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464. Classifying Schizophrenia Patients and Healthy Individuals via Whole Brain SPECT Functional Connectivity

Biological Psychiatry 2025年第9期97卷 S288-S288页

作者： Harikumar, Amritha Amen, Daniel Keator, David Calhoun, Vince Georgia State University Amen Clinics Inc University of California Irvine Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University

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Statelets: A Novel Multi-Dimensional State-Shape Representation Of Brain Functional Connectivity Dynamics

Statelets: A Novel Multi-Dimensional State-Shape Representat...

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IEEE International Symposium on Biomedical Imaging

作者： Md Abdur Rahaman Eswar Damaraju Debbrata Kumar Saha Vince D. Calhoun Sergey M. Plis Georgia Institute of Technology Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (纸本)9781665412469;9781665429474

Time series motifs discovery and summarization is an established powerful tool for modeling and analyzing dynamical systems. In a similar spirit, we propose a state-space data mining approach called “statelets.” It is a probabilistic pattern-summarization framework operating on time series correlations and capturing their dynamics. Statelets rely on earth mover distance (EMD): a simple yet effective similarity metric that provides a scale-independent, variable-length comparison between substructures and accounts for partial matching. The efficiency of computing EMD supports its application in a kernel density estimator to approximate local motifs' probability density across the dataset. We validate statelets' utility on the dynamic functional network connectivity (dFNC) of patients with schizophrenia and healthy controls. The typical approach to the dFNC analysis, sliding window plus clustering (SWC), models the system continuously through a fixed set of connectivity states. Here, rather than using all the time points and searching for the patterns that span throughout the brain, we adopt time series motifs to capture the most recurring and transient patterns of the signals. Statelets produce the synopsis of dynamic connectivity shapes from both dynamics that provide valuable insights into the human brain's dynamic features. We observe these state-shapes are highly informative about the disorder. Also, they reveal significant group differences in connectivity strength across various regions of the brain.

关键词： Measurement Analytical models Shape Time series analysis Tools Probabilistic logic Brain modeling

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A metabolic acidity-activatable calcium phosphate probe with fluorescence signal amplification capabilities for non-invasive imaging of tumor malignancy

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science Bulletin 2022年第3期67卷 288-298页

作者： Qian Jia Ruili Zhang Yongdong Wang Haohao Yan Zheng Li Yanbin Feng Yu Ji Zuo Yang Yang Yang Kanyi Pu Zhongliang Wang Laboratory of Molecular Imaging and Translational Medicine(MITM) Engineering Research Center of Molecular&NeuroimagingMinistry of EducationSchool of Life Science and TechnologyXidian UniversityXi’an 710126China Academy of Advanced Interdisciplinary Research Xidian UniversityXi’an 710071China Department of Thoracic Surgery Shanghai Pulmonary HospitalSchool of MedicineTongji UniversityShanghai 200433China School of Materials Science and Engineering Tongji UniversityShanghai 201804China School of Chemical and Biomedical Engineering Nanyang Technological UniversitySingapore 637457Singapore

Dysregulated energy metabolism has recently been recognized as an emerging hallmark of *** cells,which are characterized by abnormal glycolysis,exhibit a lower extracellular pH(6.5–7.0)than nor-mal tissues(7.2–7.4),providing a promising target for tumor-specific imaging and ***,most pH-sensitive materials are unable to distinguish such a subtle pH difference owing to their wide and continuous pH-responsive *** this study,we developed an efficient strategy for the fabrication of a tumor metabolic acidity-activatable calcium phosphate(CaP)fluorescent probe(termed MACaP9).Unlike traditional CaP-based biomedical nanomaterials,which only work within more acidic organelles,such as endosomes and lysosomes(pH 4.0–6.0),MACaP9 could not only specifically respond to the tumor extra-cellular pH but also rapidly convert pH variations into a distinct fluorescence signal to visually dis-tinguish tumor from normal *** superior sensitivity and specificity of MACaP9 enabled high-contrast visualization of a broad range of tumors,as well as small tumor lesions.

关键词： Calcium phosphate Activatable probe Cancer imaging Tumor microenvironment Tumor metabolism

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On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease

On Self-Supervised Multimodal Representation Learning: An Ap...

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IEEE International Symposium on Biomedical Imaging

作者： Alex Fedorov Lei Wu Tristan Sylvain Margaux Luck Thomas P. DeRamus Dmitry Bleklov Sergey M. Plis Vince D. Calhoun Georgia Institute of Technology Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Georgia State University Mila Université de Montréal Montréal Québec Canada Emory University

Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning), impairing its value in the discovery process. Deep unsupervised and, recently, contrastive self-supervised approaches, not biased to classification, are better candidates for the task. Their multimodal options specifically offer additional regularization via modality interactions. This paper introduces a way to exhaustively consider multimodal architectures for a contrastive self-supervised fusion of fMRI and MRI of AD patients and controls. We show that this multimodal fusion results in representations that improve the downstream classification results for both modalities. We investigate the fused self-supervised features projected into the brain space and introduce a numerically stable way to do so.

关键词： Training Brain data integration Tools Predictive models Functional magnetic resonance imaging Alzheimer's disease

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Recent advances in small-molecular therapeutics for COVID-19

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Precision Clinical Medicine 2022年第4期5卷 222-243页

作者： Lei Zhong Zhipeng Zhao Xuerun Peng Jun Zou Shengyong Yang Department of Pharmacy Personalized Drug Therapy Key Laboratory of Sichuan ProvinceSichuan Provincial People’s HospitalSchool of MedicineUniversity of Electronic Science and Technology of ChinaChengdu 610072China Center for Clinical Translational Innovations Molecular Medicine Research CenterWest China Biomedical Big Data CenterState Key Laboratory of BiotherapyWest China HospitalWest China School of MedicineSichuan UniversityChengdu 610041China State Key Laboratory of Biotherapy and Cancer Center West China HospitalWest China School of MedicineSichuan UniversityChengdu 610041China

The COVID-19 pandemic poses a fundamental challenge to global *** the outbreak of SARS-CoV-2,great efforts have been made to identify antiviral strategies and develop therapeutic drugs to combat the *** are different strategies for developing small molecular anti-SARS-CoV-2 drugs,including targeting coronavirus structural proteins(*** protein),non-structural proteins(nsp)(***,Mpro,PLpro,helicase,nsp14,and nsp16),host proteases(***2,cathepsin,and furin)and the pivotal proteins mediating endocytosis(***),as well as developing endosome acidification agents and immune response *** and chloroquine are the anti-SARS-CoV-2 agents that were identified earlier in this epidemic and repurposed for COVID-19 clinical therapy based on these ***,their efficacies are ***,three small molecular anti-SARSCoV-2 agents,remdesivir,molnupiravir,and Paxlovid(PF-07321332 plus ritonavir),have been granted emergency use authorization or approved for COVID-19 therapy in many countries due to their significant curative effects in phase Ⅲ ***,a large number of promising anti-SARS-CoV-2 drug candidates have entered clinical *** development of these drugs brings hope for us to finally conquer *** this account,we conducted a comprehensive review of the recent advances in small molecule anti-SARS-CoV-2 agents according to the target *** we present all the approved drugs and most of the important drug candidates for each target,and discuss the challenges and perspectives for the future research and development of anti-SARS-CoV-2 drugs.

关键词： COVID-19 SARS-CoV-2 RdRp inhibitor Mpro inhibitor host-targeted inhibitor small molecule

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Decision-Level Scheme for Fusing Multi-Omics and Histology Slide Image Using Deep Neural Network for Tumor Prognosis Prediction

SSRN

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SSRN 2023年

作者： Kong, Yan Lu, Hui Ren, Yongyong Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai Children's Hospital Shanghai200240 China School of Life Sciences and Biotechnology India

Molecular bio-statistic workflows in oncology often rely on predictive models from multimodal data. Multimodal data fusion is becoming a fundamental but challenging task. Advances in deep learning (DL), and artificial intelligence technology (AI), have enabled the multimodal fusion from huge volume of data. Here, we present a decision level multimodal data fusion framework for integrating multi-omics and pathological tissue slide images for prognosis prediction. Basic convolution neural networks aim at extracting representation features from the perspective of perception and not fully explore the spatial information between nuclei instances. Our approach establish the spatial map of instances by connecting the neighboring nuclei in space and calculated the characteristic tensor via graph convolution layers for the input pathological tissue slide image. The subsequent Global Average Pooling calculation behind, we eliminated the heterogeneity between the feature tensors from pathological images and the multi-omics. We performed our proposed approach using beast cancer and non-small cell lung cancer datasets from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images, transcriptome data, genotype, epienetic, and survival information. In a 10-fold cross-validation, the comparison results demonstrate that the multimodal fusion paradigm improves outcome predictions from single modal data alone with the average C-Index increasing from 0.61 and 0.52 to 0.75 and 0.67 for Breast cancer and Non-small cell lung cancer cohort separately. The proposed decision-level multimodal data fusion framework is hoped to provide insights and technically methodologies for the follow-up studies that seed to merge heterogeneous data in disease mechanisms. © 2023, The Authors. All rights reserved.

关键词： Tensors

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A Cross-Feature Mutual Learning Framework to Integrate Functional Connectivity and Activity for Brain Disorder Classification

A Cross-Feature Mutual Learning Framework to Integrate Funct...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Min Zhao Rongtao Xu Dongmei Zhi Shan Yu Vince D Calhoun Jing Sui Brainnetome Center Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory of Cognitive Neuroscience Beijing Normal University Beijing China Laboratory of Brain Atlas and Brain-Inspired Intelligence Institute of Automation Chinese Academy of Sciences Beijing China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Center Georgia State University Atlanta GA USA IDG/McGovern Institute for Brain Research State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Time courses (TC) and functional network connectivity (FNC) features, derived from functional magnetic resonance imaging, show considerable potential in the study of brain disorders. Despite significant advancements, most deep learning approaches tend to either directly concatenate complementary MRI features at the input level or ensemble decisions after separately learning each feature, whereas an end-to-end, mixed feature learning framework is still lacking. To bridge this gap, we introduce a cross-feature mutual learning (CFML) to enable collaborative learning of TC-specific and FNC-specific models and facilitate mutual knowledge transfer to distill shared and robust characteristics from the high-level representations of TC and FNC, thereby enhancing brain disorder classification performance. Specifically, we first develop a recurrent neural network-based TC-specific encoder to capture temporal dynamic dependencies within TCs, alongside a transformer-based FNC-specific encoder to discern global high-order functional dependencies among independent components in FNCs. Subsequently, we design a cross-modal module for the adaptive integration of TC-specific and FNC-specific features. Additionally, the CFML strategy is proposed to collaboratively train these modules, incorporating feature-specific loss, feature-exchange loss, and joint loss. Empirical results reveal that CFML achieves an accuracy of 85.1% in differentiating healthy controls (HC) from schizophrenia (SZ) patients, surpassing 12 comparative models by a margin of 3.0-9.2% accuracy using either static FNC or TCs or both. These findings underscore the efficacy of CFML in classifying brain disorders, highlighting its potential in advancing this field.

关键词： Representation learning Deep learning neuroimaging Accuracy Recurrent neural networks Mental disorders Transformers Brain modeling Object recognition Knowledge transfer

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Perceptions and Discussions of Snus on Twitter: Observational Study

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JMIR Medical Informatics 2022年第8期10卷 e38174页

作者： Chen, Jiarui Xue, Siyu Xie, Zidian Li, Dongmei Goergen Institute for Data Science University of Rochester Rochester NY United States Department of Clinical & Translational Research University of Rochester Medical Center Rochester NY United States

Background: With the increasing popularity of snus, it is essential to understand the public perception of this oral tobacco product. Twitter—a popular social media platform that is being used to share personal experiences and opinions—provides an ideal data source for studying the public perception of snus. Objective: This study aims to examine public perceptions and discussions of snus on Twitter. Methods: Twitter posts (tweets) about snus were collected through the Twitter streaming application programming interface from March 11, 2021, to February 26, 2022. A temporal analysis was conducted to examine the change in number of snus-related tweets over time. A sentiment analysis was conducted to examine the sentiments of snus-related tweets. Topic modeling was applied to tweets to determine popular topics. Finally, a keyword search and hand-coding were used to understand the health symptoms mentioned in snus-related tweets. Results: The sentiment analysis showed that the proportion of snus-related tweets with a positive sentiment was significantly higher than the proportion of negative sentiment tweets (4341/11,631, 37.32% vs 3094/11,631, 26.60%;P<.001). The topic modeling analysis revealed that positive tweets focused on snus’s harm reduction and snus use being an alternative to smoking, while negative tweets focused on health concerns related to snus. Mouth and respiratory symptoms were the most mentioned health symptoms in snus-related tweets. Conclusions: This study examined the public perception of snus and popular snus-related topics discussed on Twitter, thus providing a guide for policy makers with regard to the future formulation and adjustment of tobacco regulation policies. ©Jiarui Chen, Siyu Xue, Zidian Xie, Dongmei Li.

关键词： sentiment smokeless tobacco products snus topic modeling Twitter

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A New Hypergraph Clustering Method For Exploring Transdiagnostic Biotypes In Mental Illnesses: Application To Schizophrenia And Psychotic Bipolar Disorder

A New Hypergraph Clustering Method For Exploring Transdiagno...

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IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Ju Niu Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (纸本)9781665412469;9781665429474

It is difficult to distinguish schizophrenia (SZ) and bipolar disorder with psychosis (BPP) due to their overlapping symptoms. Indeed, there has been evidence supporting different subtypes within them. data-driven clustering approaches are commonly used to explore biologically meaningful biotypes using neuroimaging features. However, previous studies typically consider pair-wise subject relationships. Here, we propose a hypergraph clustering method to explore biotypes. Our method extracts high-order features via hyperedges sampling, measures similarity and then regroups subjects using community detection. We applied it to identify biotypes of 100 BPP and 100 SZ patients using brain functional connectivity estimated from resting-state fMRI data, and compared with solutions from K-means and normalized cut (Ncut). Two reliable biotypes were identified and had greater differences in functional connectivity than groups determined by clinical diagnosis. Our method also outperformed K-means and Ncut for the clustering ability and computation efficiency. In summary, the proposed method is promising for developing biotypes, targeting accurate clinical diagnosis for psychosis.

关键词： neuroimaging Clustering methods Functional magnetic resonance imaging Biomedical measurement Feature extraction Biology Computational efficiency

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A New Semi-Supervised Non-Negative Matrix Factorization Method For Brain Dynamic Functional Connectivity Analysis

A New Semi-Supervised Non-Negative Matrix Factorization Meth...

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IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Xingyu He Vince D Calhoun School of Computer and Information Technology Shanxi University Taiyuan China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (纸本)9781665412469;9781665429474

To overcome the shortcoming of static brain functional connectivity analysis, recent studies have analyzed brain functional connectivity from a time-varying view to identify subtle group differences as potential biomarkers. However, how to obtain reliable functional connectivity states from dynamic functional connectivity (DFC) is challenging, since the resulting states are often sensitive to the cluster or component number. In our present work, we propose a new semi-supervised non-negative matrix factorization method combining with a community detection technique, which can automatically estimate reliable functional connectivity states from DFC without a need of setting a cluster or component number. We applied our method to fMRI data of 36 schizophrenia patients and 49 healthy controls to investigate brain connectivity changes in schizophrenia. We found significant and meaningful group differences in four of five connectivity states. Our findings suggest that our method can help understand the mechanism of mental illness in a more effective way.

关键词： Functional magnetic resonance imaging Biomarkers Reliability Biomedical imaging

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