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The Impact of Enriching Electroencephalogram in Spatial Metadata on Interpretability and Generalization Ability of Graph Neural Networks

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PATTERN RECOGNITION AND IMAGE ANALYSIS 2024年第4期34卷 1255-1263页

作者： Sidorov, L. S. Maysuradze, A. I. Lomonosov Moscow State Univ Moscow 119991 Russia

This paper proposes a formalization of current approaches to processing electroencephalograms, as well as a generalization of the original problem of processing multidimensional time series in terms of graph neural networks. The proposed architecture exploits information about the recording device by encoding information about the relative position of the recording electrodes as a graph. The effectiveness of the method has been demonstrated in tasks of emotion recognition, as well as P300 impulse recognition. The model achieved outstanding results on the considered datasets using the same set of hyperparameters. The experiments conducted demonstrate the usefulness of graphs composed of metainformation, as well as the dependence of the structure of the optimal graph on the problem being solved. Potentially, this technique can be extended to other subject areas where additional information about the connectivity of multivariate time series channels is available.

关键词： multimodal data integration multivariate time series electroencephalogram spatial data deep learning graph neural network P300 wave emotion recognition

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Cell-type brain-region specific changes in prefrontal cortex of a mouse model of alcohol dependence

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NEUROBIOLOGY OF DISEASE 2024年 190卷 106361-106361页

作者： Salem, Nihal A. Manzano, Lawrence Keist, Michael W. Ponomareva, Olga Roberts, Amanda J. Roberto, Marisa Mayfield, R. Dayne Univ Texas Austin Waggoner Ctr Alcohol & Addict Res Austin TX 78712 USA Univ Texas Austin Dept Neurosci Austin TX 78712 USA Scripps Res Inst Anim Models Core Facil 10550 North Torrey Pines Rd La Jolla CA 92037 USA Scripps Res Inst Dept Mol Med La Jolla CA 92037 USA Scripps Res Inst Dept Neurosci La Jolla CA 92037 USA

The prefrontal cortex is a crucial regulator of alcohol drinking, and dependence, and other behavioral phenotypes associated with AUD. Comprehensive identification of cell-type specific transcriptomic changes in alcohol dependence will improve our understanding of mechanisms underlying the excessive alcohol use associated with alcohol dependence and will refine targets for therapeutic development. We performed single nucleus RNA sequencing (snRNA-seq) and Visium spatial gene expression profiling on the medial prefrontal cortex (mPFC) obtained from C57BL/6 J mice exposed to the two-bottle choice-chronic intermittent ethanol (CIE) vapor exposure (2BC-CIE, defined as dependent group) paradigm which models phenotypes of alcohol dependence including escalation of alcohol drinking. Gene co-expression network analysis and differential expression analysis identified highly dysregulated co-expression networks in multiple cell types. Dysregulated modules and their hub genes suggest novel understudied targets for studying molecular mechanisms contributing to the alcohol dependence state. A subtype of inhibitory neurons was the most alcohol-sensitive cell type and contained a downregulated gene co-expression module;the hub gene for this module is Cpa6, a gene previously identified by GWAS to be associated with excessive alcohol consumption. We identified an astrocytic Gpc5 module significantly upregulated in the alcohol-dependent group. To our knowledge, there are no studies linking Cpa6 and Gpc5 to the alcohol-dependent phenotype. We also identified neuroinflammation related gene expression changes in multiple cell types, specifically enriched in microglia, further implicating neuroinflammation in the escalation of alcohol drinking. Here, we present a comprehensive atlas of cell-type specific alcohol dependence mediated gene expression changes in the mPFC and identify novel cell type-specific targets implicated in alcohol dependence.

关键词： Alcohol dependence Chronic intermittent ethanol exposure Alcohol dependence cell -type specific re sponses Single nucleus RNA sequencing Spatial transcriptomics multimodal data integration Gene co-expression networks

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Lymphocyte networks are dynamic cellular communities in the immunoregulatory landscape of lung adenocarcinoma

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CANCER CELL 2023年第5期41卷 871-886.e10页

作者： Gaglia, Giorgio Burger, Megan L. Ritch, Cecily C. Rammos, Danae Dai, Yang Crossland, Grace E. Tavana, Sara Z. Warchol, Simon Jaeger, Alex M. Naranjo, Santiago Coy, Shannon Nirmal, Ajit J. Krueger, Robert Lin, Jia-Ren Pfister, Hanspeter Sorger, Peter K. Jacks, Tyler Santagata, Sandro Harvard Med Sch Dept Syst Biol Lab Syst Pharmacol Boston MA 02115 USA Harvard Med Sch Ludwig Ctr Harvard Boston MA 02115 USA Harvard Med Sch Brigham & Womens Hosp Dept Pathol Boston MA 02115 USA MIT David H Koch Inst Integrat Canc Res Cambridge MA 02139 USA Oregon Hlth & Sci Univ Dept Cell Dev & Canc Biol Portland OR 97212 USA Oregon Hlth & Sci Univ Sch Med Div Hematol & Oncol Portland OR 97212 USA Harvard Univ Sch Engn & Appl Sci Boston MA 02134 USA MIT Dept Biol Cambridge MA 02139 USA

Lymphocytes are key for immune surveillance of tumors, but our understanding of the spatial organization and physical interactions that facilitate lymphocyte anti-cancer functions is limited. We used multiplexed im-aging, quantitative spatial analysis, and machine learning to create high-definition maps of lung tumors from a Kras/Trp53-mutant mouse model and human resections. Networks of interacting lymphocytes ("lympho-nets") emerged as a distinctive feature of the anti-cancer immune response. Lymphonets nucleated from small T cell clusters and incorporated B cells with increasing size. CXCR3-mediated trafficking modulated lymphonet size and number, but T cell antigen expression directed intratumoral localization. Lymphonets preferentially harbored TCF1+ PD-1+ progenitor CD8+ T cells involved in responses to immune checkpoint blockade (ICB) therapy. Upon treatment of mice with ICB or an antigen-targeted vaccine, lymphonets re-tained progenitor and gained cytotoxic CD8+ T cell populations, likely via progenitor differentiation. These data show that lymphonets create a spatial environment supportive of CD8+ T cell anti-tumor responses.

关键词： multiplexed imaging systems biology computational biology spatial profiling CyCIF lung adenocarcinoma immunotherapy cancer vaccines spatial biology multimodal data integration

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iCEMIGE: integration of CEll-morphometrics, MIcrobiome, and GEne biomarker signatures for risk stratification in breast cancers

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World Journal of Clinical Oncology 2022年第7期13卷 616-629页

作者： Xuan-Yu Mao Jesus Perez-Losada Mar Abad Marta Rodríguez-González Cesar A Rodríguez Jian-Hua Mao Hang Chang Division of Biological Systems and Engineering Lawrence Berkeley National LaboratoryBerkeley94720United States Instituto de Biología Molecular y Celular del Cáncer Universidad de SalamancaSalamanca 37007Spain Department of Pathology Universidad de SalamancaSalamanca 37007Spain Department of Medical Oncology Universidad de SalamancaSalamanca 37007Spain

BACKGROUND The development of precision medicine is essential for personalized treatment and improved clinical outcome,whereas biomarkers are critical for the success of precision *** To investigate whether iCEMIGE(integration of CEll-morphometrics,MIcro-biome,and GEne biomarker signatures)improves risk stratification of breast cancer(BC)*** We used our recently developed machine learning technique to identify cellular morphometric biomarkers(CMBs)from the whole histological slide images in The Cancer Genome Atlas(TCGA)breast cancer(TCGA-BRCA)*** Cox regression was used to assess whether cell-morphometrics prognosis score(CMPS)and our previously reported 12-gene expression prognosis score(GEPS)and 15-microbe abundance prognosis score(MAPS)were independent prognostic *** was built upon the sparse representation learning *** iCEMIGE scoring model performance was measured by the area under the receiver operating characteristic curve compared to CMPS,GEPS,or MAPS *** models were created to predict overall survival(OS)and progress-free survival(PFS)rates at 5-and 10-year in the TCGA-BRCA *** We identified 39 CMBs that were used to create a CMPS system in ***,GEPS,and MAPS were found to be significantly independently associated with *** then established an iCEMIGE scoring system for risk stratification of BC *** iGEMIGE score has a significant prognostic value for OS and PFS independent of clinical factors(age,stage,and estrogen and progesterone receptor status)and PAM50-based molecular ***,the iCEMIGE score significantly increased the power to predict OS and PFS compared to CMPS,GEPS,or MAPS *** Our study demonstrates a novel and generic artificial intelligence framework for multimodal data integration toward improving prognosis risk stratification of BC patients,which can be extended to other types of cancer.

关键词： Breast cancer Gene signature Microbiome signature Cellular morphometrics signature multimodal data integration Prognosis

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Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction

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JHEP REPORTS 2022年第4期4卷 100443-100443页

作者： Nam, David Chapiro, Julius Paradis, Valerie Seraphin, Tobias Paul Kather, Jakob Nikolas Yale Sch Med Dept Radiol & Biomed Imaging Sect Intervent Radiol New Haven CT USA Univ Paris Ctr Rech inflammat CRI INSERM U1149 Paris France Univ Paris Hop Beaujon AP HP Dept Pathol Clichy France Heinrich Heine Univ Dusseldorf Univ Hosp Dusseldorf Med Fac Dept Gastroenterol Hepatol & Infect Dis Dusseldorf Germany Univ Hosp RWTH Aachen Dept Med 3 Aachen Germany Univ Leeds Leeds Inst Med Res St Jamess Pathol & Data Analyt Leeds England Univ Hosp Heidelberg Natl Ctr Tumor Dis NCT Med Oncol Heidelberg Germany RWTH Univ Hosp Dept Med 3 D-52074 Aachen Germany

Clinical routine in hepatology involves the diagnosis and treatment of a wide spectrum of meta-bolic, infectious, autoimmune and neoplastic diseases. Clinicians integrate qualitative and quanti-tative information from multiple data sources to make a diagnosis, prognosticate the disease course, and recommend a treatment. In the last 5 years, advances in artificial intelligence (AI), particularly in deep learning, have made it possible to extract clinically relevant information from complex and diverse clinical datasets. In particular, histopathology and radiology image data contain diagnostic, prognostic and predictive information which AI can extract. Ultimately, such AI systems could be implemented in clinical routine as decision support tools. However, in the context of hepatology, this requires further large-scale clinical validation and regulatory approval. Herein, we summarise the state of the art in AI in hepatology with a particular focus on histopathology and radiology data. We present a roadmap for the further development of novel biomarkers in hep-atology and outline critical obstacles which need to be overcome.(c) 2022 The Authors. Published by Elsevier B.V. on behalf of European Association for the Study of the Liver (EASL). This is an open access article under the CC BY license (http://***/licenses/ by/4.0/).

关键词： Artificial intelli-gence deep learning machine learning diagnostic support system imaging multimodal data integration

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Whole-body integration of gene expression and single-cell morphology

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CELL 2021年第18期184卷 4819-+页

作者： Vergara, Hernando M. Pape, Constantin Meechan, Kimberly, I Zinchenko, Valentyna Genoud, Christel Wanner, Adrian A. Mutemi, Kevin Nzumbi Titze, Benjamin Templin, Rachel M. Bertucci, Paola Y. Simakov, Oleg Duerichen, Wiebke Machado, Pedro Savage, Emily L. Schermelleh, Lothar Schwab, Yannick Friedrich, Rainer W. Kreshuk, Anna Tischer, Christian Arendt, Detlev European Mol Biol Lab EMBL Dev Biol Unit Meyerhofstr 1 D-69117 Heidelberg Germany Sainsbury Wellcome Ctr Neural Circuits & Behav Howland St 25 London W1T 4JG England European Mol Biol Lab EMBL Cell Biol & Biophys Unit Meyerhofstr 1 D-69117 Heidelberg Germany Heidelberg Univ Inst Wissensch Rechnen Heidelberg Collaboratory Image Proc Heidelberg Germany Friedrich Miescher Inst Biomed Res Maulbeerstr 66 CH-4058 Basel Switzerland Princeton Univ Princeton Neurosci Inst Washington Rd Princeton NJ 08544 USA Univ Vienna Dept Neurosci & Dev Biol Althanstr 14 A-1090 Vienna Austria Kings Coll London Ctr Ultrastruct Imaging New Hunts House Guys Campus London SE1 1UL England Univ Oxford Dept Biochem Micron Adv Bioimaging Unit South Parks Rd Oxford OX1 3QU England European Mol Biol Lab EMBL Cell Biol & Biophys Unit Electron Microscopy Core Facil Meyerhofstr 1 D-69117 Heidelberg Germany European Mol Biol Lab EMBL Ctr Bioimage Anal Core Facil Meyerhofstr 1 D-69117 Heidelberg Germany EMBL & Heidelberg Univ Fac Biosci Heidelberg Germany Univ Lausanne Fac Biol & Med Electron Microscopy Facil CH-1015 Lausanne Switzerland Paul Scherrer Inst PSI Forsch Str 111 CH-5232 Villigen Switzerland Heidelberg Univ Ctr Organismal Studies COS Heidelberg Germany

Animal bodies are composed of cell types with unique expression programs that implement their distinct locations, shapes, structures, and functions. Based on these properties, cell types assemble into specific tissues and organs. To systematically explore the link between cell-type-specific gene expression and morphology, we registered an expression atlas to a whole-body electron microscopy volume of the nereid Platynereis dumerilii. Automated segmentation of cells and nuclei identifies major cell classes and establishes a link between gene activation, chromatin topography, and nuclear size. Clustering of segmented cells according to gene expression reveals spatially coherent tissues. In the brain, genetically defined groups of neurons match ganglionic nuclei with coherent projections. Besides interneurons, we uncover sensory-neurosecretory cells in the nereid mushroom bodies, which thus qualify as sensory organs. They furthermore resemble the vertebrate telencephalon by molecular anatomy. We provide an integrated browser as a Fiji plugin for remote exploration of all available multimodal datasets.

关键词： volume electron microscopy image registration automatic segmentation gene expression atlas Platynereis dumerilii cell types multimodal data integration machine learning mushroom bodies telencephalon

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Navigating the landscape of multimodal AI in medicine: A scoping review on technical challenges and clinical applications

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Medical Image Analysis 2025年

作者： Daan Schouten Giulia Nicoletti Bas Dille Catherine Chia Pierpaolo Vendittelli Megan Schuurmans Geert Litjens Nadieh Khalili Department of Pathology Research Institute for Medical Innovation Radboud University Medical Center Nijmegen The Netherlands Oncode Institute Utrecht The Netherlands Department of Electronics and Telecommunications Polytechnic University of Turin Turin Italy Department of Surgical-Medical Sciences Unversity of Turin Turin Italy Department of Radiology and Nuclear Medicine Erasmus University Medical Centre Rotterdam The Netherlands Department of Dermatology Erasmus University Medical Centre Rotterdam The Netherlands Department of Pathology Erasmus University Medical Centre Rotterdam The Netherlands

Recent technological advances in healthcare have led to unprecedented growth in patient data quantity and diversity. While artificial intelligence (AI) models have shown promising results in analyzing individual data modalities, there is increasing recognition that models integrating multiple complementary data sources, so-called multimodal AI, could enhance clinical decision-making. This scoping review examines the landscape of deep learning-based multimodal AI applications across the medical domain, analyzing 432 papers published between 2018 and 2024. We provide an extensive overview of multimodal AI development across different medical disciplines, examining various architectural approaches, fusion strategies, and common application areas. Our analysis reveals that multimodal AI models consistently outperform their unimodal counterparts, with an average improvement of 6.2 percentage points in AUC. However, several challenges persist, including cross-departmental coordination, heterogeneous data characteristics, and incomplete datasets. We critically assess the technical and practical challenges in developing multimodal AI systems and discuss potential strategies for their clinical implementation, including a brief overview of commercially available multimodal AI models for clinical decision-making. Additionally, we identify key factors driving multimodal AI development and propose recommendations to accelerate the field’s maturation. This review provides researchers and clinicians with a thorough understanding of the current state, challenges, and future directions of multimodal AI in medicine.

关键词： multimodal AI Deep learning multimodal data integration Medical imaging

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A mixture-of-experts deep generative model for integrated analysis of single-cell multiomics data

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CELL REPORTS METHODS 2021年第5期1卷 100071页

作者： Minoura, Kodai Abe, Ko Nam, Hyunha Nishikawa, Hiroyoshi Shimamura, Teppei Nagoya Univ Div Syst Biol Grad Sch Med Nagoya Aichi Japan Nagoya Univ Dept Immunol Grad Sch Med Nagoya Aichi Japan Natl Canc Ctr Res Inst EPOC Div Canc Immunol Tokyo Japan Kobe Pharmaceut Univ Lab Med Stat Kobe Hyogo Japan

The recentdevelopment of single-cellmultiomics analysis has enabled simultaneous detectionofmultiple traits at the single-cell level, providing deeper insights into cellular phenotypes and functions in diverse tissues. However, currently, it is challenging to infer the joint representations and learn relationships among multiplemodalities fromcomplex multimodal single-cell data. Here, we present scMM, a novel deep generative model-based framework for the extraction of interpretable joint representations and crossmodal generation. scMM addresses the complexity of data by leveraging a mixture-of-experts multimodal variational autoencoder. The pseudocell generation strategy of scMM compensates for the limited interpretability of deep learning models, and the proposed approach experimentally discoveredmultimodal regulatory programs associatedwith latent dimensions. Analysis of recently produced datasets validated that scMM facilitates high-resolution clustering with rich interpretability. Furthermore, we show that crossmodal generation by scMM leads to more precise prediction and data integration compared with the state-of-the-art and conventional approaches.

关键词： single-cell multiomics deep generative model dimensionality reduction multimodal data integration multimodal prediction

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Mirror Mirror on the Wall ... An Unobtrusive Intelligent Multisensory Mirror for Well-Being Status Self-Assessment and Visualization

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第7期19卷 1467-1481页

作者： Henriquez, Pedro Matuszewski, Bogdan J. Andreu-Cabedo, Yasmina Bastiani, Luca Colantonio, Sara Coppini, Giuseppe D'Acunto, Mario Favilla, Riccardo Germanese, Danila Giorgi, Daniela Marraccini, Paolo Martinelli, Massimo Morales, Maria-Aurora Pascali, Maria Antonietta Righi, Marco Salvetti, Ovidio Larsson, Marcus Stromberg, Tomas Randeberg, Lise Bjorgan, Asgeir Giannakakis, Giorgos Pediaditis, Matthew Chiarugi, Franco Christinaki, Eirini Marias, Kostas Tsiknakis, Manolis Univ Cent Lancashire Preston PR1 2HE Lancs England CNR IFC Inst Clin Physiol I-56124 Pisa Italy CNR ISTI Inst Informat Sci & Technol I-56124 Pisa Italy CNR ISM Inst Struct Matter I-00185 Rome Italy Linkoping Univ S-58183 Linkoping Sweden Norwegian Univ Sci & Technol N-7491 Trondheim Norway Fdn Res & Technol Hellas Inst Comp Sci Iraklion 70013 Greece Technol Educ Inst Crete Dept Informat Engn Iraklion 71410 Greece

A person's well-being status is reflected by their face through a combination of facial expressions and physical signs. The SEMEOTICONS project translates the semeiotic code of the human face into measurements and computational descriptors that are automatically extracted from images, videos, and three-dimensional scans of the face. SEMEOTICONS developed a multisensory platform in the form of a smart mirror to identify signs related to cardio-metabolic risk. The aim was to enable users to self-monitor their well-being status over time and guide them to improve their lifestyle. Significant scientific and technological challenges have been addressed to build the multisensory mirror, from touchless data acquisition, to real-time processing and integration of multimodal data.

关键词： Cardio-metabolic risk unobtrusive well-being monitoring multimodal data integration 3D face detection and tracking 3D morphometric analysis psychosomatic status recognition multispectral imaging breath analysis

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Wize Mirror - a smart, multisensory cardio-metabolic risk monitoring system

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COMPUTER VISION AND IMAGE UNDERSTANDING 2016年 148卷 3-22页

作者： Andreu, Yasmina Chiarugi, Franco Colantonio, Sara Giannakakis, Giorgos Giorgi, Daniela Henriquez, Pedro Kazantzaki, Eleni Manousos, Dimitris Marias, Kostas Matuszewski, Bogdan J. Pascali, Maria Antonietta Pediaditis, Matthew Raccichini, Giovanni Tsiknakis, Manolis Univ Cent Lancashire Sch Comp Engn & Phys Sci Robot & Comp Vis Res Lab Preston PR1 2HE Lancs England Natl Res Council Italy Inst Informat Sci & Technol Via G Moruzzi 1 I-56124 Pisa Italy Fdn Res & Technol Hellas FORTH Inst Comp Sci N Plastira 100 GR-70013 Iraklion Crete Greece Technol Educ Inst Crete Biomed Informat & eHlth Lab GR-71004 Iraklion Crete Greece

In the recent years personal health monitoring systems have been gaining popularity, both as a result of the pull from the general population, keen to improve well-being and early detection of possibly serious health conditions and the push from the industry eager to translate the current significant progress in computer vision and machine learning into commercial products. One of such systems is the Wize Mirror, built as a result of the FP7 funded SEMEOTICONS (SEMEiotic Oriented Technology for Individuals CardiOmetabolic risk self-assessmeNt and Self-monitoring) project. The project aims to translate the semeiotic code of the human face into computational descriptors and measures, automatically extracted from videos, multispectral images, and 3D scans of the face. The multisensory platform, being developed as the result of that project, in the form of a smart mirror, looks for signs related to cardio-metabolic risks. The goal is to enable users to self-monitor their well-being status over time and improve their life-style via tailored user guidance. This paper is focused on the description of the part of that system. utilising computer vision and machine learning techniques to perform 3D morphological analysis of the face and recognition of psycho-somatic status both linked with cardio-metabolic risks. The paper describes the concepts, methods and the developed implementations as well as reports on the results obtained on both real and synthetic datasets. (C) 2016 The Authors. Published by Elsevier Inc.

关键词： Unobtrusive health monitoring 3D face detection Tracking and reconstruction 3D morphometric analysis Psycho-somatic status recognition multimodal data integration

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