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Neural mechanisms of top-down divided and selective spatial attention in visual and auditory perception

Brain Science Advances 2023年第2期9卷 95-113页

作者： Zhongtian Guan Meng Lin Qiong Wu Jinglong Wu Kewei Chen Hongbin Han Dehua Chui Xu Zhang Chunlin Li School of Biomedical Engineering Capital Medical UniversityBeijing 100069China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Capital Medical UniversityBeijing 100069China Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application Capital Medical UniversityBeijing 100069China Peking University First Hospital Beijing 100034China Biomedical Engineering Laboratory Graduate School of Natural Science and TechnologyOkayama University3-1-1 Tsushima-nakaOkayamaJapan Key Laboratory of Biomimetic Robots and Systems Ministry of EducationBeijing Institute of TechnologyBeijing 100081China Computational Image Analysis Banner Alzheimer’s Institute and Banner Good Samaritan Medical CentrePET CentrePhoenixAZ 85006USA Radiology Peking University Third HospitalBeijing 100191China Neuroscience Research Institute/Peking University Third Hospital Beijing 100191China

Top-down attention mechanisms require the selection of specificobjects or locations;however,the brain mechanism involved when attention is allocated across different modalities is not well *** aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlyingdivided and selective spatial attention.A concurrent audiovisual stimulus was used,and subjects were prompted to focus on a visual,auditory and audiovisual stimulus in a Posner *** results confirmed the better performance of selectiveattention compared to devided *** found differences in the activation level of the frontoparietal network,visual/auditorycortex,the putamen and the salience network under differentattention *** further used Granger causality(GC)toexplore effective connectivity differences between *** in GC connectivity between visual and auditory selective tasksreflected the visual dominance effect under spatial *** addition,our results supported the role of the putamen inredistributing attention and the functional separation of the *** summary,we explored the audiovisual top-down allocation of attention and observed the differences in neuralmechanisms under endogenous attention modes,which revealedthe differences in cross-modal expression in visual and auditory attention under attentional modulation.

关键词： top-down mechanism divided spatial attention selective spatial attention fMRI Granger causality

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Explainable and Interpretable Diabetic Retinopathy Classification Based on Neural-Symbolic Learning

arXiv

引用

arXiv 2022年

作者： Jang, Se-In Girard, Michaël J.A. Thiery, Alexandre H. Gordon Center for Medical Imaging Massachusetts General Hospital Harvard Medical School Boston United States Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA United States Ophthalmic Engineering and Innovation Laboratory Singapore Eye Research Institute Singapore Singapore Duke-NUS Medical School Singapore Singapore Institute for Molecular and Clinical Ophthalmology Basel Switzerland Department of Statistics and Data Science National University of Singapore Singapore Singapore

In this paper, we propose an explainable and interpretable diabetic retinopathy (ExplainDR) classification model based on neural-symbolic learning. To gain explainability, a highlevel symbolic representation should be considered in decision making. Specifically, we introduce a human-readable symbolic representation, which follows a taxonomy style of diabetic retinopathy characteristics related to eye health conditions to achieve explainability. We then include humanreadable features obtained from the symbolic representation in the disease prediction. Experimental results on a diabetic retinopathy classification dataset show that our proposed ExplainDR method exhibits promising performance when compared to that from state-of-the-art methods applied to the IDRiD dataset, while also providing interpretability and explainability. © 2022, CC BY.

关键词： Eye protection

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

arXiv

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arXiv 2024年

作者： Rokuss, Maximilian R. Kirchhoff, Yannick Roy, Saikat Kovacs, Balint Ulrich, Constantin Wald, Tassilo Zenk, Maximilian Denner, Stefan Isensee, Fabian Vollmuth, Philipp Kleesiek, Jens Maier-Hein, Klaus Heidelberg Division of Medical Image Computing Germany Faculty of Mathematics and Computer Science Heidelberg University Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Medical Faculty Heidelberg Heidelberg University Heidelberg Germany Helmholtz Imaging German Cancer Research Center Heidelberg Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Neuroimaging Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany University Hospital Essen Essen Germany West German Cancer Center Essen University Hospital Essen Essen Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Germany

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level F1 score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at: ***/MIC-DKFZ/ Longitudinal-Difference-Weighting. © 2024, CC BY.

关键词： Image segmentation

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Progressive Dual Priori Network for Generalized Breast Tumor Segmentation

arXiv

引用

arXiv 2023年

作者： Wang, Li Wang, Lihui Kuai, Zixiang Tang, Lei Ou, Yingfeng Ye, Chen Zhu, Yuemin Wu, Min Shi, Tianliang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China Radiology Department Guizhou Provincial People’s Hospital Guiyang550002 China Radiology Department Tongren People’s Hospital Tongren554300 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 U1206 LyonF-69621 France

To promote the generalization ability of breast tumor segmentation models, as well as to improve the segmentation performance for breast tumors with smaller size, low-contrast and irregular shape, we propose a progressive dual priori network (PDPNet) to segment breast tumors from dynamic enhanced magnetic resonance images (DCE-MRI) acquired at different centers. The PDPNet first cropped tumor regions with a coarse-segmentation based localization module, then the breast tumor mask was progressively refined by using the weak semantic priori and cross-scale correlation prior knowledge. To validate the effectiveness of PDPNet, we compared it with several state-of-the-art methods on multi-center datasets. The results showed that, comparing against the suboptimal method, the DSC and HD95 of PDPNet were improved at least by 5.13% and 7.58% respectively on multi-center test sets. In addition, through ablations, we demonstrated that the proposed localization module can decrease the influence of normal tissues and therefore improve the generalization ability of the model. The weak semantic priors allow focusing on tumor regions to avoid missing small tumors and low-contrast tumors. The cross-scale correlation priors are beneficial for promoting the shape-aware ability for irregular tumors. Thus integrating them in a unified framework improved the multicenter breast tumor segmentation performance. The source code and open data can be accessed at https://github. com/wangli100209/PDPNet. Copyright © 2023, The Authors. All rights reserved.

关键词： Magnetic resonance

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Benchmarking the performance of a low-cost Magnetic Resonance Control System at multiple sites in the open MaRCoS community

arXiv

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arXiv 2022年

作者： Guallart-Naval, Teresa O'Reilly, Thomas Algarín, José M. Pellicer-Guridi, Ruben Vives-Gilabert, Yolanda Craven-Brightman, Lincoln Negnevitsky, Vlad Menküc, Benjamin Galve, Fernando Stockmann, Jason P. Webb, Andrew Alonso, Joseba Valencia Spain Tesoro Imaging S.L. Valencia Spain Department of Radiology Leiden University Medical Center Leiden Netherlands Asociación de investigación MPC Donostia-San Sebastián Spain Intelligent Data Analysis Laboratory Department of Electronic Engineering Universitat de València Valencia Spain Massachusetts General Hospital A. A. Martinos Center for Biomedical Imaging CharlestownMA United States Oxford Ionics Ltd Oxford United Kingdom University of Applied Sciences and Arts Dortmund Dortmund Germany

Purpose: To describe the current properties and capabilities of an open-source hardware and software package that is being developed by many sites internationally with the aim of providing an inexpensive yet flexible platform for low-cost MRI. Methods: This paper describes three different setups from 50 to 360 mT in different settings, all of which used the MaRCoS console for acquiring data, and different types of software interfaces (custom-built GUI or PulSeq overlay) to acquire the data. Results: Images are presented from both phantoms and in vivo from healthy volunteers to demonstrate the image quality that can be obtained from the MaRCoS hardware/software interfaced to different low-field magnets. Conclusions: The results presented here show that a number of different sequences commonly used in the clinic can be programmed into an open-source system relatively quickly and easily, and can produce good quality images even at this early stage of development. Both the hardware and software will continue to develop, and it is an aim of this paper to encourage other groups to join this international consortium. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance

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Deep Learning-based Segmentation of Pleural Effusion From Ultrasound Using Coordinate Convolutions

arXiv

引用

arXiv 2022年

作者： Morilhat, Germain Kifle, Naomi FinesilverSmith, Sandra Ruijsink, Bram Vergani, Vittoria Desita, Habtamu Tegegne Desita, Zerubabel Tegegne Puyol-Antón, Esther Carass, Aaron King, Andrew P. School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom IMT Atlantique France Image Analysis and Communications Laboratory Johns Hopkins University United States Big Data Institute Oxford University United Kingdom Department of Cardiology University Medical Center Utrecht Netherlands King’s College Hospital London United Kingdom Department of Information Technology University of Gondar Ethiopia Department of Radiology University of Gondar Ethiopia

Ultrasound imaging plays a crucial role in assessing disease and making diagnoses for a range of conditions, especially so in low-to-middle-income (LMIC) countries. One such application is the assessment of pleural effusion, which can be associated with multiple morbidities including tuberculosis (TB). Currently, assessment of pleural effusion is performed manually by the sonographer during the ultrasound examination, leading to significant intra-/inter-observer variability. In this work, we investigate the use of deep learning (DL) to automate the process of pleural effusion segmentation from ultrasound images. On two ultrasound datasets of suspected TB patients acquired in a LMIC setting, we achieve median Dice Similarity Coefficients (DSCs) of 0.82 and 0.74 respectively using the nnU-net DL model. We also investigate the use of coordinate convolutions in the DL model and find that this results in a statistically significant improvement in the median DSC on the first dataset to 0.85, with no significant change on the second dataset. This work showcases, for the first time, the potential of DL in automating the process of effusion assessment from ultrasound imaging and paves the way for future work on artificial intelligence-assisted acquisition and interpretation of ultrasound images. This could enable accurate and robust assessment of pleural effusion in LMIC settings where there is often a lack of experienced radiologists to perform such assessments. © 2022, CC BY.

关键词： Convolution

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Hijacking and integration of algal plastids and mitochondria in a polar planktonic host

引用

Current Biology 2025年第11期35卷 2509-2523.e7页

作者： Rao, Ananya Kedige Yee, Daniel Chevalier, Fabien LeKieffre, Charlotte Pavie, Marie Olivetta, Marine Dudin, Omaya Gallet, Benoit Hehenberger, Elisabeth Seifi, Mehdi Jug, Florian Deschamps, Joran Wu, Ting-Di Gast, Rebecca Jouneau, Pierre-Henri Decelle, Johan Cell and Plant Physiology Laboratory CNRS CEA INRAE IRIG Université Grenoble Alpes Grenoble 38054 France Department of Biochemistry Université de Genève Genève 1211 Switzerland Université Grenoble Alpes CEA CNRS IBS Grenoble 38000 France Institute of Parasitology Biology Centre Czech Academy of Sciences České Budějovice 37005 Czech Republic Computational Biology Research Centre Human Technopole Milan 20157 Italy Bioimage Analysis Unit National Facility for Data Handling and Analysis Human Technopole Milan 20157 Italy Institut Curie PSL University University of Paris-Saclay CNRS UAR2016 Inserm US43 Multimodal Imaging Center Orsay 91400 France Woods Hole Oceanographic Institution Woods Hole 02543 MA United States University Grenoble Alpes CEA IRIG-MEM Grenoble 38054 France

In oceanic plankton, various hosts are capable of engulfing and temporarily integrating microalgae (photosymbiosis) or just their photosynthetic plastids (kleptoplastidy) from the environment. These cellular interactions have been hypothesized to be representative of evolutionary steps in plastid acquisition in eukaryotes, but the underlying mechanisms are not fully understood. Here, we studied a polar kleptoplastidic dinoflagellate, which is known to steal plastids of the microalga Phaeocystis antarctica. We tracked the morphology and activity of stolen plastids over several months by combining multimodal subcellular imaging and photophysiology. Upon integration inside a host vacuole, the volume of plastids and pyrenoids significantly increased, and photosynthetic activity was boosted. This may be supported by the retention of a 50-fold larger algal nucleus for ∼1 week. Once the algal nucleus was lost, there was a decrease in plastid volume and photosynthesis, but nucleus- and plastid-encoded photosystem subunits were still detected. Carbon fixation and transfer to the host were also maintained after >2 months. We also showed that the algal mitochondrion was stolen and retained for several months, transforming into an extensive network interacting with plastids. This highlights a complex strategy in plankton along the continuum of plastid symbioses, where both plastids and mitochondria of a microalga are hijacked by a host for several months without the algal nucleus. This association, which we found to be widely distributed in polar regions, suggests that plastid-mitochondrion interaction may have played a role in the evolution of plastid acquisition and opens new questions about host control and organelle maintenance. © 2025 The Authors

关键词： 3D electron microscopy dinoflagellate kleptoplastidy marine plankton microalga mitochondria photosynthesis plastid symbiosis

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Rapid isolation and immune profiling of SARS-CoV-2 specific memory B cell in convalescent COVID-19 patients via LIBRA-seq

引用

Signal Transduction and Targeted Therapy 2021年第6期6卷 1833-1844页

作者： Bing He Shuning Liu Yuanyuan Wang Mengxin Xu Wei Cai Jia Liu Wendi Bai Shupei Ye Yong Ma Hengrui Hu Huicui Meng Tao Sun Yanling Li Huanle Luo Mang Shi Xiangjun Du Wenjing Zhao Shoudeng Chen Jingyi Yang Haipeng Zhu Yusheng Jie Yuedong Yang Deyin Guo Qiao Wang Yuwen Liu Huimin Yan Manli Wang Yao-Qing Chen School of Public Health(Shenzhen) Sun Yat-sen UniversityShenzhenChina Wuhan Institute of Virology Center for Biosafety Mega-ScienceChinese Academy of SciencesWuhanChina Pulmonary and critical care medicine The Third Peopled Hospital of Dongguan CityDongguanGuangdong ProvinceChina Hangzhou ImmuQuad Biotechnologies HangzhouChina Zhejiang-California International NanoSystems Institute Zhejiang UniversityHangzhouChina lnfection and Immunity Center School of MedicineSun Yat-sen UniversityShenzhenChina Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases The Sixth Affiliated HospitalSchool of MedicineSun Yat-sen UniversityGuangzhouChina Guangdong Provincial Key Laboratory of Biomedical Imaging The Fifth Affiliated HospitalSun Yat-sen UniversityZhuhaiChina Department of Infectious Diseases The Ninth Peopled Hospital of Dongguan CityDongguanGuangdong ProvinceChina Department of Infectious Diseases The Third Affiliated Hospital of Sun Yat-sen UniversityGuangzhouGuangdong ProvinceChina School of Data and Computer Science Sun Yat-sen UniversityGuangzhouChina Key Laboratory of Medical Molecular Virology(MOE/NHC/CAMS) School of Basic Medical SciencesShanghai Medical CollegeFudan UniversityShanghaiChina Shenzhen Branch Guangdong Laboratory for Lingnan Modern AgricultureShenzhenChina Genome Analysis Laboratory of the Ministry of Agriculture ShenzhenChina Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural SciencesShenzhenChina Key Laboratory of Tropical Disease Control(Sun Yat-sen University) Ministry of EducationGuangzhouChina

B cell response plays a critical role against SARS-CoV-2 ***,little is known about the diversity and frequency of the paired SARS-CoV-2 antigen-specific BCR repertoire after SARS-CoV-2 ***,we performed single-cell RNA sequencing and VDJ sequencing using the memory and plasma B cells isolated from five convalescent COVID-19 patients,and analyzed the spectrum and transcriptional heterogeneity of antibody immune *** linking BCR to antigen specificity through sequencing(LIBRA-seq),we identified a distinct activated memory B cell subgroup(CD11c^(high) CD95^(high))had a higher proportion of SARS-CoV-2 antigen-labeled cells compared with memory B *** results revealed the diversity of paired BCR repertoire and the non-stochastic pairing of SARS-CoV-2 antigen-specific immunoglobulin heavy and light chains after SARS-CoV-2 *** public antibody clonotypes were shared by distinct convalescent ***,several antibodies isolated by LIBRA-seq showed high binding affinity against SARS-CoV-2 receptor-binding domain(RBD)or nucleoprotein(NP)via ELISA *** RBD-reactive antibodies C14646P3S and C2767P3S isolated by LIBRA-seq exhibited high neutralizing activities against both pseudotyped and authentic SARS-CoV-2 viruses in *** study provides fundamental insights into B cell response following SARS-CoV-2 infection at the single-cell level.

关键词： patients specificity linking

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Pathway Activity Score Learning for Dimensionality Reduction of Gene Expression data 23rd

Pathway Activity Score Learning for Dimensionality Reduction...

引用

23rd International Conference on Discovery Science, DS 2020

作者： Karagiannaki, Ioulia Pantazis, Yannis Chatzaki, Ekaterini Tsamardinos, Ioannis Department of Computer Science University of Crete Heraklion Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Greece Laboratory of Pharmacology Medical School Democritus University of Thrace Alexandroupoli Greece Institute of Agri-food and Life Sciences University Research Center Hellenic Mediterranean University HeraklionCrete71410 Greece Gnosis Data Analysis PC HeraklionCrete Greece

ISBN: (纸本)9783030615260

Molecular gene-expression datasets consist of samples with tens of thousands of measured quantities (e.g., high dimensional data). However, there exist lower-dimensional representations that retain the useful information. We present a novel algorithm for such dimensionality reduction called Pathway Activity Score Learning (PASL). The major novelty of PASL is that the constructed features directly correspond to known molecular pathways and can be interpreted as pathway activity scores. Hence, unlike PCA and similar methods, PASL’s latent space has a relatively straight-forward biological interpretation. As a use-case, PASL is applied on two collections of breast cancer and leukemia gene expression datasets. We show that PASL does retain the predictive information for disease classification on new, unseen datasets, as well as outperforming PLIER, a recently proposed competitive method. We also show that differential activation pathway analysis provides complementary information to standard gene set enrichment analysis. The code is available at https://***/mensxmachina/PASL. © 2020, The Author(s).

关键词： Activation analysis

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MaRCoS, an open-source electronic control system for low-field MRI

arXiv

引用

arXiv 2022年

作者： Negnevitsky, Vlad Vives-Gilabert, Yolanda Algarín, José M. Craven-Brightman, Lincoln Pellicer-Guridi, Rubén O'Reilly, Thomas Stockmann, Jason P. Webb, Andrew Alonso, Joseba Menküc, Benjamin Oxford Ionics Ltd OxfordOX5 1PF United Kingdom Intelligent Data Analysis Laboratory Department of Electronic Engineering Universitat de València Valencia Spain Valencia Spain Massachusetts General Hospital A. A. Martinos Center for Biomedical Imaging CharlestownMA United States Asociación de Investigación Mpc Donostia-San Sebastián Spain Department of Radiology Leiden University Medical Center Leiden Netherlands University of Applied Sciences and Arts Dortmund Dortmund Germany

Every magnetic resonance imaging (MRI) device requires an electronic control system that handles pulse sequences and signal detection and processing. Here we provide details on the architecture and performance of MaRCoS, a MAgnetic Resonance COntrol System developed by an open international community of low-field MRI researchers. MaRCoS is inexpensive and can handle cycle-accurate sequences without hard length limitations, rapid bursts of events, and arbitrary waveforms. It can also be easily adapted to meet further specifications required by the various academic and private institutions participating in its development. We describe the MaRCoS hardware, firmware and software that enable all of the above, including a Python-based graphical user interface for pulse sequence implementation, data processing and image reconstruction. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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