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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Head Time series analysis Noise Independent component analysis Functional magnetic resonance imaging Feature extraction Brain modeling Magnetic heads data models Engineering in medicine and biology

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Improved Dictionary Learning for FMRI data Analysis Capturing Common and Individual Activation Maps 58

Improved Dictionary Learning for FMRI Data Analysis Capturin...

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58th Asilomar Conference on Signals, Systems and Computers, ACSSC 2024

作者： Xu, Shuai Jin, Rui Kim, Seung-Jun Adali, Tülay Calhoun, Vince D. University of Maryland Baltimore County Department of Computer Science and Electrical Engineering United States Tri-institutional Center for Translational Research in Neuroimaging and Data Science Georgia State University Georgia Institute of Technology United States Emory University United States

ISBN: (纸本)9798350354058

A dictionary learning (DL)-based method for mul-tisubject functional magnetic resonance imaging (fMRI) data analysis is proposed. The method can incorporate group attributes to find the neural activation maps that are common across groups as well as those that are explanatory of the group differences. The key contribution is to significantly improve the map estimation performance by mitigating undesirable local optima obtained from a supervised DL formulation for fMRI data analysis. We introduce two novel ideas, namely, developing a variant of Fisher's discriminant cost, which is imposed on the non-discriminative features, and optimally permuting the dictionary and the sparse factor such that the solution is not trapped to unwanted local optima. Preliminary tests on synthetic and real data sets verify the effectiveness of the proposed approach. © 2024 IEEE.

关键词： Functional neuroimaging

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A Deep Biclustering Framework for Brain Network Analysis

A Deep Biclustering Framework for Brain Network Analysis

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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)9798350365474

ISBN: (纸本)9798350365481

Brain functional connectivity (FC) analysis has emerged as a compelling quest to understand human brain dynamics and clarify disorder-related aberrations. Typically, FC can be portrayed as a graph of brain components (nodes) and their functional links (edges) known as the brain network (BN). The brain operates as a modular unit, with different regions forming semantically cohesive submodules to execute essential neuronal processing. Identifying these granules can provide insights into the underlying neurobiological mechanisms. Consequently, substantial research efforts have been directed toward clustering the constituents of brain networks. However, the inherent subject heterogeneity in the biological population and the wide spectrum of brain disease manifestation significantly impede cluster generalization. Thus, it often delivers a suboptimal solution and misses insightful nuances of neural systems. Therefore, we propose a deep neural network (DNN) framework for a more granular subgrouping of brain networks by simultaneously stratifying subjects and feature dimensions. The framework adapts discrete learning of BN edges and jointly optimizes instance and feature assignment probability distributions for a novel bicluster retrieval. Extensive experiments on multiple neuroimaging datasets show our model outperforms state-of-the-art biclustering methods. In addition, the extracted biclusters render more modular and semantically meaningful communities in the brain network highlighting significant neuroscientific relevance.

关键词： neuroimaging Artificial neural networks Network analyzers Brain modeling Feature extraction Probability distribution Physiology

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SpaDE: Semantic Locality Preserving Biclustering for neuroimaging data

SpaDE: Semantic Locality Preserving Biclustering for Neuroim...

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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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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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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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Deep multimodal predictome for studying mental disorders (vol 44, pg 509, 2023)

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HUMAN BRAIN MAPPING 2023年第14期44卷 4967-4967页

作者： Rahaman, M. A. Chen, J. Fu, Z. Lewis, N. Iraji, A. van Erp, T. G. M. Calhoun, V. D. Department of Computational Science and Engineering Georgia Institute of Technology Atlanta Georgia USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Clinical Translational Neuroscience Laboratory Department of Psychiatry and Human Behavior University of California Irvine Irvine California USA Center for the Neurobiology of Learning and Memory University of California Irvine Irvine California USA

Characterizing neuropsychiatric disorders is challenging due to heterogeneity in the population. We propose combining structural and functional neuroimaging and genomic data in a multimodal classification framework to leverage their complementary information. Our objectives are two-fold (i) to improve the classification of disorders and (ii) to introspect the concepts learned to explore underlying neural and biological mechanisms linked to mental disorders. Previous multimodal studies have focused on naïve neural networks, mostly perceptron, to learn modality-wise features and often assume equal contribution from each modality. Our focus is on the development of neural networks for feature learning and implementing an adaptive control unit for the fusion phase. Our mid fusion with attention model includes a multilayer feed-forward network, an autoencoder, a bi-directional long short-term memory unit with attention as the features extractor, and a linear attention module for controlling modality-specific influence. The proposed model acquired 92% (p < .0001) accuracy in schizophrenia prediction, outperforming several other state-of-the-art models applied to unimodal or multimodal data. Post hoc feature analyses uncovered critical neural features and genes/biological pathways associated with schizophrenia. The proposed model effectively combines multimodal neuroimaging and genomics data for predicting mental disorders. Interpreting salient features identified by the model may advance our understanding of their underlying etiological mechanisms.

关键词： functional network connectivity multimodal deep learning resting-state functional and structural MRI saliency schizophrenia classification single nucleotide polymorphism

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

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

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

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

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Training Deep learning Analytical models Perturbation methods Training data Brain modeling Depression Electroencephalography data models Robustness

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Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG Deep Learning Classifier Performance in Major Depressive Disorder Diagnosis

Cross-Sampling Rate Transfer Learning for Enhanced Raw EEG D...

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IEEE International Symposium on Biomedical Imaging

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

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Transfer learning offers a route for developing robust deep learning models on small raw electroencephalography (EEG) datasets. Nevertheless, the utility of transferring representations from large datasets with lower sampling rates to smaller datasets with higher sampling rates is relatively unexplored. In this study, we transfer representations learned by a convolutional neural network on a large, publicly available sleep dataset (100 Hertz sampling rate) to a major depressive disorder (MDD) diagnosis task (200 Hertz sampling rate) and examine their effects upon the MDD models via spectral and spatial explainability analyses. Importantly, we find that the early convolutional layers have representations that are generalizable across tasks. Moreover, our approach significantly increases mean model accuracy from 82.33% to 86.99%, increases use of lower frequencies, (θ-band), and increases robustness to channel loss. We expect this analysis to provide useful guidance and enable more widespread use of transfer learning in EEG deep learning studies.

关键词： Deep learning Sleep Biological system modeling Transfer learning Brain modeling Depression Electroencephalography

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Graph-based deep learning models in the prediction of early-stage Alzheimers 46

Graph-based deep learning models in the prediction of early-...

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

作者： Thapaliya, Bishal Wu, Zundong Sapkota, Ram Ray, Bhaskar Suresh, Pranav Ghimire, Santosh Calhoun, Vince Liu, Jingyu Georgia State University Department of Computer Science Atlanta United States Tri-Institutional Center for Translational Research in Neuroimaging and Data Science United States Georgia Institute of Technology School of Electrical and Computer Engineering Atlanta United States Tribhuvan University Department of Applied Sciences and Chemical Engineering Nepal

ISBN: (纸本)9798350371499

Alzheimer's disease is the most common age-related problem and progresses in different stages, from cognitively normal to early mild cognitive impairment, and severe dementia. This study investigates the predictive potential of resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity (FC) in understanding Alzheimer's progression. Leveraging deep learning and graph-based models, we introduce two key contributions: 1) a comparative analysis of rs-fMRI time points and FC for Alzheimer's prediction. 2) an innovative graph transformer variant incorporating self-clustering for enhanced prediction accuracy. Experiments on the Alzheimer's Disease neuroimaging Initiative dataset with 830 subjects reveal two notable conclusions. Firstly, rs-fMRI time points offer limited utility compared to functional network connectivity for transformer-based models, even when considering temporal information. Secondly, a clustering-based attention module proves effective for classifying brain networks in predicting Alzheimer's disease progression, providing valuable insights for future research and clinical applications. © 2024 IEEE.

关键词： Prediction models

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