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

作者： Yuhui Du Yating Guo 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

Many studies have shown that changes in the functional connectivity are diverse along with aging. However, few studies have addressed how aging affects connectivity among large-scale brain networks, and it is challenging to examine gradual aging trajectories from middle adulthood to old age. In this work, based on large-sample fMRI data from 6300 subjects aged between 49 to 73 years, we apply an independent component analysis-based method called NeuroMark to extract brain functional networks and their connectivity (i.e., functional network connectivity (FNC)), and then propose a two-level statistical analysis method to explore robust aging-related changes in functional network connectivity. We found that the enhanced FNCs mainly occur between different functional domains, involving the default mode and cognitive control networks, while the reduced FNCs come from not only between different domains but also within the same domain, primarily relating to the visual network, cognitive control network and cerebellum. Our results emphasize the diversity of brain aging and provide new evidence for non-pathological aging of the whole *** Relevance—This provides new evidence for non-pathological aging of functional network connectivity in the whole brain.

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Complexity Measures of Psychotic Brain Activity In The FMRI Signal

Complexity Measures of Psychotic Brain Activity In The FMRI ...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Qiang Li Masoud Seraji Vince D Calhoun Armin Iraji Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA School of Psychology University of Texas at Austin Austin TX USA Department of Computer Science Georgia State University Atlanta GA USA

When viewing the brain as a sophisticated, nonlinear dynamic system, employing complexity measures offers a valuable way to measure the intricate and dynamic aspects of spontaneous psychotic brain activity. These measures can help us identify irregularities and patterns in complex systems. In our study, we utilized fuzzy recurrence plots and sample entropy to evaluate the dynamic characteristics of psychiatric disorders. This assessment focused on understanding the temporal and spatial neural activity patterns, and more specifically, we applied complexity measures to investigate the functional connectivity within the psychotic brain. This involves understanding how different brain regions synchronize their activity, and complexity measures can reveal the patterns of these connections. It provides a means to understand how different brain regions interact and communicate under resting-state abnormal conditions. This study offers evidence demonstrating that fuzzy recurrence plots can serve as descriptors for functional connectivity and discusses their relevance to sample entropy in the context of the psychotic brain. In summary, complexity measures offer valuable insights that enrich our comprehension of atypical brain activity and the complexities present in the psychotic brain 1 . 1 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI 2024)

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An Adaptive Semi-Supervised Deep Clustering and Its Application to Identifying Biotypes of Psychiatric Disorders

An Adaptive Semi-Supervised Deep Clustering and Its Applicat...

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IEEE International Symposium on Biomedical Imaging

作者： Yuhui Du Fulin Wu 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

Symptoms overlap among different types of psychiatric disorder, which makes diagnosis challenging. There has been evidence that exploring new biotypes may help solve the problem. Using neuroimaging data, clustering technique has been applied to identify biotypes, however deep learning has not been well studied for this goal. Here, we propose a new semi-supervised deep clustering method, which takes full advantage of slight amount of labeled samples to adaptively extract useful information from unlabeled samples with high confidence level to facilitate the semi-supervised clustering. We validate our method using the Fashion MNIST data and then apply it to 137 patients with autism spectrum disorder (ASD) and 137 patients with schizophrenia (SZ) to explore psychiatric biotypes by using functional magnetic resonance imaging (fMRI) data. Compared with traditional methods, our method obtains better clustering performance for the Fashion MNIST data and identifies interesting transdiagnostic biotypes in ASD and SZ. In summary, our proposed semi-supervised deep clustering method is promising in extracting biotypes of psychiatric disorders.

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Topological Correction of Subject-Level Intrinsic Connectivity Networks

Topological Correction of Subject-Level Intrinsic Connectivi...

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IEEE International Symposium on Biomedical Imaging

作者： Noah Lewis Armin Iraji Robyn Miller Vince D. Calhoun School of Computational Science and Engineering Georgia Institute of Technology Tri-Institutional Center for Translational Research in Neuroimaging Data Science (TReNDS) Georgia Institute of Technology Georgia State University Emory University School of Electrical and Computer Engineering Georgia Institute of Technology

Over the last several decades, researchers have sought to capture the underlying functional activity of the human brain from functional magnetic resonance imaging (fMRI). One well-studied and promising avenue of research is independent component analysis (ICA) to capture the maximally independent set of elements known as functional networks. These functional networks are represented as spatial maps or 3D images in which each voxel has a value associated with the given network. The current state-of-the-art methods use group-level spatial maps as a reference to provide estimates of subject-level maps, which are vulnerable to the low contrast-to-noise (CNR) ratio of fMRI signals. However, such approaches do not account for all group-level spatial information. As such, their subject-level estimate is still quite noisy. This work presents a novel method that leverages the topological properties of the group maps to improve subject-level estimations. We show that adding topological similarity constraints also improves subject-specific information.

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Reproducibility in Joint Blind Source Separation: Application to fMRI Analysis

Reproducibility in Joint Blind Source Separation: Applicatio...

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Asilomar Conference on Signals, Systems & Computers

作者： Francisco Laport Trung Vu Hanlu Yang Vince Calhoun Tulay Adali Department of Computer Science and Electrical Engineering University of Maryland Baltimore County MD USA CITIC Research Center University of A Coruna A Coruña Spain Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

Joint blind source separation (JBSS) techniques have been successfully applied for the analysis of multi-subject functional magnetic resonance imaging (fMRI) data. However, convergence in JBSS can be only guaranteed to a local optimum, since typically cost functions are non-convex. Also, iterative methods are usually implemented with random initialization for best performance, resulting in high variability, especially for more flexible solutions. Yet, the assessment of the reproducibility of JBSS has been limited in the literature, even though it has been demonstrated that when not taken into account, the solutions can be highly suboptimal. In this work, we propose a framework for the evaluation of the reproducibility of independent vector analysis, an important JBSS solution. We introduce a mechanism for selecting the model complexity that offers the most consistent and accurate solution, and demonstrate results to underline its importance using resting state fMRI data.

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CfMRIPrep: A MATLAB toolbox for integrated preprocessing of complex-valued fMRI data

CfMRIPrep: A MATLAB toolbox for integrated preprocessing of ...

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International Conference on Information science and Technology (ICIST)

作者： Si-Qi Li Ming-Jun Ma Qiu-Hua Lin Vince D. Calhoun School of Information and Communication Engineering Dalian University of Technology Dalian 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

Functional magnetic resonance imaging (fMRI) has been widely used in the study of brain function due to its noninvasiveness and high spatial resolution. Preprocessing of fMRI data is essential to remove the noise caused by head motion and scanners, and to enable comparison across different subjects and various studies. To date, a number of toolboxes such as Statistical Parametric Mapping (SPM) and FMRIB Software Library (FSL) have been developed for preprocessing magnitude-only fMRI data. In fact, complete fMRI data are complex-valued including both magnitude and phase data. Complex-valued fMRI data can detect additional and meaningful brain activities beyond the magnitude-only fMRI data. However, there is a lack of a toolbox for preprocessing complex-valued fMRI data. As such, we design a new MATLAB toolbox named CfMRIPrep to perform this role. CfMRIPrep includes phase unwrapping of phase fMRI data, motion correction, spatial normalization and spatial smoothing for both magnitude and phase fMRI data. To do this, the parameters of motion correction and spatial normalization are computed from the magnitude data and applied to the phase data. Experimental results show that CfMRIPrep provides the same preprocessing results as a manual approach and is easy-to-use allowing for automated processing and also can be used for preprocessing of magnitude-only fMRI data.

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Objective Assessment of the Bias Introduced by Baseline Signals in XAI Attribution Methods

Objective Assessment of the Bias Introduced by Baseline Sign...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Giorgio Dolci Federica Cruciani Ilaria Boscolo Galazzo Vince D. Calhoun Gloria Menegaz Dept. of Engineering for Innovative Medicine University of Verona Verona Italy Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

This work represents a first step towards a systematic analysis of the impact of the choice of the baseline signals to be used in explainable baseline-dependent methods for multi-modal and multi-dimensional data relying on single-input deep networks, in view of the generalization to multi -channel architectures. This point is critical for ensuring the soundness of the attribution values and enabling their subsequent validation through association studies. In this work, two different CNNs were implemented to predict Alzheimer's disease patients from control subjects using structural Magnetic Resonance Imaging volumes and genetics data. The Integrated Gradients method was applied to both models for post-hoc attribution visualization relying on different baselines. Differences in the attribution maps were found with respect to the attributions of the reference baseline in both modalities highlighting the importance of finding and using the “optimal” baseline. We believe this work is highly relevant for the community in the framework of the validation of XAI post-hoc methods, as it provides evidence of the impact of the choice of the baselines for deriving feature attribution values with the Integrated Gradients method which determines the reliability of the outcomes, improving both the awareness of the users and their trust in the methods.

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Integrated medical resource consumption stratification in hospitalized patients:an Auto Triage Management model based on accurate risk,cost and length of stay prediction

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science China(Life sciences) 2022年第5期65卷 988-999页

作者： Qin Zhong Zongren Li Wenjun Wang Lei Zhang Kunlun He Medical Big Data Research Center Chinese PLA General HospitalBeijing 100853China Medical Artificial Intelligence Research Center Chinese PLA General HospitalBeijing 100853China Bio-engineering Research Center Chinese PLA General HospitalBeijing 100039China Department of Cardiology Fourth Medical Center of Chinese PLA General HospitalBeijing 100048China Key Laboratory of Ministry of Industry and Information Technology of Biomedical Engineering and Translational Medicine Chinese PLA General HospitalBeijing 100048China China-Australia Joint Research Center for Infectious Diseases School of Public HealthXi’an Jiaotong University Health Science CenterXi’an 710061China

Triage management plays important roles in hospitalized patients for disease severity stratification and medical burden *** progression risks have been extensively researched for numbers of diseases,other crucial indicators that reflect patients’economic and time costs have not been systematically *** address the problems,we developed an automatic deep learning based Auto Triage Management(ATM)Framework capable of accurately modelling patients’disease progression risk and health economic *** on them,we can first discover the relationship between disease progression and medical system cost,find potential features that can more precisely aid patient triage in resource allocation,and allow treatment plan searching that has cured *** ATM in COVID-19,we built a joint model to predict patients’risk,the total length of stay(Lo S)and cost when at-admission,and remaining Lo S and cost at a given hospitalized time point,with C-index0.930 and 0.869 for risk prediction,mean absolute error(MAE)of 5.61 and 5.90 days for total Lo S prediction in internal and external validation data.

关键词： patient triage AutoML electronic medical records

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GottBERT: a pure German Language Model

GottBERT: a pure German Language Model

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Scheible, Raphael Frei, Johann Thomczyk, Fabian He, Henry Tippmann, Patric Knaus, Jochen Jaravine, Victor Kramer, Frank Boeker, Martin Institute for AI and Informatics in Medicine University Hospital rechts der Isar Technical University Munich Germany IT-Infrastructure for Translational Medical Research Faculty of Applied Computer Science University of Augsburg Germany Data Integration Center Faculty of Medicine University of Freiburg Germany School of Computation Information and Technology Technical University Munich Germany Institute of Medical Biometry and Statistics Medical Center Faculty of Medicine University of Freiburg Germany Freiburg Center for Data Analysis and Modeling University of Freiburg Germany Hengrui Europe Biosciences Zurich Switzerland

ISBN: (纸本)9798891761643

Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa. While initial research focused on English, single-language models can be advantageous compared to multilingual ones in terms of pre-training effort, overall resource efficiency or downstream task performance. Despite the growing popularity of prompt-based LLMs, more compute-efficient BERT-like models remain highly relevant. In this work, we present the first German single-language RoBERTa model, GottBERT, pre-trained exclusively on the German portion of the OSCAR dataset. Additionally, we investigated the impact of filtering the OSCAR corpus. GottBERT was pre-trained using fairseq and standard hyperparameters. We evaluated its performance on two Named Entity Recognition (NER) tasks (Conll 2003 and GermEval 2014) and three text classification tasks (GermEval 2018 fine and coarse, and 10kGNAD) against existing German BERT models and two multilingual models. Performance was measured using the F1 score and accuracy. The GottBERT base and large models showed competitive performance, with GottBERT leading among the base models in 4 of 6 tasks. Contrary to our expectation, the applied filtering did not significantly affect the results. To support the German NLP research community, we are releasing the GottBERT models under the MIT license. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Evaluating Trade-Offs in IVA of Multimodal neuroimaging using Cross-Platform Multidataset Independent Subspace Analysis

Evaluating Trade-Offs in IVA of Multimodal Neuroimaging usin...

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IEEE International Symposium on Biomedical Imaging

作者： Xinhui Li Daniel Khosravinezhad Vince D. Calhoun Rogers F. Silva Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA Department of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA USA

Multidataset independent subspace analysis (MISA) unifies multiple linear blind source separation methods to analyze joint and unique information across multiple datasets. MISA can jointly analyze large multimodal neuroimaging datasets to advance our understanding of the brain from multiple perspectives. However, a systematic evaluation of the trade-offs between problem scale and sample size is still absent in the literature. Aiming to support flexibility and replicability of deep latent variable modeling, and equip practitioners with crucial tools and usage guidelines, we developed a MISA PyTorch module incorporating the linked multi-network architecture and loss function of the original MISA MATLAB. We then investigated critical performance trade-offs between latent space and sample sizes in independent vector analysis (IVA) problems. Both platforms were highly similar in hundreds of simulation settings, demonstrating successful replication of the original framework and flexibility to evaluate multiple configurations. We observed that a larger sample size, fewer datasets and fewer sources can lead to better IVA model performance. We then performed an IVA experiment on a large multimodal neuroimaging dataset and observed high cross-modal correlation linkage among the identified sources in both platforms, supporting MISA’s effectiveness for replicable multimodal linkage detection.

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