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IEEE International Symposium on Biomedical Imaging

作者： Md Abdur Rahaman Eswar Damaraju Debbrata Kumar Saha Vince D. Calhoun Sergey M. Plis Georgia Institute of Technology Atlanta GA USA Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta GA USA

ISBN: (纸本)9781665412469;9781665429474

Time series motifs discovery and summarization is an established powerful tool for modeling and analyzing dynamical systems. In a similar spirit, we propose a state-space data mining approach called “statelets.” It is a probabilistic pattern-summarization framework operating on time series correlations and capturing their dynamics. statelets rely on earth mover distance (EMD): a simple yet effective similarity metric that provides a scale-independent, variable-length comparison between substructures and accounts for partial matching. The efficiency of computing EMD supports its application in a kernel density estimator to approximate local motifs' probability density across the dataset. We validate statelets' utility on the dynamic functional network connectivity (dFNC) of patients with schizophrenia and healthy controls. The typical approach to the dFNC analysis, sliding window plus clustering (SWC), models the system continuously through a fixed set of connectivity states. Here, rather than using all the time points and searching for the patterns that span throughout the brain, we adopt time series motifs to capture the most recurring and transient patterns of the signals. statelets produce the synopsis of dynamic connectivity shapes from both dynamics that provide valuable insights into the human brain's dynamic features. We observe these state-shapes are highly informative about the disorder. Also, they reveal significant group differences in connectivity strength across various regions of the brain.

关键词： Measurement Analytical models Shape Time series analysis Tools Probabilistic logic Brain modeling

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

Improved Dictionary Learning for FMRI Data Analysis Capturin...

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Asilomar Conference on Signals, Systems & Computers

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

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

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.

关键词： Computers data analysis Dictionaries Costs Estimation Machine learning Functional magnetic resonance imaging

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Any-Way Independent Component Analysis with Reference

Any-Way Independent Component Analysis with Reference

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IEEE International Symposium on Biomedical Imaging

作者： Kuaikuai Duan Rogers F. Silva Jingyu Liu Oktay Agcaoglu Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta Georgia USA Department of Computer Science Georgia State University Atlanta USA Department of Psychology Georgia State University Atlanta GA USA

Multimodal fusion with reference allows a prior-guided exploration of coherent multimodal patterns to facilitate the interpretation. Multiset canonical correlation analysis with reference + joint independent component analysis (mCCAR + jICA) is one such method, and it is limited to decompose the same number of components for each modality, and two-step processing (i.e., mCCAR for correlation optimization, jICA for independence optimization) may hinder the optimization arriving at an optimal global solution. To overcome these limitations, we propose aNy-way ICA with reference, which simultaneously maximizes the independence of sources, cross-modality correlations, and correlations between reference and modalities without orthogonality requirements on the loadings, and allows different number of sources for different modalities. Simulation results demonstrated that aNy-way ICA with reference recovered the designed linkages between reference and modalities, and the designed cross-modality correlations, as well as recovered loading and component matrices with improved accuracy, compared to mCCAR+jICA under noisy conditions.

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ON SELF-SUPERVISED MULTIMODAL REPRESENTATION LEARNING: AN APPLICATION TO ALZHEIMER'S DISEASE

arXiv

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arXiv 2020年

作者： Fedorov, Alex Wu, Lei Sylvain, Tristan Luck, Margaux DeRamus, Thomas P. Bleklov, Dmitry Plis, Sergey M. Calhoun, Vince D. Georgia Institute of Technology Georgia Georgia State University Georgia Emory University Georgia Center for Translational Research in Neuroimaging and Data Science AtlantaGA United States Mila Université de Montréal MontrealQC Canada

Introspection of deep supervised predictive models trained on functional and structural brain imaging may uncover novel markers of Alzheimer's disease (AD). However, supervised training is prone to learning from spurious features (shortcut learning), impairing its value in the discovery process. Deep unsupervised and, recently, contrastive self-supervised approaches, not biased to classification, are better candidates for the task. Their multimodal options specifically offer additional regularization via modality interactions. This paper introduces a way to exhaustively consider multimodal architectures for a contrastive self-supervised fusion of fMRI and MRI of AD patients and controls. We show that this multimodal fusion results in representations that improve the downstream classification results for both modalities. We investigate the fused self-supervised features projected into the brain space and introduce a numerically stable way to do so. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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Multimodal Imaging Feature Extraction with Reference Canonical Correlation Analysis Underlying Intelligence

Multimodal Imaging Feature Extraction with Reference Canonic...

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

作者： Ram Sapkota Bishal Thapaliya Pranav Suresh Bhaskar Ray Vince D. Calhoun Jingyu Liu Translational Research in Neuroimaging and Data Science (TReNDS) Department of Computer Science Georgia State University Atlanta USA School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta USA

With neuroimaging data scientists have gained substantial information of the neuronal underpinning of intelligence. Yet how to integrate multimodal neuronal features effectively in relation to intelligence remains elusive. In this paper, we have developed a reference Canonical Correlation Analysis (RCCA) model that extracts latent, correlated multimodal features while enhancing correlation to a reference of interest. We applied RCCA to gray matter and white matter images from 7874 participants, and compared the derived features with those from Principle Components Analysis (PCA) and sparse CCA (SCCA), in terms of association with intelligence and prediction effectiveness using LASSO regression models. Eight RCCA features explained 10%, 16% and 17% variance of fluid intelligence, crystallized intelligence, and total composite score, respectively, which are similar to the percentage of variance explained by over 100 principle components. SCCA features presented the least variance of intelligence. Our results indicate RCCA model can successfully extract features of interest. The top brain regions that contribute to intelligence include the frontal regions and cingulate gyrus.

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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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Uncovering Effects of Schizophrenia upon a Maximally Significant, Minimally Complex Subset of Default Mode Network Connectivity Features

Uncovering Effects of Schizophrenia upon a Maximally Signifi...

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

作者： Masoud Seraji Charles A. Ellis Mohammad S.E. Sendi Robyn L. Miller Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS Center) Georgia State University Georgia Institute of Technology Emory University Atlanta USA TReNDS Center Georgia State University Georgia Institute of Technology Emory University Atlanta USA Department of Psychiatry Harvard Medical School Boston USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

A common analysis approach for resting state functional magnetic resonance imaging (rs-fMRI) dynamic functional network connectivity (dFNC) data involves clustering windowed correlation time-series and assigning time windows to clusters (i.e., states) that can be quantified to summarize aspects of the dFNC dynamics. However, those methods can be dominated by a select few features and obscure key dynamics related to less dominant features. This study presents an iterative feature learning approach to identify a maximally significant and minimally complex subset of dFNC features within the default mode network (DMN) in schizophrenia (SZ). Utilizing dFNC data from individuals with SZ and healthy controls (HC), our approach uncovers a subset of features that has a greater number of dFNC states with disorder-related dynamics than is found when all features are present in the clustering. We find that anterior cingulate cortex/posterior cingulate cortex (ACC/PCC) interactions are consistently related to SZ across the most significant iterations of the feature learning analysis and that individuals with SZ tend to spend more time in states with greater intra-ACC anticorrelation and almost no time in a state of high intra-ACC correlation that HCs periodically enter. Our findings highlight the need for nuanced analyses to reveal disorder-related dynamics and advance our understanding of neuropsychiatric disorders.

关键词： Representation learning Neurological diseases Correlation Functional magnetic resonance imaging Iterative methods 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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IEEE International Conference on Big data

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

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

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.

关键词： neuroimaging data privacy Machine learning algorithms Federated learning Artificial neural networks Independent component analysis Functional magnetic resonance imaging Schizophrenia data models Feeds

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Analysis of High-Order Brain Networks Resolved in Time and Frequency Using CP Decomposition

Analysis of High-Order Brain Networks Resolved in Time and F...

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

作者： Ashkan Faghiri Armin Iraji Tulay Adali Vince Calhoun 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 University of Maryland Baltimore County Baltimore MD USA

To capture different aspects of a complex system, the modeling approach should be able to take these effectively into consideration. Two aspects of the human brain we are quite interested in are its interconnected nature and its dynamism. One modeling approach that can capture these two aspects is based on networks that change with time and go beyond pairwise interactions. Partly because of the size of these temporal high-order networks, analyzing and visualizing them is quite a challenge. In this work, we propose a pipeline based on canonical polyadic (CP) decomposition to analyze high-order networks that are resolved in both time and frequency estimated from resting-state functional magnetic resonance imaging (fMRI) data. We show that we can combine different subjects' information into a common frame of reference for comparison. We also show that different factors provide different patterns that are easy to visualize and interpret. To the best of our knowledge, this is the first work that has proposed a pipeline for analyzing different subjects' brain networks while also incorporating temporal and spectral information about high-order interactions.

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On Self-Supervised Multimodal Representation Learning: An Application To Alzheimer’s Disease

On Self-Supervised Multimodal Representation Learning: An Ap...

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IEEE International Symposium on Biomedical Imaging

作者： Alex Fedorov Lei Wu Tristan Sylvain Margaux Luck Thomas P. DeRamus Dmitry Bleklov Sergey M. Plis Vince D. Calhoun Georgia Institute of Technology Center for Translational Research in Neuroimaging and Data Science Atlanta GA USA Georgia State University Mila Université de Montréal Montréal Québec Canada Emory University

关键词： Training Brain data integration Tools Predictive models Functional magnetic resonance imaging Alzheimer's disease

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