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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Rekha Saha Debbrata K. Saha Zening Fu Rogers F. Silva Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Functional and structural magnetic resonance imaging (fMRI/sMRI) are extensively used modalities for studying brain development. While individual modalities may overlook crucial aspects of brain analysis, combining multiple modalities allows us to leverage the benefits of revealing hidden brain connections. To analyze multivariate change patterns in brain function and structure with increasing age across the entire brain, we employ a symmetric multimodal fusion approach that combines multiset canonical correlation analysis and joint independent component analysis. In this study, we present a novel approach to analyze linked longitudinal change patterns in functional network connectivity (FNC) and gray matter (GM) data derived from the large-scale Adolescent Brain and Cognitive Development dataset. Our approach uncovers significant pattern changes in both modalities. Specifically, we identify highly structured functional change patterns and structural change patterns that include increased brain functional connectivity between the visual and sensorimotor domains in the fMRI data, as well as changes in the bilateral sensorimotor cortex in the sMRI data. Overall, our study demonstrates the strength of our approach in uncovering longitudinal changes in FNC and GM, provides valuable insights into the dynamic nature of brain connectivity and structure during adolescence, and sheds light on potential gender-related differences in these processes.

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Constrained Source-based Salience: Network-based Visualization of Deep Learning neuroimaging Models

Constrained Source-based Salience: Network-based Visualizati...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Ishaan Batta Anees Abrol Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Developing frameworks using high-dimensional magnetic resonance imaging (MRI) data to characterize underlying brain changes in neurological disorders is crucial and challenging. While deep learning models offer a better prediction, tracking automated higher-order explanations at the level of brain networks is harder in learned models. We introduce a novel constrained source-based salience (cSBS) framework to automatically learn and visualize multiple independently salient brain networks associated with clinical diagnostic assessments. This is achieved by performing active subspace learning (ASL) and spatially constrained independent component analysis (scICA) in the saliency space of trained convolutional neural networks (CNNs), such that the resultant components are interpretable in terms of brain network components from existing templates. By employing a robust analysis across repeated training scenarios for an Alzheimer's disease (AD) classification task, we visualize cSBS components via full-brain back-reconstruction. We show that the cSBS components and their corresponding loadings are consistent and relevant in terms of AD-related brain areas. Our approach is able to synthesize multiple objectives of utilization of high-dimensional MRI data for deep learning along with automated detection of low-dimensional representations of the consistently involved features in terms of intrinsically salient brain networks. Our framework of automated identification of consistent underlying brain subsystems associated with clinically observed assessments is an important step toward biomarker development for various clinically observed characteristics and disorders.

关键词： Deep learning Neurological diseases Training Magnetic resonance imaging Biological system modeling Loading data visualization Predictive models Brain modeling Load modeling

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Brain community detection in the general children population

Brain community detection in the general children population

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

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) Atlanta USA Department of Computer Science Georgia State University Atlanta USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The fingerprint is known to be unique in every individual, and there is evidence that such individuality exists with the brain. neuroimaging studies that research brain fingerprint patterns typically consider relationships between individuals and their brain patterns. However, there remains a question as to how such fingerprint patterns can be grouped among the general population. In this study, we implemented clustering-based methods to evaluate whether such subgrouping exists among individuals and evaluated the relationships between these clusters and individuals’ developmental, cognitive, demographical, psychological status in the Adolescent Brain and Cognitive Development study cohort. Multiplex community detection and K-means clustering revealed the existence of clusters in our cohort, as well as significant group differences between these clusters in these datasets, indicative of heterogeneous subgrouping of brain fingerprint patterns in the general population.

关键词： neuroimaging Multiplexing Pediatrics Psychology Fingerprint recognition Feature extraction Engineering in medicine and biology

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Deep learning imaging analysis to identify bacterial metabolic states associated with carcinogen production

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Discover imaging 2025年第1期2卷 2页

作者： Maysam Orouskhani Sarwesh Rauniyar Norma Morella Daniel Lachance Samuel S Minot Neelendu Dey Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA USA. Microbiome Research Initiative Fred Hutchinson Cancer Center Seattle WA USA. Data Core Fred Hutchinson Cancer Center Seattle WA USA. Department of Laboratory Medicine and Pathology University of Washington Seattle WA USA. Department of Medicine Division of Gastroenterology University of Washington Seattle WA USA.

Background:Colorectal cancer (CRC) is a globally prevalent cancer. Emerging research implicates the gut microbiome in CRC pathogenesis. Bacteria such as can produce the carcinogenic bile acid deoxycholic acid (DCA). It is unknown whether imaging methods can differentiate DCA-producing and DCA-non-producing cells. Methods:Light microscopy images of anaerobically cultured in four conditions were acquired at 100× magnification using the Tissue FAX system: in media alone (DCA-non-producing state), in media with cholic acid (DCA-producing state), or in co-culture with one of two species (intermediate DCA production states). We evaluated three approaches: whole-image classification, per-cell classification, and image segmentation-based classification. For whole-image classification, we used a custom Convolutional Neural Network (CNN), pre-trained DenseNet, pre-trained ResNet, and ResNet enhanced by integrating the Digital Images of Bacterial Species (DIBaS) dataset. For cell detection and classification, we applied thresholding (OTSU or adaptive thresholding) followed by a ResNet model. Finally, image segmentation-based classification was performed using nnU-Net. Results:For whole-image analysis, DIBaS-enhanced ResNet models achieved the best performance in distinguishing states in monoculture (accuracy 0.89 ± 0.006) and in co-cultures (accuracy 0.86 ± 0.004). Per-cell analysis was optimal at a C constant value of 3, with the ResNet model achieving 62-74% accuracy for states in monoculture. Segmentation-based analysis using nnU-Net resulted in Dice coefficients of 87% for and 74-76% for the species. Conclusions:This study demonstrates feasibility of image-based deep learning models in identifying health-relevant gut bacterial metabolic states. Supplementary Information:The online version contains supplementary material available at 10.1007/s44352-025-00006-1.

关键词： Colorectal cancer Deep learning Deoxycholic acid Microbiome

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CanSeer: a translational methodology for developing personalized cancer models and therapeutics

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Scientific Reports 2025年第1期15卷 1-22页

作者： Butt, Rida Nasir Amina, Bibi Sultan, Muhammad Umer Tanveer, Zain Bin Gondal, Mahnoor Naseer Hussain, Risham Khan, Salaar Akbar, Rida Nasir, Zainab Khalid, Muhammad Farhan Channan-Khan, Asher Alban Faisal, Amir Shoaib, Muhammad Chaudhary, Safee Ullah Biomedical Informatics and Engineering Research Laboratory (BIRL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor 48109 MI United States Michigan Center for Translational Pathology University of Michigan Ann Arbor 48109 MI United States Data Science Institute Lancaster University Lancaster LA1 4YW United Kingdom Center for Eukaryotic Gene Regulation Department of Biochemistry and Molecular Biology Penn State University University Park 16802 PA United States Mayo Clinic School of Medicine New York University NY United States Cancer Therapeutics Lab Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan Epigenome and Genome Integrity Lab (EaGIL) Syed Babar Ali School of Science and Engineering Department of Life Sciences Lahore University of Management Sciences Lahore 54792 Pakistan

Computational modeling and analysis of biomolecular network models annotated with omics data are emerging as a versatile tool for designing personalized therapies. Current endeavors aimed at employing in silico models towards personalized cancer therapeutics remain limited in providing all-in-one approach that ascertains actionable targets, re-positions FDA (Food and Drug Administration) approved drugs, furnishes quantitative cues on therapy responses such as efficacy and cytotoxic effect, and identifies novel drug combinations. Here we propose “CanSeer”—a methodology for developing personalized therapeutics. CanSeer employs patient-specific genetic alterations and RNA-seq data to annotate in silico models followed by dynamical network analyses towards assessment of treatment responses. To exemplify, three use cases involving paired samples, unpaired samples, and cancer samples only, of lung squamous cell carcinoma (LUSC) patients are provided. CanSeer reveals the effectiveness of repositioned drugs along with the identification of several novel LUSC treatment combinations including Afuresertib + Palbociclib, Dinaciclib + Trametinib, Afatinib + Oxaliplatin, Ulixertinib + Olaparib, etc. © The Author(s) 2025.

关键词： Biomolecular network modeling Dynamical analysis of in silico models Integrative omics Personalized mono- and combinatorial therapies Therapeutic screening translational cancer systems biology

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Conserved spatial subtypes and cellular neighborhoods of cancer-associated fibroblasts revealed by single-cell spatial multi-omics

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Cancer Cell 2025年第5期43卷 905-924.e6页

作者： Liu, Yunhe Sinjab, Ansam Min, Jimin Han, Guangchun Paradiso, Francesca Zhang, Yuanyuan Wang, Ruiping Pei, Guangsheng Dai, Yibo Liu, Yang Cho, Kyung Serk Dai, Enyu Basi, Akshay Burks, Jared K. Rajapakshe, Kimal I. Chu, Yanshuo Jiang, Jiahui Zhang, Daiwei Yan, Xinmiao Guerrero, Paola A. Serrano, Alejandra Li, Mingyao Hwang, Tae Hyun Futreal, Andrew Ajani, Jaffer A. Solis Soto, Luisa M. Jazaeri, Amir A. Kadara, Humam Maitra, Anirban Wang, Linghua Department of Genomic Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Translational Molecular Pathology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Sheikh Ahmed Center for Pancreatic Cancer Research The University of Texas MD Anderson Cancer Center Houston 77030 TX United States The University of Texas MD Anderson Cancer Center UTHealth Houston Graduate School of Biomedical Sciences (GSBS) Houston 77030 TX United States Department of Leukemia The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Biostatistics Epidemiology and Informatics Perelman School of Medicine University of Pennsylvania Philadelphia 19104 PA United States Department of Surgery Vanderbilt University Medical Center Nashville 37232 TN United States Department of Gastrointestinal Medical Oncology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Department of Gynecologic Oncology and Reproductive Medicine The University of Texas MD Anderson Cancer Center Houston 77030 TX United States The James P. Allison Institute The University of Texas MD Anderson Cancer Center Houston 77030 TX United States Institute for Data Science in Oncology The University of Texas MD Anderson Cancer Center Houston 77030 TX United States

Cancer-associated fibroblasts (CAFs) are a multifaceted cell population essential for shaping the tumor microenvironment (TME) and influencing therapy responses. Characterizing the spatial organization and interactions of CAFs within complex tissue environments provides critical insights into tumor biology and immunobiology. In this study, through integrative analyses of over 14 million cells from 10 cancer types across 7 spatial transcriptomics and proteomics platforms, we discover, validate, and characterize four distinct spatial CAF subtypes. These subtypes are conserved across cancer types and independent of spatial omics platforms. Notably, they exhibit distinct spatial organizational patterns, neighboring cell compositions, interaction networks, and transcriptomic profiles. Their abundance and composition vary across tissues, shaping TME characteristics, such as levels, distribution, and state composition of tumor-infiltrating immune cells, tumor immune phenotypes, and patient survival. This study enriches our understanding of CAF spatial heterogeneity in cancer and paves the way for novel approaches to target and modulate CAFs. © 2025 Elsevier Inc.

关键词： cancer-associated fibroblast cell-cell communication cellular neighborhood lymphoid aggregate pan-cancer spatial multi-omics spatial transcriptomics tertiary lymphoid structure tumor associated macrophage tumor microenvironment

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Improving Multichannel Raw Electroencephalography-based Diagnosis of Major Depressive Disorder via Transfer Learning with Single Channel Sleep Stage data*

Improving Multichannel Raw Electroencephalography-based Diag...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Charles A. Ellis Abhinav Sattiraju Robyn L. Miller Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University Atlanta USA

As the field of deep learning has grown in recent years, its application to the domain of raw resting-state electroencephalography (EEG) has also increased. Relative to traditional machine learning methods or deep learning methods applied to manually engineered features, there are fewer methods for developing deep learning models on small raw EEG datasets. One potential approach for enhancing deep learning performance, in this case, is the use of transfer learning. While a number of studies have presented transfer learning approaches for manually engineered EEG features, relatively few approaches have been developed for raw resting-state EEG. In this study, we propose a novel EEG transfer learning approach wherein we first train a model on a large publicly available single-channel sleep stage classification dataset. We then use the learned representations to develop a classifier for automated major depressive disorder diagnosis with raw multichannel EEG. Statistical testing reveals that our approach significantly improves the performance of our model (p < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.

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Denoising Diffusion Probabilistic Models for High-Fidelity fMRI Intrinsic Connectivity Network data Generation

Denoising Diffusion Probabilistic Models for High-Fidelity f...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Meenu Ajith Vince D. Calhoun The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) @ Georgia State Georgia Tech and Emory University Atlanta GA USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

The emergence of diffusion models such as Glide, Dalle-2, Imagen, and Stable Diffusion marks a significant breakthrough in generative AI-based image generation. This paper introduces an rs-fMRI image synthesis framework that leverages the nonlinear capabilities of denoising diffusion probabilistic models (DDPMs) to overcome the limitations of linear methods like independent component analysis (ICA) in neuroimaging analysis. Unlike ICA, which assumes linearity, DDPMs capture the intricate and complex patterns inherent in neuroimaging data. Our approach advances from 2D to 3D representations, providing a comprehensive visualization of intrinsic connectivity networks (ICNs). This framework also addresses the challenge of sparse training datasets commonly encountered in deep learning applications for neuroimaging. Trained on a large database, our model captures the intricate variability of different ICNs, generating realistic connectivity patterns. The proposed method is evaluated quantitatively to compare the synthesized ICNs against ground truth data. Results demonstrate that the proposed DDPM-based framework showcases competitive performance accuracy in reflecting the true complexity of neural connectivity patterns.

关键词： neuroimaging Training Three-dimensional displays Image synthesis Noise reduction Memory management Diffusion models data models Probability distribution Complexity theory

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A Novel Activation Maximization-based Approach for Insight into Electrophysiology Classifiers

A Novel Activation Maximization-based Approach for Insight i...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Charles A. Ellis Mohammad S.E. Sendi Robyn Miller Vince Calhoun Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

ISBN: (纸本)9781665429825

Spectral analysis remains a hallmark approach for gaining insight into electrophysiology modalities like electroencephalography (EEG). As the field of deep learning has progressed, more studies have begun to train deep learning classifiers on raw EEG data, which presents unique problems for explainability. A growing number of studies have presented explainability approaches that provide insight into the spectral features learned by deep learning classifiers. However, existing approaches only attribute importance to different frequency bands. Most of the methods cannot provide insight into the actual spectral values or the relationship between spectral features that models have learned. Here, we present a novel adaptation of activation maximization for electrophysiology time-series that generates samples that indicate the features learned by classifiers by optimizing their spectral content. We evaluate our approach within the context of EEG sleep stage classification with a convolutional neural network, and we find that our approach is able to identify spectral patterns known to be associated with each sleep stage. We also find surprising results suggesting that our classifier may have prioritized the use of eye and motion artifact when identifying Awake samples. Our approach is the first adaptation of activation maximization to the domain of raw electrophysiology classification. Additionally, our approach has implications for explaining any classifier trained on highly dynamic, long time-series.

关键词： Deep learning Electric potential Sleep Frequency-domain analysis Dynamics Electrophysiology Electroencephalography

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Classification of Schizophrenia using Intrinsic Connectivity Networks and Incremental Boosting Convolution Neural Networks

Classification of Schizophrenia using Intrinsic Connectivity...

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

作者： Duc My Vo Sergey Plis Vince D. Calhourn Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory University Atlanta GA USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

One of the key challenges in the use of resting brain functional magnetic resonance imaging (fMRI) network analysis for predicting mental illnesses such as schizophrenia (SZ) is the high noise levels variability among individuals including age, sex, and different protocols used in labs. To deal with these challenging problems, we designed a recognition method for using brain functional networks to classify SZs and healthy controls (HCs). Our method includes two stages of training. In the first stage, we use a deep convolutional neural network (DCNN) to extract valuable deep features from functional network connectivity (FNC) images. In the next stage, these deep features are used as inputs to a gradient-boosting trees classifier. After the training process, the boosting trees classifier gains a remarkable performance compared to the DCNN classifier. We evaluate this approach using a large dataset of schizophrenia and healthy controls divided into separate validation and training sets. Experimental results showed that the recognition accuracy is over 98 %, compared to a support vector machine baseline of 77% demonstrating the ability of our system to distinguish differences between the two groups. We also estimate heatmaps for each FNC image, representing a 2D FNC matrix indicated which pairs of networks are most predictive of SZ. Our method thus provides both high accuracy, and provides insights into the relevant brain regions for SZ.

关键词： Training Support vector machines Accuracy Protocols Functional magnetic resonance imaging Boosting Feature extraction Convolutional neural networks Random forests Biological neural networks

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