with the original CT images, the difference in dosimetric parameters D98, D95, D2 and D_mean of PTV in 8 patients was less than 0.5%. The gamma passed rates of PTV and whole body volume were: 1%/1 mm: 93.96%±6.75%, 2%/2 mm: 99.87%±0.30%, 3%/3 mm: 100.00%±0.00%; and 1%/1 mm: 99.14%±0.80%, 2%/2 mm: 99.92%±0.08%, 3%/3 mm: 99.99%±0.01%.

Conclusion: MR images can be used both in delineation and treatment efficacy evaluation and in dose calculation. Using the deep learning way to convert MR image to CT image is a viable method and can be further used in dose calculation.

Objective: Ganglion cell layer thickness (GCLT) may be used as a potential marker for central neural changes. We compared GCLT by using spectral domain optical coherence tomography (SD-OCT) in patients with primary headache disorders and healthy controls. We seek whether there was any difference between the headache groups and whether any clinical parameters correlated to GCLT.

Methods: Fifty-three primary headache patients, 11 age and sex-matched healthy subjects were included in this cross-sectional study after power analysis. All subjects underwent SD-OCT. The duration of disorder, headache frequency, severity, duration of pain, presence of ocular pain, and accompanying symptoms have been collected.

Results: Mean GCLT of the headache group was 15.7 ± 3.8 µm (mean ± standard deviation), and the control group was 17.5 ± 2.4. The difference was not statistically significant. When we compared the controls, migraine and tension-type headache patients' GCLT values, we found a significant difference (ANOVA, p = 0.001). Migraine patients had thinner GCLT compared to all non-migraine headache patients (p = 0.01). Intraocular pressure values of migraine patients and non-migraine patients were not statistically significantly different (p = 0.13). The only clinical parameter that correlated with GCLT was pain duration (r = -0.43 and p = 0.01). The patients with white matter lesions had thinner GCLT (p = 0.046).

Conclusion: Our results suggest that not long-term suffering from pain but migraine pathophysiology itself seems to affect neuroretinal tissue. Pain duration was moderately and inversely correlated to GCLT, meaning that the longer the headache, the thinner the ganglion cell layer is.

Aim: We examined associations among injury severity, white matter structural connectivity within functionally defined brain networks and psychosocial/adaptive outcomes in children with traumatic brain injury (TBI).

Method: Participants included 58 youths (39 male) with complicated-mild TBI (cmTBI; n = 12, age =  12.6 ± 2.0), moderate/severe TBI (msTBI; n =  16, age =  11.4 ± 2.9) and a comparison group with orthopedic injury (OI; n =  24, age =  11.7 ± 2.1), at least 1 year post-injury. Participants underwent diffusion tensor imaging and parents rated children's behavioral and adaptive function on the CBCL and ABAS-3, respectively. Probabilistic tractography quantified streamline density. Group differences were analyzed for structural connectivity and behavioral outcomes.

Results: Groups differed in structural connectivity within regions of the default mode and central executive networks (ps < .05, FDR corrected). The msTBI group displayed decreased connectivity relative to cmTBI and OI, whereas the cmTBI group displayed increased connectivity relative to msTBI and OI. Similar patterns emerged in several behavioral domains. Ordinary least squares path analyses showed that structural connectivity mediated the relationship between injury severity and multiple parent-reported outcomes for msTBI.

Interpretation: White matter structural connectivity may explain unique variance in long-term psychosocial and adaptive outcome in children with TBI, particularly in cases of moderate-to-severe injury.

Background: Sciatica is a phrase used to describe radiating leg discomfort. The most common cause is lumbar disc herniation (LDH), which is considered to start in the nucleus pulposus. Advancements in lipidomics and metabolomics have unveiled the complex role of fatty acid metabolism (FAM) in both healthy and pathological states. However, the specific roles of fatty acid metabolism-related genes (FAMGs) in shaping therapeutic approaches, especially in LDH, remain largely unexplored and are a subject of ongoing research.

Methods: The junction of the weighted correlation network analysis (WGCNA) test with 6 FAMGs enabled the finding of FAMGs. Gene set variation analysis (GSVA) was used to identify the possible biological activities and pathways of FAMGs. LASSO was used to determine diagnostic effectiveness of the four FAMGs in diagnosing LDH. GSE124272, GSE147383, GSE150408, and GSE153761 were utilized to confirm the levels of expression of four FAMGs.

Results: Four FAMGs were discovered [Acyl-CoA Thioesterase 4 (ACOT4), Cytochrome P450 Family 4 Subfamily A Member 11 (CYP4A11), Acyl-CoA Dehydrogenase Long Chain (ACADL), Enoyl-CoA Hydratase and 3-Hydroxyacyl CoA Dehydrogenase (EHHADH)] For biological function analysis, mhc class ib receptor activity, response to thyroxine, response to l phenylalanine derivative were emphasized.

Conclusions: FAMGs can help with prognosis and immunology, and provide evidence for fatty acid metabolism-related targeted therapeutics. In LDH, FAMGs and their interactions with immune cells might be therapeutic targets.

Purpose: To evaluate cardiorespiratory fitness in patients with early to mid-stage Parkinson's disease by cardiopulmonary exercise test (CPET) on a stationary cycle ergometer.

Methods: To compare the differences in each index of the cardiopulmonary exercise test between the two groups of subjects; general data such as disease duration, medication use and exercise habits were also collected.

Results: (1) Finally, 36 Parkinson's disease patients and 12 healthy controls successfully completed the cardiopulmonary exercise test without any adverse events. (2) The V'O₂peak, Metspeak, RERpeak, MVVpeak, Wpeak, HRpeak, HRpeak/pre, percentage of HRR-1 min decay > 12 bpm, SBPpeak in the Parkinson's disease group were lower than those in the control group (p < .05, each). Detailed data: V'O2peak (15.7 ± 4.5vs21.5 ± 3.6 ml/kg/min, p < .01), Metspeak (4.5 ± 1.3 vs 6.1 ± 1.0, p < .01), RERpeak (1.04 ± 0.10 vs 1.15 ± 0.10, p = .001), MVVpeak (37.22 ± 11.58 vs 53.00 ± 16.85L/min, p = .009), Wpeak (49.17 ± 29.72 vs 49.17 ± 29.72W, p < .01), HRpeak (111.08 ± 16.67 vs 111.08 ± 16.67bpm, p < .01), HRpeak/pre (71.19 ± 10.06 vs 96.00 ± 21.13, p = .002), percentage of HRR-1min decay > 12bpm (33.3% vs 100%, p < .01), systolic blood pressure (155.81 ± 31.83 vs 175.83 ± 17.84 mmHg, p = .01). (3) Divided Parkinson's disease patients into high V'O₂peak group (V'O₂peak ≥ 15 mL/kg/min) and low V'O₂peak group (V'O₂peak < 15 mL/kg/min). The age of patients, Hoehn-Yahr grade and incidence of symptom fluctuation in high V'O₂peak group were lower (p < .05, respectively), percentage of males and percentage of HRR-1 min decay > 12 bpm were higher (p < .05, respectively); p < .05 is considered a statistically significant difference. Detailed data: age of patients(61.05 ± 6.93 vs 68.57 ± 7.99 years, p = .005), Hoehn-Yahr grade(1.75 ± 0.48 vs 2.18 ± 0.64, p = .028), incidence of symptom fluctuation (59.1 vs 92.9%, p = .03), percentage of males (77.7 vs 42.9%, p = .041), percentage of HRR-1 min decay > 12 bpm (50 vs 7.1%, p = .008).

Conclusions: Cardiopulmonary exercise test was safe to perform and the cardiorespiratory fitness is significantly reduced in patients with early and middle stage Parkinson's disease. Patients with Parkinson's disease presented blunted heart rate and systolic blood pressure responses to exercise test. Females, older age, fluctuating symptoms, high H-Y staging and higher activities of daily living may be associated with lower oxygen uptake.

Background: Chemoradiotherapy is the major means in the treatment of gliomas followed by surgery. Ferroptosis has been shown to play an important role in carcinogenesis by many studies. However, its underlying effect on chemoradiotherapy sensitivity in gliomas remains unclear.

Methods: The genetic and clinical information and ferroptosis-related genes were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to perform hub gene expression and survival analysis. Cell Counting Kit 8 (CCK-8), colony formation, 5-Ethynyl-2'-Deoxyuridine (EdU), Transwell and chemoradiotherapy sensitivity experiments were performed to confirm the biological function of RGS4 in glioma cells. The molecular mechanism of RGS4 on ferroptosis in gliomas was explored in vitro.

Results: 385 ferroptosis-related genes were identified via bioinformatics analysis. 16 differential expressed genes (DEGs) were identified as radiation-related genes. Among them, RGS4, HSPA5, and SLC40A1 had prognostic values in further analysis. The calculated risk score could significantly distinguish the high-risk population. Moreover, RGS4 expression was closely related with immune infiltration and regulators. RGS4 knockdown could inhibit the proliferation and migration of glioma cells. Down-regulation of RGS4 expression induced ferroptosis to promote cancer sensitivity to chemoradiotherapy.

Conclusions: A three-gene signature was developed in a risk-score model, which could be used to predict the prognosis of glioma patients. RGS4 is dysregulated in many types of cancers, and is a candidate prognostic biomarker for many types of cancers. Moreover, RGS4 may be a target for predicting and enhancing the chemoradiotherapy sensitivity of gliomas.