Brain Informatics最新文献_第3页

Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy. 基于 RNN-GC 与癫痫患者颅内脑电图信号的有效连通性分析，进行直角发病定位。

Brain Informatics Pub Date : 2024-08-23 DOI: 10.1186/s40708-024-00233-y

Xiaojia Wang, Yanchao Liu, Chunfeng Yang

{"title":"Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy.","authors":"Xiaojia Wang, Yanchao Liu, Chunfeng Yang","doi":"10.1186/s40708-024-00233-y","DOIUrl":"10.1186/s40708-024-00233-y","url":null,"abstract":"Epilepsy is one of the most common clinical diseases of the nervous system. The occurrence of epilepsy will bring many serious consequences, and some patients with epilepsy will develop drug-resistant epilepsy. Surgery is an effective means to treat this kind of patients, and lesion localization can provide a basis for surgery. The purpose of this study was to explore the functional types and connectivity evolution patterns of relevant regions of the brain during seizures. We used intracranial EEG signals from patients with epilepsy as the research object, and the method used was GRU-GC. The role of the corresponding area of each channel in the seizure process was determined by the introduction of group analysis. The importance of each area was analysed by introducing the betweenness centrality and PageRank centrality. The experimental results show that the classification method based on effective connectivity has high accuracy, and the role of the different regions of the brain could also change during the seizures. The relevant methods in this study have played an important role in preoperative assessment and revealing the functional evolution patterns of various relevant regions of the brain during seizures.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"22"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11343958/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142047303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HyEpiSeiD: a hybrid convolutional neural network and gated recurrent unit model for epileptic seizure detection from electroencephalogram signals. HyEpiSeiD：从脑电图信号中检测癫痫发作的混合卷积神经网络和门控递归单元模型。

Brain Informatics Pub Date : 2024-08-21 DOI: 10.1186/s40708-024-00234-x

Rajdeep Bhadra, Pawan Kumar Singh, Mufti Mahmud

引用次数: 0

Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator. 感知的皮层动力学表现为一连串连贯的伽马振荡，而脉络膜是中心协调器。

Brain Informatics Pub Date : 2024-08-20 DOI: 10.1186/s40708-024-00235-w

J Farineau, R Lestienne

{"title":"Cortical dynamics of perception as trains of coherent gamma oscillations, with the pulvinar as central coordinator.","authors":"J Farineau, R Lestienne","doi":"10.1186/s40708-024-00235-w","DOIUrl":"10.1186/s40708-024-00235-w","url":null,"abstract":"Synchronization of spikes carried by the visual streams is strategic for the proper binding of cortical assemblies, hence for the perception of visual objects as coherent units. Perception of a complex visual scene involves multiple trains of gamma oscillations, coexisting at each stage in visual and associative cortex. Here, we analyze how this synchrony is managed, so that the perception of each visual object can emerge despite this complex interweaving of cortical activations. After a brief review of structural and temporal facts, we analyze the interactions which make the oscillations coherent for the visual elements related to the same object. We continue with the propagation of these gamma oscillations within the sensory chain. The dominant role of the pulvinar and associated reticular thalamic nucleus as cortical coordinator is the common thread running through this step-by-step description. Synchronization mechanisms are analyzed in the context of visual perception, although the present considerations are not limited to this sense. A simple experiment is described, with the aim of assessing the validity of the elements developed here. A first set of results is provided, together with a proposed method to go further in this investigation.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"20"},"PeriodicalIF":0.0,"publicationDate":"2024-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11336127/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus. 基于深度学习的认知模型，探究闪烁刺激的心理物理学与电生理学之间的关系。

Brain Informatics Pub Date : 2024-07-10 DOI: 10.1186/s40708-024-00231-0

Keerthi S Chandran, Kuntal Ghosh

{"title":"A deep learning based cognitive model to probe the relation between psychophysics and electrophysiology of flicker stimulus.","authors":"Keerthi S Chandran, Kuntal Ghosh","doi":"10.1186/s40708-024-00231-0","DOIUrl":"10.1186/s40708-024-00231-0","url":null,"abstract":"The flicker stimulus is a visual stimulus of intermittent illumination. A flicker stimulus can appear flickering or steady to a human subject, depending on the physical parameters associated with the stimulus. When the flickering light appears steady, flicker fusion is said to have occurred. This work aims to bridge the gap between the psychophysics of flicker fusion and the electrophysiology associated with flicker stimulus through a Deep Learning based computational model of flicker perception. Convolutional Recurrent Neural Networks (CRNNs) were trained with psychophysics data of flicker stimulus obtained from a human subject. We claim that many of the reported features of electrophysiology of the flicker stimulus, including the presence of fundamentals and harmonics of the stimulus, can be explained as the result of a temporal convolution operation on the flicker stimulus. We further show that the convolution layer output of a CRNN trained with psychophysics data is more responsive to specific frequencies as in human EEG response to flicker, and the convolution layer of a trained CRNN can give a nearly sinusoidal output for 10 hertz flicker stimulus as reported for some human subjects.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"18"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236830/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach. 改进心理测量健康经济学中的李克特量表大数据分析：新构成数据方法的可靠性。

Brain Informatics Pub Date : 2024-07-10 DOI: 10.1186/s40708-024-00232-z

René Lehmann, Bodo Vogt

{"title":"Improving Likert scale big data analysis in psychometric health economics: reliability of the new compositional data approach.","authors":"René Lehmann, Bodo Vogt","doi":"10.1186/s40708-024-00232-z","DOIUrl":"10.1186/s40708-024-00232-z","url":null,"abstract":"Bipolar psychometric scales data are widely used in psychologic healthcare. Adequate psychological profiling benefits patients and saves time and costs. Grant funding depends on the quality of psychotherapeutic measures. Bipolar Likert scales yield compositional data because any order of magnitude of agreement towards an item assertion implies a complementary order of magnitude of disagreement. Using an isometric log-ratio (ilr) transformation the bivariate information can be transformed towards the real valued interval scale yielding unbiased statistical results increasing the statistical power of the Pearson correlation significance test if the Central Limit Theorem (CLT) of statistics is satisfied. In practice, however, the applicability of the CLT depends on the number of summands (i.e., the number of items) and the variance of the data generating process (DGP) of the ilr transformed data. Via simulation we provide evidence that the ilr approach also works satisfactory if the CLT is violated. That is, the ilr approach is robust towards extremely large or infinite variances of the underlying DGP increasing the statistical power of the correlation test. The study generalizes former results pointing out the universality and reliability of the ilr approach in psychometric big data analysis affecting psychometric health economics, patient welfare, grant funding, economic decision making and profits.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"19"},"PeriodicalIF":0.0,"publicationDate":"2024-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11236837/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141581096","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques. 基于脑电图的神经营销系统综述：最新趋势和分析技术。

Brain Informatics Pub Date : 2024-06-05 DOI: 10.1186/s40708-024-00229-8

Md Fazlul Karim Khondakar, Md Hasib Sarowar, Mehdi Hasan Chowdhury, Sumit Majumder, Md Azad Hossain, M Ali Akber Dewan, Quazi Delwar Hossain

{"title":"A systematic review on EEG-based neuromarketing: recent trends and analyzing techniques.","authors":"Md Fazlul Karim Khondakar, Md Hasib Sarowar, Mehdi Hasan Chowdhury, Sumit Majumder, Md Azad Hossain, M Ali Akber Dewan, Quazi Delwar Hossain","doi":"10.1186/s40708-024-00229-8","DOIUrl":"10.1186/s40708-024-00229-8","url":null,"abstract":"Neuromarketing is an emerging research field that aims to understand consumers' decision-making processes when choosing which product to buy. This information is highly sought after by businesses looking to improve their marketing strategies by understanding what leaves a positive or negative impression on consumers. It has the potential to revolutionize the marketing industry by enabling companies to offer engaging experiences, create more effective advertisements, avoid the wrong marketing strategies, and ultimately save millions of dollars for businesses. Therefore, good documentation is necessary to capture the current research situation in this vital sector. In this article, we present a systematic review of EEG-based Neuromarketing. We aim to shed light on the research trends, technical scopes, and potential opportunities in this field. We reviewed recent publications from valid databases and divided the popular research topics in Neuromarketing into five clusters to present the current research trend in this field. We also discuss the brain regions that are activated when making purchase decisions and their relevance to Neuromarketing applications. The article provides appropriate illustrations of marketing stimuli that can elicit authentic impressions from consumers' minds, the techniques used to process and analyze recorded brain data, and the current strategies employed to interpret the data. Finally, we offer recommendations to upcoming researchers to help them investigate the possibilities in this area more efficiently in the future.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11153447/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141248879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection. 利用基于注意力的 ResNet 方法估计脑年龄差距，用于阿尔茨海默病检测。

Brain Informatics Pub Date : 2024-06-04 DOI: 10.1186/s40708-024-00230-1

Atefe Aghaei, Mohsen Ebrahimi Moghaddam

{"title":"Brain age gap estimation using attention-based ResNet method for Alzheimer's disease detection.","authors":"Atefe Aghaei, Mohsen Ebrahimi Moghaddam","doi":"10.1186/s40708-024-00230-1","DOIUrl":"10.1186/s40708-024-00230-1","url":null,"abstract":"This study investigates the correlation between brain age and chronological age in healthy individuals using brain MRI images, aiming to identify potential biomarkers for neurodegenerative diseases like Alzheimer's. To achieve this, a novel attention-based ResNet method, 3D-Attention-Resent-SVR, is proposed to accurately estimate brain age and distinguish between Cognitively Normal (CN) and Alzheimer's disease (AD) individuals by computing the brain age gap (BAG). Unlike conventional methods, which often rely on single datasets, our approach addresses potential biases by employing four datasets for training and testing. The results, based on a combined dataset from four public sources comprising 3844 data points, demonstrate the model's efficacy with a mean absolute error (MAE) of 2.05 for brain age gap estimation. Moreover, the model's generalizability is showcased by training on three datasets and testing on a separate one, yielding a remarkable MAE of 2.4. Furthermore, leveraging BAG as the sole biomarker, our method achieves an accuracy of 92% and an AUC of 0.87 in Alzheimer's disease detection on the ADNI dataset. These findings underscore the potential of our approach in assisting with early detection and disease monitoring, emphasizing the strong correlation between BAG and AD.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"16"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11150363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-view graph-based interview representation to improve depression level estimation. 基于多视图的访谈表征，改善抑郁程度估计。

Brain Informatics Pub Date : 2024-06-04 DOI: 10.1186/s40708-024-00227-w

Navneet Agarwal, Gaël Dias, Sonia Dollfus

{"title":"Multi-view graph-based interview representation to improve depression level estimation.","authors":"Navneet Agarwal, Gaël Dias, Sonia Dollfus","doi":"10.1186/s40708-024-00227-w","DOIUrl":"10.1186/s40708-024-00227-w","url":null,"abstract":"Depression is a serious mental illness that affects millions worldwide and consequently has attracted considerable research interest in recent years. Within the field of automated depression estimation, most researchers focus on neural network architectures while ignoring other research directions. Within this paper, we explore an alternate approach and study the impact of input representations on the learning ability of the models. In particular, we work with graph-based representations to highlight different aspects of input transcripts, both at the interview and corpus levels. We use sentence similarity graphs and keyword correlation graphs to exemplify the advantages of graphical representations over sequential models for binary classification problems within depression estimation. Additionally, we design multi-view architectures that split interview transcripts into question and answer views in order to take into account dialogue structure. Our experiments show the benefits of multi-view based graphical input encodings over sequential models and provide new state-of-the-art results for binary classification on the gold standard DAIC-WOZ dataset. Further analysis establishes our method as a means for generating meaningful insights and visual summaries of interview transcripts that can be used by medical professionals.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"14"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11150354/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity. 中尺度的连接信息学：绘制大脑连接图的图像处理和分析的最新进展。

Brain Informatics Pub Date : 2024-06-04 DOI: 10.1186/s40708-024-00228-9

Yoon Kyoung Choi, Linqing Feng, Won-Ki Jeong, Jinhyun Kim

{"title":"Connecto-informatics at the mesoscale: current advances in image processing and analysis for mapping the brain connectivity.","authors":"Yoon Kyoung Choi, Linqing Feng, Won-Ki Jeong, Jinhyun Kim","doi":"10.1186/s40708-024-00228-9","DOIUrl":"10.1186/s40708-024-00228-9","url":null,"abstract":"Mapping neural connections within the brain has been a fundamental goal in neuroscience to understand better its functions and changes that follow aging and diseases. Developments in imaging technology, such as microscopy and labeling tools, have allowed researchers to visualize this connectivity through high-resolution brain-wide imaging. With this, image processing and analysis have become more crucial. However, despite the wealth of neural images generated, access to an integrated image processing and analysis pipeline to process these data is challenging due to scattered information on available tools and methods. To map the neural connections, registration to atlases and feature extraction through segmentation and signal detection are necessary. In this review, our goal is to provide an updated overview of recent advances in these image-processing methods, with a particular focus on fluorescent images of the mouse brain. Our goal is to outline a pathway toward an integrated image-processing pipeline tailored for connecto-informatics. An integrated workflow of these image processing will facilitate researchers' approach to mapping brain connectivity to better understand complex brain networks and their underlying brain functions. By highlighting the image-processing tools available for fluroscent imaging of the mouse brain, this review will contribute to a deeper grasp of connecto-informatics, paving the way for better comprehension of brain connectivity and its implications.","PeriodicalId":37465,"journal":{"name":"Brain Informatics","volume":"11 1","pages":"15"},"PeriodicalIF":0.0,"publicationDate":"2024-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11150223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141236712","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correction: Semantic representation of neural circuit knowledge in Caenorhabditis elegans. 更正：秀丽隐杆线虫神经回路知识的语义表征

Brain Informatics Pub Date : 2024-05-15 DOI: 10.1186/s40708-024-00226-x

Sharan J Prakash, Kimberly M Van Auken, David P Hill, Paul W Sternberg

引用次数: 0