The role of the lateral habenula (LHb) as a hub for receiving and relaying signals from the limbic system to serotonergic, dopaminergic, and norepinephrinergic regions in the brainstem makes this area a critical region in the control of reward and addiction. Behavioral evidence reveals the vital role of the LHb in negative symptoms during withdrawal. In this investigation, we study the role of the LHb N-Methyl D-Aspartate receptor (NMDAR) in the modulation of tramadol reward. Male adult Wistar rats were used in this study. The effect of intra-LHb micro-injection of NMDAR agonist (NMDA, 0.1, 0.5, 2 µg/rat) and antagonist (D-AP5, 0.1, 0.5, 1 µg/rat) was evaluated in conditioned place preference (CPP) paradigm. The obtained results showed that intra-LHb administration of NMDA induced place aversion dose-dependently, while blockade of NMDAR in the LHb using D-AP5 micro-injection led to an increased preference score in the CPP task. Co-administration of NMDA (0.5 µg/rat) with tramadol (4 mg/kg) reduced preference score, while co-administration of D-AP5 (0.5 µg/rat) with a non-effective dose of tramadol (1 mg/kg) potentiate the rewarding effect of tramadol. LHb receives inputs from the limbic system and projects to the monoaminergic nuclei in the brainstem. It has been declared that NMDAR is expressed in LHb, and as obtained data revealed, these receptors could modulate the rewarding effect of tramadol. Therefore, NMDA receptors in the LHb might be a new target for modulating tramadol abuse.
Carpal tunnel syndrome (CTS) is a common peripheral nerve entrapment disorder that is diagnosed using clinical signs and symptoms and confirmed via nerve conduction studies (NCSs). While NCS is a semi-invasive procedure, magnetic resonance imaging (MRI) is a non-invasive diagnostic tool that detects macroscopic nerve abnormalities and evaluates a patient's surgical or medication treatment options. This study assessed magnetic resonance neurography (MRN)'s diagnostic and grading value by comparing it to electrodiagnostic studies in patients with CTS and healthy individuals.
Various treatment methods for drug abusers will result in different success rates. This is partly due to different neural assumptions and partly due to various rate of relapse in abusers because of different circumstances. Investigating the brain activation networks of treated subjects can reveal the hidden mechanisms of the therapeutic methods.We studied three groups of subjects: heroin abusers treated with abstinent based therapy (ABT) method, heroin abusers treated with Methadone Maintenance Therapy (MMT) method, and a control group. They were all scanned with functional magnetic resonance imaging (fMRI), using a 6-block task, where each block consisted of the rest-craving-rest-neutral sequence. Using the dynamic causal modeling (DCM) algorithm, brain effective connectivity network (caused by the drug craving stimulation) was quantified for all groups. In this regard, 4 brain areas were selected for this analysis based on previous findings: ventromedial prefrontal cortex (VMPFC), dorsolateral prefrontal cortex (DLPFC), amygdala, and ventral striatum.Our results indicated that the control subjects did not show significant brain activations after craving stimulations, but the two other groups showed significant brain activations in all 4 regions. In addition, VMPFC showed higher activations in the ABT group compared to the MMT group. The effective connectivity network suggested that the control subjects did not have any direct input from drug-related cue indices, while the other two groups showed reactions to these cues. Also, VMPFC displayed an important role in ABT group. In encountering the craving pictures, MMT subjects manifest a very simple mechanism compared to other groups.This study revealed an activation network similar to the emotional and inhibitory control networks observed in drug abusers in previous works. The results of DCM analysis also support the regulatory role of frontal regions on bottom regions. Furthermore, this study demonstrates the different effective connectivity patterns after drug abuse treatment and in this way helps the experts in the field.
Introduction: Intracranial chondroma and chondrosarcoma are very rare tumors that mainly originate from the base of the skull. Advanced neuroimaging studies, including magnetic resonance spectroscopy (MRS), play a pivotal role in both tumor diagnosis and presurgical planning. Case Presentation: We present two cases of intracranial cartilaginous tumors, including a chondroma and a chondrosarcoma, both of which presented with severe headaches. Due to inconclusive conventional MRI and MRS results, they were both primarily diagnosed as intra-axial brain tumors. However, pathological reports later confirmed the diagnosis of a chondroma and a chondrosarcoma. Conclusion: Based on the present findings, the use of advanced neuroimaging techniques, such as MRS, may improve diagnostic accuracy. We believe that MRS can play a significant role in the surgical planning of similar cases. Also, reporting rare cases worldwide can contribute to the improvement of radiographic diagnosis.
Background: Preliminary studies have shown that electrical source imaging (ESI) has numerous advantages for the pre-surgical evaluation of epileptic patients. However, the role of ESI for children with non-lesional drug resistance in focal epilepsy has been poorly characterized. Objectives: This study aimed to investigate this issue according to interictal epileptiform discharges (IEDs) and constraints in developing countries. Methods: The present study used long-term video electroencephalography (EEG) monitoring (LTM) data that were recorded using the standard 19 scalp electrodes (10 - 20 system) and 3 tesla T1 image data. Accordingly, first, IEDs were clustered and then assessed by an epileptologist. Afterward, some operations were conducted that included EEG inverse problem solving with three known methods, namely brain electrical source analysis (BESA) with the individual head model, cortical classical LORETA analysis recursively analysis (CLARA) with the individual head model, and BESA with the age template head model. Seven children were processed in this project. Results: In most cases (n = 5, 71%), the seizure onset zone (SOZ) was the same in the LTM report and the present proposed methodology. Moreover, this study succeeded in localizing the region of the predicted SOZ. Conclusions: According to limitations in a developing country, for the configuration of multi-modal studies (e.g., 3T magnetic resonance imaging, LTM, and ESI) with a specific and valuable protocol, this investigation defined a pilot study with a 7 data sample for the first step. These findings, based on the small sample size, suggest that ESI based on combining ensemble methods improves information for children with focal drug-resistant epilepsy. It is hoped that future studies with large sample sizes show the role of ESI in developing countries more than before.
This study aimed to investigate the potential relationship between diffusion kurtosis imaging (DKI)- derived parameters and lymphovascular space invasion (LVSI) in patients with cervical carcinoma. This prospective study included 30 patients with cervical carcinoma. The patients underwent MRI, diffusion-weighted imaging (DWI), and DKI prior to surgery. The surgical pathology results were accepted as the reference standard for determining the LVSI status. The DKI-derived parameters, including mean diffusivity (MD) and mean kurtosis (MK), were measured. The apparent diffusion coefficient (ADC) value was also assessed. The MD value of LVSI positive cervical carcinomas was significantly lower than LVSI negative carcinomas (p-value = 0.01). MK value was significantly higher in LVSI positive tumors (p-value = 0.01). However, the ADC value did not show a significant difference between LVSI positive and LVSI negative tumors (p-value = 0.2). MD and MK parameters showed similar diagnostic accuracy in identifying the LVSI status, with the area under the curve of 0.77 and 0.78, respectively. In this study, DKI-derived parameters were associated with the LVSI status in cervical carcinomas. Further studies with larger sample size are required to confirm these results.
In recent years, multiple data-driven fiber orientation distribution function (fODF) estimation algorithms and automatic tractography pipelines have been proposed to address the limitations of traditional methods. However, these approaches lack precision and generalizability. To tackle these shortcomings, we introduce a transformer-based pipeline to estimate fODFs and perform tractography. In this approach, a convolutional neural network (CNN) module is employed to project the resampled diffusion-weighted magnetic resonance imaging (DW-MRI) data to a lower dimension. Then, a transformer model estimates the fiber orientation distribution functions using the projected data within a local block around each voxel. The proposed model represents the extracted fODFs by spherical harmonics coefficients. The predicted fiber ODFs can be used for both deterministic and probabilistic tractography. Our pipeline was tested in terms of the precision and robustness in estimating fODFs and performing tractography using both simulated and real diffusion data. The Tractometer tool was employed to compare our method with the classical and data-driven tractography approaches. The qualitative and quantitative assessments illustrate the competitive performance of our framework compared to other available algorithms.
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