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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Kumar, Samujjwal Kinsey, Spencer Jensen, Kyle M. Bajracharya, Prerana Calhoun, Vince D. Iraji, Armin Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia State University United States

ISBN: (纸本)9798350371499

Resting-state functional magnetic resonance imaging (rsfMRI) plays a pivotal role in estimating intrinsic brain functional connectivity within healthy and clinical populations. However, the pervasive impact of head motion confounds the interpretation of rsfMRI data and is typically addressed through preprocessing without further exploration. This investigation aims to scrutinize the intricate interplay between head motion and neurobiologically relevant BOLD signal as well as its potential clinical implications. Here, we use independent component analysis (ICA) to extract large-scale brain networks from BOLD fMRI and modeled head motion time series for 508 subjects sourced from three major psychosis projects. Our approach uncovers the presence of latent network information within modeled head motion data. Moreover, we find altered functional network connectivity (FNC) between healthy controls (HC) and individuals with schizophrenia (SZ) for BOLD and motion networks, revealing that projections of BOLD time series onto network features extracted from head motion data reflect cohort-specific information. Our approach challenges conventional perspectives by treating motion-related signals not as mere noise, but as potential repositories of valuable insights into functional brain connectivity across diverse populations. © 2024 IEEE.

关键词： Time series

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MART(Splitting-Merging Assisted Reliable)Independent Component Analysis for Extracting Accurate Brain Functional Networks

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Neuroscience Bulletin 2024年第7期40卷 905-920页

作者： Xingyu He Vince D.Calhoun Yuhui Du School of Computer and Information Technology Shanxi UniversityTaiyuan 030006China Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Georgia State UniversityGeorgia Institute of TechnologyEmory UniversityAtlanta 30303USA

Functional networks(FNs)hold significant promise in understanding brain *** component analysis(ICA)has been applied in estimating FNs from functional magnetic resonance imaging(fMRI).However,determining an optimal model order for ICA remains challenging,leading to criticism about the reliability of FN ***,we propose a SMART(splitting-merging assisted reliable)ICA method that automatically extracts reliable FNs by clustering independent components(ICs)obtained from multi-model-order ICA using a simplified graph while providing linkages among FNs deduced from different-model *** extend SMART ICA to multi-subject fMRI analysis,validating its effectiveness using simulated and real fMRI *** on simulated data,the method accurately estimates both group-common and group-unique components and demonstrates robustness to *** two age-matched cohorts of resting fMRI data comprising 1,950 healthy subjects,the resulting reliable group-level FNs are greatly similar between the two cohorts,and interestingly the subject-specific FNs show progressive changes while age ***,both small-scale and large-scale brain FN templates are provided as benchmarks for future *** together,SMART ICA can automatically obtain reliable FNs in analyzing multi-subject fMRI data,while also providing linkages between different FNs.

关键词： Independent component analysis Functional magnetic resonance imaging-Brain functional networks Clustering Multi-model-order

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Functionally-Adaptive Gray and White Matter Structural Basis Sets via Dynamic Fusion of Multimodal MRI data 46

Functionally-Adaptive Gray and White Matter Structural Basis...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Duda, Marlena Calhoun, Vince D. Georgia State University Georgia Tech Emory University Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States

ISBN: (纸本)9798350371499

The exact nature of the coupling of brain structure and function has long been an open area of research. Often, this question is approached by first defining a single structural basis set, and then estimating functional brain activation time courses as a linear combination of these structural bases. However, knowing that functional brain activity and connectivity vary over time, so might the nature of these structural/functional couplings. Thus, a single rigidly defined, "functionally unaware"structural manifold may be insufficient to describe structure/function linkages across a whole functional time series. Here, we introduce dynamic fusion, an ICA-based symmetric fusion, and show evidence that challenges current approaches and suggests time-resolved structural basis sets can better represent changing functional manifolds. We perform dynamic fusion using measures of both gray matter (GM) and white matter (WM) structure and present results that may indicate a stronger link between WM structure and dynamic brain function than in GM. © 2024 IEEE.

关键词： data fusion

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Evaluating Augmentation Approaches for Deep Learning-based Major Depressive Disorder Diagnosis with Raw Electroencephalogram data 46

Evaluating Augmentation Approaches for Deep Learning-based M...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Georgia State University Emory University Georgia Institute of Technology Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350371499

While deep learning methods are increasingly applied in research contexts for neuropsychiatric disorder diagnosis, small dataset size limits their potential for clinical translation. data augmentation (DA) could address this limitation, but the utility of EEG DA methods remains relatively underexplored in neuropsychiatric disorder diagnosis. In this study, we train a model for major depressive disorder diagnosis. We then evaluate the utility of 6 EEG DA approaches. Importantly, to remove the bias that could be introduced by comparing performance for models trained on larger augmented training sets to models trained on smaller baseline sets, we also introduce a new baseline trained on duplicate training data. We lastly examine the effects of the DA approaches upon representations learned by the model with a pair of explainability analyses. We find that while most approaches boost model performance, they do not improve model performance beyond that of simply using a duplicate training set without DA. The exception to this is channel dropout augmentation, which does improve model performance. These findings suggest the importance of comparing EEG DA methods to a baseline with a duplicate training set of equal size to the augmented training set. We also found that some DA methods increased model robustness to frequency (Fourier transform surrogates) and channel (channel dropout) perturbation. While our findings on EEG DA efficacy are restricted to our dataset and model, we hope that future studies on deep learning for small EEG datasets and on new EEG DA methods will find our findings helpful. © 2024 IEEE.

关键词： Electroencephalography

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Identifying Reproducibly Important EEG Markers of Schizophrenia with an Explainable Multi-Model Deep Learning Approach 46

Identifying Reproducibly Important EEG Markers of Schizophre...

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46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2024

作者： Sancho, Martina Lapera Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. Georgia State University Georgia Institute of Technology Emory University Tri-Institutional Center for Translational Research in Neuroimaging and Data Science Atlanta United States

ISBN: (纸本)9798350371499

The diagnosis of schizophrenia (SZ) can be challenging due to its diverse symptom presentation. As such, many studies have sought to identify diagnostic biomarkers of SZ using explainable machine learning methods. However, the generalizability of identified biomarkers in many machine learning-based studies is highly questionable given that most studies only analyze explanations from a small number of models. In this study, we present (1) a novel feature interaction-based explainability approach and (2) several new approaches for summarizing multi-model explanations. We implement our approach within the context of electroencephalogram (EEG) spectral power data. We further analyze both training and test set explanations with the goal of extracting generalizable insights from the models. Importantly, our analyses identify effects of SZ upon the α, β, and θ frequency bands, the left hemisphere of the brain, and interhemispheric interactions across a majority of cross-validation folds. We hope that our analysis will provide helpful insights into SZ and inspire the development of robust approaches for identifying neuropsychiatric disorder biomarkers from explainable machine learning models. © 2024 IEEE.

关键词： Electroencephalography

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data augmentation for schizophrenia diagnosis via vision transformer-based latent diffusion model 4

Data augmentation for schizophrenia diagnosis via vision tra...

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4th International Conference on Biomedicine and Bioinformatics Engineering, ICBBE 2024

作者： Yang, Yuxuan Ma, Shidong Cao, Shengjie Jia, Sihan Bi, Yuda Calhoun, Vince D. Shandong University of Science and Technology Shandong Qingdao China Tri-institutional Center for Translational Research in Neuroimaging and Data Science GSU GATech Emory AtlantaGA30303 United States

ISBN: (纸本)9781510682443

Schizophrenia (SZ) is a complex and heterogeneous disorder. Characterizing functional magnetic resonance imaging (fMRI) imaging patterns specific to SZ is challenging and can benefit greatly from advanced artificial intelligence techniques. Prior research has shown that fMRI data can be summarized in functional network connectivity matrices (FNCs) using group independent component analysis (ICA), significantly reducing computational burden for tasks such as classification which also maintaining relevant information. Recent advances in the development of state-of-the-art latent diffusion models (LDMs) have shown promise for various generative tasks, such as image generation and data augmentation. This study leverages a lightweight vision transformer (ViT) encoder-decoder architecture instead of the traditional U-net as an effective denoising function in LDMs for FNC generation. This approach addresses the challenge of small datasets that often lead to lower accuracy such as in the use of neuroimaging data to diagnose schizophrenia. © 2024 SPIE.

关键词： Magnetic resonance imaging

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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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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

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

ISBN: (纸本)9798350337488

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 © 2023 IEEE.

关键词： deep learning electroencephalography explainable AI major depressive disorder transfer learning

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Improving Explainability for Single-Channel EEG Deep Learning Classifiers via Interpretable Filters and Activation Analysis

Improving Explainability for Single-Channel EEG Deep Learnin...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

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

ISBN: (纸本)9798350337488

Deep learning methods are increasingly being applied to raw electroencephalography (EEG) data. Relative to traditional machine learning methods, deep learning methods can increase model performance through automated feature extraction. Nevertheless, they are also less explainable. Existing raw EEG explainability methods identify relative feature importance but do not identify how features relate to model predictions. In this study, we combine well-characterized first layer filters with a novel post hoc statistical analysis of the filter activations that linearly relates properties of model activations with predictions. We implement our approach within the context of automated sleep stage classification, finding that the model uncovers positive relationships between waveforms resembling sleep spindles and NREM2, between high frequency filters and Awake samples, and between δ activity and NREM3. Our approach represents a significant step forward for raw EEG explainability and has the potential to provide many insights relating learned features and model predictions in future studies. © 2023 IEEE.

关键词： activation analysis electroencephalography explainable AI sleep stage classification deep learning

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An Explainable and Robust Deep Learning Approach for Automated Electroencephalography-based Schizophrenia Diagnosis 23

An Explainable and Robust Deep Learning Approach for Automat...

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23rd IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2023

作者： Sattrraju, Abhmav Ellis, Charles A. Miller, Robyn L. Calhoun, Vince D. The Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology Emory University AtlantaGA30303 United States

ISBN: (纸本)9798350393118

Schizophrenia (SZ) is a neuropsychiatric disorder that affects millions globally. Current diagnosis of SZ is symptom-based, which poses difficulty due to the variability of symptoms across patients. To this end, many recent studies have developed deep learning methods for automated diagnosis of SZ, especially using raw EEC, which provides high temporal precision. For such methods to be productionized, they must be both explainable and robust. ExplaLble models are essential to identify biomarkers of SZ, and robust models are critical to learn generalizable patterns, especially amidst changes in the implementation environment. One common example I channel loss during EEC recording, which could be detrimental to classifier performance. In this study, we developed a novel channel dropout (CD) approach to increase the robustness of explainable deep learning models trained on EEC data for SZ diagnosis to channel loss. We developed a baseline convolutional neural network (CNN) architecture and implement our approach as a CD layer added to the baseline (CNN-CD). We then applied two explainability approaches to both models for insight into learned spatial and spectral features and show that the application of CD decreases model sensitivity to channel loss. The CNN and CNN-CD achieved accuracies of 81.9% and 80.9% on testing data, respectively. Furthermore, our models heavily prioritized the parietal electrodes and the a-band, which is supported by exising literature. It is our hope that this study motivates the further development of explainable and robust models and bridges the transftion from research to application in a clinical decision support role. © 2023 IEEE.

关键词： Diseases

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

Cross-Modal Synthesis of Structural MRI and Functional Conne...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuda Bi Anees Abrol Jing Sui Vince Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science @{GSU GATech Emory}

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.

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