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

作者： Md Abdur Rahaman Zening Fu Armin Iraji Vince Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) School of Computational Science and Engineering Georgia Institute of Technology

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The most discriminative and revealing patterns in the neuroimaging population are often confined to smaller subdivisions of the samples and features. Especially in neuropsychiatric conditions, symptoms are expressed within micro subgroups of individuals and may only underly a subset of neurological mechanisms. As such, running a whole-population analysis yields suboptimal outcomes leading to reduced specificity and interpretability. Biclustering is a potential solution since subject heterogeneity makes one-dimensional clustering less effective in this realm. Yet, high dimensional sparse input space and semantically incoherent grouping of attributes make post hoc analysis challenging. Therefore, we propose a deep neural network called semantic locality preserving auto decoder (SpaDE), for unsupervised feature learning and biclustering. SpaDE produces coherent subgroups of subjects and neural features preserving semantic locality and enhancing neurobiological interpretability. Also, it regularizes for sparsity to improve representation learning. We employ SpaDE on human brain connectome collected from schizophrenia (SZ) and healthy control (HC) subjects. The model outperforms several state-of-the-art biclustering methods. Our method extracts modular neural communities showing significant (HC/SZ) group differences in distinct brain networks including visual, sensorimotor, and subcortical. Moreover, these bi-clustered connectivity substructures exhibit substantial relations with various cognitive measures such as attention, working memory, and visual learning.

关键词： neuroimaging Representation learning Visualization Semantics Network analyzers Brain modeling Feature extraction Physiology Performance analysis Engineering in medicine and biology

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Cross-Modal Synthesis of Structural MRI and Functional Connectivity Networks via Conditional ViT-GANs

arXiv

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arXiv 2023年

作者： Bi, Yuda Abrol, Anees Sui, Jing Calhoun, Vince Tri-institutional Center for Translational Research in Neuroimaging and Data Science GSU GATech Emory United States

The cross-modal synthesis between structural magnetic resonance imaging (sMRI) and functional network connectivity (FNC) is a relatively unexplored area in medical imaging, especially with respect to schizophrenia. This study employs conditional Vision Transformer Generative Adversarial Networks (cViT-GANs) to generate FNC data based on sMRI inputs. After training on a comprehensive dataset that included both individuals with schizophrenia and healthy control subjects, our cViT-GAN model effectively synthesized the FNC matrix for each subject, and then formed a group difference FNC matrix, obtaining a Pearson correlation of 0.73 with the actual FNC matrix. In addition, our FNC visualization results demonstrate significant correlations in particular subcortical brain regions, highlighting the model’s capability of capturing detailed structural-functional associations. This performance distinguishes our model from conditional CNN-based GAN alternatives such as Pix2Pix. Our research is one of the first attempts to link sMRI and FNC synthesis, setting it apart from other cross-modal studies that concentrate on T1- and T2-weighted MR images or the fusion of MRI and CT scans. © 2023, CC BY.

关键词： Magnetic resonance imaging

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Bi-allelic UGGT1 variants cause a congenital disorder of glycosylation

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American Journal of Human Genetics 2025年第5期112卷 1139-1157页

作者： Dardas, Zain Harrold, Laura Calame, Daniel G. Salter, Claire G. Kikuma, Takashi Guay, Kevin P. Ng, Bobby G. Sano, Kanae Saad, Ahmad K. Du, Haowei Sangermano, Riccardo Patankar, Sohil G. Jhangiani, Shalini N. Gürsoy, Semra Abdel-Hamid, Mohamed S. Ahmed, Mahmoud K.H. Maroofian, Reza Kaiyrzhanov, Rauan Salayev, Kamran Jones, Wendy D. Pérez Caballero, Ana McGavin, Lucy Spiller, Michael Durkie, Miranda Wood, Nick Goldenberg, Paula Neumeyer, Ann M. Begtrup, Amber Abdel-Ghafar, Sherif F. Zaki, Maha S. Van Esch, Hilde Posey, Jennifer E. Wenger, Olivia K. Scott, Ethan M. Bujakowska, Kinga M. Gibbs, Richard A. Pehlivan, Davut Marafi, Dana Leslie, Joseph S. Ubeyratna, Nishanka Day, Jacob Owens, Martina Settle, Jessica Balkhy, Soher Tamim, Abdullah Alabdi, Lama Alkuraya, Fowzan S. Takeda, Yoichi Freeze, Hudson H. Hebert, Daniel N. Lupski, James R. Crosby, Andrew H. Baple, Emma L. Department of Molecular and Human Genetics Baylor College of Medicine Houston TX United States Department of Clinical and Biomedical Sciences (Medical School) Faculty of Health and Life Sciences University of Exeter Exeter United Kingdom Texas Children's Hospital Houston TX United States Section of Pediatric Neurology and Developmental Neuroscience Department of Pediatrics Baylor College of Medicine Houston TX United States Wessex Clinical Genetics Service Princess Anne Hospital Southampton United Kingdom Department of Biotechnology College of Life Sciences Ritsumeikan University 1-1-1 Noji-Higashi Shiga Kusatsu 525-8577 Japan Program in Molecular and Cellular Biology University of Massachusetts Amherst Amherst MA United States Department of Biochemistry and Molecular Biology University of Massachusetts Amherst Amherst MA United States Human Genetics Program Sanford Burnham Prebys Medical Discovery Institute La Jolla CA United States Medical Molecular Genetics Department Human Genetics and Genome Research Institute National Research Centre Cairo Egypt Ocular Genomics Institute Department of Ophthalmology Massachusetts Eye and Ear Harvard Medical School Boston MA United States Human Genome Sequencing Center Baylor College of Medicine Houston TX United States Department of Pediatric Genetics Dokuz Eylul University Faculty of Medicine Izmir Turkey Department of Prenatal Diagnosis and Fetal Medicine Human Genetics and Genome Research Institute National Research Centre Cairo Egypt Department of Neuromuscular Diseases UCL Queen Square Institute of Neurology University College London London United Kingdom Fetal-Neonatal Neuroimaging & Developmental Science Center Division of Newborn Medicine Boston Children's Hospital Boston MA United States The North East Thames Regional Genetics Service Great Ormond Street Hospital London United Kingdom North Thames Genomic Laboratory Hub Great Ormond Street NHS Foundation Trust London United Kingdom University

Congenital disorders of glycosylation (CDGs) comprise a large heterogeneous group of metabolic conditions caused by defects in glycoprotein and glycolipid glycan assembly and remodeling, a fundamental molecular process with wide-ranging biological roles. Herein, we describe bi-allelic UGGT1 variants in fifteen individuals from ten unrelated families of various ethnic backgrounds as a cause of a distinctive CDG of variable severity. The cardinal clinical features of UGGT1-CDG involve developmental delay, intellectual disability, seizures, characteristic facial features, and microcephaly in the majority (9/11 affected individuals for whom measurements were available). The more severely affected individuals display congenital heart malformations, variable skeletal abnormalities including scoliosis, and hepatic and renal involvement, including polycystic kidneys mimicking autosomal recessive polycystic kidney disease. Clinical studies defined genotype-phenotype correlations, showing bi-allelic UGGT1 loss-of-function variants associated with increased disease severity, including death in infancy. UGGT1 encodes UDP-glucose:glycoprotein glucosyltransferase 1, an enzyme critical for maintaining quality control of N-linked glycosylation. Molecular studies showed that pathogenic UGGT1 variants impair UGGT1 glucosylation and catalytic activity, disrupt mRNA splicing, or inhibit endoplasmic reticulum (ER) retention. Collectively, our data provide a comprehensive genetic, clinical, and molecular characterization of UGGT1-CDG, broadening the spectrum of N-linked glycosylation disorders. © 2025 The Author(s)

关键词： autosomal recessive congential disorder of glycosylation microcephaly MOGS monogenic disorder N-linked glycosylation neurodevelopmental disorder UDP-glucose:glycoprotein glucosyltransferase 1 UGGT1

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A clinical solution for tracking clonal evolution of acute myeloid leukemia after allogeneic transplantation using bulk next generation sequencing

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Bone Marrow Transplantation 2025年 1-9页

作者： Krakow, Elizabeth F. Lee, Nathan Jenkins, Isaac Sala-Torra, Olga Beppu, Lan Radich, Jerald P. Fukuda, Bryce Sandmaier, Brenda M. Yeung, Cecilia CS Bozic, Ivana Clinical Research Division Fred Hutchinson Cancer Center Seattle WA United States Division of Hematology-Oncology University of Washington Seattle WA United States Department of Applied Mathematics University of Washington Seattle WA United States Department of Biostatistics Fred Hutchinson Cancer Center Seattle WA United States Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA United States Department of Laboratory Medicine and Pathology University of Washington Seattle WA United States Translational Data Science Integrated Research Core Fred Hutchinson Cancer Center Seattle WA United States Berlin Institute for the Foundations of Learning and Data (BIFOLD) & Institute for Computational Cancer Biology Cologne Germany

Clinical next generation sequencing (NGS) typically relies on limited gene panels run on bulk marrow or blood. Current computational tools for inferring clonal relationships is generally limited by the use of a small panel of pathogenic mutations to define clones. We developed an online software (CloneTracker) that uses ‘incidentally-sequenced’ single nucleotide polymorphisms (SNPs) in the regions of recurrent somatic mutations in addition to conventional mutation data from bulk NGS gene panels to provide detailed visualizations of clonal evolution during cancer treatment, alongside clinical data. Tested on 29 patients who underwent non-myeloablative transplantation for AML, CloneTracker successfully reconstructed the evolutionary dynamics of donor engraftment from bulk NGS and rendered intuitive visualizations of residual patient-derived hematopoiesis and relapsing malignant clones. The software does not require sequencing donor samples, as donor-derived clones are identifiable from post-HCT SNP data. This manuscript aims to introduce CloneTracker to the BMT community and make it available for those who would ascertain its clinical utility, e.g, in BMT trials leveraging molecular minimal residual disease (MRD) monitoring and targeted interventions to pre-empt relapse. (Figure presented.) © The Author(s), under exclusive licence to Springer Nature Limited 2025.

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Advanced machine learning in neuroimaging studies via federated learning

Advanced machine learning in neuroimaging studies via federa...

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2024 IEEE International Conference on Big data, Bigdata 2024

作者： Basodi, Sunitha Romero, Javier Panta, Sandeep Martin, Dylan Plis, Sergey Sarwate, Anand D. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States Rutgers University Department of Electrical & Computer Engineering NJ United States

ISBN: (纸本)9798350362480

Federated analysis can help perform large-scale analyses using neuroimaging datasets across various research groups overcoming the limitations of institutional data-sharing policies, privacy or regulatory concerns as it requires no data sharing. In this work, we employ a federated neuromark algorithm to generate independent component analysis (ICA) time courses data from functional magnetic resonance imaging (fMRI) data and feed it to a federated deep neural network (DNN) model to perform classification of schizophrenia patients versus controls. © 2024 IEEE.

关键词： Federated learning

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Voxel-Wise Fusion of Resting fMRI Networks and Gray Matter Volume for Alzheimer's Disease Classification Using Deep Multimodal Learning

SSRN

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SSRN 2025年

作者： Itkyal, Vaibhavi Sanjeet Abrol, Anees LaGrow, Theodore J. Fedorov, Alex Calhoun, Vince D. Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology Emory University GA United States Department of Electrical and Computer Engineering Georgia Institute of Technology GA United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University GA United States Department of Neuroscience Emory University GA United States Emory University United States

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder requiring accurate and early diagnosis to support clinical decision-making and future intervention strategies. Resting-state functional magnetic resonance imaging (rs-fMRI) and gray matter volume analysis from structural MRI (sMRI) are promising tools for identifying AD-related brain alterations. In this study, we propose a novel deep learning framework that fuses spatial maps of rs-fMRI functional networks—via a new voxelwise intensity projection (iVIP) method—and gray matter segmentations to classify subjects from the Alzheimer’s Disease neuroimaging Initiative (ADNI). Our multi-channel 3D convolutional neural network (CNN), inspired by the AlexNet architecture, integrates anatomical and functional modalities to perform both two-way (AD vs. CN) and three-way (AD vs. MCI vs. CN) classification tasks. While the two-way classification achieves high performance (94.12% accuracy, 97.79 AUC), the three-way classification offers clinically relevant utility, achieving 62.68% accuracy and 68.31 AUC, and enables finer differentiation among diagnostic subtypes. Comparative analysis shows that our iVIP representation outperforms traditional rs-fMRI measures such as ALFF and fALFF, and the fusion of iVIP with sMRI consistently outperforms unimodal models. Saliency map visualizations further reveal discriminative regions—including the hippocampus, amygdala, caudate nucleus, and thalamus—aligned with AD pathology. Our findings highlight the potential of deep multimodal fusion to enhance early diagnosis and subtype differentiation in Alzheimer’s disease. © 2025, The Authors. All rights reserved.

关键词： Brain mapping

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Neural Complexity Unveiled: Doubly Functionally Independent Primitives (dFIPs) in Psychiatric Risk Score Assessment

Neural Complexity Unveiled: Doubly Functionally Independent ...

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

作者： Najme Soleimani Vince D. Calhoun Department of Computer Science Center for Translational Research in Neuroimaging and Data Science(TReNDS) GSU GATech Emory Atlanta USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Understanding and predicting intricate neural underpinnings of psychiatric disorders has become an area of intensive research in neuroimaging. Current assessment methods, such as genetic testing, face limitations in providing adaptable biomarkers. This study introduces an innovative perspective by focusing on doubly functionally independent primitives (dFIPs) to assess risk scores modeled by employing multiple linear regression. Departing from traditional polygenic risk scores, our novel approach assesses an individual’s psychiatric risk by contrasting their functional network connectivity (FNC) with reference patterns from psychiatric disorder. Leveraging a large imaging dataset (N=5805) encompassing schizophrenia (SZ), autism spectrum disorder (ASD), bipolar disorder (BPD) and major depressive disorder (MDD), alongside corresponding healthy controls, the risk score is computed and applied to a diverse dataset of Adolescent Brain and Cognitive Development (ABCD, N=8191). Our major findings unveiled the nuanced significance of reconstructed FNCs based on the most discriminative dFIP patterns in the 10% of the ABCD individuals with highest risk score for the specific disorder. Furthermore, the 10% ABCD individuals with highest risk score of ASD and MDD showed greater overlap than individuals with other disorders. Overall, these findings underscored the relative importance of each dFIP pattern, providing valuable insight into the differential contributions of these patterns to the accurate prediction of elevated risk score. Also, this pioneering approach provides a promising avenue for comprehensive risk assessment.

关键词： neuroimaging Linear regression Focusing Genetics Depression Risk management Engineering in medicine and biology Image reconstruction Faces Testing

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data-driven multimodal fusion:approaches and applications in psychiatric research

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Psychoradiology 2023年第1期3卷 135-153页

作者： Jing Sui Dongmei Zhi Vince D Calhoun State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal UniversityBeijing 100875China Tni-institutional Center for Translational Research in Neuroimaging and Data Science(TReNDS) Georgia Institute of TechnologyEmory University and Georgia State UniversityAtlantaGA 30303United States

In the era of big data,where vast amounts of information are being generated and collected at an unprecedented rate,there is a pressing demand for innovative data-driven multi-modal fusion *** methods aim to integrate diverse neuroimaging per-spectives to extract meaningful insights and attain a more comprehensive understanding of complex psychiatric ***,analyzing each modality separately may only reveal partial insights or miss out on important correlations between different types of *** is where data-driven multi-modal fusion techniques come into *** combining information from multiple modalities in a synergistic manner,these methods enable us to uncover hidden patterns and relationships that would otherwise remain *** this paper,we present an extensive overview of data-driven multimodal fusion approaches with or without prior information,with specific emphasis on canonical correlation analysis and independent component *** applications of such fusion methods are wide-ranging and allow us to incorporate multiple factors such as genetics,environment,cognition,and treatment outcomes across various brain *** summarizing the diverse neuropsychiatric magnetic resonance imaging fusion applications,we further discuss the emerging neuroimaging analyzing trends in big data,such as N-way multimodal fusion,deep learning approaches,and clinical ***,multimodal fusion emerges as an imperative approach providing valuable insights into the under-lying neural basis of mental disorders,which can uncover subtle abnormalities or potential biomarkers that may benefit targeted treatments and personalized medical interventions.

关键词： multimodal fusion approach data driven functional magnetic resonance imaging(fMRI) structural MRI diffusion mag-netic resonance imaging independent component analysis canonical correlation analysis psychiatric disorder

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Confirmatory Factor Analysis on Mental Health Status using ABCD Cohort

Confirmatory Factor Analysis on Mental Health Status using A...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Britny Farahdel Bishal Thapaliya Pranav Suresh Bhaskar Ray Vince D. Calhoun Jingyu Liu Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta USA

ISBN: (纸本)9781665429825

The general psychopathology factor (p factor), derived from a wide range of psychological symptoms, is proposed to approximate an individual’s tendency to develop a broad range of psychiatric disorders. The aim of this study was to extract the general p factor in a bifactor model from confirmatory factor analysis (CFA), as well as internalizing and externalizing factors, and test the stability of the factors derived from different behavioral measures. Using data from the Adolescent Brain and Cognitive Development (ABCD) study, we compared the latent factors derived from Child Behavior Checklist measure (a standard approach) with those from broader measures. Multiple linear regression was used to assess the relationship of the p factor with other behavioral or cognition measures and clinical diagnoses. The results showed that the p factor had greater stability and a higher correlation between models (r=0.87) than internalizing and externalizing factors (r=0.32,0.02, respectively). The p factor explained significant variances in all dimensional behavioral variables as well as neurocognition and was significantly related to all mental disorders available. A mixed-effects model was constructed to measure the association of the p factor with screen time activity, sleep duration, physical activity, household income, and gender (altogether, these explained 8.02% of the variance in the p factor). The findings from this study also show that the general p factor defined in the bifactor model has sufficient determinacy.

关键词： Correlation Mental disorders Conferences Linear regression Mental health Biomedical measurement Stability analysis

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Time-dependent apparent diffusion coefficient changes in breast MR images with contrast for tumor characterization

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Clinical Breast Cancer 2025年

作者： Yongsheng Ao Xuanle Li Lan Mu Jierui Zhao Hongliang Chen Yunling Wang Shuheng Zhang Shimin Yang Na Zhang Lihua Qiu Medical Imaging Center the Second People 's Hospital of Yibin Yibin 644000 China Faculty of Data Science City University of Macau Macau China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen 518055 China Department of Radiology Third Affiliated Hospital of Chengdu Medical College· Pidu District People 's Hospital Chengdu 611730 China Department of Radiology The First Affiliated Hospital of Xinjiang Medical University Urumqi 830054 China United Imaging Healthcare Shanghai 201807 China State Key Laboratory of Biomedical Imaging Science and System Key Laboratory of Biomedical Imaging Science and System Chinese Academy of Sciences Shenzhen China Neuroimaging Big Data Research Center Clinical Research and Translational Center The Second People’s Hospital of Yibin Yibin 644000 China

Background: To assess the effect of scan time after contrast injection on breast MRI DWI sequence ADC values and its role in lesion differentiation. Patients and Methods: Between 2022 and 2023, 251 breast magnetic resonance (MR) images were collected from 251 patients, who had a total of 258 lesions. Pathology results obtained within one month of the MR imaging were utilized. Apparent diffusion coefficient (ADC) values were measured at three time - points: pre - contrast, 3 minutes post - contrast, and 6 minutes post - contrast. For the analysis, statistical methods including the Friedman test, linear mixed models, Bonferroni correction, receiver operating characteristic (ROC) curve analysis, and DeLong test were applied. Results: Contrast agents cause ADC values to decrease within 3 minutes after injection and recover within 6 minutes (e.g., invasive ductal carcinoma). ADC can distinguish between benign tumors and cancers but not between carcinoma in situ and invasive carcinoma. Time-dependent changes do not affect the differentiation of benign from malignant lesions. Conclusion: Contrast injection time has little effect on the effectiveness of ADC in differentiating benign from malignant lesions on DWI sequences. The findings support the feasibility of post-contrast DWI without compromising diagnostic accuracy.

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