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Zamani J, Jafadideh AT. Predicting the Conversion from Mild Cognitive Impairment to Alzheimer's Disease Using Graph Frequency Bands and Functional Connectivity-Based Features. RESEARCH SQUARE 2024:rs.3.rs-4549428. [PMID: 38947050 PMCID: PMC11213162 DOI: 10.21203/rs.3.rs-4549428/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2024]
Abstract
Accurate prediction of the progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD) is crucial for disease management. Machine learning techniques have demonstrated success in classifying AD and MCI cases, particularly with the use of resting-state functional magnetic resonance imaging (rs-fMRI) data.This study utilized three years of rs-fMRI data from the ADNI, involving 142 patients with stable MCI (sMCI) and 136 with progressive MCI (pMCI). Graph signal processing was applied to filter rs-fMRI data into low, middle, and high frequency bands. Connectivity-based features were derived from both filtered and unfiltered data, resulting in a comprehensive set of 100 features, including global graph metrics, minimum spanning tree (MST) metrics, triadic interaction metrics, hub tendency metrics, and the number of links. Feature selection was enhanced using particle swarm optimization (PSO) and simulated annealing (SA). A support vector machine (SVM) with a radial basis function (RBF) kernel and a 10-fold cross-validation setup were employed for classification. The proposed approach demonstrated superior performance, achieving optimal accuracy with minimal feature utilization. When PSO selected five features, SVM exhibited accuracy, specificity, and sensitivity rates of 77%, 70%, and 83%, respectively. The identified features were as follows: (Mean of clustering coefficient, Mean of strength)/Radius/(Mean Eccentricity, and Modularity) from low/middle/high frequency bands of graph. The study highlights the efficacy of the proposed framework in identifying individuals at risk of AD development using a parsimonious feature set. This approach holds promise for advancing the precision of MCI to AD progression prediction, aiding in early diagnosis and intervention strategies.
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Affiliation(s)
- Jafar Zamani
- Department of Psychiatry and Behavioral Sciences, Stanford University, California, USA
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2
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Becske M, Marosi C, Molnár H, Fodor Z, Farkas K, Rácz FS, Baradits M, Csukly G. Minimum spanning tree analysis of EEG resting-state functional networks in schizophrenia. Sci Rep 2024; 14:10495. [PMID: 38714807 PMCID: PMC11076461 DOI: 10.1038/s41598-024-61316-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 05/03/2024] [Indexed: 05/10/2024] Open
Abstract
Schizophrenia is a serious and complex mental disease, known to be associated with various subtle structural and functional deviations in the brain. Recently, increased attention is given to the analysis of brain-wide, global mechanisms, strongly altering the communication of long-distance brain areas in schizophrenia. Data of 32 patients with schizophrenia and 28 matched healthy control subjects were analyzed. Two minutes long 64-channel EEG recordings were registered during resting, eyes closed condition. Average connectivity strength was estimated with Weighted Phase Lag Index (wPLI) in lower frequencies: delta and theta, and Amplitude Envelope Correlation with leakage correction (AEC-c) in higher frequencies: alpha, beta, lower gamma and higher gamma. To analyze functional network topology Minimum Spanning Tree (MST) algorithms were applied. Results show that patients have weaker functional connectivity in delta and alpha frequency bands. Concerning network differences, the result of lower diameter, higher leaf number, and also higher maximum degree and maximum betweenness centrality in patients suggest a star-like, and more random network topology in patients with schizophrenia. Our findings are in accordance with some previous findings based on resting-state EEG (and fMRI) data, suggesting that MST network structure in schizophrenia is biased towards a less optimal, more centralized organization.
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Affiliation(s)
- Melinda Becske
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Csilla Marosi
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Hajnalka Molnár
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Zsuzsanna Fodor
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Kinga Farkas
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | | | - Máté Baradits
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary
| | - Gábor Csukly
- Department of Psychiatry and Psychotherapy, Semmelweis University, Balassa u. 6., Budapest, 1083, Hungary.
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Wang H, Zhu R, Tian S, Shao J, Dai Z, Xue L, Sun Y, Chen Z, Yao Z, Lu Q. Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI. Cogn Neurodyn 2023; 17:1609-1619. [PMID: 37974586 PMCID: PMC10640554 DOI: 10.1007/s11571-022-09907-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Revised: 07/19/2022] [Accepted: 10/28/2022] [Indexed: 12/04/2022] Open
Abstract
The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information The online version contains supplementary material available at 10.1007/s11571-022-09907-x.
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Affiliation(s)
- Huan Wang
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Rongxin Zhu
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Shui Tian
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Junneng Shao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhongpeng Dai
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Li Xue
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Yurong Sun
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
| | - Zhilu Chen
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
| | - Zhijian Yao
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Department of Psychiatry, The Affiliated Brain Hospital of Nanjing Medical University, Nanjing, 210029 China
- Nanjing Brain Hospital, Medical School of Nanjing University, Nanjing, 210093 China
| | - Qing Lu
- School of Biological Sciences & Medical Engineering, Southeast University, No.2 Sipailou, Nanjing, 210096 Jiangsu Province China
- Child Development and Learning Science, Key Laboratory of Ministry of Education, Nanjing, China
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Yang J, Wang F, Li Z, Yang Z, Dong X, Han Q. Constructing high-order functional networks based on hypergraph for diagnosis of autism spectrum disorders. Front Neurosci 2023; 17:1257982. [PMID: 37719159 PMCID: PMC10501447 DOI: 10.3389/fnins.2023.1257982] [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: 07/13/2023] [Accepted: 08/17/2023] [Indexed: 09/19/2023] Open
Abstract
Introduction High-order functional connectivity networks (FCNs) that reflect the connection relationships among multiple brain regions have become important tools for exploring the deep workings of the brain and revealing the mechanisms of brain diseases. The traditional high-order FCN constructed based on the "correlation of correlations" strategy, is a representative method for conducting whole-brain connectivity analysis and revealing global network characteristics. However, whole-brain connectivity analysis may be affected by noise carried by less important brain regions, resulting in redundant information and affecting the accuracy and reliability of the analysis. Moreover, this type of analysis has a high computational complexity. Methods To address these issues, a new method for constructing high-order FCN based on hypergraphs is proposed in this article, which is used to accurately capture the real interaction relationships among brain regions. Specifically, first, a low-order FCN reflecting the connection relationships between pairs of brain regions based on resting-state functional Magnetic Resonance Imaging (rs-fMRI) time series is constructed, the method first constructs the low-order FCN that reflects the connection relationships between pairs of brain regions based on rs-fMRI time series, and then selects the "good friends" of each brain region from hypergraph perspective, which refers to the local friend circles with closer relationships. Then, the rs-fMRI time series corresponding to the "good friends" in each brain region's friend circle are averaged to obtain a sequence that reflects the intimacy between brain regions in each friend circle. Finally, hypergraph high-order FCN, which reflects the interaction relationships among multiple brain regions, is obtained by calculating the correlations based on the sequence of friend circles. Results The experimental results demonstrate that the proposed method outperforms traditional high-order FCN construction methods. Furthermore, integrating the high-order FCN constructed based on hypergraphs and the low-order FCN through feature fusion to achieve complementary information improves the accuracy of assisting in the diagnosis of brain diseases. Discussion In addition, the effectiveness of our method has only been validated in the diagnosis of ASD. For future work, we plan to extend this method to other brain connectivity patterns.
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Affiliation(s)
- Jie Yang
- Faculty of Nature, Mathematical & Engineering Sciences, King’s College London, London, United Kingdom
| | - Fang Wang
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Zhen Li
- Hydrological Center of Zaozhuang, Zaozhuang, China
| | - Zhen Yang
- School of Artificial Intelligence, Zaozhuang University, Zaozhuang, China
| | - Xishang Dong
- School of Information Science and Engineering, Zaozhuang University, Zaozhuang, China
| | - Qinghua Han
- School of Artificial Intelligence, Zaozhuang University, Zaozhuang, China
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Tassinari V, La Rosa P, Guida E, Colopi A, Caratelli S, De Paolis F, Gallo A, Cenciarelli C, Sconocchia G, Dolci S, Cesarini V. Contribution of A-to-I RNA editing, M6A RNA Methylation, and Alternative Splicing to physiological brain aging and neurodegenerative diseases. Mech Ageing Dev 2023; 212:111807. [PMID: 37023929 DOI: 10.1016/j.mad.2023.111807] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/31/2023] [Accepted: 04/03/2023] [Indexed: 04/08/2023]
Abstract
Aging is a physiological and progressive phenomenon in all organisms' life cycle, characterized by the accumulation of degenerative processes triggered by several alterations within molecular pathways. These changes compromise cell fate, resulting in the loss of functions in tissues throughout the body, including the brain. Physiological brain aging has been linked to structural and functional alterations, as well as to an increased risk of neurodegenerative diseases. Post-transcriptional RNA modifications modulate mRNA coding properties, stability, translatability, expanding the coding capacity of the genome, and are involved in all cellular processes. Among mRNA post-transcriptional modifications, the A-to-I RNA editing, m6A RNA Methylation and Alternative Splicing play a critical role in all the phases of a neuronal cell life cycle and alterations in their mechanisms of action significantly contribute to aging and neurodegeneration. Here we review our current understanding of the contribution of A-to-I RNA editing, m6A RNA Methylation, and Alternative Splicing to physiological brain aging process and neurodegenerative diseases.
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Affiliation(s)
- Valentina Tassinari
- Department of Molecular Medicine, Sapienza University of Rome, Rome, Italy; Department of Experimental Medicine, University of Rome Tor Vergata, Rome, Italy.
| | - Piergiorgio La Rosa
- Division of Neuroscience, Department of Psychology, Sapienza University of Rome, Rome, Italy; European Center for Brain Research, IRCCS Fondazione Santa Lucia, Rome, Italy
| | - Eugenia Guida
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Ambra Colopi
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Sara Caratelli
- Department of Biomedicine, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Rome, Italy
| | - Francesca De Paolis
- Department of Biomedicine, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Rome, Italy
| | - Angela Gallo
- RNA Editing Lab., Oncohaematology Department, Cellular and Gene Therapy Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Carlo Cenciarelli
- Department of Biomedicine, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Rome, Italy
| | - Giuseppe Sconocchia
- Department of Biomedicine, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Rome, Italy
| | - Susanna Dolci
- Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy
| | - Valeriana Cesarini
- Department of Biomedicine, Institute of Translational Pharmacology (IFT), National Research Council (CNR), Rome, Italy.
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Li Y, Chu T, Liu Y, Zhang H, Dong F, Gai Q, Shi Y, Ma H, Zhao F, Che K, Mao N, Xie H. Classification of major depression disorder via using minimum spanning tree of individual high-order morphological brain network. J Affect Disord 2023; 323:10-20. [PMID: 36403803 DOI: 10.1016/j.jad.2022.11.029] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2022] [Revised: 10/09/2022] [Accepted: 11/07/2022] [Indexed: 11/19/2022]
Abstract
BACKGROUND Major depressive disorder (MDD) is an overbroad and heterogeneous diagnosis with no reliable or quantifiable markers. We aim to combine machine-learning techniques with the individual minimum spanning tree of the morphological brain network (MST-MBN) to determine whether the network properties can provide neuroimaging biomarkers to identify patients with MDD. METHOD Eight morphometric features of each region of interest (ROI) were extracted from 3D T1 structural images of 106 patients with MDD and 97 healthy controls. Six feature distances of the eight morphometric features were calculated to generate a feature distance matrix, which was defined as low-order MBN. Further linear correlations of feature distances between ROIs were calculated on the basis of low-order MBN to generate individual high-order MBN. The Kruskal's algorithm was used to generate the MST to obtain the core framework of individual low-order and high-order MBN. The regional and global properties of the individual MSTs were defined as the feature. The support vector machine and back-propagation neural network was used to diagnose MDD and assess its severity, respectively. RESULT The low-order and high-order MST-MBN constructed by cityblock distance had the excellent classification performance. The high-order MST-MBN significantly improved almost 20 % diagnostic accuracy compared with the low-order MST-MBN, and had a maximum R2 value of 0.939 between the predictive and true Hamilton Depression Scale score. The different group-level connectivity strength mainly involves the central executive network and default mode network (no statistical significance after FDR correction). CONCLUSION We proposed an innovative individual high-order MST-MBN to capture the cortical high-order morphological correlation and make an excellent performance for individualized diagnosis and assessment of MDD.
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Affiliation(s)
- Yuna Li
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, PR China
| | - Tongpeng Chu
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing 100070, PR China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Fanghui Dong
- School of Medical Imaging, Binzhou Medical University, Yantai, Shandong 264000, PR China
| | - Qun Gai
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Yinghong Shi
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Heng Ma
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China
| | - Feng Zhao
- Compute Science and Technology, Shandong Technology and Business University Yantai, Shandong 264000, PR China
| | - Kaili Che
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China; Big data & Artificial Intelligence Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
| | - Haizhu Xie
- Department of Radiology, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong 264000, PR China.
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Estimating high-order brain functional networks by correlation-preserving embedding. Med Biol Eng Comput 2022; 60:2813-2823. [DOI: 10.1007/s11517-022-02628-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Accepted: 06/07/2022] [Indexed: 11/26/2022]
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Zhao F, Pan H, Li N, Chen X, Zhang H, Mao N, Ren Y. High-order brain functional network for electroencephalography-based diagnosis of major depressive disorder. Front Neurosci 2022; 16:976229. [PMID: 36017184 PMCID: PMC9396245 DOI: 10.3389/fnins.2022.976229] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/15/2022] [Indexed: 12/04/2022] Open
Abstract
Brain functional network (BFN) based on electroencephalography (EEG) has been widely used to diagnose brain diseases, such as major depressive disorder (MDD). However, most existing BFNs only consider the correlation between two channels, ignoring the high-level interaction among multiple channels that contain more rich information for diagnosing brain diseases. In such a sense, the BFN is called low-order BFN (LO-BFN). In order to fully explore the high-level interactive information among multiple channels of the EEG signals, a scheme for constructing a high-order BFN (HO-BFN) based on the “correlation’s correlation” strategy is proposed in this paper. Specifically, the entire EEG time series is firstly divided into multiple epochs by sliding window. For each epoch, the short-term correlation between channels is calculated to construct a LO-BFN. The correlation time series of all channel pairs are formulated by these LO-BFNs obtained from all epochs to describe the dynamic change of short-term correlation along the time. To construct HO-BFN, we cluster all correlation time series to avoid the problems caused by high dimensionality, and the correlation of the average correlation time series from different clusters is calculated to reflect the high-order correlation among multiple channels. Experimental results demonstrate the efficiency of the proposed HO-BFN in MDD identification, and its integration with the LO-BFN can further improve the recognition rate.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Hongxin Pan
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Na Li
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China
| | - Haicheng Zhang
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, China
| | - Yande Ren
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, China
- *Correspondence: Yande Ren,
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Neuroimaging Modalities in Alzheimer’s Disease: Diagnosis and Clinical Features. Int J Mol Sci 2022; 23:ijms23116079. [PMID: 35682758 PMCID: PMC9181385 DOI: 10.3390/ijms23116079] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Revised: 05/25/2022] [Accepted: 05/26/2022] [Indexed: 11/17/2022] Open
Abstract
Alzheimer’s disease (AD) is a neurodegenerative disease causing progressive cognitive decline until eventual death. AD affects millions of individuals worldwide in the absence of effective treatment options, and its clinical causes are still uncertain. The onset of dementia symptoms indicates severe neurodegeneration has already taken place. Therefore, AD diagnosis at an early stage is essential as it results in more effective therapy to slow its progression. The current clinical diagnosis of AD relies on mental examinations and brain imaging to determine whether patients meet diagnostic criteria, and biomedical research focuses on finding associated biomarkers by using neuroimaging techniques. Multiple clinical brain imaging modalities emerged as potential techniques to study AD, showing a range of capacity in their preciseness to identify the disease. This review presents the advantages and limitations of brain imaging modalities for AD diagnosis and discusses their clinical value.
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10
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Hybrid High-order Brain Functional Networks for Schizophrenia-Aided Diagnosis. Cognit Comput 2022. [DOI: 10.1007/s12559-022-10014-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
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11
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An autism spectrum disorder adaptive identification based on the Elimination of brain connections: a proof of long-range underconnectivity. Soft comput 2022. [DOI: 10.1007/s00500-022-06890-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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12
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Li L, Jiang H, Wen G, Cao P, Xu M, Liu X, Yang J, Zaiane O. TE-HI-GCN: An Ensemble of Transfer Hierarchical Graph Convolutional Networks for Disorder Diagnosis. Neuroinformatics 2021; 20:353-375. [PMID: 34761367 DOI: 10.1007/s12021-021-09548-1] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/14/2021] [Indexed: 11/25/2022]
Abstract
Accurate diagnosis of psychiatric disorders plays a critical role in improving the quality of life for patients and potentially supports the development of new treatments. Graph convolutional networks (GCNs) are shown to be successful in modeling applications with graph structures. However, training an accurate GCNs model for brain networks faces several challenges, including high dimensional and noisy correlation in the brain networks, limited labeled training data, and depth limitation of GCN learning. Generalization and interpretability are important in developing predictive models for clinical diagnosis. To address these challenges, we proposed an ensemble framework involving hierarchical GCN and transfer learning for sparse brain networks, which allows GCN to capture the intrinsic correlation among the subjects and domains, to improve the network embedding learning for disease diagnosis. Extensive experiments on two real medical clinical applications: diagnosis of Autism spectrum disorder (ASD) and diagnosis of Alzheimer's disease (AD) on both the ADNI and ABIDE databases, showing the effectiveness of the proposed framework. We achieved state-of-the-art accuracy and AUC for AD/MCI and ASD/NC (Normal control) classification in comparison with studies that used functional connectivity as features or GCN models. The proposed TE-HI-GCN model achieves the best classification performance, leading to about 27.93% (31.38%) improvement for ASD and 16.86% (44.50%) for AD in terms of accuracy and AUC compared with the traditional GCN model. Moreover, the obtained clustering results show high correspondence with the previous neuroimaging derived evidence of within and between-networks biomarkers for ASD. The discovered subnetworks are used as evidence for the proposed TE-HI-GCN model. Furthermore, this work is the first attempt of transfer learning on the two related disorder domains to uncover the correlation among the two diseases with a transfer learning scheme.
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Affiliation(s)
- Lanting Li
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Hao Jiang
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Guangqi Wen
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China.
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
| | - Mingyi Xu
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Xiaoli Liu
- Department of Chemical and Biomolecular Engineering, Faculty of Engineering, National University of Singapore, Singapore, Singapore
| | - Jinzhu Yang
- Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Amii, University of Alberta, Edmonton, Alberta, Canada
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Zhao F, Zhang X, Thung KH, Mao N, Lee SW, Shen D. Constructing Multi-view High-order Functional Connectivity Networks for Diagnosis of Autism Spectrum Disorder. IEEE Trans Biomed Eng 2021; 69:1237-1250. [PMID: 34705632 DOI: 10.1109/tbme.2021.3122813] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
Brain functional connectivity network (FCN) based on resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used to identify neuropsychiatric disorders such as autism spectrum disorder (ASD). Most existing FCN-based methods only estimate the correlation between brain regions of in terest (ROIs), without exploring more informative higher-level inter actions among multiple ROIs which could be beneficial to disease diagnosis. To fully explore the discriminative information provided by different brain networks, a cluster-based multi-view high-order FCN (Ho-FCN) framework is proposed in this paper. Specifically, we first group the functional connectivity (FC) time series into different clusters and compute the multi-order central moment series for the FC time series in each cluster. Then we utilize the correlation of central moment series between different clusters to reveal the high-order FC relationships among multiple ROIs. In addition, to address the phase mismatch issue in conventional FCNs, we also adopt the central moments of the correlation time series as the temporal-invariance features to capture the dynamic characteristics of low-order dynamic FCN (Lo-D-FCN). Experimental results on the ABIDE dataset validate that: 1) the proposed multi-view Ho-FCNs is able to explore rich discriminative information for ASD diagnosis; 2) the phase mismatch issue can be well circumvented by using central moments; and 3) the combination of different types of FCNs can significantly improve the diagnostic accuracy of ASD (86.2%).
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14
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Guo T, Zhang Y, Xue Y, Qiao L, Shen D. Brain Function Network: Higher Order vs. More Discrimination. Front Neurosci 2021; 15:696639. [PMID: 34497485 PMCID: PMC8419271 DOI: 10.3389/fnins.2021.696639] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Accepted: 07/26/2021] [Indexed: 12/20/2022] Open
Abstract
Brain functional network (BFN) has become an increasingly important tool to explore individual differences and identify neurological/mental diseases. For estimating a "good" BFN (with more discriminative information for example), researchers have developed various methods, in which the most popular and simplest is Pearson's correlation (PC). Despite its empirical effectiveness, PC only encodes the low-order (second-order) statistics between brain regions. To model high-order statistics, researchers recently proposed to estimate BFN by conducting two sequential PCs (denoted as PC 2 in this paper), and found that PC 2-based BFN can provide additional information for group difference analysis. This inspires us to think about (1) what will happen if continuing the correlation operation to construct much higher-order BFN by PC n (n>2), and (2) whether the higher-order correlation will result in stronger discriminative ability. To answer these questions, we use PC n -based BFNs to predict individual differences (Female vs. Male) as well as identify subjects with mild cognitive impairment (MCI) from healthy controls (HCs). Through experiments, we have the following findings: (1) with the increase of n, the discriminative ability of PC n -based BFNs tends to decrease; (2) fusing the PC n -based BFNs (n>1) with the PC 1-based BFN can generally improve the sensitivity for MCI identification, but fail to help the classification accuracy. In addition, we empirically find that the sequence of BFN adjacency matrices estimated by PC n (n = 1,2,3,⋯ ) will converge to a binary matrix with elements of ± 1.
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Affiliation(s)
- Tingting Guo
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yining Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Yanfang Xue
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
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15
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Xie Q, Zhang X, Rekik I, Chen X, Mao N, Shen D, Zhao F. Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder. PeerJ 2021; 9:e11692. [PMID: 34268010 PMCID: PMC8269664 DOI: 10.7717/peerj.11692] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 06/08/2021] [Indexed: 01/23/2023] Open
Abstract
The sliding-window-based dynamic functional connectivity network (D-FCN) has been becoming an increasingly useful tool for understanding the changes of brain connectivity patterns and the association of neurological diseases with these dynamic variations. However, conventional D-FCN is essentially low-order network, which only reflects the pairwise interaction pattern between brain regions and thus overlooking the high-order interactions among multiple brain regions. In addition, D-FCN is innate with temporal sensitivity issue, i.e., D-FCN is sensitive to the chronological order of its subnetworks. To deal with the above issues, we propose a novel high-order functional connectivity network framework based on the central moment feature of D-FCN. Specifically, we firstly adopt a central moment approach to extract multiple central moment feature matrices from D-FCN. Furthermore, we regard the matrices as the profiles to build multiple high-order functional connectivity networks which further capture the higher level and more complex interaction relationships among multiple brain regions. Finally, we use the voting strategy to combine the high-order networks with D-FCN for autism spectrum disorder diagnosis. Experimental results show that the combination of multiple functional connectivity networks achieves accuracy of 88.06%, and the best single network achieves accuracy of 79.5%.
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Affiliation(s)
- Qingsong Xie
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Xiangfei Zhang
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Islem Rekik
- School of Science and Engineering, Computing, University of Dundee, Dundee, Dundee, United Kingdom.,BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Istanbul, Turkey
| | - Xiaobo Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
| | - Ning Mao
- Department of Radiology, Yantai Yuhuangding Hospital, Yantai, Shandong, China
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai, China.,Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China.,Department of Artificial Intelligence, Korea University, Seoul, South Korea
| | - Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, Shandong, China
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16
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Jiang H, Cao P, Xu M, Yang J, Zaiane O. Hi-GCN: A hierarchical graph convolution network for graph embedding learning of brain network and brain disorders prediction. Comput Biol Med 2020; 127:104096. [DOI: 10.1016/j.compbiomed.2020.104096] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/24/2020] [Accepted: 10/24/2020] [Indexed: 02/06/2023]
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17
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Li Y, Liu J, Tang Z, Lei B. Deep Spatial-Temporal Feature Fusion From Adaptive Dynamic Functional Connectivity for MCI Identification. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:2818-2830. [PMID: 32112678 DOI: 10.1109/tmi.2020.2976825] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Dynamic functional connectivity (dFC) analysis using resting-state functional Magnetic Resonance Imaging (rs-fMRI) is currently an advanced technique for capturing the dynamic changes of neural activities in brain disease identification. Most existing dFC modeling methods extract dynamic interaction information by using the sliding window-based correlation, whose performance is very sensitive to window parameters. Because few studies can convincingly identify the optimal combination of window parameters, sliding window-based correlation may not be the optimal way to capture the temporal variability of brain activity. In this paper, we propose a novel adaptive dFC model, aided by a deep spatial-temporal feature fusion method, for mild cognitive impairment (MCI) identification. Specifically, we adopt an adaptive Ultra-weighted-lasso recursive least squares algorithm to estimate the adaptive dFC, which effectively alleviates the problem of parameter optimization. Then, we extract temporal and spatial features from the adaptive dFC. In order to generate coarser multi-domain representations for subsequent classification, the temporal and spatial features are further mapped into comprehensive fused features with a deep feature fusion method. Experimental results show that the classification accuracy of our proposed method is reached to 87.7%, which is at least 5.5% improvement than the state-of-the-art methods. These results elucidate the superiority of the proposed method for MCI classification, indicating its effectiveness in the early identification of brain abnormalities.
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18
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Zhao F, Chen Z, Rekik I, Lee SW, Shen D. Diagnosis of Autism Spectrum Disorder Using Central-Moment Features From Low- and High-Order Dynamic Resting-State Functional Connectivity Networks. Front Neurosci 2020; 14:258. [PMID: 32410930 PMCID: PMC7198826 DOI: 10.3389/fnins.2020.00258] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2019] [Accepted: 03/09/2020] [Indexed: 01/06/2023] Open
Abstract
The sliding-window-based dynamic functional connectivity networks (D-FCNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) are effective methods for diagnosing various neurological diseases, including autism spectrum disorder (ASD). However, traditional D-FCNs are low-order networks based on pairwise correlation between brain regions, thus overlooking high-level interactions across multiple regions of interest (ROIs). Moreover, D-FCNs suffer from the temporal mismatching issue, i.e., subnetworks in the same temporal window do not have temporal correspondence across different subjects. To address the above problems, we first construct a novel high-order D-FCNs based on the principle of “correlation’s correlation” to further explore the higher level and more complex interaction relationships among multiple ROIs. Furthermore, we propose to use a central-moment method to extract temporal-invariance properties contained in either low- or high-order D-FCNs. Finally, we design and train an ensemble classifier by fusing the features extracted from conventional FCN, low-order D-FCNs, and high-order D-FCNs for the diagnosis of ASD and normal control subjects. Our method achieved the best ASD classification accuracy (83%), and our results revealed the features extracted from different networks fingerprinting the autistic brain at different connectional levels.
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Affiliation(s)
- Feng Zhao
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.,Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Zhiyuan Chen
- School of Computer Science and Technology, Shandong Technology and Business University, Yantai, China.,Shandong Co-Innovation Center of Future Intelligent Computing, Yantai, China
| | - Islem Rekik
- BASIRA Lab, CVIP Group, Computing, School of Science and Engineering, University of Dundee, Dundee, United Kingdom
| | - Seong-Whan Lee
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Central, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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19
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Cui X, Xiao J, Guo H, Wang B, Li D, Niu Y, Xiang J, Chen J. Clustering of Brain Function Network Based on Attribute and Structural Information and Its Application in Brain Diseases. Front Neuroinform 2020; 13:79. [PMID: 32116624 PMCID: PMC7020566 DOI: 10.3389/fninf.2019.00079] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2019] [Accepted: 12/24/2019] [Indexed: 12/02/2022] Open
Abstract
At present, the diagnosis of brain disease is mainly based on the self-reported symptoms and clinical signs of the patient, which can easily lead to psychiatrists' bias. The purpose of this study is to develop a brain network clustering model to accurately identify brain diseases based on resting state functional magnetic resonance imaging (fMRI) in the absence of clinical information. We use cosine similarity and sub-network kernels to measure attribute similarity and structure similarity, respectively. By integrating the structure similarity and attribute similarity into one matrix, spectral clustering is used to achieve brain network clustering. Finally, we evaluate this method on three diseases: Alzheimer's disease, Bipolar disorder patients, and Schizophrenia. The performance of methods is evaluated by measuring clustering consistency. Clustering consistency is similar to clustering accuracy, which is used to evaluate the consistency between the clustering labels and clinical diagnostic labels of the subjects. The experimental results show that our proposed method can significantly improve clustering performance, with a consistency of 60.6% for Alzheimer's disease, with a consistency of 100% for Schizophrenia, with a consistency of 100% for Bipolar disorder patients.
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Affiliation(s)
| | | | | | | | | | | | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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20
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Resting-State Functional Network Scale Effects and Statistical Significance-Based Feature Selection in Machine Learning Classification. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2019; 2019:9108108. [PMID: 31781290 PMCID: PMC6875180 DOI: 10.1155/2019/9108108] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/18/2019] [Revised: 08/04/2019] [Accepted: 09/06/2019] [Indexed: 12/17/2022]
Abstract
In recent years, functional brain network topological features have been widely used as classification features. Previous studies have found that network node scale differences caused by different network parcellation definitions significantly affect the structure of the constructed network and its topological properties. However, we still do not know how network scale differences affect the classification accuracy, performance of classification features, and effectiveness of the feature selection strategy using P values in terms of the machine learning method. This study used five scale parcellations, involving 90, 256, 497, 1003, and 1501 nodes. Three local properties of resting-state functional brain networks were selected (degree, betweenness centrality, and nodal efficiency), and the support vector machine method was used to construct classifiers to identify patients with major depressive disorder. We analyzed the impact of the five scales on classification accuracy. In addition, the effectiveness and redundancy of features obtained by the different scale parcellations were compared. Finally, traditional statistical significance (P value) was verified as a feature selection criterion. The results showed that the feature effectiveness of different scales was similar; in other words, parcellation with more regions did not provide more effective discriminative features. Nevertheless, parcellation with more regions did provide a greater quantity of discriminative features, which led to an improvement in the accuracy of the classification. However, due to the close distance between brain regions, the redundancy of parcellation with more regions was also greater. The traditional P value feature selection strategy is feasible with different scales, but our analysis showed that the traditional P < 0.05 threshold was too strict for feature selection. This study provides an important reference for the selection of network scales when applying topological properties of brain networks to machine learning methods.
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21
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Identification of Alzheimer’s Disease on the Basis of a Voxel-Wise Approach. APPLIED SCIENCES-BASEL 2019. [DOI: 10.3390/app9153063] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Robust prediction of Alzheimer’s disease (AD) helps in the early diagnosis of AD and may support the treatment of AD patients. In this study, for early detection of AD and prediction of mild cognitive impairment (MCI) conversion, we develop an automatic computer-aided diagnosis (CAD) framework based on a merit-based feature selection method through a whole-brain voxel-wise analysis using baseline magnetic resonance imaging (MRI) data. We also explore the impact of different MRI spatial resolution on the voxel-wise metric AD classification and MCI conversion prediction. We assessed the proposed CAD framework using the whole-brain voxel-wise MRI features of 507 J-ADNI participants (146 healthy controls [HCs], 102 individuals with stable MCI [sMCI], 112 with progressive MCI [pMCI], and 147 with AD) among four clinically relevant pairs of diagnostic groups at different imaging resolutions (i.e., 2, 4, 8, and 16 mm). Using a support vector machine classifier through a 10-fold cross-validation strategy at a spatial resolution of 2 mm, the proposed CAD framework yielded classification accuracies of 91.13%, 74.77%, 81.12%, and 81.78% in identifying AD/healthy control, sMCI/pMCI, sMCI/AD, and pMCI/HC, respectively. The experimental results show that a lower spatial resolution (i.e., 2 mm) may provide more robust information to trace the neuronal loss-related brain atrophy in AD.
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22
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Filippi M, Spinelli EG, Cividini C, Agosta F. Resting State Dynamic Functional Connectivity in Neurodegenerative Conditions: A Review of Magnetic Resonance Imaging Findings. Front Neurosci 2019; 13:657. [PMID: 31281241 PMCID: PMC6596427 DOI: 10.3389/fnins.2019.00657] [Citation(s) in RCA: 66] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 06/07/2019] [Indexed: 12/12/2022] Open
Abstract
In the last few decades, brain functional connectivity (FC) has been extensively assessed using resting-state functional magnetic resonance imaging (RS-fMRI), which is able to identify temporally correlated brain regions known as RS functional networks. Fundamental insights into the pathophysiology of several neurodegenerative conditions have been provided by studies in this field. However, most of these studies are based on the assumption of temporal stationarity of RS functional networks, despite recent evidence suggests that the spatial patterns of RS networks may change periodically over the time of an fMRI scan acquisition. For this reason, dynamic functional connectivity (dFC) analysis has been recently implemented and proposed in order to consider the temporal fluctuations of FC. These approaches hold promise to provide fundamental information for the identification of pathophysiological and diagnostic markers in the vast field of neurodegenerative diseases. This review summarizes the main currently available approaches for dFC analysis and reports their recent applications for the assessment of the most common neurodegenerative conditions, including Alzheimer’s disease, Parkinson’s disease, dementia with Lewy bodies, and frontotemporal dementia. Critical state-of-the-art findings, limitations, and future perspectives regarding the analysis of dFC in these diseases are provided from both a clinical and a technical point of view.
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Affiliation(s)
- Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
| | - Edoardo G Spinelli
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Camilla Cividini
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Federica Agosta
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy.,Vita-Salute San Raffaele University, Milan, Italy
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23
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Fu Z, Caprihan A, Chen J, Du Y, Adair JC, Sui J, Rosenberg GA, Calhoun VD. Altered static and dynamic functional network connectivity in Alzheimer's disease and subcortical ischemic vascular disease: shared and specific brain connectivity abnormalities. Hum Brain Mapp 2019; 40:3203-3221. [PMID: 30950567 DOI: 10.1002/hbm.24591] [Citation(s) in RCA: 87] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Revised: 03/19/2019] [Accepted: 03/23/2019] [Indexed: 12/16/2022] Open
Abstract
Subcortical ischemic vascular disease (SIVD) is a major subtype of vascular dementia with features that overlap clinically with Alzheimer's disease (AD), confounding diagnosis. Neuroimaging is a more specific and biologically based approach for detecting brain changes and thus may help to distinguish these diseases. There is still a lack of knowledge regarding the shared and specific functional brain abnormalities, especially functional connectivity changes in relation to AD and SIVD. In this study, we investigated both static functional network connectivity (sFNC) and dynamic FNC (dFNC) between 54 intrinsic connectivity networks in 19 AD patients, 19 SIVD patients, and 38 age-matched healthy controls. The results show that both patient groups have increased sFNC between the visual and cerebellar (CB) domains but decreased sFNC between the cognitive-control and CB domains. SIVD has specifically decreased sFNC within the sensorimotor domain while AD has specifically altered sFNC between the default-mode and CB domains. In addition, SIVD has more occurrences and a longer dwell time in the weakly connected dFNC states, but with fewer occurrences and a shorter dwell time in the strongly connected dFNC states. AD has both similar and opposite changes in certain dynamic features. More importantly, the dynamic features are found to be associated with cognitive performance. Our findings highlight similar and distinct functional connectivity alterations in AD and SIVD from both static and dynamic perspectives and indicate dFNC to be a more important biomarker for dementia since its progressively altered patterns can better track cognitive impairment in AD and SIVD.
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Affiliation(s)
- Zening Fu
- The Mind Research Network, Albuquerque, New Mexico
| | | | - Jiayu Chen
- The Mind Research Network, Albuquerque, New Mexico
| | - Yuhui Du
- The Mind Research Network, Albuquerque, New Mexico.,School of Computer and Information Technology, Shanxi University, Taiyuan, China
| | - John C Adair
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Jing Sui
- The Mind Research Network, Albuquerque, New Mexico.,Chinese Academy of Sciences (CAS), Centre for Excellence in Brain Science and Intelligence Technology, University of Chinese Academy of Sciences, Beijing, China
| | - Gary A Rosenberg
- Department of Neurology, University of New Mexico Health Sciences Center, Albuquerque, New Mexico
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, New Mexico.,Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, New Mexico
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24
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Saba V, Premi E, Cristillo V, Gazzina S, Palluzzi F, Zanetti O, Gasparotti R, Padovani A, Borroni B, Grassi M. Brain Connectivity and Information-Flow Breakdown Revealed by a Minimum Spanning Tree-Based Analysis of MRI Data in Behavioral Variant Frontotemporal Dementia. Front Neurosci 2019; 13:211. [PMID: 30930736 PMCID: PMC6427927 DOI: 10.3389/fnins.2019.00211] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Accepted: 02/25/2019] [Indexed: 12/12/2022] Open
Abstract
Brain functional disruption and cognitive shortfalls as consequences of neurodegeneration are among the most investigated aspects in current clinical research. Traditionally, specific anatomical and behavioral traits have been associated with neurodegeneration, thus directly translatable in clinical terms. However, these qualitative traits, do not account for the extensive information flow breakdown within the functional brain network that deeply affect cognitive skills. Behavioural variant Frontotemporal Dementia (bvFTD) is a neurodegenerative disorder characterized by behavioral and executive functions disturbances. Deviations from the physiological cognitive functioning can be accurately inferred and modeled from functional connectivity alterations. Although the need for unbiased metrics is still an open issue in imaging studies, the graph-theory approach applied to neuroimaging techniques is becoming popular in the study of brain dysfunction. In this work, we assessed the global connectivity and topological alterations among brain regions in bvFTD patients using a minimum spanning tree (MST) based analysis of resting state functional MRI (rs-fMRI) data. Whilst several graph theoretical methods require arbitrary criteria (including the choice of network construction thresholds and weight normalization methods), MST is an unambiguous modeling solution, ensuring accuracy, robustness, and reproducibility. MST networks of 116 regions of interest (ROIs) were built on wavelet correlation matrices, extracted from 41 bvFTD patients and 39 healthy controls (HC). We observed a global fragmentation of the functional network backbone with severe disruption of information-flow highways. Frontotemporal areas were less compact, more isolated, and concentrated in less integrated structures, respect to healthy subjects. Our results reflected such complex breakdown of the frontal and temporal areas at both intra-regional and long-range connections. Our findings highlighted that MST, in conjunction with rs-fMRI data, was an effective method for quantifying and detecting functional brain network impairments, leading to characteristic bvFTD cognitive, social, and executive functions disorders.
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Affiliation(s)
- Valentina Saba
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Enrico Premi
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Viviana Cristillo
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Stefano Gazzina
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Fernando Palluzzi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Orazio Zanetti
- Alzheimer's Research Unit, IRCCS Fatebenefratelli, Brescia, Italy
| | | | - Alessandro Padovani
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Barbara Borroni
- Neurology Unit, Department of Clinical and Experimental Sciences, Centre for Neurodegenerative Disorders, University of Brescia, Brescia, Italy
| | - Mario Grassi
- Medical and Genomic Statistics Unit, Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
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25
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Zhou Y, Zhang L, Teng S, Qiao L, Shen D. Improving Sparsity and Modularity of High-Order Functional Connectivity Networks for MCI and ASD Identification. Front Neurosci 2018; 12:959. [PMID: 30618582 PMCID: PMC6305547 DOI: 10.3389/fnins.2018.00959] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 12/03/2018] [Indexed: 01/01/2023] Open
Abstract
High-order correlation has recently been proposed to model brain functional connectivity network (FCN) for identifying neurological disorders, such as mild cognitive impairment (MCI) and autism spectrum disorder (ASD). In practice, the high-order FCN (HoFCN) can be derived from multiple low-order FCNs that are estimated separately in a series of sliding windows, and thus it in fact provides a way of integrating dynamic information encoded in a sequence of low-order FCNs. However, the estimation of low-order FCN may be unreliable due to the fact that the use of limited volumes/samples in a sliding window can significantly reduce the statistical power, which in turn affects the reliability of the resulted HoFCN. To address this issue, we propose to enhance HoFCN based on a regularized learning framework. More specifically, we first calculate an initial HoFCN using a recently developed method based on maximum likelihood estimation. Then, we learn an optimal neighborhood network of the initially estimated HoFCN with sparsity and modularity priors as regularizers. Finally, based on the improved HoFCNs, we conduct experiments to identify MCI and ASD patients from their corresponding normal controls. Experimental results show that the proposed methods outperform the baseline methods, and the improved HoFCNs with modularity prior consistently achieve the best performance.
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Affiliation(s)
- Yueying Zhou
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Limei Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Shenghua Teng
- College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China.,College of Electronic and Information Engineering, Shandong University of Science and Technology, Qingdao, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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26
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Cui X, Xiang J, Wang B, Xiao J, Niu Y, Chen J. Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment. Front Neurosci 2018; 12:701. [PMID: 30349451 PMCID: PMC6186843 DOI: 10.3389/fnins.2018.00701] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/18/2018] [Indexed: 02/03/2023] Open
Abstract
Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI.
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Affiliation(s)
- Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Bin Wang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jihai Xiao
- Center of Information Management and Development, Taiyuan University of Technology, Taiyuan, China
| | - Yan Niu
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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27
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An Efficient Mixed Integer Linear Programming Model for the Minimum Spanning Tree Problem. MATHEMATICS 2018. [DOI: 10.3390/math6100183] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Finding a minimum spanning tree in a given network is a famous combinatorial optimization problem that appears in different engineering applications. Even though this problem is solvable in polynomial time, having efficient mathematical programming models is important as they can provide insights for formulating larger models that integrate other decisions in more complex applications. In the literature, there are ten different integer and mixed integer linear programming (MILP) models for this problem. They are variants of set packing, cuts, network flow and node level formulations. In addition, this paper introduces an efficient node level MILP model. Comparisons for the eleven models are provided. First, the models are compared in terms of the number of decision variables and the number of constraints. Then, computational comparisons using a commercial MILP solver on sets of randomly generated instances of different sizes are conducted. Results provide evidence that the proposed MILP model is competitive in terms of the computational time needed for proving optimality of generated solutions for instances with up to 50 nodes. Meanwhile, the LP relaxation of a multi-commodity flow MILP model which has integer polyhedron provides stable computational times despite its larger size.
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Li Y, Liu J, Huang J, Li Z, Liang P. Learning Brain Connectivity Sub-networks by Group- Constrained Sparse Inverse Covariance Estimation for Alzheimer's Disease Classification. Front Neuroinform 2018; 12:58. [PMID: 30258358 PMCID: PMC6143825 DOI: 10.3389/fninf.2018.00058] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2018] [Accepted: 08/17/2018] [Indexed: 11/15/2022] Open
Abstract
Background/Aims: Brain functional connectivity networks constructed from resting-state functional magnetic resonance imaging (rs-fMRI) have been widely used for classifying Alzheimer's disease (AD) from normal controls (NC). However, conventional correlation analysis methods only capture the pairwise information, which may not be capable of revealing an adequate and accurate functional connectivity relationship among brain regions in the whole brain. Additionally, the non-sparse connectivity networks commonly contain a large number of spurious or insignificant connections, which are inconsistent with the sparse connectivity of actual brain networks in nature and may deteriorate the classification performance of Alzheimer's disease. Methods: To address these problems, in this paper, a new classification framework is proposed by combining the Group-constrained topology structure detection with sparse inverse covariance estimation (SICE) method to build the functional brain sub-network for each brain region. Particularly, to tune the sensitive analysis of the regularized parameters in the SICE method, a nested leave-one-out cross-validation (LOOCV) method is adopted. Sparse functional connectivity networks are thus effectively constructed by using the optimal regularized parameters. Finally, a decision classification tree (DCT) classifier is trained for classifying AD from NC based on these optimal functional brain sub-networks. The convergence performance of our proposed method is furthermore evaluated by the trend of coefficient variation. Results: Experiment results indicate that a LOOCV classification accuracy of 81.82% with a sensitivity of 80.00%, and a specificity of 83.33% can be obtained by using the proposed method for the classification AD from NC, and outperforms the most state-of-the-art methods in terms of the classification accuracy. Additionally, the experiment results of the convergence performance further suggest that our proposed scheme has a high rate of convergence. Particularly, the abnormal brain regions and functional connections identified by our proposed framework are highly associated with the underpinning pathological mechanism of the AD, which are consistent with previous studies. Conclusion: These results have demonstrated the effectiveness of the proposed Group- constrained SICE method, and are capable of clinical value to the diagnosis of Alzheimer's disease.
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Affiliation(s)
- Yang Li
- School of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, China.,Beijing Advanced Innovation Center for Big Date-Based Precision Medicine, Beihang University, Beijing, China.,Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian, China
| | - Jingyu Liu
- School of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China
| | - Jie Huang
- School of Automation Sciences and Electrical Engineering, Beihang University, Beijing, China
| | - Zuoyong Li
- Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Minjiang University, Fujian, China
| | - Peipeng Liang
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.,School of Psychology, Capital Normal University, Beijing, China
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Du Y, Fu Z, Calhoun VD. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front Neurosci 2018; 12:525. [PMID: 30127711 PMCID: PMC6088208 DOI: 10.3389/fnins.2018.00525] [Citation(s) in RCA: 166] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022] Open
Abstract
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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Jarrell JT, Gao L, Cohen DS, Huang X. Network Medicine for Alzheimer's Disease and Traditional Chinese Medicine. Molecules 2018; 23:molecules23051143. [PMID: 29751596 PMCID: PMC6099497 DOI: 10.3390/molecules23051143] [Citation(s) in RCA: 45] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2018] [Revised: 05/07/2018] [Accepted: 05/09/2018] [Indexed: 12/20/2022] Open
Abstract
Alzheimer’s Disease (AD) is a neurodegenerative condition that currently has no known cure. The principles of the expanding field of network medicine (NM) have recently been applied to AD research. The main principle of NM proposes that diseases are much more complicated than one mutation in one gene, and incorporate different genes, connections between genes, and pathways that may include multiple diseases to create full scale disease networks. AD research findings as a result of the application of NM principles have suggested that functional network connectivity, myelination, myeloid cells, and genes and pathways may play an integral role in AD progression, and may be integral to the search for a cure. Different aspects of the AD pathology could be potential targets for drug therapy to slow down or stop the disease from advancing, but more research is needed to reach definitive conclusions. Additionally, the holistic approaches of network pharmacology in traditional Chinese medicine (TCM) research may be viable options for the AD treatment, and may lead to an effective cure for AD in the future.
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Affiliation(s)
- Juliet T Jarrell
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
| | - Li Gao
- Modern Research Center for Traditional Chinese Medicine, Shanxi University, Taiyuan 030006, China.
| | - David S Cohen
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
| | - Xudong Huang
- Neurochemistry Laboratory, Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA.
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Cui X, Xiang J, Guo H, Yin G, Zhang H, Lan F, Chen J. Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Normal Controls With Subnetwork Selection and Graph Kernel Principal Component Analysis Based on Minimum Spanning Tree Brain Functional Network. Front Comput Neurosci 2018; 12:31. [PMID: 29867424 PMCID: PMC5954113 DOI: 10.3389/fncom.2018.00031] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2017] [Accepted: 04/24/2018] [Indexed: 12/26/2022] Open
Abstract
Effective and accurate diagnosis of Alzheimer's disease (AD), as well as its early stage (mild cognitive impairment, MCI), has attracted more and more attention recently. Researchers have constructed threshold brain function networks and extracted various features for the classification of brain diseases. However, in the construction of the brain function network, the selection of threshold is very important, and the unreasonable setting will seriously affect the final classification results. To address this issue, in this paper, we propose a minimum spanning tree (MST) classification framework to identify Alzheimer's disease (AD), MCI, and normal controls (NCs). The proposed method mainly uses the MST method, graph-based Substructure Pattern mining (gSpan), and graph kernel Principal Component Analysis (graph kernel PCA). Specifically, MST is used to construct the brain functional connectivity network; gSpan, to extract features; and subnetwork selection and graph kernel PCA, to select features. Finally, the support vector machine is used to perform classification. We evaluate our method on MST brain functional networks of 21 AD, 25 MCI, and 22 NC subjects. The experimental results show that our proposed method achieves classification accuracy of 98.3, 91.3, and 77.3%, for MCI vs. NC, AD vs. NC, and AD vs. MCI, respectively. The results show our proposed method can achieve significantly improved classification performance compared to other state-of-the-art methods.
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Affiliation(s)
- Xiaohong Cui
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Jie Xiang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Hao Guo
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Guimei Yin
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.,Department of Computer Science and Technology, Taiyuan Normal University, Taiyuan, China
| | - Huijun Zhang
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China.,Department of Digital Media Technology, Communication University of Shanxi, Jinzhong, China
| | - Fangpeng Lan
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
| | - Junjie Chen
- College of Information and Computer, Taiyuan University of Technology, Taiyuan, China
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