检索结果-内蒙古大学图书馆

arXiv 2024年

作者： Haefner, Ralf M. Beck, Jeff Savin, Cristina Salmasi, Mehrdad Pitkow, Xaq Department of Brain and Cognitive Sciences University of Rochester RochesterNY United States Department of Neurobiology Duke University DurhamNC United States Departments of Neural Science and Data Science New York University New YorkNY United States Gatsby Computational Neuroscience Unit Max Planck UCL Centre for Computational Psychiatry and Ageing Research University College London United Kingdom Neuroscience Institute Department of Machine Learning Carnegie Mellon University PittsburghPA United States Department of Neuroscience Center for Neuroscience and Artificial Intelligence Baylor College of Medicine HoustonTX United States Department of Electrical and Computer Engineering Department of Computer Science Rice University HoustonTX United States

This perspective piece is the result of a Generative Adversarial Collaboration (GAC) tackling the question 'How does neural activity represent probability distributions?'. We have addressed three major obstacles to progress on answering this question: first, we provide a unified language for defining competing hypotheses. Second, we explain the fundamentals of three prominent proposals for probabilistic computations - Probabilistic Population Codes (PPCs), Distributed Distributional Codes (DDCs), and Neural Sampling Codes (NSCs) - and describe similarities and differences in that common language. Third, we review key empirical data previously taken as evidence for at least one of these proposal, and describe how it may or may not be explainable by alternative proposals. Finally, we describe some key challenges in resolving the debate, and propose potential directions to address them through a combination of theory and *** Codes 92 Copyright © 2024, The Authors. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Nonlinear EEG analysis of mindfulness training using interpretable machine learning

Nonlinear EEG analysis of mindfulness training using interpr...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Pankaj Pandey Krishna Prasad Miyapuram Computer Science and Engineering Indian Institute of Technology Gandhinagar Gandhinagar India Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gandhinagar India

ISBN: (纸本)9781665429825

With an increasing effort from the scientific community to quantify the effects of meditation practices, various approaches have been used to establish neural correlates of experienced meditators. Numerous techniques have been studied to identify meditation-related changes in brain signals, including network analysis, synchronization metrics, and spectral features. Here, we employ nonlinear measures to explore the dynamical aspects of electroencephalography (EEG) signals in participants who had completed an 8-week Mindfulness Based Stress Reduction training program via pre-post intervention changes. Three nonlinear complexity measures comprising Detrended Fluctuation Analysis (DFA), Higuchi fractal dimension (FD), and Katz FD are implemented to measure the complexity of EEG signals. We examine theta and alpha frequency bands with a specific focus on four sub-bands, along with four anterior and posterior regions. Random Forest (RF) with SHAP explainability method is used to generate the results. RF models are trained for classification on features extracted from pre- and post-session. Our findings reveal that (a) Higuchi FD exhibits a decline post-training session and delivers the best classifying results; (b) the alpha wave contributes the most in the left-right frontal and parietal regions; (c) and overall, the right hemisphere has greater involvement. These findings add to the growing body of knowledge about the neural correlates of meditation. This research’s implications for designing neurotechnology products that improve attention, awareness, and kindness have been discussed.

关键词： Training Radio frequency Atmospheric measurements Time series analysis Particle measurements Feature extraction Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Predicting learning stages during the serial reaction time task using event-related potentials

Predicting learning stages during the serial reaction time t...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Ishita Arun Pankaj Pandey Goldy Yadav Krishna Prasad Miyapuram Centre for Cognitive and Brain Sciences Indian Institute of Technology Gandhinagar Gandhinagar India Computer Science and Engineering Indian Institute of Technology Gandhinagar Gandhinagar India

ISBN: (纸本)9781665429825

Learning a sequence of movements is akin to the acquisition of a motor skill. We investigated event-related potentials (ERPs) changes, particularly the error-related negativity (ERN) and P200 components, as participants learned a motor sequence using a serial reaction time task. Unlike previous studies that investigated error-related negativity for only incorrect motor responses, we tracked ERN changes for all responses. We found that ERN decreased significantly from early to later stages of learning. We also observed a significant change in the P200 component associated with increased selective attention to relevant stimuli as learning occurred. Scalp topography showed the differences in neural activity during motor sequence learning in the frontal, central, and parietal regions. We then employed machine learning to identify the best predictors of motor learning stages. Using random forest, we found the most discriminating pattern for early versus late learning phases. The combination of three electrodes, including ‘C3-C4-P3’, obtained the maximum accuracy of 82% while classifying EEG signals corresponding to early and late stages of learning. Our study demonstrates that ERN and P200 signals can serve as temporal neural markers for motor skill learning.

关键词： Electrodes Electric potential Tracking Scalp Neural activity Forestry Surfaces

来源：评论

学校读者我要写书评

暂无评论

Multi-Omics Data Clustering via the Guidance of Highly Correlated Features

Multi-Omics Data Clustering via the Guidance of Highly Corre...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Bin Zhang Hongmin Cai School of Computer Science and Engineering South China University of Technology Guangzhou China Brain and Affective Cognitive Research Center Pazhou Lab Guangzhou China

ISBN: (纸本)9781665429825

Multi-omics data clustering aims at assigning elements to their respective categories via exploiting the complementary information among multi-omics samples, with the widely application on data mining, bioinformatics, biomedicine and so on. Current multi-omics data clustering methods rely heavily on accurate sample-wise affinity estimation, which is limited by large heterogeneous gap among different sampling spaces. In this paper, we propose a novelly collaborative clustering method, which measures feature-wise matching instead of sample-wise affinity for sample assignment via the guidance of the highly matched features. The method consists of three modules. Firstly, the feature matching is learned by feature-wise correlation. Secondly, the feature matching matrix is factorized into two coded bases within yielded feature spaces. Thirdly, the sample assignment is achieved by joint sample reconstruction employing the two bases. The sample-wise assignment and feature-wise matching are optimized in a collaborative scheme with mutually reinforcing relationship. Extensive experiments demonstrate the superior performance of our proposed method compared with benchmark ones.

关键词： Correlation Clustering methods Current measurement Conferences Collaboration Estimation Benchmark testing

来源：评论

学校读者我要写书评

暂无评论

Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech

arXiv

引用

arXiv 2021年

作者： Szubert, Ida Abend, Omri Schneider, Nathan Gibbon, Samuel Mahon, Louis Goldwater, Sharon Steedman, Mark The School of Informatics The University of Edinburgh United Kingdom The Centre for Clinical Brain Sciences The University of Edinburgh United Kingdom The School of Computer Science and Engineering The Department of Cognitive Science The Hebrew University of Jerusalem Israel The Department of Linguistics and Computer Science Georgetown University DC United States

Corpora of child speech and child-directed speech (CDS) have enabled major contributions to the study of child language acquisition, yet semantic annotation for such corpora is still scarce and lacks a uniform standard. Semantic annotation of CDS is particularly important for understanding the nature of the input children receive and developing computational models of child language acquisition. For example, under the assumption that children are able to infer meaning representations for (at least some of) the utterances they hear, the acquisition task is to learn a grammar that can map novel adult utterances onto their corresponding meaning representations, in the face of noise and distraction by other contextually possible meanings. To study this problem and to develop computational models of it, we need corpora that provide both adult utterances and their meaning representations, ideally using annotation that is consistent across a range of languages in order to facilitate cross-linguistic comparative studies. This paper proposes a methodology for constructing such corpora of CDS paired with sentential logical forms, and uses this method to create two such corpora, in English and Hebrew. The approach enforces a cross-linguistically consistent representation, building on recent advances in dependency representation and semantic parsing. Specifically, the approach involves two steps. First, we annotate the corpora using the Universal Dependencies (UD) scheme for syntactic annotation, which has been developed to apply consistently to a wide variety of domains and typologically diverse languages. Next, we further annotate these data by applying an automatic method for transducing sentential logical forms (LFs) from UD structures. The UD and LF representations have complementary strengths: UD structures are language-neutral and support consistent and reliable annotation by multiple annotators, whereas LFs are neutral as to their syntactic derivation and transparently

关键词： Syntactics

来源：评论

学校读者我要写书评

暂无评论

Analysis of regions of interest and distractor regions in breast biopsy images

Analysis of regions of interest and distractor regions in br...

引用

2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Lu, Ximing Mehta, Sachin Brunyé, Tad T. Weaver, Donald L. Elmore, Joann G. Shapiro, Linda G. Paul G. Allen School of Computer Science and Engineering University of Washington Seattle United States Center for Applied Brain and Cognitive Sciences School of Engineering Tufts University Medford United States Department of Medicine University of Vermont Burlington United States David Geffen School of Medicine University of California Los Angeles United States

ISBN: (纸本)9781665403580

This paper studies why pathologists can misdiagnose diagnostically challenging breast biopsy cases, using a data set of 240 whole slide images (WSIs). Three experienced pathologists agreed on a consensus reference ground-truth diagnosis for each slide and also a consensus region of interest (ROI) from which the diagnosis could best be made. A study group of 87 other pathologists then diagnosed test sets (60 slides each) and marked their own regions of interest. Diagnoses and ROIs were categorized such that if on a given slide, their ROI differed from the consensus ROI and their diagnosis was incorrect, that ROI was called a distractor. We used the HATNet transformer-based deep learning classifier to evaluate the visual similarities and differences between the true (consensus) ROIs and the distractors. Results showed high accuracy for both the similarity and difference networks, showcasing the challenging nature of feature classification with breast biopsy images. This study is important in the potential use of its results for teaching pathologists how to diagnose breast biopsy slides. © 2021 IEEE

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Left inferior parietal lobe and auditory cortex jointly contribute to sound knowledge retrieval

引用

brain stimulation 2025年第4期18卷 1037-1047页

作者： Philipp Kuhnke Ole Numssen Johannah Voeller Vincent K M Cheung Konstantin Weise Markus Kiefer Gesa Hartwigsen Wilhelm Wundt Institute for Psychology Leipzig University Germany Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany. Electronic address: philipp.kuhnke@uni-leipzig.de. Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany Methods and Development Group Brain Networks MPI CBS Leipzig Germany TMS Group Department of Neuroscience and Biomedical Engineering Aalto University Finland TMS Lab Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Boston US. Department of Experimental Psychology University of Granada Spain Mind Brain and Behavior Research Center (CIMCYC) University of Granada Spain. Sony Computer Science Laboratories Tokyo Japan. Leipzig University of Applied Sciences HTWK Leipzig Germany. Department of Psychiatry Ulm University Germany. Research Group Cognition and Plasticity Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) Leipzig Germany.

BACKGROUND:Conceptual knowledge is central to human cognition. Neuroimaging studies suggest that conceptual processing relies on the joint contribution of modality-specific perceptual-motor and multimodal brain regions. In particular, the multimodal left inferior parietal lobe (IPL) couples with auditory association cortex during sound knowledge retrieval and with somatomotor cortex during action feature retrieval. However, it remains unknown whether the interaction between modality-specific and multimodal cortices is causally relevant for conceptually-guided behavior. METHODS:To tackle this issue, we applied offline transcranial magnetic stimulation (TMS) over modality-specific cortex (somatomotor, auditory, or sham), before 24 healthy participants received online TMS over multimodal cortex (IPL, or sham) during action and sound knowledge tasks on written words. For the first time in a TMS study on higher cognition, TMS targeting and dosing were optimized using individual a priori computational electric field simulations. RESULTS:We found that combined stimulation of auditory association cortex and multimodal IPL selectively impaired sound judgments, but not action judgments, on low sound-low action words (BF = 5.316). Unifocal TMS over auditory cortex or IPL alone did not disrupt behavior, suggesting adaptive compensatory plasticity. CONCLUSIONS:These results indicate that the joint contribution of multimodal IPL and auditory cortex is causally relevant for sound knowledge retrieval: The functional relevance of left IPL depends on the integrity of the auditory cortex, and vice versa, as single perturbation of either region did not disrupt performance. Our findings support "hybrid theories" of conceptual processing, which propose that task-dependent functional interactions between modality-specific and multimodal regions are crucial.

关键词： TMS compensation computational modeling concepts plasticity semantics

来源：评论

学校读者我要写书评

暂无评论

Initial Investigations of Pinhole Numbers and Projection Views in Multi-pinhole brain SPECT

Initial Investigations of Pinhole Numbers and Projection Vie...

引用

IEEE Symposium on Nuclear science (NSS/MIC)

作者： Wenbo Huang Greta S. P. Mok Department of Electrical and Computer Engineering Faculty of Science and Technology and the Center for Cognitive and Brain Sciences Institute of Collaborative Innovation University of Macau Taipa Macau SAR China

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

SPECT is a powerful tool for diagnosing neurological diseases such as Alzheimer’s disease (AD) and Parkinson’s disease (PD), while multi-pinhole (MPH) SPECT could offer superior resolution-detection efficiency trade-off. Here we aim to investigate the relationship of pinhole numbers and projection views in MPH brain SPECT for a conventional dual-head SPECT scanner. The NCAT brain phantom was used to simulate 2 activity distributions, i.e., 99m Tc-HMPAO for AD and 99m Tc-TRODAT for PD. The target system spatial resolution was set to 12 mm for a 200 mm FOV to cover the whole patient’s head. We used a 3D analytical MPH projector to generate 64, 32, 16, 8, 4 and 2 noise free projections for 1-15 pinholes and noisy projections for 1-20 pinholes respectively. The noise levels for different pinholes were modelled by scaling the clinical count level for 2 brain applications obtained from a LEHR parallel hole collimator. Noise free and noisy projections were reconstructed using the ML-EM algorithm up to data convergence. Normalized mean square error (NMSE) was calculated over the whole brain region for HMPAO and striatum region for TRODAT in both noise free and noisy simulation, respectively. Based on the noise-free NMSE analysis for 64 to 2 projections, the optimum pinhole number increased from 3 to 11 for HMPAO and from 1 to 10 for TRODAT. While for noisy data, the optimum pinhole number increased from 5 to 18 for HMPAO and from 2 to 13 for TRODAT for 64 to 2 projections. We conclude that more number of pinholes are needed as the number of projections decreases. More number of pinholes are needed for HMPAO as compared to TRODAT.

关键词： Neurological diseases Three-dimensional displays Phantoms Mean square error methods brain modeling Magnetic heads Noise measurement

来源：评论

学校读者我要写书评

暂无评论

SamRobNODDI: Q-Space Sampling-Augmented Continuous Representation Learning for Robust and Generalized NODDI

arXiv

引用

arXiv 2024年

作者： Xiao, Taohui Cheng, Jian Fan, Wenxin Dong, Enqing Zheng, Hairong Wang, Shanshan The School of Mechanical Electrical & Information Engineering Shandong University Weihai264209 China The Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The School of Computer Science and Engineering Beihang University Beijing100191 China The State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing100191 China

Neurite Orientation Dispersion and Density Imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-based methods accelerate the speed of NODDI parameter estimation and improve the accuracy. However, most methods require the number and coordinates of gradient directions during testing and training to remain strictly consistent, significantly limiting the generalization and robustness of these models in NODDI parameter estimation. In this paper, we propose a q-space sampling augmentation-based continuous representation learning framework (SamRobNODDI) to achieve robust and generalized NODDI. Specifically, a continuous representation learning method based on q-space sampling augmentation is introduced to fully explore the information between different gradient directions in q-space. Furthermore, we design a sampling consistency loss to constrain the outputs of different sampling schemes, ensuring that the outputs remain as consistent as possible, thereby further enhancing performance and robustness to varying q-space sampling schemes. SamRobNODDI is also a flexible framework that can be applied to different backbone networks. To validate the effectiveness of the proposed method, we compared it with 7 state-of-the-art methods across 18 different q-space sampling schemes, demonstrating that the proposed SamRobNODDI has better performance, robustness, generalization, and flexibility. Copyright © 2024, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

来源：评论

学校读者我要写书评

暂无评论

Artificial Intelligence-Based Face Transformation in Patient Seizure Videos for Privacy Protection

引用

Mayo Clinic Proceedings: Digital Health 2023年第4期1卷 619-628页

作者： Hou, Jen-Cheng Li, Chin-Jou Chou, Chien-Chen Shih, Yen-Cheng Fong, Si-Lei Dufau, Stephane E. Lin, Po-Tso Tsao, Yu McGonigal, Aileen Yu, Hsiang-Yu Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Neurology Neurological Institute Taipei Veterans General Hospital Taipei Taiwan School of Medicine National Yang Ming Chiao Tung University College of Medicine Taipei Taiwan Brain Research Center National Yang Ming Chiao Tung University Taipei Taiwan Department of Medicine University of Malaya Kuala Lumpur Malaysia Laboratoire de psychologie cognitive (UMR7290) CNRS & Aix-Marseille University France Queensland Brain Institute The University of Queensland Brisbane QLD Australia CROSSING IRL 2010 CNRS Adelaide Australia Department of Neurosciences Mater Misericordiae Hospital Brisbane Queensland Australia and Mater Research Institute Faculty of Medicine University of Queensland Australia

Objective: To investigate the feasibility and accuracy of artificial intelligence (AI) methods of facial deidentification in hospital-recorded epileptic seizure videos, for improved patient privacy protection while preserving clinically important features of seizure semiology. Patients and Methods: Videos of epileptic seizures displaying seizure-related involuntary facial changes were selected from recordings at Taipei Veterans General Hospital Epilepsy Unit (between August 1, 2020 and February 28, 2023), and a single representative video frame was prepared per seizure. We tested 3 AI transformation models: (1) morphing the original facial image with a different male face;(2) substitution with a female face;and (3) cartoonization. Facial deidentification and preservation of clinically relevant facial detail were calculated based on: (1) scoring by 5 independent expert clinicians and (2) objective computation. Results: According to the clinician scoring of 26 facial frames in 16 patients, the best compromise between deidentification and preservation of facial semiology was the cartoonization model. A male facial morphing model was superior to the cartoonization model for deidentification, but clinical detail was sacrificed. Objective similarity testing of video data reported deidentification scores in agreement with the clinicians’ scores;however, preservation of semiology gave mixed results likely due to inadequate existing comparative databases. Conclusion: Artificial intelligence-based face transformation of medical seizure videos is feasible and may be useful for patient privacy protection. In our study, the cartoonization approach provided the best compromise between deidentification and preservation of seizure semiology. © 2023 The Authors

关键词： AI artificial intelligence EEG electroencephalography 3D three-dimensional FD facial deidentification FSP facial semiology preservation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：