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Ji Y, Pearlson G, Bustillo J, Kochunov P, Turner JA, Jiang R, Shao W, Zhang X, Fu Z, Li K, Liu Z, Xu X, Zhang D, Qi S, Calhoun VD. Identifying psychosis subtypes use individualized covariance structural differential networks and multi-site clustering. Schizophr Res 2024; 264:130-139. [PMID: 38128344 DOI: 10.1016/j.schres.2023.12.013] [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: 08/29/2022] [Revised: 07/19/2023] [Accepted: 12/10/2023] [Indexed: 12/23/2023]
Abstract
BACKGROUND Similarities among schizophrenia (SZ), schizoaffective disorder (SAD) and bipolar disorder (BP) including clinical phenotypes, brain alterations and risk genes, make it challenging to perform reliable separation among them. However, previous subtype identification that transcend traditional diagnostic boundaries were based on group-level neuroimaging features, ignoring individual-level inferences. METHODS 455 psychoses (178 SZs, 134 SADs and 143 BPs), their first-degree relatives (N = 453) and healthy controls (HCs, N = 220) were collected from Bipolar-Schizophrenia Network on Intermediate Phenotypes (B-SNIP I) consortium. Individualized covariance structural differential networks (ICSDNs) were constructed for each patient and multi-site clustering was used to identify psychosis subtypes. Group differences between subtypes in clinical phenotypes and voxel-wise fractional amplitude of low frequency fluctuation (fALFF) were calculated, as well as between the corresponding relatives. RESULTS Two psychosis subtypes were identified with increased whole brain structural covariance, with decreased connectivity between amygdala-hippocampus and temporal-occipital cortex in subtype I (S-I) compared to subtype II (S-II), which was replicated under different clustering methods, number of edges and across datasets (B-SNIP II) and different brain atlases. S-I had higher emotional-related symptoms than S-II and showed significant fALFF decrease in temporal and occipital cortex, while S-II was more similar to HC. This pattern was consistently validated on relatives of S-I and S-II in both fALFF and clinical symptoms. CONCLUSIONS These findings reconcile categorical and dimensional perspectives of psychosis neurobiological heterogeneity, indicating that relatives of S-I might have greater predisposition in developing psychosis, while relatives of S-II are more likely to be healthy. This study contributes to the development of neuroimaging informed diagnostic classifications within psychosis spectrum.
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Affiliation(s)
- Yixin Ji
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Godfrey Pearlson
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA; Olin Neuropsychiatry Research Center, Institute of Living, Hartford, CT, USA
| | - Juan Bustillo
- Departments of Neurosciences and Psychiatry and Behavioral Sciences, University of New Mexico, Albuquerque, NM, USA
| | - Peter Kochunov
- Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jessica A Turner
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Rongtao Jiang
- Departments of Psychiatry and Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Wei Shao
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China
| | - Xiao Zhang
- Peking University Sixth Hospital/Institute of Mental Health, Beijing, China
| | - Zening Fu
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA, USA
| | - Kaicheng Li
- Department of Radiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Zhaowen Liu
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Xijia Xu
- Department of Psychiatry, Affiliated Nanjing Brain Hospital, Nanjing Medical University, Nanjing, China
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, China.
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, 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, USA; Department of Electrical and Computer Engineering, Georgia Tech University, Atlanta, GA, USA
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Wen G, Cao P, Liu L, Yang J, Zhang X, Wang F, Zaiane OR. Graph Self-Supervised Learning With Application to Brain Networks Analysis. IEEE J Biomed Health Inform 2023; 27:4154-4165. [PMID: 37159311 DOI: 10.1109/jbhi.2023.3274531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
The less training data and insufficient supervision limit the performance of the deep supervised models for brain disease diagnosis. It is significant to construct a learning framework that can capture more information in limited data and insufficient supervision. To address these issues, we focus on self-supervised learning and aim to generalize the self-supervised learning to the brain networks, which are non-Euclidean graph data. More specifically, we propose an ensemble masked graph self-supervised framework named BrainGSLs, which incorporates 1) a local topological-aware encoder that takes the partially visible nodes as input and learns these latent representations, 2) a node-edge bi-decoder that reconstructs the masked edges by the representations of both the masked and visible nodes, 3) a signal representation learning module for capturing temporal representations from BOLD signals and 4) a classifier used for the classification. We evaluate our model on three real medical clinical applications: diagnosis of Autism Spectrum Disorder (ASD), diagnosis of Bipolar Disorder (BD) and diagnosis of Major Depressive Disorder (MDD). The results suggest that the proposed self-supervised training has led to remarkable improvement and outperforms state-of-the-art methods. Moreover, our method is able to identify the biomarkers associated with the diseases, which is consistent with the previous studies. We also explore the correlation of these three diseases and find the strong association between ASD and BD. To the best of our knowledge, our work is the first attempt of applying the idea of self-supervised learning with masked autoencoder on the brain network analysis.
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Chaari N, Akdağ HC, Rekik I. Comparative survey of multigraph integration methods for holistic brain connectivity mapping. Med Image Anal 2023; 85:102741. [PMID: 36638747 DOI: 10.1016/j.media.2023.102741] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 12/27/2022] [Accepted: 01/03/2023] [Indexed: 01/09/2023]
Abstract
One of the greatest scientific challenges in network neuroscience is to create a representative map of a population of heterogeneous brain networks, which acts as a connectional fingerprint. The connectional brain template (CBT), also named network atlas, presents a powerful tool for capturing the most representative and discriminative traits of a given population while preserving its topological patterns. The idea of a CBT is to integrate a population of heterogeneous brain connectivity networks, derived from different neuroimaging modalities or brain views (e.g., structural and functional), into a unified holistic representation. Here we review current state-of-the-art methods designed to estimate well-centered and representative CBT for populations of single-view and multi-view brain networks. We start by reviewing each CBT learning method, then we introduce the evaluation measures to compare CBT representativeness of populations generated by single-view and multigraph integration methods, separately, based on the following criteria: Centeredness, biomarker-reproducibility, node-level similarity, global-level similarity, and distance-based similarity. We demonstrate that the deep graph normalizer (DGN) method significantly outperforms other multi-graph and all single-view integration methods for estimating CBTs using a variety of healthy and disordered datasets in terms of centeredness, reproducibility (i.e., graph-derived biomarkers reproducibility that disentangle the typical from the atypical connectivity variability), and preserving the topological traits at both local and global graph-levels.
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Affiliation(s)
- Nada Chaari
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Faculty of Management, Istanbul Technical University, Istanbul, Turkey
| | | | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Computing, Imperial-X Translation and Innovation Hub, Imperial College London, London, UK.
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4
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Moridian P, Ghassemi N, Jafari M, Salloum-Asfar S, Sadeghi D, Khodatars M, Shoeibi A, Khosravi A, Ling SH, Subasi A, Alizadehsani R, Gorriz JM, Abdulla SA, Acharya UR. Automatic autism spectrum disorder detection using artificial intelligence methods with MRI neuroimaging: A review. Front Mol Neurosci 2022; 15:999605. [PMID: 36267703 PMCID: PMC9577321 DOI: 10.3389/fnmol.2022.999605] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Accepted: 08/09/2022] [Indexed: 12/04/2022] Open
Abstract
Autism spectrum disorder (ASD) is a brain condition characterized by diverse signs and symptoms that appear in early childhood. ASD is also associated with communication deficits and repetitive behavior in affected individuals. Various ASD detection methods have been developed, including neuroimaging modalities and psychological tests. Among these methods, magnetic resonance imaging (MRI) imaging modalities are of paramount importance to physicians. Clinicians rely on MRI modalities to diagnose ASD accurately. The MRI modalities are non-invasive methods that include functional (fMRI) and structural (sMRI) neuroimaging methods. However, diagnosing ASD with fMRI and sMRI for specialists is often laborious and time-consuming; therefore, several computer-aided design systems (CADS) based on artificial intelligence (AI) have been developed to assist specialist physicians. Conventional machine learning (ML) and deep learning (DL) are the most popular schemes of AI used for diagnosing ASD. This study aims to review the automated detection of ASD using AI. We review several CADS that have been developed using ML techniques for the automated diagnosis of ASD using MRI modalities. There has been very limited work on the use of DL techniques to develop automated diagnostic models for ASD. A summary of the studies developed using DL is provided in the Supplementary Appendix. Then, the challenges encountered during the automated diagnosis of ASD using MRI and AI techniques are described in detail. Additionally, a graphical comparison of studies using ML and DL to diagnose ASD automatically is discussed. We suggest future approaches to detecting ASDs using AI techniques and MRI neuroimaging.
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Affiliation(s)
- Parisa Moridian
- Faculty of Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
| | - Navid Ghassemi
- Department of Computer Engineering, Ferdowsi University of Mashhad, Mashhad, Iran
| | - Mahboobeh Jafari
- Faculty of Electrical and Computer Engineering, Semnan University, Semnan, Iran
| | - Salam Salloum-Asfar
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Delaram Sadeghi
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Marjane Khodatars
- Department of Medical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran
| | - Afshin Shoeibi
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Abbas Khosravi
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Sai Ho Ling
- Faculty of Engineering and IT, University of Technology Sydney (UTS), Ultimo, NSW, Australia
| | - Abdulhamit Subasi
- Faculty of Medicine, Institute of Biomedicine, University of Turku, Turku, Finland
- Department of Computer Science, College of Engineering, Effat University, Jeddah, Saudi Arabia
| | - Roohallah Alizadehsani
- Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC, Australia
| | - Juan M. Gorriz
- Data Science and Computational Intelligence Institute, University of Granada, Granada, Spain
| | - Sara A. Abdulla
- Neurological Disorders Research Center, Qatar Biomedical Research Institute, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - U. Rajendra Acharya
- Ngee Ann Polytechnic, Singapore, Singapore
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung, Taiwan
- Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore, Singapore
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5
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A unified framework of graph structure learning, graph generation and classification for brain network analysis. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03891-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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6
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Wang N, Yao D, Ma L, Liu M. Multi-site clustering and nested feature extraction for identifying autism spectrum disorder with resting-state fMRI. Med Image Anal 2021; 75:102279. [PMID: 34731776 DOI: 10.1016/j.media.2021.102279] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 12/22/2022]
Abstract
Brain functional connectivity (FC) derived from resting-state functional magnetic resonance imaging (rs-fMRI) has been widely employed to study neuropsychiatric disorders such as autism spectrum disorder (ASD). Existing studies usually suffer from (1) significant data heterogeneity caused by different scanners or studied populations in multiple sites, (2) curse of dimensionality caused by millions of voxels in each fMRI scan and a very limited number (tens or hundreds) of training samples, and (3) poor interpretability, which hinders the identification of reproducible disease biomarkers. To this end, we propose a Multi-site Clustering and Nested Feature Extraction (MC-NFE) method for fMRI-based ASD detection. Specifically, we first divide multi-site training data into ASD and healthy control (HC) groups. To model inter-site heterogeneity within each category, we use a similarity-driven multiview linear reconstruction model to learn latent representations and perform subject clustering within each group. We then design a nested singular value decomposition (SVD) method to mitigate inter-site heterogeneity and extract FC features by learning both local cluster-shared features across sites within each category and global category-shared features across ASD and HC groups, followed by a linear support vector machine (SVM) for ASD detection. Experimental results on 609 subjects with rs-fMRI from the ABIDE database with 21 imaging sites suggest that the proposed MC-NFE outperforms several state-of-the-art methods in ASD detection. The most discriminative FCs identified by the MC-NFE are mainly located in default mode network, salience network, and cerebellum region, which could be used as potential biomarkers for fMRI-based ASD analysis.
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Affiliation(s)
- Nan Wang
- East China Normal University, Shanghai 200062, China
| | - Dongren Yao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Lizhuang Ma
- East China Normal University, Shanghai 200062, China; Shanghai Jiao Tong University, Shanghai 200240, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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7
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Xin B, Huang J, Zhang L, Zheng C, Zhou Y, Lu J, Wang X. Dynamic topology analysis for spatial patterns of multifocal lesions on MRI. Med Image Anal 2021; 76:102267. [PMID: 34929461 DOI: 10.1016/j.media.2021.102267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2021] [Revised: 07/26/2021] [Accepted: 10/07/2021] [Indexed: 01/01/2023]
Abstract
Quantitatively analysing the spatial patterns of multifocal lesions on clinical MRI is an important step towards a better understanding of the disease and for precision medicine, which is yet to be properly explored by feature engineering and deep learning methods. Network science addresses this issue by explicitly modeling the inter-lesion topology. However, the construction of the informative graph with optimal edge sparsity and quantification of community graph structures are the current challenges in network science. In this paper, we address these challenges with a novel Dynamic Topology Analysis framework on the basis of persistent homology, aiming to investigate the predictive values of global geometry and local clusters of multifocal lesions. Firstly, Dynamic Hierarchical Network is proposed to construct informative global and community-level topology over multi-scale networks from sparse to dense. Multi-scale global topology is constructed with a nested sequence of Rips complexes, from which a new K-simplex Filtration is designed to generate a higher-level topological abstraction for community identification based on the connectivity of k-simplices in the Rips Complex. Secondly, to quantify multi-scale community structures, we design a new Decomposed Community Persistence algorithm to track the dynamic evolution of communities, and then summarise the evolutionary communities incorporated with a customisable descriptor. The quantified community features are encapsulated with global geometric invariants for topological pattern analysis. The proposed framework was evaluated on both diagnostic differentiation and prognostic prediction for multiple sclerosis that is a typical multifocal disease, and achieved ROC_AUC 0.875 and 0.767, respectively, outperforming seven state-of-the-art persistent homology methods and the reported performance of six feature engineering and deep learning methods.
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Affiliation(s)
- Bowen Xin
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Jing Huang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Lin Zhang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia
| | - Chaojie Zheng
- Central Research Institute, United Imaging Healthcare Group Co, Ltd, Shanghai, China
| | - Yun Zhou
- Central Research Institute, United Imaging Healthcare Group Co, Ltd, Shanghai, China
| | - Jie Lu
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Xiuying Wang
- School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
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Ji J, Chen Z, Yang C. Convolutional Neural Network with Sparse Strategies to Classify Dynamic Functional Connectivity. IEEE J Biomed Health Inform 2021; 26:1219-1228. [PMID: 34314368 DOI: 10.1109/jbhi.2021.3100559] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Classification of dynamic functional connectivity (DFC) is becoming a promising approach for diagnosing various neurodegenerative diseases. However, the existing methods generally face the problem of overfitting. To solve it, this paper proposes a convolutional neural network with three sparse strategies named SCNN to classify DFC. Firstly, an element-wise filter is designed to impose sparse constraints on the DFC matrix by replacing the redundant elements with zeroes, where the DFC matrix is specially constructed to quantify the spatial and temporal variation of DFC. Secondly, a 11 convolutional filter is adopted to reduce the dimensionality of the sparse DFC matrix, and remove meaningless features resulted from zero elements in the subsequent convolution process. Finally, an extra sparse optimization classifier is employed to optimize the parameters of the above two filters, which can effectively improve the ability of SCNN to extract discriminative features. Experimental results on multiple resting-state fMRI datasets demonstrate that the proposed model provides a better classification performance of DFC compared with several state-of-the-art methods, and can identify the abnormal brain functional connectivity.
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Bessadok A, Mahjoub MA, Rekik I. Brain multigraph prediction using topology-aware adversarial graph neural network. Med Image Anal 2021; 72:102090. [PMID: 34004494 DOI: 10.1016/j.media.2021.102090] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2020] [Revised: 03/21/2021] [Accepted: 04/20/2021] [Indexed: 12/21/2022]
Abstract
Brain graphs (i.e, connectomes) constructed from medical scans such as magnetic resonance imaging (MRI) have become increasingly important tools to characterize the abnormal changes in the human brain. Due to the high acquisition cost and processing time of multimodal MRI, existing deep learning frameworks based on Generative Adversarial Network (GAN) focused on predicting the missing multimodal medical images from a few existing modalities. While brain graphs help better understand how a particular disorder can change the connectional facets of the brain, synthesizing a target brain multigraph (i.e, multiple brain graphs) from a single source brain graph is strikingly lacking. Additionally, existing graph generation works mainly learn one model for each target domain which limits their scalability in jointly predicting multiple target domains. Besides, while they consider the global topological scale of a graph (i.e., graph connectivity structure), they overlook the local topology at the node scale (e.g., how central a node is in the graph). To address these limitations, we introduce topology-aware graph GAN architecture (topoGAN), which jointly predicts multiple brain graphs from a single brain graph while preserving the topological structure of each target graph. Its three key innovations are: (i) designing a novel graph adversarial auto-encoder for predicting multiple brain graphs from a single one, (ii) clustering the encoded source graphs in order to handle the mode collapse issue of GAN and proposing a cluster-specific decoder, (iii) introducing a topological loss to force the prediction of topologically sound target brain graphs. The experimental results using five target domains demonstrated the outperformance of our method in brain multigraph prediction from a single graph in comparison with baseline approaches.
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Affiliation(s)
- Alaa Bessadok
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia; National Engineering School of Sousse, University of Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia
| | - Mohamed Ali Mahjoub
- Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia; National Engineering School of Sousse, University of Sousse, LATIS- Laboratory of Advanced Technology and Intelligent Systems, Sousse, 4023, Tunisia
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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Bessadok A, Mahjoub MA, Rekik I. Brain graph synthesis by dual adversarial domain alignment and target graph prediction from a source graph. Med Image Anal 2020; 68:101902. [PMID: 33338871 DOI: 10.1016/j.media.2020.101902] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Revised: 11/09/2020] [Accepted: 11/10/2020] [Indexed: 11/19/2022]
Abstract
Developing predictive intelligence in neuroscience for learning how to generate multimodal medical data from a single modality can improve neurological disorder diagnosis with minimal data acquisition resources. Existing deep learning frameworks are mainly tailored for images, which might fail in handling geometric data (e.g., brain graphs). Specifically, predicting a target brain graph from a single source brain graph remains largely unexplored. Solving such problem is generally challenged with domain fracturecaused by the difference in distribution between source and target domains. Besides, solving the prediction and domain fracture independently might not be optimal for both tasks. To address these challenges, we unprecedentedly propose a Learning-guided Graph Dual Adversarial Domain Alignment (LG-DADA) framework for predicting a target brain graph from a source brain graph. The proposed LG-DADA is grounded in three fundamental contributions: (1) a source data pre-clustering step using manifold learning to firstly handle source data heterogeneity and secondly circumvent mode collapse in generative adversarial learning, (2) a domain alignment of source domain to the target domain by adversarially learning their latent representations, and (3) a dual adversarial regularization that jointly learns a source embedding of training and testing brain graphs using two discriminators and predict the training target graphs. Results on morphological brain graphs synthesis showed that our method produces better prediction accuracy and visual quality as compared to other graph synthesis methods.
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Affiliation(s)
- Alaa Bessadok
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia
| | - Mohamed Ali Mahjoub
- Higher Institute of Informatics and Communication Technologies, University of Sousse, Tunisia
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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Ghribi O, Li G, Lin W, Shen D, Rekik I. Multi-Regression based supervised sample selection for predicting baby connectome evolution trajectory from neonatal timepoint. Med Image Anal 2020; 68:101853. [PMID: 33264713 DOI: 10.1016/j.media.2020.101853] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Revised: 07/27/2020] [Accepted: 09/14/2020] [Indexed: 01/06/2023]
Abstract
The connectional map of the baby brain undergoes dramatic changes over the first year of postnatal development, which makes its mapping a challenging task, let alone learning how to predict its evolution. Currently, learning models for predicting brain connectomic developmental trajectories remain broadly absent despite their great potential in spotting atypical neurodevelopmental disorders early. This is most likely due to the scarcity and often incompleteness of longitudinal infant neuroimaging studies for training such models. In this paper, we propose the first approach for progressively predicting longitudinal development of brain networks during the postnatal period solely from a baseline connectome around birth. To this end, a supervised multi-regression sample selection strategy is designed to learn how to identify the best set of neighbors of a testing baseline connectome to eventually predict its evolution trajectory at follow-up timepoints. However, given that the training dataset may have missing samples (connectomes) at certain timepoints, this may affect the training of the predictive model. To overcome this problem, we perform a low-rank tensor completion based on a robust principal component analysis to impute the missing training connectomes by linearly approximating similar complete training networks. In the prediction step, our sample selection strategy aims to preserve spatiotemporal relationships between consecutive timepoints. Therefore, the proposed method learns how to identify the set of the local closest neighbors to a target network by training an ensemble of bidirectional regressors leveraging temporal dependency between consecutive timepoints with a recall to the baseline observations to progressively predict the evolution of a testing network over time. Our method achieves the best prediction results and better captures the dynamic changes of each brain connectome over time in comparison to its ablated versions using leave-one-out cross-validation strategy.
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Affiliation(s)
- Olfa Ghribi
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; National School of Engineers of Sfax, University of Sfax, Tunisia
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Research and Development, Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Islem Rekik
- BASIRA lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey; School of Science and Engineering, Computing, University of Dundee, UK.
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Adversarial brain multiplex prediction from a single brain network with application to gender fingerprinting. Med Image Anal 2020; 67:101843. [PMID: 33129149 DOI: 10.1016/j.media.2020.101843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Revised: 07/25/2020] [Accepted: 09/07/2020] [Indexed: 11/22/2022]
Abstract
Brain connectivity networks, derived from magnetic resonance imaging (MRI), non-invasively quantify the relationship in function, structure, and morphology between two brain regions of interest (ROIs) and give insights into gender-related connectional differences. However, to the best of our knowledge, studies on gender differences in brain connectivity were limited to investigating pairwise (i.e., low-order) relationships across ROIs, overlooking the complex high-order interconnectedness of the brain as a network. A few recent works on neurological disorders addressed this limitation by introducing the brain multiplex which is composed of a source network intra-layer, a target intra-layer, and a convolutional interlayer capturing the high-level relationship between both intra-layers. However, brain multiplexes are built from at least two different brain networks hindering their application to connectomic datasets with single brain networks (e.g., functional networks). To fill this gap, we propose Adversarial Brain Multiplex Translator (ABMT), the first work for predicting brain multiplexes from a source network using geometric adversarial learning to investigate gender differences in the human brain. Our framework comprises: (i) a geometric source to target network translator mimicking a U-Net architecture with skip connections, (ii) a conditional discriminator which distinguishes between predicted and ground truth target intra-layers, and finally (iii) a multi-layer perceptron (MLP) classifier which supervises the prediction of the target multiplex using the subject class label (e.g., gender). Our experiments on a large dataset demonstrated that predicted multiplexes significantly boost gender classification accuracy compared with source networks and unprecedentedly identify both low and high-order gender-specific brain multiplex connections. Our ABMT source code is available on GitHub at https://github.com/basiralab/ABMT.
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Mhiri I, Khalifa AB, Mahjoub MA, Rekik I. Brain graph super-resolution for boosting neurological disorder diagnosis using unsupervised multi-topology connectional brain template learning. Med Image Anal 2020; 65:101768. [DOI: 10.1016/j.media.2020.101768] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2019] [Revised: 04/12/2020] [Accepted: 06/23/2020] [Indexed: 10/24/2022]
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