Neuroscience informatics最新文献

Background and purpose

Effective communication of radiologic findings is crucial in the acute evaluation of stroke. Conventional methods (CM) of communication can present challenges leading to delays in reporting, ultimately impacting patient outcomes. The goal of this retrospective study was to assess the utility of a HIPAA-compliant messaging system (MS) for expediting communication of CT findings by radiologists to members of the stroke team.

Materials and methods

Multimodal stroke protocol CT examinations performed at a high-volume stroke center between January and December 2019 were identified as part of this IRB-approved retrospective study. Read-back times were determined based on templated documentation within individual radiologic reports. The time interval between identification of findings and notification was calculated for each report, and usage of CM versus MS was documented. In addition, an online voluntary survey was disseminated to staff to assess user satisfaction with the MS.

Results and conclusions

In total, 442 CTs were included, and 126 (29%) documented use of the MS. The MS was associated with significant reductions in median read-back times, both for all reported findings (1.5 versus 16 minutes, p<0.01) and for critical findings (3 versus 15 minutes, p<0.01). The majority of MS users (48/57, 84%) felt that it was an effective method of communication beneficial to patient care. Use of the MS was associated with a significant decrease in read-back times and was well accepted as a mode of communicating CT findings between radiologists and interdisciplinary members of the stroke team.

The Flow Re-direction Endoluminal Device (FRED) is widely used to treat large intracranial unruptured cerebral aneurysms; however, some cases of deployment failure have been observed. Most of these cases occur in internal carotid artery aneurysms around the siphon. We report a method that we developed to predict the success of FRED deployment by using a preoperative angiography in simple lateral view.

In patients with Medial Temporal Lobe Epilepsy (MTLE), more severe impairment in the ipsilateral than the contralateral hemisphere white matter tracts, including Superior Longitudinal Fasciculus (SLF), are demonstrated on diffusion tensor imaging (DTI). Many clinicians and researchers conclude that drawing regions of interest (ROI) in the white matter can demonstrate these asymmetries. In this study we demonstrate that fractional anisotropy (FA) values derived from manually drawing ROI's on diffusion tensor imaging (DTI) of SLF differ between the side of seizure onset compared to the contralateral side in each individual patient with MTLE does not demonstrate these previous conclusions. We therefore believe that clinicians should recognize that this method of measurement can be inaccurate and should not be interpreted independently.

Processing large cohorts of data is very important to increase statistical power in neuroscience and neuroimaging. The Brain Imaging Data Structure (BIDS) standard has been developed to facilitate this task, but researchers are still facing the challenge of reusing scripts to analyse data coming from different projects or different centres. Here, our goal was to develop a tool, BIDS Manager-Pipeline, to process automated analyses using open-source research software solutions on several subjects at the same time. BIDS Manager-Pipeline builds upon BIDS Manager, a software recently developed in our lab to collect, organise, and manage multimodal multicentre datasets. When processing data, BIDS Manager-Pipeline coordinates the use of several modules originating from different software environments and executable programs and can thus be considered as a bridge between BIDS datasets and software solutions. Through a graphical user interface, users can filter the subjects of interest in the dataset by ID or by other subject criteria and common input analysis parameters. BIDS Manager-Pipeline first controls whether the selected values can be applied to the selected subset, provided the inputs and the outputs, and then run the process on all selected data. The results of analyses are stored using specifications for BIDS derivatives. In addition, BIDS Manager-Pipeline creates a table gathering the different metrics resulting from the analyses across subjects, for later statistical analyses. In short, BIDS Manager-Pipeline allows processing data from large cohorts, either in basic neuroscience or in clinical research. This framework can take advantage of tools developed by the neuroscience community, by centralizing and facilitating their use.

Image classification is a significant way in the field of image processing to automatically categorize large numbers of images. Brain tumor classification is mainly a helpful and widely desired process in the medical system. Brain tumor classification is a significant way to automatically categorize brain tumors images. Many methods have been introduced for solving the classification task with leverage low-level features. However, it has few limitations for achieving the higher accuracy of image tumor classification with minimum time. To overcome the issue, this study has proposed a novel technique called Regularized Anisotropic Filtered Tanimoto Indexive Deep Multilayer Perceptive Connectionist Network (RAFTIDMPCN), consisting of many layers of nodes for deeply analyzing the input and providing better classification results. The proposed architecture helps in improving the accuracy and reducing the time. The input layer receives the number of MRI images and natural image datasets collected from the dataset. Then the images are sent to the first hidden layer where the preprocessing is carried out to improve the image quality by removing the noise pixels using Regularized Anisotropic diffusion filtering technique. Followed by, shape, color, texture, and size features of input images are extracted in the second hidden layer. The classification is performed at the third hidden layer based on the Tanimoto similarity measure. Finally, the Heaviside step activation function is applied to obtain the classification results with higher accuracy. Experimental evaluation is carried out with different qualitative and quantitative results discussion by using brain tumor MRI dataset and natural image datasets. The obtained results indicate that the proposed technique provides better results in terms of Peak signal to noise ratio, accuracy, false-positive rate, time complexity, and space complexity. The analyzed results show the superior performance of our proposed RAFTIDMPCN technique accuracy by 6%, minimizes the false-positive rate by 40%, and time complexity by 11% in brain tumor detection when compared with the two state-of-the-art methods. This paper also presents several discoveries that could be helpful to the neurological community.

Objective: To investigate the molecular mechanism of enriched rehabilitation training in improving cognitive dysfunction of ischemic stroke.

Material and method: We established a rat model of cerebral ischemia with cognitive impairment and then carried out them to 4 groups according to different treatments. The all groups were performed Water maze training and cognitive function assessment. Western blot and Immunofluorescence were used to analyze the expression of synaptic plasticity-related proteins, regulatory proteins, and PKMζ in hippocampus. The patch clamp technique was used to record the field potential changes in the hippocampus and analyze the induction of LTP, and to observe the changes in field potential and LTP after the addition of PKMζ inhibitors.

Results: The results showed that enriched rehabilitation training improved cognitive function in stroke rats, increased the PKMζ and SYN protein levels, and enhanced LTP in the ischemia hippocampus. The enhanced LTP for the rats after enriched rehabilitation training was attenuated by ZIP.

Conclusion: We discuss this enriched rehabilitation training adaptive response to stroke as a potential underlying mechanism, which may play a role in improving synaptic plasticity and cognitive dysfunction.

Objective

Dural arteriovenous fistula (DAVF) is a complex condition and several different classifications have been proposed focusing on venous drainage directions. This study was to evaluate a practical and easy-to-follow Zipfel's classification for DAVFs based on new natural history data.

Methods

From 2014 to 2018, 143 patients of DAVFs (including 134 cerebral and 9 spinal) were consecutively retrospectively reviewed. Patients' pre-treatment status (pre-mRS) were analyzed using one-way ANOVA between Borden, Cognard and Zipfel's classifications. Patients' characteristics, treatments and outcomes were also analyzed between 3 groups of Zipfel's type I, II and III.

Results

The results of one-way ANOVA indicated that pre-mRS was significantly different between Zipfel's type and Cognard classifications (p<0.001 and p<0.001, respectively). This suggested that patients' pre-mRS was better distributed in Zipfel's classification than Cognard classification. In Zipfel's classification, male and non-sinus location were correlated with the Zipfel's type III DAVF (p<0.001). More than 3 arterial feeders and pial arterial suppliers were associated with a high grade (Zipfel's type II and III) DAVF (p=0.003). Worse symptoms were observed in Zipfel's type II and III DAVF (p=0.003) and aggressive endovascular treatment was needed (p<0.001). Transarterial treatment was usually used in Zipfel's type III DAVF and transvenous treatment was used commonly in Zipfel's type II DAVF (p<0.001).

Conclusions

The Zipfel's classification could evaluate the hemodynamic and clinical status of DAVFs and guide their treatment. The Zipfel's classification was informative by providing an effective assessment for the natural history of patients with simple but precise method.

To investigate prospective key genes and pathways associated with the pathogenesis and prognosis of stroke types along with subtypes. Human genes using genome assembly build 38 patch release 13 with known gene symbols through NCBI gene database (https://www.ncbi.nlm.nih.gov/gene) were fetched. PubMed advanced queries were constructed using stroke-related keywords and associations were calculated using Normalized pointwise mutual information (nPMI) between each gene symbol and queries. Genes related with stroke risk within their types and subtypes were investigated in order to discover genetic markers to predict individuals who are at the risk of developing stroke with their subtypes. A total of 2,785 (9.4%) genes were found to be linked to the risk of stroke. Based on stroke types, 1,287 (46.2%) and 376 (13.5%) genes were found to be related with ischemic stroke (IS) and hemorrhagic stroke (HS) respectively. Further stratification of IS based on TOAST classification, 86 (6.6%) genes were confined to Large artery atherosclerosis; 131 (10.1%) and 130 (10%) genes were related with the risk of small vessel disease and Cardioembolism subtypes of IS. Evidences reported through published meta-analysis and GWAS studies suggested for the association of nine highly associated genes and therefore, we concluded a prognostic panel of nine genes comprising of CYP4A11, ALOX5P, NOTCH, NINJ2, FGB, MTHFR, PDE4D, HDAC9, and ZHFX3 can be treated as a diagnostic marker to predict individuals who are at the risk of developing stroke. Validation of these nine prognostic markers are further required by conducting case-control studies embedded with large sample size.

Purpose

Several studies have indicated the relevance of spinal cord grey matter atrophy for Multiple Sclerosis (MS) related disability. This work aimed at evaluating feasibility and clinical relevance of MRI-derived estimations of spinal cord grey matter atrophy in clinical practice.

Methods

A convenience sample of MS patients (n=48) and healthy controls (HC, n=11) was scanned using brain sagittal 3D T1w (MPRAGE) and transverse T2w-FLAIR, and transverse single-slice spinal cord 2D heavily T1w (PSIR: phase-sensitive inversion recovery) sequences. An experienced technician with neuroradiological supervision used a semiautomated thresholding method implemented in JIM software on PSIR sequences to obtain cross-sectional area (CSA) of the spinal canal (CSAcanal), whole cord (CSAcord), grey (CSAgm) and white matter (CSAwm) at C2-C3. MPRAGE and T2w-FLAIR sequences were used to obtain brain parenchymal, grey and white matter fractions (BPF, GMF and WMF) using the Statistical Parametric Mapping software. Appropriate statistical tests were used before and after age and sex adjustment.

Results

CSAgm and CSAwm could be obtained in all HC, but only 18 patients (37.5%) due to presence of lesions at C2-C3. Significant univariate associations between EDSS and BPF (rho =−0.376, p=0.015), GMF (rho =−0.287, p=0.069), CSAcanal (rho =−0.310, p=0.049), CSAcord (rho =−0.533 p<0.001) and CSAgm (rho =−0.511, p=0.062) were detected. After age/sex adjustment trends were observed for CSAcanal and CSAcord.

Conclusion

In a clinical practice setting, relevance of spinal cord grey matter is confirmed, but cervical cord lesions could greatly hamper its application. Clinical associations with disability have been observed and seem stronger for spinal cord than for brain atrophy measures.