Neuroscience informatics最新文献

{"title":"Automated diagnosis of epileptic seizures using EEG image representations and deep learning","authors":"Taranjit Kaur,&nbsp;Tapan Kumar Gandhi","doi":"10.1016/j.neuri.2023.100139","DOIUrl":"10.1016/j.neuri.2023.100139","url":null,"abstract":"<div><h3>Background</h3><p>The identification of seizure and its complex waveforms in electroencephalography (EEG) through manual examination is time consuming, tedious, and susceptible to human mistakes. These issues have prompted the design of an automated seizure detection system that can assist the neurophysiologists by providing a fast and accurate analysis.</p></div><div><h3>Methods</h3><p>Existing automated seizure detection systems are either machine learning based or deep learning based. Machine learning based algorithms employ handcrafted features with sophisticated feature selection approaches. As a result of which their performance varies with the choice of the feature extraction and selection techniques employed. On the other hand, deep learning-based methods automatically deduce the best subset of features required for the categorization task but they are computationally expensive and lacks generalization on clinical EEG datasets. To address the above stated limitations and motivated by the advantage of continuous wavelet transform's (CWT) in elucidating the non-stationary nature of the EEG signals in a better way, we propose an approach based on EEG image representations (constructed via applying WT at different scale and time intervals) and transfer learning for seizure detection. Firstly, the pre-trained model is fine-tuned on the EEG image representations and thereafter features are extracted from the trained model by performing activations on different layers of the network. Subsequently, the features are passed through a Support Vector Machine (SVM) for categorization using a 10-fold data partitioning scheme.</p></div><div><h3>Results and comparison with existing methods</h3><p>The proposed mechanism results in a ceiling level of classification performance (accuracy=99.50/98.67, sensitivity=100/100 &amp; specificity=99/96) for both the standard and the clinical dataset that are better than the existing state-of-the art works.</p></div><div><h3>Conclusion</h3><p>The rapid advancement in the field of deep learning has created a paradigm shift in automated diagnosis of epilepsy. The proposed tool has effectually marked the relevant EEG segments for the clinician to review thereby reducing the time burden in scanning the long duration EEG records.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 3","pages":"Article 100139"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49528741","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Usefulness of novel fusion imaging with zero TE sequence and contrast-enhanced T1WI for cavernous sinus dural arteriovenous fistula","authors":"Takeru Umemura ,&nbsp;Yuko Tanaka ,&nbsp;Toru Kurokawa ,&nbsp;Satoru Ide ,&nbsp;Takatoshi Aoki ,&nbsp;Junkoh Yamamoto","doi":"10.1016/j.neuri.2023.100137","DOIUrl":"10.1016/j.neuri.2023.100137","url":null,"abstract":"<div><p>Evaluation of access routes and shunting points plays a crucial role in the treatment of cavernous sinus dural arteriovenous fistulas (CS-dAVF). Generally, these evaluations are performed using three-dimensional rotation angiography. However, assessing access routes becomes challenging in cases lacking anterior or posterior drainage routes. Zero TE magnetic resonance imaging (MRI) is an innovative technique enabling the visualization of cortical bone. By merging fusion images of zero TE and contrast-enhanced T1 weighted imaging (CE-T1WI), enhanced arteries can be visualized, resembling cranial bone-like three-dimensional rotation angiography. To determine the usefulness of fusion images in evaluating access routes and shunting points for dural arteriovenous fistulas, a comparison was made between these fusion images and three-dimensional rotation angiography in the same case. This report describes the application of fusion images in evaluating access routes and shunting points.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 3","pages":"Article 100137"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47526929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cortico-cortical connectivity changes during motor execution associated with sensory gating to frontal cortex: An rTMS study","authors":"Yosuke Fujiwara ,&nbsp;Koji Aono ,&nbsp;Osamu Takahashi ,&nbsp;Yoshihisa Masakado ,&nbsp;Junichi Ushiba","doi":"10.1016/j.neuri.2023.100136","DOIUrl":"10.1016/j.neuri.2023.100136","url":null,"abstract":"<div><p>As a change in the electroencephalogram (EEG) during motor tasks, the phenomenon in the sensorimotor area (SM1) is called event-related desynchronization (ERD). Motor commands are discharged from the primary motor area (M1) to the muscle through the corticospinal pathway and feedback to the primary somatosensory area (S1). This sensory input from the peripheral nerve stimulation to the central nervous system is attenuated during motor tasks by motor commands. This phenomenon is known as movement gating and is observed not only in S1, but also in non-primary motor areas. However, the brain circuits that trigger these motor-related changes and how the brain circuit modulates them as a controller remain unsolved. In this study, we evaluated the effects of spontaneous EEG changes and movement gating of somatosensory evoked potentials (SEPs) during motor execution by modulating cortical excitability with low-frequency repetitive transcranial magnetic stimulation (rTMS) over the PMc. Low frequency rTMS is known as an application where cortical excitability is suppressed after the stimulation. After rTMS, not only the previously known ERD, but also the newly gating of SEPs N30 and corticocortical spontaneous EEG changes were evaluated by Granger causality, which indicates that the time-varying causal relationship from the frontal to parietal area was significantly attenuated among eight healthy participants. These results suggest that spontaneous changes in EEG on SM1 and cortico-cortical connectivity during motor tasks are related to sensory feedback suppression of the frontal cortex.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 3","pages":"Article 100136"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46903604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cerebral AVM segmentation from 3D rotational angiography images by convolutional neural networks","authors":"Mounir Lahlouh ,&nbsp;Raphaël Blanc ,&nbsp;Michel Piotin ,&nbsp;Jérôme Szewczyk ,&nbsp;Nicolas Passat ,&nbsp;Yasmina Chenoune","doi":"10.1016/j.neuri.2023.100138","DOIUrl":"10.1016/j.neuri.2023.100138","url":null,"abstract":"<div><h3>Background and objective</h3><p>3D rotational angiography (3DRA) provides high quality images of the cerebral arteriovenous malformation (AVM) nidus that can be reconstructed in 3D. However, these reconstructions are limited to only 3D visualization without possible interactive exploration of geometric characteristics of cerebral structures. Refined understanding of the AVM angioarchitecture prior to treatment is mandatory and vascular segmentation is an important preliminary step that allow physicians analyze the complex vascular networks and can help guide microcatheters navigation and embolization of AVM.</p></div><div><h3>Methods</h3><p>A deep learning method was developed for the segmentation of 3DRA images of AVM patients. The method uses a fully convolutional neural network with a U-Net-like architecture and a DenseNet backbone. A compound loss function, combining Cross Entropy and Focal Tversky, is employed for robust segmentation. Binary masks automatically generated from region-growing segmentation have been used to train and validate our model.</p></div><div><h3>Results</h3><p>The developed network was able to achieve the segmentation of the vessels and the malformation and significantly outperformed the region-growing algorithm. Our experiments were performed on 9 AVM patients. The trained network achieved a Dice Similarity Coefficient (DSC) of 80.43%, surpassing other U-Net like architectures and the region-growing algorithm on the manually approved test set by physicians.</p></div><div><h3>Conclusions</h3><p>This work demonstrates the potential of a learning-based segmentation method for characterizing very complex and tiny vascular structures even when the training phase is performed with the results of an automatic or a semi-automatic method. The proposed method can contribute to the planning and guidance of endovascular procedures.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 3","pages":"Article 100138"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49161986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rules-based natural language processing to extract features of large vessel occlusion and cerebral edema from radiology reports in stroke patients","authors":"Zohair Siddiqui ,&nbsp;Kunal Bhatia ,&nbsp;Aaron Corbin ,&nbsp;Rajat Dhar","doi":"10.1016/j.neuri.2023.100129","DOIUrl":"10.1016/j.neuri.2023.100129","url":null,"abstract":"<div><h3>Background</h3><p>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.</p></div><div><h3>Methods</h3><p>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.</p></div><div><h3>Results</h3><p>The algorithm identified occlusions in the development (<span><math><mi>n</mi><mo>=</mo><mn>577</mn></math></span>) and test cohorts (<span><math><mi>n</mi><mo>=</mo><mn>442</mn></math></span>) 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.</p></div><div><h3>Conclusion</h3><p>A rules-based NLP pipeline can accurately identify and phenotype an LVO cohort, yielding clinical associations with stroke research implications.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100129"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42469001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"‘Tortured phrases’ in the neurosciences: A call for greater vigilance","authors":"Jaime A. Teixeira da Silva,&nbsp;Timothy Daly","doi":"10.1016/j.neuri.2023.100127","DOIUrl":"10.1016/j.neuri.2023.100127","url":null,"abstract":"","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100127"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46194307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach for communicating with patients suffering from completely locked-in-syndrome (CLIS) via thoughts: Brain computer interface system using EEG signals and artificial intelligence","authors":"Sharmila Majumdar ,&nbsp;Amin Al-Habaibeh ,&nbsp;Ahmet Omurtag ,&nbsp;Bubaker Shakmak ,&nbsp;Maryam Asrar","doi":"10.1016/j.neuri.2023.100126","DOIUrl":"10.1016/j.neuri.2023.100126","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100126"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46715985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Motor Imagery Tasks Based Electroencephalogram Signals Classification Using Data-Driven Features","authors":"Vikram Singh Kardam ,&nbsp;Sachin Taran ,&nbsp;Anukul Pandey","doi":"10.1016/j.neuri.2023.100128","DOIUrl":"10.1016/j.neuri.2023.100128","url":null,"abstract":"<div><p>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.</p><p>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.</p><p>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).</p><p>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.</p><p>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.</p><p>Our experimental results on the BNCI Horizon 2022 dataset show that the proposed method (EEMD) with three channels outperforms &gt; 15% with EMD based filtering with narrow NN based classification.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100128"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41931684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiomic features of contralateral and ipsilateral hemispheres for prediction of glioma genetic markers","authors":"Nicholas C. Wang ,&nbsp;Johann Gagnon-Bartsch ,&nbsp;Ashok Srinivasan ,&nbsp;Michelle M. Kim ,&nbsp;Douglas C. Noll ,&nbsp;Arvind Rao","doi":"10.1016/j.neuri.2023.100116","DOIUrl":"10.1016/j.neuri.2023.100116","url":null,"abstract":"<div><p>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.</p><p>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.</p><p>Main Findings: Contralateral features alone were as predictive of IDH mutation status as tumor features and had predictive power for several genetic markers.</p><p>Conclusion: Normalization with contralateral brain should be done carefully, and further investigation of potential radiological changes to the contralateral is warranted.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100116"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46768740","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Eldo-care: EEG with Kinect sensor based telehealthcare for the disabled and the elderly","authors":"Sima Das ,&nbsp;Arpan Adhikary ,&nbsp;Asif Ali Laghari ,&nbsp;Solanki Mitra","doi":"10.1016/j.neuri.2023.100130","DOIUrl":"10.1016/j.neuri.2023.100130","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":74295,"journal":{"name":"Neuroscience informatics","volume":"3 2","pages":"Article 100130"},"PeriodicalIF":0.0,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46862655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}