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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] [MESH Headings] [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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Liu R, Huang ZA, Hu Y, Zhu Z, Wong KC, Tan KC. Spatial-Temporal Co-Attention Learning for Diagnosis of Mental Disorders From Resting-State fMRI Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:10591-10605. [PMID: 37027556 DOI: 10.1109/tnnls.2023.3243000] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging techniques have been widely adopted to detect the neurological brain structures and functions of the nervous system. As an effective noninvasive neuroimaging technique, functional magnetic resonance imaging (fMRI) has been extensively used in computer-aided diagnosis (CAD) of mental disorders, e.g., autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). In this study, we propose a spatial-temporal co-attention learning (STCAL) model for diagnosing ASD and ADHD from fMRI data. In particular, a guided co-attention (GCA) module is developed to model the intermodal interactions of spatial and temporal signal patterns. A novel sliding cluster attention module is designed to address global feature dependency of self-attention mechanism in fMRI time series. Comprehensive experimental results demonstrate that our STCAL model can achieve competitive accuracies of 73.0 ± 4.5%, 72.0 ± 3.8%, and 72.5 ± 4.2% on the ABIDE I, ABIDE II, and ADHD-200 datasets, respectively. Moreover, the potential for feature pruning based on the co-attention scores is validated by the simulation experiment. The clinical interpretation analysis of STCAL can allow medical professionals to concentrate on the discriminative regions of interest and key time frames from fMRI data.
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Li WX, Lin QH, Zhang CY, Han Y, Calhoun VD. A new transfer entropy method for measuring directed connectivity from complex-valued fMRI data. Front Neurosci 2024; 18:1423014. [PMID: 39050665 PMCID: PMC11266018 DOI: 10.3389/fnins.2024.1423014] [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: 04/25/2024] [Accepted: 06/21/2024] [Indexed: 07/27/2024] Open
Abstract
Background Inferring directional connectivity of brain regions from functional magnetic resonance imaging (fMRI) data has been shown to provide additional insights into predicting mental disorders such as schizophrenia. However, existing research has focused on the magnitude data from complex-valued fMRI data without considering the informative phase data, thus ignoring potentially important information. Methods We propose a new complex-valued transfer entropy (CTE) method to measure causal links among brain regions in complex-valued fMRI data. We use the transfer entropy to model a general non-linear magnitude-magnitude and phase-phase directed connectivity and utilize partial transfer entropy to measure the complementary phase and magnitude effects on magnitude-phase and phase-magnitude causality. We also define the significance of the causality based on a statistical test and the shuffling strategy of the two complex-valued signals. Results Simulated results verified higher accuracy of CTE than four causal analysis methods, including a simplified complex-valued approach and three real-valued approaches. Using experimental fMRI data from schizophrenia and controls, CTE yields results consistent with previous findings but with more significant group differences. The proposed method detects new directed connectivity related to the right frontal parietal regions and achieves 10.2-20.9% higher SVM classification accuracy when inferring directed connectivity using anatomical automatic labeling (AAL) regions as features. Conclusion The proposed CTE provides a new general method for fully detecting highly predictive directed connectivity from complex-valued fMRI data, with magnitude-only fMRI data as a specific case.
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Affiliation(s)
- Wei-Xing Li
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Qiu-Hua Lin
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Chao-Ying Zhang
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - Yue Han
- School of Information and Communication Engineering, Dalian University of Technology, Dalian, China
| | - 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, GA, United States
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Zhang F, Li Y, Liu L, Liu Y, Wang P, Biswal BB. Corticostriatal causality analysis in children and adolescents with attention-deficit/hyperactivity disorder. Psychiatry Clin Neurosci 2024; 78:291-299. [PMID: 38444215 DOI: 10.1111/pcn.13650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 12/26/2023] [Accepted: 01/16/2024] [Indexed: 03/07/2024]
Abstract
AIM The effective connectivity between the striatum and cerebral cortex has not been fully investigated in attention-deficit/hyperactivity disorder (ADHD). Our objective was to explore the interaction effects between diagnosis and age on disrupted corticostriatal effective connectivity and to represent the modulation function of altered connectivity pathways in children and adolescents with ADHD. METHODS We performed Granger causality analysis on 300 participants from a publicly available Attention-Deficit/Hyperactivity Disorder-200 dataset. By computing the correlation coefficients between causal connections between striatal subregions and other cortical regions, we estimated the striatal inflow and outflow connection to represent intermodulation mechanisms in corticostriatal pathways. RESULTS Interactions between diagnosis and age were detected in the superior occipital gyrus within the visual network, medial prefrontal cortex, posterior cingulate gyrus, and inferior parietal lobule within the default mode network, which is positively correlated with hyperactivity/impulsivity severity in ADHD. Main effect of diagnosis exhibited a general higher cortico-striatal causal connectivity involving default mode network, frontoparietal network and somatomotor network in ADHD compared with comparisons. Results from high-order effective connectivity exhibited a disrupted information pathway involving the default mode-striatum-somatomotor-striatum-frontoparietal networks in ADHD. CONCLUSION The interactions detected in the visual-striatum-default mode networks pathway appears to be related to the potential distraction caused by long-term abnormal information input from the retina in ADHD. Higher causal connectivity and weakened intermodulation may indicate the pathophysiological process that distractions lead to the impairment of motion planning function and the inhibition/control of this unplanned motion signals in ADHD.
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Affiliation(s)
- Fanyu Zhang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yilu Li
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Lin Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Yefen Liu
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Pan Wang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Bharat B Biswal
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
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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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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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Zhou XC, Huang YB, Liu Z, Wu HJ, Huang HZ, Tian Y, Hong SW, Hu HJ, Lv LJ, Lv ZZ. Bibliometric Analysis of Functional Magnetic Resonance Imaging Studies on Manual Therapy Analgesia from 2002-2022. J Pain Res 2023; 16:2115-2129. [PMID: 37361428 PMCID: PMC10289250 DOI: 10.2147/jpr.s412658] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 06/13/2023] [Indexed: 06/28/2023] Open
Abstract
Background Research on the brain mechanisms underlying manual therapy (MT)-induced analgesia has been conducted worldwide. However, no bibliometric analysis has been performed on functional magnetic resonance imaging (fMRI) studies of MT analgesia. To provide a theoretical foundation for the practical application of MT analgesia, this study examined the current incarnation, hotspots, and frontiers of fMRI-based MT analgesia research over the previous 20 years. Methods All publications were obtained from the Science Citation Index-Expanded (SCI-E) of Web of Science Core Collection (WOSCC). We used CiteSpace 6.1.R3 to analyze publications, authors, cited authors, countries, institutions, cited journals, references, and keywords. We also evaluated keyword co-occurrences and timelines, and citation bursts. The search was conducted from 2002-2022 and was completed within one day on October 7, 2022. Results In total, 261 articles were retrieved. The total number of annual publications showed a fluctuating but overall increasing trend. Author B. Humphreys had the highest number of publications (eight articles) and J. E. Bialosky had the highest centrality (0.45). The United States of America (USA) was the country with the most publications (84 articles), accounting for 32.18% of all publications. Output institutions were mainly the University of Zurich, University of Switzerland, and the National University of Health Sciences of the USA. The Spine (118) and the Journal of Manipulative and Physiological Therapeutics (80) were most frequently cited. The four hot topics in fMRI studies on MT analgesia were "low back pain", "magnetic resonance imaging", "spinal manipulation", and "manual therapy." The frontier topics were "clinical impacts of pain disorders" and "cutting-edge technical capabilities offered by magnetic resonance imaging". Conclusion fMRI studies of MT analgesia have potential applications. fMRI studies of MT analgesia have linked several brain areas, with the default mode network (DMN) garnering the most attention. Future research should include international collaboration and RCTs on this topic.
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Affiliation(s)
- Xing-Chen Zhou
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yu-Bo Huang
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Zhen Liu
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Hong-Jiao Wu
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Hua-Zhi Huang
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Yu Tian
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Shuang-Wei Hong
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Hui-Jie Hu
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Li-Jiang Lv
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
| | - Zhi-Zhen Lv
- The Third Clinical Medical College of Zhejiang Chinese Medical University, Hangzhou, Zhejiang, People’s Republic of China
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Time Series Reconstruction and Classification: A Comprehensive Comparative Study. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02926-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Ji J, Liu J, Han L, Wang F. Estimating Effective Connectivity by Recurrent Generative Adversarial Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3326-3336. [PMID: 34038358 DOI: 10.1109/tmi.2021.3083984] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Estimating effective connectivity from functional magnetic resonance imaging (fMRI) time series data has become a very hot topic in neuroinformatics and brain informatics. However, it is hard for the current methods to accurately estimate the effective connectivity due to the high noise and small sample size of fMRI data. In this paper, we propose a novel framework for estimating effective connectivity based on recurrent generative adversarial networks, called EC-RGAN. The proposed framework employs the generator that consists of a set of effective connectivity generators based on recurrent neural networks to generate the fMRI time series of each brain region, and uses the discriminator to distinguish between the joint distributions of the real and generated fMRI time series. When the model is well-trained and generated fMRI data is similar to real fMRI data, EC-RGAN outputs the effective connectivity by means of the causal parameters of the effective connectivity generators. Experimental results on both simulated and real-world fMRI time series data demonstrate the efficacy of our proposed framework.
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