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

Identifying Reproducibly Important EEG Markers of Schizophre...

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

作者： Martina Lapera Sancho Charles A. Ellis Robyn L. Miller Vince D. 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: (数字)9798350371499

ISBN: (纸本)9798350371505

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.

关键词： Training Deep learning Analytical models Biological system modeling Closed box Biomarkers Brain modeling Feature extraction Electroencephalography Engineering in medicine and biology

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Multiset FMRI data Analysis with Subject Group Information using Structured Dictionary Learning 22

Multiset FMRI Data Analysis with Subject Group Information u...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

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

ISBN: (纸本)9798331520526

A dictionary learning (DL)-based method for analyzing multiple functional magnetic resonance imaging (fMRI) data sets is developed. The algorithm can incorporate subject group information to extract neural activation maps indicative of the group differences and the maps shared across groups. Furthermore, multiple data sets are jointly analyzed to obtain the maps common across data sets and those unique to individual data sets. For this, a novel structured supervised DL problem is formulated. Numerical tests on synthetic and real fMRI data sets verify the effectiveness of the proposed method. © 2025 IEEE.

关键词： Dictionary learning fMRI data analysis group sparsity multiset analysis

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[18F]Florbetapir Pet Uptake Differences in White Matter Across the Alzheimer's Disease Continuum: From Cognitively Normal to MCI and Dementia

[18F]Florbetapir Pet Uptake Differences in White Matter Acro...

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IEEE International Symposium on Biomedical Imaging

作者： Nigar Khasayeva Kyle M. Jensen Cyrus Eierud Helen Petropoulos 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

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Alzheimer's disease (AD) is widely recognized for hallmark gray matter (GM) changes, including extracellular amyloid plaques and neurofibrillary tangles. However, recent neuroimaging research suggests that white matter (WM) aberrations may also play a significant role in AD risk and development. This study investigates AD dementia by examining regional covariance patterns of WM changes, expanding beyond the conventional focus on GM. Using [18F]Florbetapir ([18F]FBP) Positron Emission Tomography (PET) data from 716 participants in the Alzheimer's Disease neuroimaging Initiative (ADNI), we analyzed inter-subject covarying patterns to reveal latent WM sources associated with AD dementia. Employing a model order of 100 and independent component analysis (ICA), we identified 13 highly reliable and distinct WM components, along with 80 GM components, enabling a comparative analysis of group effects across the AD continuum and highlighting the differential significance of WM integrity. We assessed the discrimination ability of WM components between cognitively normal (CN), mild cognitive impairment (MCI), and AD dementia diagnostic groups. Statistical analysis on 13 WM component weights highlighted significant diagnostic contrasts, with 11 components differentiating AD dementia from CN, 10 distinguishing AD dementia from MCI, and 8 distinguishing MCI from CN. Notably, results were replicable, reinforcing the robustness of latent WM sources. Our findings underscore the importance of WM integrity in AD pathology, suggesting WM degeneration as a potentially integral feature of the AD continuum. This work demonstrates the potential of combining ICA with [18F]FBP PET to illuminate nuanced neurobiological mechanisms underlying AD-related WM degeneration, offering valuable insights that may inform future diagnostic approaches across the AD continuum.

关键词： neuroimaging Pathology Statistical analysis Grey matter Independent component analysis Robustness Positron emission tomography Alzheimer's disease White matter Extracellular

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Improving Age Prediction: Utilizing LSTM-Based Dynamic Forecasting For data Augmentation in Multivariate Time Series Analysis

Improving Age Prediction: Utilizing LSTM-Based Dynamic Forec...

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IEEE Southwest Symposium on Image Analysis and Interpretation

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

The high dimensionality and complexity of neuroimaging data necessitate large datasets to develop robust and high- performing deep learning models. However, the neuroimaging field is notably hampered by the scarcity of such datasets. In this work, we proposed a data augmentation and validation framework that utilizes dynamic forecasting with Long Short-Term Memory (LSTM) networks to enrich datasets. We extended multivariate time series data by predicting the time courses of independent component networks (ICNs) in both one-step and recursive configurations. The effectiveness of these augmented datasets was then compared with the original data using various deep learning models designed for chronological age prediction tasks. The results suggest that our approach improves model performance, providing a robust solution to overcome the challenges presented by the limited size of neuroimaging datasets.

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Coupling between Time-Varying EEG Spectral Bands and Spatial Dynamic FMRI Networks

Coupling between Time-Varying EEG Spectral Bands and Spatial...

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IEEE International Symposium on Biomedical Imaging

作者： Souvik Phadikar Krishna Pusuluri Kyle M. Jensen Lei Wu Armin Iraji Vince D. 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

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Functional brain networks exhibit changes in both temporal and spatial dimensions. Simultaneous electroencephalography (EEG) and resting-state functional magnetic resonance imaging (rs-fMRI) studies have enabled the investigation of the spatial dynamics of functional brain networks. However, most studies estimate fixed spatial networks that modulate together over time. In this paper, we estimate voxel-wise spatially dynamic brain networks and link these to concurrently recorded EEG spectral properties. First, we computed the dynamic brain networks and then extracted the connectivity with four EEG spectral bands (delta, theta, alpha, and beta) as a function of time. Subsequently, by cross correlating the time-varying connectivity matrix, we identified certain network pairs that exhibit different EEG spectral powers during the resting state. The coupling of spatially dynamic networks in relation to neurological spectral properties observed during rest may provide insight into the intricacies of the brain and aid in early diagnosis and personalized treatment approaches for neural disorders.

关键词： Couplings Time-frequency analysis Correlation Cognitive processes Functional magnetic resonance imaging Electroencephalography Task analysis

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Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold Signal Applied To an Imaging Study of Schizophrenia

Markov Spatial Flows in Bold FMRI: A Novel Lens on the Bold ...

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IEEE Southwest Symposium on Image Analysis and Interpretation

作者： Robyn L. Miller Victor M. Vergara Vince D. Calhoun The Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS Center) Georgia State University Georgia Institute of Technology and Emory University Atlanta GA USA

While the analysis of temporal signal fluctuations and co-fluctuations has long been a fixture of blood oxygenation-level dependent (BOLD) functional magnetic resonance imaging CMRl) research, the role and implications of spatial propagation within the 4D neurovascular BOLD signal has been almost entirely neglected As part of a larger research program aimed at capturing and analyzing spatially propagative dynamics in BOLDjMRl, we report here a method that exposes large-scale functional attractors of flow processes defined via Markov processes defined at the voxel level. The brainwide stationary distributions of these voxel-level Markov processes represent patterns of signal accumulation toward which the brain exerts a probabilistic propagative undertow. These probabilistic propagative attractors are spatially structured and organized interpretably over functional regions. They also differ significantly between schizophrenia patients and healthy controls.

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Label Noise-Robust Ensemble Deep Multimodal Framework For neuroimaging data

Label Noise-Robust Ensemble Deep Multimodal Framework For Ne...

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

作者： Hooman Rokham Haleh Falakshahi 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 United States

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

neuroimaging data have become widely studied in the context of identifying brain-based markers of mental illness. however, this work is hampered by the use of symptom and self-report assessments of diagnosis, as well as lack of clarity in the nosological categories. Hence, treating existing diagnostic categories as label noise problems might be beneficial. Ensemble methods and deep learning models were used in many applications and revealed remarkable findings dealing with label noise. In this study, we incorporated deep convolutional frameworks and bagging approaches for diagnostic classification, identifying potential biomarkers and mitigating the effects of label noise across mood and psychosis categories using structural and functional MRI data. We conducted repeated k-fold cross-validation techniques to train individual base models on different subsets of data and aggregate independent models for final classification. Moreover, we interpreted the results and identified class-specific relevant learned features contributing to a successful diagnosis and highlighted differences for different modalities. Overall, our proposed method shows improvement in classification performance.

关键词： neuroimaging Deep learning Mood Biological system modeling Magnetic resonance imaging Noise data models Noise robustness Ensemble learning Engineering in medicine and biology

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Capturing Stretching and Shrinking of Inter-Network Temporal Coupling in FMRI Via WARP Elasticity

Capturing Stretching and Shrinking of Inter-Network Temporal...

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IEEE International Symposium on Biomedical Imaging

作者： Sir-Lord Wiafe Ashkan Faghiri Zening Fu Robyn Miller Vince D. 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

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

In this study, we present a novel approach for examining time-resolved functional network connectivity (trFNC) in fMRI data. Utilizing a concept, we term 'warp elasticity,' our method dynamically employs varying windows across time and brain network pairs, a feature often missed by standard analyses, emphasizing the elastic nature of brain interactions over time. This technique leverages dynamic time warping (DTW) to capture the nuanced stretching and shrinking relationships between timescales of brain regions. It allows us to see how elasticity changes over time, offering a new and nuanced way of viewing trFNC and brain function. Our method demonstrates consistency across subjects and is robust against noise, providing a solid foundation for exploring the temporal complexity of brain connectivity, and potentially advancing our understanding of neurological behavior.

关键词： Couplings Noise Elasticity Functional magnetic resonance imaging Solids Complexity theory Standards

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