Neuroscience informatics最新文献

Background

Large vessel occlusion (LVO) stroke research is limited regarding high-risk patient groups for complications including cerebral edema. Large, well-phenotyped cohorts hold potential insights, but identifying cohorts and manually extracting outcomes is impractical. Natural language processing (NLP) software has previously extracted stroke characteristics from radiology reports, but there has not been an integrated extraction of both LVO classification and acute stroke outcomes.

Methods

We constructed a rules-based NLP pipeline that extracted presence/location of arterial occlusion and core/penumbral volumes from multimodal CT reports, along with presence of edema and midline shift on follow-up CTs. The algorithm flagged inconsistent reports for manual adjudication. We validated performance over two cohorts and analyzed the associations between NLP-extracted variables and clinical edema outcomes.

Results

The algorithm identified occlusions in the development ($n=577$) and test cohorts ($n=442$) with 94% and 85% recall, increasing to 97% and 93% after review of flagged reports. It could distinguish proximal ICA/M1 from distal occlusions with 96% recall and correctly extracted 98% of core/penumbral volumes. NLP recall was 93% and 86% for identifying edema and midline shift from follow-up reports of 213 patients with ICA/MCA occlusions. NLP-extracted radiographic edema captured 89% of those who developed clinical cerebral edema, which was more likely in those with NLP-identified proximal vs distal occlusions and associated with significantly higher core/penumbral volumes.

Conclusion

A rules-based NLP pipeline can accurately identify and phenotype an LVO cohort, yielding clinical associations with stroke research implications.

This paper investigates the development of an intelligent system method to address completely locked-in-syndrome (CLIS) that is caused by some illnesses such as Amyotrophic Lateral Sclerosis (ALS) as the most predominant type of Motor Neuron Disease (MND). In the last stages of ALS and despite the limitations in body movements, patients however will have a fully functional brain and cognitive capabilities and able to feel pain but fail to communicate. This paper aims to address the CLIS problem by utilizing EEG signals that human brain generates when thinking about a specific feeling or imagination as a way to communicate. The aim is to develop a low-cost and affordable system for patients to use to communicate with carers and family members. In this paper, the novel implementation of the ASPS (Automated Sensor and Signal Processing Selection) approach for feature extraction of EEG is presented to select the most suitable Sensory Characteristic Features (SCFs) to detect human thoughts and imaginations. Artificial Neural Networks (ANN) are used to verify the results. The findings show that EEG signals are able to capture imagination information that can be used as a means of communication; and the ASPS approach allows the selection of the most important features for reliable communication. This paper explains the implementation and validation of ASPS approach in brain signal classification for bespoke arrangement. Hence, future work will present the results of relatively high number of volunteers, sensors and signal processing methods.

Brain-Computer Interface (BCI) system consist of a variety of different applications based on the processing of electroencephalograph (EEG). One of the most significant categories are based on EEG signals segmentation for “Motor Imagery” (MI) classification.

When analytic methods use a fixed set of basis functions, the EEG signals frequently exhibit poor time-frequency localization. Additionally, these signals have a low signal-to-noise ratio (SNR) and highly non-stationary characteristics. As a result, BCI systems frequently have high error rates and low task detection accuracy.

This work is aiming to introduce the adaptive and data-driven based feature extraction method for MI-tasks classification. In this regard, empirical mode decomposition (EMD) and ensemble-EMD (EEMD) algorithms are explored. These data-driven decompositions decompose EEG signal into intrinsic mode functions (IMFs).

The IMFs are chosen to automatically reconstruct the EEG signal. The reconstructed EEG signal contains only information correlated to a specific motor imagery task and high SNR.

The time-domain features are extracted from both the algorithms and compared for the classification of right-hand and feet MI movements. The results have been compared to determine the suitability of each method. Different classifiers, including tree, naive bayes, support vector machine, k-nearest neighbors, ensemble average, and neural network (NN), have been tested for the proposed features in order to classify the features into right hand motor imagery and feet motor imagery tasks.

Our experimental results on the BNCI Horizon 2022 dataset show that the proposed method (EEMD) with three channels outperforms > 15% with EMD based filtering with narrow NN based classification.

Purpose: Radiomic features of gliomas are often used to predict genetic markers from radiological studies. Radiomic features were extracted from the contralateral brain to test if tumor texture is driving the predictive power of machine learning models. Ideally, these contralateral models would be a negative control for tumor radiomics models, since many studies use contralateral normal appearing white matter for normalization. This study used those features to attempt to predict IDH mutation status, MGMT promoter methylation, TERT promoter mutation, and ATRX mutation status with random forests.

Methods: Radiomic features were extracted from the tumor region, a mirrored contralateral region, a spherical region within the tumor, a spherical region on the contralateral, and a spherical sample of the ipsilateral side. These features were used independently to predict IDH, MGMT, TERT, and ATRX using random forests.

Main Findings: Contralateral features alone were as predictive of IDH mutation status as tumor features and had predictive power for several genetic markers.

Conclusion: Normalization with contralateral brain should be done carefully, and further investigation of potential radiological changes to the contralateral is warranted.

Telehealthcare systems are nowadays becoming a massive daily helping kit for elderly and disabled people. By using the Kinect sensors, remote monitoring has become easy. Also, the sensors' data are useful for the further improvement of the device. In this paper, we have discussed our newly developed “Eldo-care” system. This system is designed for the assessment and management of diverse neurological illnesses. The telemedical system is developed to monitor the psycho-neurological condition. People with disabilities and the elderly frequently experience access issues to essential services. Researchers today are concentrating on rehabilitative technologies based on human-computer interfaces that are closer to social-emotional intelligence. The goal of the study is to help old and disabled persons with cognitive rehabilitation using machine learning techniques. Human brain activity is observed using electroencephalograms, while user movement is tracked using Kinect sensors. Chebyshev filter is used for feature extraction and noise reduction. Utilizing the autoencoder technique, categorization is carried out by a Convolutional neural network with an accuracy of 95% and higher based on transfer learning. A better quality of life for older and disabled persons will be attained through the application of the suggested system in real time. The proposed device is attached to the subject under monitoring.

Automated segmentation of anatomical sub-regions with high precision has become a necessity to enable the quantification and characterization of cells/ tissues in histology images. An automated model to do this task is currently unavailable. One area of the brain which requires precise sub-region segmentation and downstream analysis is Substantia Nigra (SN). The loss of dopaminergic (DA) neurons in SN is the primary endpoint for majority of Parkinson's disease (PD) preclinical studies. The scientists rely on manually segmenting anatomical sub-regions of the brain which is extremely time-consuming and prone to labeler-dependent bias. In this study, we employed a UNet-based architecture to segment two sub-regions of SN-dorsal tier of substantia nigra pars compacta (SNCD) and reticulata (SNr). We compared model performance with various combinations of encoders, image sizes and sample selection techniques. The model is trained on approximately one thousand annotated 2D brain images stained with Nissl/ Haematoxylin and Tyrosine Hydroxylase enzyme (TH, indicator of dopaminergic neuron viability). The framework's output are: segmentation of SNr and SNCD irrespective of the tissue staining, quantitative readout for TH intensity indicating DA health status in the segmented regions. With the availability of training data, this model can be expanded to other 2D sub-region segmentation tasks. The shorter turnaround time, high accuracy and unbiased data output of this model will fulfill the ever increasing demands of data analysis in PD preclinical research.

Autism is a developmental disorder that affects around 62.1 million people globally. Estimates of its prevalence have been on the rise. Recent research suggests that in the United States alone, the cost of caring for individuals with autism could reach $461 billion by 2025, including medical expenses. Autism results from a combination of genetic and environmental factors, and molecular diagnosis can often be challenging. Therefore, there is a need for more reliable biomarkers to assist in clinical evaluation. Here, we employed a bioinformatics technique, Gene Set Enrichment Analysis (GSEA), that allows for the evaluation of whether specific genes associated with autism are related to common biological pathways and particular molecular processes using data extracted from genetic biobanks. Thus, it was possible to validate 910 genes related to autism by means of GSEA. The generated data indicated genetic convergence in a molecular pathway, suggesting that the disordered activation of the RAS-MAPK and PI3K-AKT signaling cascades converges in the mTOR pathway. Cell typification in silico indicated high expression in striated neurons, type D1 (p=5,947e-04) and type D2 (p=1,292e-05). In conclusion, our molecular pathway data can be used to assess, using computer modeling, whether new drug candidates for treating autism interact with proteins involved in the mTOR pathway, thus optimizing the screening of new drugs. In addition, with the evidence of such biomarkers and the development of easily accessible laboratory tests, in the future, the early clinical diagnosis of autism could be significantly improved.

The effects of the breathing pathway (nasal vs. oral) on the processing of emotions are not yet well-understood although there is evidence of respiratory entrainment of local field potential activity in human limbic networks and the importance of nasal airflow in shaping this entrainment. In this study, we compared the degree of various emotions triggered by different pieces of music during oral breathing (OB) and nasal breathing (NB). In addition, correlation of different musical features with emotions was investigated. Our results showed that during NB, subjects found songs more relaxing (p = 0.00013) and happier (p = 0.069), and they felt more arousal states from songs (p = 0.036) when compared to the same songs during OB, while during OB subjects' average rating for more negative emotions was higher when compared to NB (NS). During both OB and NB, we observed that the consonance degree of songs had significantly high positive correlations with positive emotions (valence: p < 0.01, happy: p < 0.05, relaxed: NB: p < 0.05, OB: NS) and significantly high negative correlations with negative emotions (angry: p < 0.001, fear: p < 0.05, frustrated: NB: p < 0.001, OB: NS), while the higher complexity rate of songs had a positive correlation with negative emotions (fear: p < 0.01, sad < 0.05, frustrated: p < 0.05, angry: OB: p < 0.05, NB: NS) and negative correlations with positive emotions (happy: p < 0.05, relaxed: p < 0.05, valence: p < 0.05).

Objective

To describe that the angle of the microguidwire on lateral projection under fluoroscopic image is a prediction of cannulation of the occluded inferior petrosal sinus in the transvenous embolization of cavernous sinus dural fistulas.

Methods

From January 2018 through January 2021, 12 cavernous sinus dural fistulas with ipsilateral inferior petrosal sinus occlusion identified in 12 consecutive patients were cured by cannulation of the occluded ipsilateral inferior petrosal sinus. Clinical, radiologic and procedure data of the 12 patients were retrospectively reviewed. The angle of microguidewire between on lateral projection under fluoroscopic image between the inferior petrosal sinus and the internal jugular vein was measured.

Results

In the 12 patients, access via the occluded ipsilateral inferior petrosal sinus was primarily attempted as the transvenous approach. During the procedure, the angle of microguidwire on lateral projection under fluoroscopic image between the inferior petrosal sinus and the internal jugular vein was 117°±7°, which is very useful to confirm the cannulation of the occluded inferior petrosal sinus. Complete occlusion was achieved in all fistulas, with no procedure-related morbidity or mortality. Postprocedural symptom was improved in all patients.

Conclusion

Cannulation of an occluded inferior petrosal sinus is possible and reasonable as an initial access attempt for cavernous sinus dural fistulas. The angle of microguidwire on the lateral projection under fluoroscopic image can help to confirm the orifice of the occluded inferior petrosal sinus.