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Bagheri A, Pasande M, Bello K, Araabi BN, Akhondi-Asl A. Discovering the effective connectome of the brain with dynamic Bayesian DAG learning. Neuroimage 2024; 297:120684. [PMID: 38880310 DOI: 10.1016/j.neuroimage.2024.120684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 05/30/2024] [Accepted: 06/10/2024] [Indexed: 06/18/2024] Open
Abstract
Understanding the complex mechanisms of the brain can be unraveled by extracting the Dynamic Effective Connectome (DEC). Recently, score-based Directed Acyclic Graph (DAG) discovery methods have shown significant improvements in extracting the causal structure and inferring effective connectivity. However, learning DEC through these methods still faces two main challenges: one with the fundamental impotence of high-dimensional dynamic DAG discovery methods and the other with the low quality of fMRI data. In this paper, we introduce Bayesian Dynamic DAG learning with M-matrices Acyclicity characterization (BDyMA) method to address the challenges in discovering DEC. The presented dynamic DAG enables us to discover direct feedback loop edges as well. Leveraging an unconstrained framework in the BDyMA method leads to more accurate results in detecting high-dimensional networks, achieving sparser outcomes, making it particularly suitable for extracting DEC. Additionally, the score function of the BDyMA method allows the incorporation of prior knowledge into the process of dynamic causal discovery which further enhances the accuracy of results. Comprehensive simulations on synthetic data and experiments on Human Connectome Project (HCP) data demonstrate that our method can handle both of the two main challenges, yielding more accurate and reliable DEC compared to state-of-the-art and traditional methods. Additionally, we investigate the trustworthiness of DTI data as prior knowledge for DEC discovery and show the improvements in DEC discovery when the DTI data is incorporated into the process.
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Affiliation(s)
- Abdolmahdi Bagheri
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran.
| | - Mohammad Pasande
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran
| | - Kevin Bello
- Machine Learning Department, Carnegie Mellon University, Pittsburgh, USA
| | - Babak Nadjar Araabi
- School of Electrical and Computer Engineering, University of Tehran, College of Engineering, Tehran, Iran
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Ji J, Zhang Z, Han L, Liu J. MetaCAE: Causal autoencoder with meta-knowledge transfer for brain effective connectivity estimation. Comput Biol Med 2024; 170:107940. [PMID: 38232454 DOI: 10.1016/j.compbiomed.2024.107940] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Revised: 11/18/2023] [Accepted: 01/01/2024] [Indexed: 01/19/2024]
Abstract
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has gradually become one of the hot subjects in the fields of neuroscience. In particular, the encoder-decoder based methods can effectively extract the connections in fMRI time series, which have achieved promising performance. However, these methods generally use Granger causality model, which may identify false directions due to the non-stationary characteristic of fMRI data. Additionally, fMRI datasets have limited sample sizes, which significantly constrains the development of these methods. In this paper, we propose a novel brain effective connectivity estimation method based on causal autoencoder with meta-knowledge transfer, called MetaCAE. The proposed approach employs a causal autoencoder (CAE) to extract causal dependencies from non-stationary fMRI time series, and leverages meta-knowledge transfer to improve the estimation accuracy on small-sample data. More specifically, MetaCAE first employs a temporal convolutional encoder to extract non-stationary temporal information from fMRI time series. Then it uses a structural equation model-based decoder to decode causal relationships between brain regions. Finally, it utilizes a model-agnostic meta-learning method to learn the meta-knowledge of the shared brain effective connectivity among different subjects, and transfers the meta-knowledge to the CAE to enhance its estimation ability on small-sample fMRI data. Comprehensive experiments on both simulated and real-world data demonstrate the efficacy of MetaCAE in estimating brain effective connectivity.
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Affiliation(s)
- Junzhong Ji
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Zuozhen Zhang
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Lu Han
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China
| | - Jinduo Liu
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing, China.
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Zhai J, Ji J, Liu J. Learning Causal Biological Networks with Parallel Ant Colony Optimization Algorithm. Bioengineering (Basel) 2023; 10:909. [PMID: 37627794 PMCID: PMC10451563 DOI: 10.3390/bioengineering10080909] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Revised: 07/22/2023] [Accepted: 07/27/2023] [Indexed: 08/27/2023] Open
Abstract
A wealth of causal relationships exists in biological systems, both causal brain networks and causal protein signaling networks are very classical causal biological networks (CBNs). Learning CBNs from biological signal data reliably is a critical problem today. However, most of the existing methods are not excellent enough in terms of accuracy and time performance, and tend to fall into local optima because they do not take full advantage of global information. In this paper, we propose a parallel ant colony optimization algorithm to learn causal biological networks from biological signal data, called PACO. Specifically, PACO first maps the construction of CBNs to ants, then searches for CBNs in parallel by simulating multiple groups of ants foraging, and finally obtains the optimal CBN through pheromone fusion and CBNs fusion between different ant colonies. Extensive experimental results on simulation data sets as well as two real-world data sets, the fMRI signal data set and the Single-cell data set, show that PACO can accurately and efficiently learn CBNs from biological signal data.
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Affiliation(s)
| | | | - Jinduo Liu
- Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China (J.J.)
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Zhang Z, Zhang Z, Ji J, Liu J. Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data. Brain Sci 2023; 13:995. [PMID: 37508927 PMCID: PMC10376969 DOI: 10.3390/brainsci13070995] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/18/2023] [Accepted: 06/22/2023] [Indexed: 07/30/2023] Open
Abstract
Using machine learning methods to estimate brain effective connectivity networks from functional magnetic resonance imaging (fMRI) data has garnered significant attention in the fields of neuroinformatics and bioinformatics. However, existing methods usually require retraining the model for each subject, which ignores the knowledge shared across subjects. In this paper, we propose a novel framework for estimating effective connectivity based on an amortization transformer, named AT-EC. In detail, AT-EC first employs an amortization transformer to model the dynamics of fMRI time series and infer brain effective connectivity across different subjects, which can train an amortized model that leverages the shared knowledge from different subjects. Then, an assisted learning mechanism based on functional connectivity is designed to assist the estimation of the brain effective connectivity network. Experimental results on both simulated and real-world data demonstrate the efficacy of our method.
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Affiliation(s)
- Zuozhen Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Ziqi Zhang
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Junzhong Ji
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
| | - Jinduo Liu
- The Beijing Municipal Key Laboratory of Multimedia and Intelligent Software Technology, Beijing Institute of Artificial Intelligence, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
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Cao Y, Kuai H, Liang P, Pan JS, Yan J, Zhong N. BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer's disease. Front Neurosci 2023; 17:1202382. [PMID: 37424996 PMCID: PMC10326383 DOI: 10.3389/fnins.2023.1202382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Accepted: 05/09/2023] [Indexed: 07/11/2023] Open
Abstract
Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer's Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.
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Affiliation(s)
- Yu Cao
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Hongzhi Kuai
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
| | - Peipeng Liang
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
| | - Jeng-Shyang Pan
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Jianzhuo Yan
- Faculty of Information Technology, Beijing University of Technology, Beijing, China
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
| | - Ning Zhong
- Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China
- Faculty of Engineering, Maebashi Institute of Technology, Maebashi, Gunma, Japan
- School of Psychology and Beijing Key Laboratory of Learning and Cognition, Capital Normal University, Beijing, China
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A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI. FUTURE INTERNET 2022. [DOI: 10.3390/fi14120351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.
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Ali H, Biswas R, Ali F, Shah U, Alamgir A, Mousa O, Shah Z. The role of generative adversarial networks in brain MRI: a scoping review. Insights Imaging 2022; 13:98. [PMID: 35662369 PMCID: PMC9167371 DOI: 10.1186/s13244-022-01237-0] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/11/2022] [Indexed: 11/23/2022] Open
Abstract
The performance of artificial intelligence (AI) for brain MRI can improve if enough data are made available. Generative adversarial networks (GANs) showed a lot of potential to generate synthetic MRI data that can capture the distribution of real MRI. Besides, GANs are also popular for segmentation, noise removal, and super-resolution of brain MRI images. This scoping review aims to explore how GANs methods are being used on brain MRI data, as reported in the literature. The review describes the different applications of GANs for brain MRI, presents the most commonly used GANs architectures, and summarizes the publicly available brain MRI datasets for advancing the research and development of GANs-based approaches. This review followed the guidelines of PRISMA-ScR to perform the study search and selection. The search was conducted on five popular scientific databases. The screening and selection of studies were performed by two independent reviewers, followed by validation by a third reviewer. Finally, the data were synthesized using a narrative approach. This review included 139 studies out of 789 search results. The most common use case of GANs was the synthesis of brain MRI images for data augmentation. GANs were also used to segment brain tumors and translate healthy images to diseased images or CT to MRI and vice versa. The included studies showed that GANs could enhance the performance of AI methods used on brain MRI imaging data. However, more efforts are needed to transform the GANs-based methods in clinical applications.
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Affiliation(s)
- Hazrat Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
| | - Rafiul Biswas
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Farida Ali
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Uzair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Asma Alamgir
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Osama Mousa
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar
| | - Zubair Shah
- College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, 34110, Doha, Qatar.
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