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检索条件"机构=Center for Translational Research in Neuroimaging and Data Science: Georgia State University"

共 248 条记录，以下是61-70 订阅

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A Multimodal Deep Learning Approach for Automated Detection and Characterization of Distinctly Salient Features of Alzheimers Disease

A Multimodal Deep Learning Approach for Automated Detection ...

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IEEE International Symposium on Biomedical Imaging

作者： Ishaan Batta Anees Abrol Vince Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology Emory University Atlanta USA

Neurological disorders generally involve multiple kinds of changes in the functional and structural properties of the brain. In this study, we develop a CNN-based multimodal deep learning pipeline by exploiting both functional and structural neuroimaging features to generate full-brain maps that encode significant differences between patient groups and between modalities in terms of their distinctive contribution towards diagnostic classification of Alzheimer’s disease. Through a repeated cross-validation procedure and robust statistical analysis, we show that our approach can be used to encode highly discriminative and abstract information from full-brain data, while also retaining the ability to identify and categorize significantly contributing voxel-level features based on their salient strength in various diagnostic and modality-related contexts. Our results on an Alzheimer’s disease classification task show that such approaches can be used for creating more elaborately defined biomarkers for brain disorders.

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A Novel Deep Subspace Learning Framework to Automatically Uncover Assessment-Specific Independent Brain Networks

A Novel Deep Subspace Learning Framework to Automatically Un...

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Annual Conference on Information sciences and Systems (CISS)

We present a novel deep learning framework to automatically compute independently salient networks in the brain that characterize the underlying changes in the brain in association with clinically observed assessments. Unsupervised approaches for high-dimensional neuroimaging data focus on computing low-dimensional brain components for subsequent analysis, while supervised learning approaches aim for predictive performance and yielding a single list of associative feature importance, thus making it hard to interpret at the level of brain subsystems. Our approach integrates the goals of decomposition into lower dimensional subspaces and, identifying salient brain subsystems into a single automated framework. We first train a convolutional neural network on structural brain features to predict clinical assessments, followed by a multi-step decomposition in the saliency space to compute salient brain networks that intrinsically characterize the brain changes associated with the assessment. Through a repeated training procedure on an Alzheimer’s disease (AD) dataset, we show that our method effectively computes AD-related salient brain subsystems directly from high-dimensional neuroimaging data, while maintaining predictive performance. Such approaches are crucial for data-driven biomarker development for brain disorders.

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Spatial Sequence Attention Network for Schizophrenia Classification from Structural Brain MR Images

Spatial Sequence Attention Network for Schizophrenia Classif...

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IEEE International Symposium on Biomedical Imaging

作者： Nagur Shareef Shaik Teja Krishna Cherukuri Vince Calhoun Dong Hye Ye Department of Computer Science Georgia State University Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State Georgia Tech Emory

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Schizophrenia is a debilitating, chronic mental disorder that significantly impacts an individual’s cognitive abilities, behavior, and social interactions. It is characterized by subtle morphological changes in the brain, particularly in the gray matter. These changes are often imperceptible through manual observation, demanding an automated approach to diagnosis. This study introduces a deep learning methodology for the classification of individuals with Schizophrenia. We achieve this by implementing a diversified attention mechanism known as Spatial Sequence Attention (SSA) which is designed to extract and emphasize significant feature representations from structural MRI (sMRI). Initially, we employ the transfer learning paradigm by leveraging pre-trained DenseNet to extract initial feature maps from the final convolutional block which contains morphological alterations associated with Schizophrenia. These features are further processed by the proposed SSA to capture and emphasize intricate spatial interactions and relationships across volumes within the brain. Our experimental studies conducted on a clinical dataset have revealed that the proposed attention mechanism outperforms the existing Squeeze & Excitation Network for Schizophrenia classification.

关键词： Attention mechanisms Mental disorders Magnetic resonance imaging Transfer learning Training data Medical services Manuals

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Dynamic Fusion of Multimodal MRI data Captures Flexible, Time-Sensitive Structure-Function Linkages in the Brain

Dynamic Fusion of Multimodal MRI Data Captures Flexible, Tim...

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IEEE International Symposium on Biomedical Imaging

作者： Marlena Duda Oktay Agcaoglu Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Tech and Emory University Atlanta GA USA

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

A major branch of neuroimaging involves capturing and understanding the relationship between brain structure and function, which is known to vary across several timescales. To this end, most recent work has approached this question by estimating a single structural basis set of the brain and then attempting to represent functional activation time series as a linear combination of that structural basis, without consideration of how the structural basis might also vary. Here, we introduce dynamic fusion, an ICA-based symmetric fusion, and show evidence that challenges current approaches and suggests time-resolved structural basis sets, derived from dynamic functional connectivity (dFNC) states, can better represent changing functional manifolds and better capture diagnostic differences.

关键词： Couplings neuroimaging Manifolds Brain Magnetic resonance imaging Time series analysis Biomedical imaging

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Constrained Source-based Salience: Network-based Visualization of Deep Learning neuroimaging Models

Constrained Source-based Salience: Network-based Visualizati...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Ishaan Batta Anees Abrol Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

ISBN: (数字)9798350351552

ISBN: (纸本)9798350351569

Developing frameworks using high-dimensional magnetic resonance imaging (MRI) data to characterize underlying brain changes in neurological disorders is crucial and challenging. While deep learning models offer a better prediction, tracking automated higher-order explanations at the level of brain networks is harder in learned models. We introduce a novel constrained source-based salience (cSBS) framework to automatically learn and visualize multiple independently salient brain networks associated with clinical diagnostic assessments. This is achieved by performing active subspace learning (ASL) and spatially constrained independent component analysis (scICA) in the saliency space of trained convolutional neural networks (CNNs), such that the resultant components are interpretable in terms of brain network components from existing templates. By employing a robust analysis across repeated training scenarios for an Alzheimer's disease (AD) classification task, we visualize cSBS components via full-brain back-reconstruction. We show that the cSBS components and their corresponding loadings are consistent and relevant in terms of AD-related brain areas. Our approach is able to synthesize multiple objectives of utilization of high-dimensional MRI data for deep learning along with automated detection of low-dimensional representations of the consistently involved features in terms of intrinsically salient brain networks. Our framework of automated identification of consistent underlying brain subsystems associated with clinically observed assessments is an important step toward biomarker development for various clinically observed characteristics and disorders.

关键词： Deep learning Neurological diseases Training Magnetic resonance imaging Biological system modeling Loading data visualization Predictive models Brain modeling Load modeling

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Multimodal Fusion of Functional and Structural data to Recognize Longitudinal Change Patterns in the Adolescent Brain

Multimodal Fusion of Functional and Structural Data to Recog...

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IEEE EMBS International Conference on Information Technology Applications in Biomedicine (ITAB)

作者： Rekha Saha Debbrata K. Saha Zening Fu Rogers F. Silva Vince D. Calhoun Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Georgia Institute of Technology and Emory University Atlanta USA

Functional and structural magnetic resonance imaging (fMRI/sMRI) are extensively used modalities for studying brain development. While individual modalities may overlook crucial aspects of brain analysis, combining multiple modalities allows us to leverage the benefits of revealing hidden brain connections. To analyze multivariate change patterns in brain function and structure with increasing age across the entire brain, we employ a symmetric multimodal fusion approach that combines multiset canonical correlation analysis and joint independent component analysis. In this study, we present a novel approach to analyze linked longitudinal change patterns in functional network connectivity (FNC) and gray matter (GM) data derived from the large-scale Adolescent Brain and Cognitive Development dataset. Our approach uncovers significant pattern changes in both modalities. Specifically, we identify highly structured functional change patterns and structural change patterns that include increased brain functional connectivity between the visual and sensorimotor domains in the fMRI data, as well as changes in the bilateral sensorimotor cortex in the sMRI data. Overall, our study demonstrates the strength of our approach in uncovering longitudinal changes in FNC and GM, provides valuable insights into the dynamic nature of brain connectivity and structure during adolescence, and sheds light on potential gender-related differences in these processes.

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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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Multimodal Analysis Uncovers Links between Grey Matter Volume and both Low-and High-frequency Dynamic Connectivity states in Schizophrenia

Multimodal Analysis Uncovers Links between Grey Matter Volum...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Marlena Duda Ashkan Faghiri Vince D. Calhoun Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University Atlanta GA USA

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Multimodal neuroimaging fusion studies of complex disorders like schizophrenia (SZ) have revealed disease-specific links between brain structure and function that unimodal analyses alone could not produce. Here, we utilize a multi-set CCA+ joint ICA fusion framework to study the connection between structural MRI and functional network connectivity (FNC) states derived via filter-banked connectivity (FBC), a unified method for estimating both static and dynamic FNC across the frequency spectrum. This joint analysis showed functional connections in both low-and high-frequency SZ-dominant states were significantly related to alterations in grey matter volume in several areas in the frontal and temporal cortices, which have formerly been implicated in SZ.

关键词： neuroimaging Brain Mental disorders Magnetic resonance imaging Grey matter Frequency estimation Bioinformatics

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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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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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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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

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 < 0.05), and we also find that the performance of our approach exceeds that of many previous studies using both engineered features and raw EEG. We further examine how transfer learning affected the representations learned by the model through a pair of explainability analyses, identifying key frequency bands and channels utilized across models. Our proposed approach represents a significant step forward for the domain of raw resting-state EEG classification and has broader implications for use with other electrophysiology and time-series modalities. Importantly, it has the potential to expand the use of deep learning methods across a greater variety of raw EEG datasets and lead to the development of more reliable EEG classifiers.

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