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Zhu C, Li H, Song Z, Jiang M, Song L, Li L, Wang X, Zheng Q. Jointly constrained group sparse connectivity representation improves early diagnosis of Alzheimer's disease on routinely acquired T1-weighted imaging-based brain network. Health Inf Sci Syst 2024; 12:19. [PMID: 38464465 PMCID: PMC10917732 DOI: 10.1007/s13755-023-00269-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 12/27/2023] [Indexed: 03/12/2024] Open
Abstract
Background Radiomics-based morphological brain networks (radMBN) constructed from routinely acquired structural MRI (sMRI) data have gained attention in Alzheimer's disease (AD). However, the radMBN suffers from limited characterization of AD because sMRI only characterizes anatomical changes and is not a direct measure of neuronal pathology or brain activity. Purpose To establish a group sparse representation of the radMBN under a joint constraint of group-level white matter fiber connectivity and individual-level sMRI regional similarity (JCGS-radMBN). Methods Two publicly available datasets were adopted, including 120 subjects from ADNI with both T1-weighted image (T1WI) and diffusion MRI (dMRI) for JCGS-radMBN construction, 818 subjects from ADNI and 200 subjects solely with T1WI from AIBL for validation in early AD diagnosis. Specifically, the JCGS-radMBN was conducted by jointly estimating non-zero connections among subjects, with the regularization term constrained by group-level white matter fiber connectivity and individual-level sMRI regional similarity. Then, a triplet graph convolutional network was adopted for early AD diagnosis. The discriminative brain connections were identified using a two-sample t-test, and the neurobiological interpretation was validated by correlating the discriminative brain connections with cognitive scores. Results The JCGS-radMBN exhibited superior classification performance over five brain network construction methods. For the typical NC vs. AD classification, the JCGS-radMBN increased by 1-30% in accuracy over the alternatives on ADNI and AIBL. The discriminative brain connections exhibited a strong connectivity to hippocampus, parahippocampal gyrus, and basal ganglia, and had significant correlation with MMSE scores. Conclusion The proposed JCGS-radMBN facilitated the AD characterization of brain network established on routinely acquired imaging modality of sMRI. Supplementary Information The online version of this article (10.1007/s13755-023-00269-0) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Chuanzhen Zhu
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Honglun Li
- Departments of Medical Oncology and Radiology, Affiliated Yantai Yuhuangding Hospital of Qingdao University Medical College, Yantai, 264099 China
| | - Zhiwei Song
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Minbo Jiang
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Limei Song
- School of Medical Imaging, Weifang Medical University, Weifang, 261000 China
| | - Lin Li
- Yantaishan Hospital Affiliated to Binzhou Medical University, Yantai, 264003 China
| | - Xuan Wang
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
| | - Qiang Zheng
- School of Computer and Control Engineering, Yantai University, No 30, Qingquan Road, Laishan District, Yantai, 264005 Shandong China
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Litwińczuk MC, Muhlert N, Trujillo‐Barreto N, Woollams A. Impact of brain parcellation on prediction performance in models of cognition and demographics. Hum Brain Mapp 2024; 45:e26592. [PMID: 38339892 PMCID: PMC10831203 DOI: 10.1002/hbm.26592] [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: 06/21/2023] [Revised: 12/18/2023] [Accepted: 12/31/2023] [Indexed: 02/12/2024] Open
Abstract
Brain connectivity analysis begins with the selection of a parcellation scheme that will define brain regions as nodes of a network whose connections will be studied. Brain connectivity has already been used in predictive modelling of cognition, but it remains unclear if the resolution of the parcellation used can systematically impact the predictive model performance. In this work, structural, functional and combined connectivity were each defined with five different parcellation schemes. The resolution and modality of the parcellation schemes were varied. Each connectivity defined with each parcellation was used to predict individual differences in age, education, sex, executive function, self-regulation, language, encoding and sequence processing. It was found that low-resolution functional parcellation consistently performed above chance at producing generalisable models of both demographics and cognition. However, no single parcellation scheme showed a superior predictive performance across all cognitive domains and demographics. In addition, although parcellation schemes impacted the graph theory measures of each connectivity type (structural, functional and combined), these differences did not account for the out-of-sample predictive performance of the models. Taken together, these findings demonstrate that while high-resolution parcellations may be beneficial for modelling specific individual differences, partial voluming of signals produced by the higher resolution of the parcellation likely disrupts model generalisability.
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Affiliation(s)
| | - Nils Muhlert
- School of Health SciencesUniversity of ManchesterManchesterUK
| | | | - Anna Woollams
- School of Health SciencesUniversity of ManchesterManchesterUK
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3
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Song X, Chai L. Graph Signal Smoothness Based Feature Learning of Brain Functional Networks in Schizophrenia. IEEE Trans Neural Syst Rehabil Eng 2023; 31:3854-3863. [PMID: 37768796 DOI: 10.1109/tnsre.2023.3320135] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
In this paper we study the brain functional network of schizophrenic patients based on resting-state fMRI data. Different from the region of interest (ROI)-level brain networks that describe the connectivity between brain regions, this paper constructs a subject-level brain functional network that describes the similarity between subjects from a graph signal processing (GSP) perspective. Based on the subject graph, we introduce the concept of graph signal smoothness to analyze the abnormal brain regions (feature brain regions) in which schizophrenic patients produce abnormal functional connections and to quantitatively rank the degree of abnormality of brain regions. We find that in the patients' brain networks, many new connections appear and some common connections are strengthened. The feature brain regions can be easily found according to the value of connection differences. Finally, we validate the learned feature brain regions by the results of two types of statistical analyses (ROI-to-ROI analysis and seed-to-voxel analysis), and the feature brain regions derived from graph signal smoothness are indeed the brain regions with significant differences in the statistical analysis, which illustrates the potential of graph signal smoothness for use in quantitative analysis of brain networks.
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Hsieh TH, Shaw FZ, Kung CC, Liang SF. Seed correlation analysis based on brain region activation for ADHD diagnosis in a large-scale resting state data set. Front Hum Neurosci 2023; 17:1082722. [PMID: 37767136 PMCID: PMC10520784 DOI: 10.3389/fnhum.2023.1082722] [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: 10/28/2022] [Accepted: 08/04/2023] [Indexed: 09/29/2023] Open
Abstract
Background Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder of multifactorial pathogenesis, which is often accompanied by dysfunction in several brain functional connectivity. Resting-state functional MRI have been used in ADHD, and they have been proposed as a possible biomarker of diagnosis information. This study's primary aim was to offer an effective seed-correlation analysis procedure to investigate the possible biomarker within resting state brain networks as diagnosis information. Method Resting-state functional magnetic resonance imaging (rs-fMRI) data of 149 childhood ADHD were analyzed. In this study, we proposed a two-step hierarchical analysis method to extract functional connectivity features and evaluation by linear classifiers and random sampling validation. Result The data-driven method-ReHo provides four brain regions (mPFC, temporal pole, motor area, and putamen) with regional homogeneity differences as second-level seeds for analyzing functional connectivity differences between distant brain regions. The procedure reduces the difficulty of seed selection (location, shape, and size) in estimations of brain interconnections, improving the search for an effective seed; The features proposed in our study achieved a success rate of 83.24% in identifying ADHD patients through random sampling (saving 25% as the test set, while the remaining data was the training set) validation (using a simple linear classifier), surpassing the use of traditional seeds. Conclusion This preliminary study examines the feasibility of diagnosing ADHD by analyzing the resting-state fMRI data from the ADHD-200 NYU dataset. The data-driven model provides a precise way to find reliable seeds. Data-driven models offer precise methods for finding reliable seeds and are feasible across different datasets. Moreover, this phenomenon may reveal that using a data-driven approach to build a model specific to a single data set may be better than combining several data and creating a general model.
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Affiliation(s)
- Tsung-Hao Hsieh
- Department of Computer Science, Tunghai University, Taichung City, Taiwan
| | - Fu-Zen Shaw
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan
| | - Chun-Chia Kung
- Department of Psychology, National Cheng Kung University, Tainan, Taiwan
| | - Sheng-Fu Liang
- Department of Computer Science and Information, National Cheng Kung University, Tainan, Taiwan
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5
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, 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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7
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Li W, Ding S, Zhao G. Static and dynamic topological organization of brain functional connectome in acute mild traumatic brain injury. Acta Radiol 2023; 64:1175-1183. [PMID: 35765198 DOI: 10.1177/02841851221109897] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Prior studies have detected topological changes of brain functional networks in patients with acute mild traumatic brain injury (mTBI). However, the alterations of dynamic topological characteristics in mTBI have been scarcely elucidated. PURPOSE To evaluate static and dynamic functional connectivity topological networks in patients with acute mTBI using resting-state functional magnetic resonance imaging (fMRI). MATERIAL AND METHODS A total of 55 patients with acute mTBI and 55 age-, sex-, and education-matched healthy controls (HCs) were enrolled in this study. All participants underwent resting-state fMRI scans, and data were analyzed using graph-theory methods and a sliding window approach. Post-traumatic cognitive performance and resting-state fMRI data were collected within one week after injury. Static and dynamic functional connectivity patterns were determined by independent component analysis. Spearman's correlation analysis was further performed between fMRI changes and Montreal cognitive assessment (MoCA) scores. RESULTS Global efficiency was lower (P = 0.02), and local efficiency (P < 0.001) and mean Cp (P < 0.001) were higher in patients with acute mTBI than in HCs. Local efficiency was correlated with visuospatial/executive performance (r = -0.421; P = 0.002) in patients with acute mTBI. Significant differences in nodal efficiency and node degree centrality (P < 0.01) were found between the mTBI and HC groups. For dynamic properties, patients with mTBI showed higher variance (P = 0.016) in global efficiency than HCs. CONCLUSIONS The present study shows that patients with mTBI have abnormal brain functional connectome topology, especially the dynamic graph theory characteristics, which provide new insights into the role of topological network properties in patients with acute mTBI.
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Affiliation(s)
- Weigang Li
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Shaohua Ding
- Department of Radiology, Taizhou People's Hospital, Fifth Affiliated Hospital of Nantong University, Taizhou, Jiangsu, PR China
| | - Guoqian Zhao
- Department of Radiology, Chinese Traditional Medicine Hospital of Danyang, Danyang, Jiangsu, PR China
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8
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Tian Y, Vitiello MV, Wang H, Wang Y, Dong D, Xu H, Yu P, Qiu J, He Q, Chen H, Feng T, Lei X. Risk of insomnia during COVID-19: effects of depression and brain functional connectivity. Cereb Cortex 2023:7030621. [PMID: 36749000 DOI: 10.1093/cercor/bhad016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 01/12/2023] [Accepted: 01/13/2023] [Indexed: 02/08/2023] Open
Abstract
Normal sleepers may be at risk for insomnia during COVID-19. Identifying psychological factors and neural markers that predict their insomnia risk, as well as investigating possible courses of insomnia development, could lead to more precise targeted interventions for insomnia during similar public health emergencies. Insomnia severity index of 306 participants before and during COVID-19 were employed to determine the development of insomnia, while pre-COVID-19 psychometric and resting-state fMRI data were used to explore corresponding psychological and neural markers of insomnia development. Normal sleepers as a group reported a significant increase in insomnia symptoms after COVID-19 outbreak (F = 4.618, P = 0.0102, df = 2, 609.9). Depression was found to significantly contribute to worse insomnia (β = 0.066, P = 0.024). Subsequent analysis found that functional connectivity between the precentral gyrus and middle/inferior temporal gyrus mediated the association between pre-COVID-19 depression and insomnia symptoms during COVID-19. Cluster analysis identified that postoutbreak insomnia symptoms followed 3 courses (lessened, slightly worsened, and developed into mild insomnia), and pre-COVID-19 depression symptoms and functional connectivities predicted these courses. Timely identification and treatment of at-risk individuals may help avoid the development of insomnia in the face of future health-care emergencies, such as those arising from COVID-19 variants.
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Affiliation(s)
- Yun Tian
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Michael V Vitiello
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Box 356560, 1959 NE Pacific Street, Seattle, WA 98195-6560, United States
| | - Haien Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Yulin Wang
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Debo Dong
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, Wilhelm-Johnen-Strasse, Jülich 52425, Germany
| | - Hongzhou Xu
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Ping Yu
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Qinghua He
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Research Center of Psychology and Social Development, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Tingyong Feng
- Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
| | - Xu Lei
- Sleep and NeuroImaging Center, Faculty of Psychology, Southwest University, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China.,Key Laboratory of Cognition and Personality, Ministry of Education, No. 2 Tiansheng Road, Beibei District, Chongqing 400715, China
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9
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A study of brain networks for autism spectrum disorder classification using resting-state functional connectivity. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2022.100290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
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10
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Zhang L, Huang G, Liang Z, Li L, Zhang Z. Estimating scale-free dynamic effective connectivity networks from fMRI using group-wise spatial–temporal regularizations. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2022.02.041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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11
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Coppola P, Spindler LRB, Luppi AI, Adapa R, Naci L, Allanson J, Finoia P, Williams GB, Pickard JD, Owen AM, Menon DK, Stamatakis EA. Network dynamics scale with levels of awareness. Neuroimage 2022; 254:119128. [PMID: 35331869 DOI: 10.1016/j.neuroimage.2022.119128] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 02/10/2022] [Accepted: 03/20/2022] [Indexed: 02/04/2023] Open
Abstract
Small world topologies are thought to provide a valuable insight into human brain organisation and consciousness. However, functional magnetic resonance imaging studies in consciousness have not yielded consistent results. Given the importance of dynamics for both consciousness and cognition, here we investigate how the diversity of small world dynamics (quantified by sample entropy; dSW-E1) scales with decreasing levels of awareness (i.e., sedation and disorders of consciousness). Paying particular attention to result reproducibility, we show that dSW-E is a consistent predictor of levels of awareness even when controlling for the underlying functional connectivity dynamics. We find that dSW-E of subcortical and cortical areas are predictive, with the former showing higher and more robust effect sizes across analyses. We find that the network dynamics of intermodular communication in the cerebellum also have unique predictive power for levels of awareness. Consequently, we propose that the dynamic reorganisation of the functional information architecture, in particular of the subcortex, is a characteristic that emerges with awareness and has explanatory power beyond that of the complexity of dynamic functional connectivity.
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Affiliation(s)
- Peter Coppola
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lennart R B Spindler
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Ram Adapa
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Lorina Naci
- Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin, Lloyd Building, Dublin 2, Ireland
| | - Judith Allanson
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Neurosciences, Addenbrooke's Hospital, Cambridge University Hospitals NHS Foundation, Hills Rd., Cambridge, CB2 0QQ, UK
| | - Paola Finoia
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK
| | - Guy B Williams
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - John D Pickard
- Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Division of Neurosurgery, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Adrian M Owen
- The Brain and Mind Institute, Western Interdisciplinary Research Building, University of Western Ontario, London, ON N6A 5B7, Canada
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge Biomedical Campus (Box 65), Cambridge CB2 0QQ, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK; Department of Clinical Neurosciences, School of Clinical Medicine, Addenbrooke's Hospital, University of Cambridge, Hills Rd., Cambridge CB2 0QQ, UK.
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12
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Li M, Zhang N. A dynamic directed transfer function for brain functional network-based feature extraction. Brain Inform 2022; 9:7. [PMID: 35304652 PMCID: PMC8933605 DOI: 10.1186/s40708-022-00154-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 02/19/2022] [Indexed: 05/31/2023] Open
Abstract
Directed transfer function (DTF) is good at characterizing the pairwise interactions from whole brain network and has been applied in discrimination of motor imagery (MI) tasks. Considering the fact that MI electroencephalogram signals are more non-stationary in frequency domain than in time domain, and the activated intensities of α band (8–13 Hz) and β band [13–30 Hz, with \documentclass[12pt]{minimal}
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\begin{document}$$\beta_{1}$$\end{document}β1(13–21 Hz) and \documentclass[12pt]{minimal}
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\begin{document}$$\beta_{2}$$\end{document}β2(21–30 Hz) included] have considerable differences for different subjects, a dynamic DTF (DDTF) with variable model order and frequency band is proposed to construct the brain functional networks (BFNs), whose information flows and outflows are further calculated as network features and evaluated by support vector machine. Extensive experiments are conducted based on a public BCI competition dataset and a real-world dataset, the highest recognition rate achieve 100% and 86%, respectively. The experimental results suggest that DDTF can reflect the dynamic evolution of BFN, the best subject-based DDTF appears in one of four frequency sub-bands (α, β, \documentclass[12pt]{minimal}
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\begin{document}$${ }\beta_{2}$$\end{document}β2) for discrimination of MI tasks and is much more related to the current and previous states. Besides, DDTF is superior compared to granger causality-based and traditional feature extraction methods, the t-test and Kappa values show its statistical significance and high consistency as well.
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Affiliation(s)
- Mingai Li
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.,Beijing Key Laboratory of Computational Intelligence and Intelligent System, Beijing, 100124, China.,Engineering Research Center of Digital Community, Ministry of Education, Beijing, 100124, China
| | - Na Zhang
- Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China.
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13
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MAGE: Automatic diagnosis of autism spectrum disorders using multi-atlas graph convolutional networks and ensemble learning. Neurocomputing 2022. [DOI: 10.1016/j.neucom.2020.06.152] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
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14
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Hou X, Mei B, Wang F, Guo H, Li S, Wu G, Zang C, Cao B. Neural activity in adults with major depressive disorder differs from that in healthy individuals: A resting-state functional magnetic resonance imaging study. Front Psychiatry 2022; 13:1028518. [PMID: 36465288 PMCID: PMC9712791 DOI: 10.3389/fpsyt.2022.1028518] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Accepted: 11/02/2022] [Indexed: 11/19/2022] Open
Abstract
OBJECTIVE Currently, findings regarding resting-state functional magnetic resonance imaging studies of major depressive disorder (MDD) are inconsistent. In contrast to the previously used a priori seed-based functional connectivity analyses, this study employed whole-brain exploratory analyses and aimed to explore neural activity patterns in Chinese adults with MDD. MATERIALS AND METHODS Specifically, this study examined the amplitude of low-frequency fluctuations within the whole brain and adopted a large-scale brain network template to explore the core dysfunctional brain regions in individuals with MDD. RESULTS Overall, 32 individuals with MDD and 32 healthy controls were evaluated. Compared to healthy controls, individuals with MDD showed more profound alterations in the amplitude of low-frequency fluctuations in the temporolimbic affective circuit (e.g., middle temporal gyrus and parahippocampus) and default mode network (e.g., precuneus and thalamus). Moreover, functional connectivity between the left mid-insula and parietal regions within the sensorimotor network was weaker in individuals with MDD than in healthy controls. CONCLUSION In conclusion, the neural characteristics of MDD correspond to cognitive deficits in self-referential processing and emotional processing and are related to a risk of sensory disorders or psychomotor retardation. These findings present neural markers that may be used to identify MDD, contributing to clinical diagnosis.
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Affiliation(s)
- Xiaofang Hou
- Laboratory of Magnetic Resonance, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Bohui Mei
- Laboratory of Magnetic Resonance, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Fukun Wang
- General Committee Office, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Hua Guo
- Committee Office, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Shilong Li
- Department of Medical, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Gang Wu
- Laboratory of Computed Tomography, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Chen Zang
- Laboratory of Magnetic Resonance, Zhumadian Second People's Hospital, Zhumadian, Henan, China
| | - Bing Cao
- Key Laboratory of Cognition and Personality, Faculty of Psychology, Ministry of Education, Southwest University, Chongqing, China
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15
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Artiles O, Saeed F. A Multi-Factorial Assessment of Functional Human Autistic Spectrum Brain Network Analysis. PROCEEDINGS. IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE 2021; 2021:3526-3531. [PMID: 35425660 PMCID: PMC9007171 DOI: 10.1109/bibm52615.2021.9669679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
The variability of the results obtained by the statistical analysis of functional human brain networks depend on multiple factors such as: the source of the fMRI data, the brain parcellations, the graph theory measures, and the threshold values applied to the functional connectivity matrices to obtain adjacency matrices of sparse graphs. Therefore, the brain network used for down-stream analysis is heavily dependent on the methods that are applied to the fMRI data to obtain and analyze such networks. In this paper we present the preliminary results of a multi-factorial assessment of the statistical analysis of functional human brain networks. The assessment was performed in the functional human brain networks obtained from the resting state fMRI data of ten imaging sites provided by the Autism Brain Imaging Data Exchange (ABIDE) preprocessed functional magnetic resonance database, with six different functional brain parcellations, six different graph theory measures, and three different threshold values applied to the corresponding connectivity matrices to obtain sparse graphs. The statistical analysis to detect differences between the networks representing autism and control subjects were performed with four different statistical methods, using the p-values to determine the levels of significance of the analysis. Our main results show a strong dependence of functional human brain networks statistical analysis on the brain parcellations, and on the graph theory measures. Our results further show that the results of these analysis are less dependent on the statistical tests methods and on the threshold values of the sparse graphs for all practical purposes. An additional result is that the levels of significance of the statistical tests obtained for data provided by individual sites were much higher than the global levels of significance obtained by averaging the results of all the sites, implying that the best results on the analysis of functional human brain networks are obtained when the source of the fMRI data is the same for all the data. Since reproducibility and reliability of functional brain network statistical analysis is strongly dependent on the graphs obtained from fMRI data; our expectation is that the novel results presented in this paper would further help researchers in this field to develop methods that are reliable and reproducible.
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Affiliation(s)
- Oswaldo Artiles
- School of Computing and Information Sciences, Florida International University, Miami, Florida
| | - Fahad Saeed
- School of Computing and Information Sciences, Florida International University, Miami, Florida
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16
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Li L, Di X, Zhang H, Huang G, Zhang L, Liang Z, Zhang Z. Characterization of whole-brain task-modulated functional connectivity in response to nociceptive pain: A multisensory comparison study. Hum Brain Mapp 2021; 43:1061-1075. [PMID: 34761468 PMCID: PMC8764484 DOI: 10.1002/hbm.25707] [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: 04/19/2021] [Revised: 10/12/2021] [Accepted: 10/21/2021] [Indexed: 11/12/2022] Open
Abstract
Previous functional magnetic resonance imaging (fMRI) studies have shown that brain responses to nociceptive pain, non-nociceptive somatosensory, visual, and auditory stimuli are extremely similar. Actually, perception of external sensory stimulation requires complex interactions among distributed cortical and subcortical brain regions. However, the interactions among these regions elicited by nociceptive pain remain unclear, which limits our understanding of mechanisms of pain from a brain network perspective. Task fMRI data were collected with a random sequence of intermixed stimuli of four sensory modalities in 80 healthy subjects. Whole-brain psychophysiological interaction analysis was performed to identify task-modulated functional connectivity (FC) patterns for each modality. Task-modulated FC strength and graph-theoretical-based network properties were compared among the four modalities. Lastly, we performed across-sensory-modality prediction analysis based on the whole-brain task-modulated FC patterns to confirm the specific relationship between brain patterns and sensory modalities. For each sensory modality, task-modulated FC patterns were distributed over widespread brain regions beyond those typically activated or deactivated during the stimulation. As compared with the other three sensory modalities, nociceptive stimulation exhibited significantly different patterns (more widespread and stronger FC within the cingulo-opercular network, between cingulo-opercular and sensorimotor networks, between cingulo-opercular and emotional networks, and between default mode and emotional networks) and global property (smaller modularity). Further, a cross-sensory-modality prediction analysis found that task-modulated FC patterns could predict sensory modality at the subject level successfully. Collectively, these results demonstrated that the whole-brain task-modulated FC is preferentially modulated by pain, thus providing new insights into the neural mechanisms of pain processing.
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Affiliation(s)
- Linling Li
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Xin Di
- Department of Biomedical Engineering, New Jersey Institute of Technology, Newark, New Jersey, USA
| | - Huijuan Zhang
- CAS Key Laboratory of Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.,Department of Psychology, University of Chinese Academy of Sciences, Beijing, China
| | - Gan Huang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Li Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Zhen Liang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China
| | - Zhiguo Zhang
- School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China.,Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.,Peng Cheng Laboratory, Shenzhen, China
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17
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Subject-Specific Cognitive Workload Classification Using EEG-Based Functional Connectivity and Deep Learning. SENSORS 2021; 21:s21206710. [PMID: 34695921 PMCID: PMC8541420 DOI: 10.3390/s21206710] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 09/22/2021] [Accepted: 10/02/2021] [Indexed: 11/16/2022]
Abstract
Cognitive workload is a crucial factor in tasks involving dynamic decision-making and other real-time and high-risk situations. Neuroimaging techniques have long been used for estimating cognitive workload. Given the portability, cost-effectiveness and high time-resolution of EEG as compared to fMRI and other neuroimaging modalities, an efficient method of estimating an individual’s workload using EEG is of paramount importance. Multiple cognitive, psychiatric and behavioral phenotypes have already been known to be linked with “functional connectivity”, i.e., correlations between different brain regions. In this work, we explored the possibility of using different model-free functional connectivity metrics along with deep learning in order to efficiently classify the cognitive workload of the participants. To this end, 64-channel EEG data of 19 participants were collected while they were doing the traditional n-back task. These data (after pre-processing) were used to extract the functional connectivity features, namely Phase Transfer Entropy (PTE), Mutual Information (MI) and Phase Locking Value (PLV). These three were chosen to do a comprehensive comparison of directed and non-directed model-free functional connectivity metrics (allows faster computations). Using these features, three deep learning classifiers, namely CNN, LSTM and Conv-LSTM were used for classifying the cognitive workload as low (1-back), medium (2-back) or high (3-back). With the high inter-subject variability in EEG and cognitive workload and recent research highlighting that EEG-based functional connectivity metrics are subject-specific, subject-specific classifiers were used. Results show the state-of-the-art multi-class classification accuracy with the combination of MI with CNN at 80.87%, followed by the combination of PLV with CNN (at 75.88%) and MI with LSTM (at 71.87%). The highest subject specific performance was achieved by the combinations of PLV with Conv-LSTM, and PLV with CNN with an accuracy of 97.92%, followed by the combination of MI with CNN (at 95.83%) and MI with Conv-LSTM (at 93.75%). The results highlight the efficacy of the combination of EEG-based model-free functional connectivity metrics and deep learning in order to classify cognitive workload. The work can further be extended to explore the possibility of classifying cognitive workload in real-time, dynamic and complex real-world scenarios.
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18
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Mousavian M, Chen J, Traylor Z, Greening S. Depression detection from sMRI and rs-fMRI images using machine learning. J Intell Inf Syst 2021. [DOI: 10.1007/s10844-021-00653-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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19
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Wang Z, Xin J, Wang Z, Yao Y, Zhao Y, Qian W. Brain functional network modeling and analysis based on fMRI: a systematic review. Cogn Neurodyn 2021; 15:389-403. [PMID: 34040667 PMCID: PMC8131458 DOI: 10.1007/s11571-020-09630-5] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 08/05/2020] [Accepted: 08/20/2020] [Indexed: 12/12/2022] Open
Abstract
In recent years, the number of patients with neurodegenerative diseases (i.e., Alzheimer's disease, Parkinson's disease, mild cognitive impairment) and mental disorders (i.e., depression, anxiety and schizophrenia) have increased dramatically. Researchers have found that complex network analysis can reveal the topology of brain functional networks, such as small-world, scale-free, etc. In the study of brain diseases, it has been found that these topologies have undergoed abnormal changes in different degrees. Therefore, the research of brain functional networks can not only provide a new perspective for understanding the pathological mechanism of neurological and psychiatric diseases, but also provide assistance for the early diagnosis. Focusing on the study of human brain functional networks, this paper reviews the research results in recent years. First, this paper introduces the background of the study of brain functional networks under complex network theory and the important role of topological properties in the study of brain diseases. Second, the paper describes how to construct a brain functional network using neural image data. Third, the common methods of functional network analysis, including network structure analysis and disease classification, are introduced. Fourth, the role of brain functional networks in pathological study, analysis and diagnosis of brain functional diseases is studied. Finally, the paper summarizes the existing studies of brain functional networks and points out the problems and future research directions.
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Affiliation(s)
- Zhongyang Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Junchang Xin
- School of Computer Science and Engineering, Northeastern University, Shenyang, China
- Key Laboratory of Big Data Management and Analytics (Liaoning Province), Northeastern University, Shenyang, China
| | - Zhiqiong Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Yudong Yao
- Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ USA
| | - Yue Zhao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
| | - Wei Qian
- College of Engineering, The University of Texas at El Paso, El Paso, TX USA
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20
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Li Y, Wang N, Wang H, Lv Y, Zou Q, Wang J. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. Neuroimage 2021; 235:118018. [PMID: 33794358 DOI: 10.1016/j.neuroimage.2021.118018] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/04/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.
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Affiliation(s)
- Yinzhi Li
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Hao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education.
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21
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The 2-D Cluster Variation Method: Topography Illustrations and Their Enthalpy Parameter Correlations. ENTROPY 2021; 23:e23030319. [PMID: 33800360 PMCID: PMC7999889 DOI: 10.3390/e23030319] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Revised: 02/22/2021] [Accepted: 03/01/2021] [Indexed: 01/02/2023]
Abstract
One of the biggest challenges in characterizing 2-D image topographies is finding a low-dimensional parameter set that can succinctly describe, not so much image patterns themselves, but the nature of these patterns. The 2-D cluster variation method (CVM), introduced by Kikuchi in 1951, can characterize very local image pattern distributions using configuration variables, identifying nearest-neighbor, next-nearest-neighbor, and triplet configurations. Using the 2-D CVM, we can characterize 2-D topographies using just two parameters; the activation enthalpy (ε0) and the interaction enthalpy (ε1). Two different initial topographies (“scale-free-like” and “extreme rich club-like”) were each computationally brought to a CVM free energy minimum, for the case where the activation enthalpy was zero and different values were used for the interaction enthalpy. The results are: (1) the computational configuration variable results differ significantly from the analytically-predicted values well before ε1 approaches the known divergence as ε1→0.881, (2) the range of potentially useful parameter values, favoring clustering of like-with-like units, is limited to the region where ε0<3 and ε1<0.25, and (3) the topographies in the systems that are brought to a free energy minimum show interesting visual features, such as extended “spider legs” connecting previously unconnected “islands,” and as well as evolution of “peninsulas” in what were previously solid masses.
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Abstract
Recent progress in transcriptomics and co-expression networks have enabled us to predict the inference of the biological functions of genes with the associated environmental stress. Microarrays and RNA sequencing (RNA-seq) are the most commonly used high-throughput gene expression platforms for detecting differentially expressed genes between two (or more) phenotypes. Gene co-expression networks (GCNs) are a systems biology method for capturing transcriptional patterns and predicting gene interactions into functional and regulatory relationships. Here, we describe the procedures and tools used to construct and analyze GCN and investigate the integration of transcriptional data with GCN to provide reliable information about the underlying biological mechanism.
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23
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Deldar Z, Gevers-Montoro C, Khatibi A, Ghazi-Saidi L. The interaction between language and working memory: a systematic review of fMRI studies in the past two decades. AIMS Neurosci 2020; 8:1-32. [PMID: 33490370 PMCID: PMC7815476 DOI: 10.3934/neuroscience.2021001] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Accepted: 11/10/2020] [Indexed: 02/06/2023] Open
Abstract
Language processing involves other cognitive domains, including Working Memory (WM). Much detail about the neural correlates of language and WM interaction remains unclear. This review summarizes the evidence for the interaction between WM and language obtained via functional Magnetic Resonance Imaging (fMRI) in the past two decades. The search was limited to PubMed, Google Scholar, Science direct and Neurosynth for working memory, language, fMRI, neuroimaging, cognition, attention, network, connectome keywords. The exclusion criteria consisted of studies including children, older adults, bilingual or multilingual population, clinical cases, music, sign language, speech, motor processing, review papers, meta-analyses, electroencephalography/event-related potential, and positron emission tomography. A total of 20 articles were included and discussed in four categories: language comprehension, language production, syntax, and networks. Studies on neural correlates of WM and language interaction are rare. Language tasks that involve WM activate common neural systems. Activated areas can be associated with cognitive concepts proposed by Baddeley and Hitch (1974), including the phonological loop of WM (mainly Broca and Wernicke's areas), other prefrontal cortex and right hemispheric regions linked to the visuospatial sketchpad. There is a clear, dynamic interaction between language and WM, reflected in the involvement of subcortical structures, particularly the basal ganglia (caudate), and of widespread right hemispheric regions. WM involvement is levered by cognitive demand in response to task complexity. High WM capacity readers draw upon buffer memory systems in midline cortical areas to decrease the WM demands for efficiency. Different dynamic networks are involved in WM and language interaction in response to the task in hand for an ultimate brain function efficiency, modulated by language modality and attention.
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Affiliation(s)
- Zoha Deldar
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada.,Language and Cognition Laboratory, Department of Communication Disorders, College of Education, University of Nebraska at Kearney, USA
| | - Carlos Gevers-Montoro
- Department of Anatomy, Université du Québec à Trois-Rivières, Trois-Rivières, QC, Canada.,Madrid College of Chiropractic, Real Centro Universitario María Cristina, San Lorenzo de El Escorial, Madrid, Spain
| | - Ali Khatibi
- Centre of Precision Rehabilitation for Spinal Pain, University of Birmingham, Birmingham, UK.,Centre for Human Brain Health, University of Birmingham, Birmingham, UK
| | - Ladan Ghazi-Saidi
- Language and Cognition Laboratory, Department of Communication Disorders, College of Education, University of Nebraska at Kearney, USA
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24
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Functional connectome-based biomarkers predict chronic codeine-containing cough syrup dependent. J Psychiatr Res 2020; 130:333-341. [PMID: 32889355 DOI: 10.1016/j.jpsychires.2020.08.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2020] [Revised: 07/26/2020] [Accepted: 08/03/2020] [Indexed: 12/30/2022]
Abstract
PURPOSE Codeine-containing cough syrup (CCS) is considered among the most popular drugs of abuse in adolescents worldwide. Accurate prediction and identification of CCS dependent (CCSD) users are crucial. This study aimed to identify a brain-connectome-based predictor of CCSD using a machine learning model based on a ten-fold cross-validation logistic regression (LR) classifier. METHODS 40 CCSD users and 40 healthy control (HC) subjects underwent functional magnetic resonance imaging to construct weight functional networks. Partial correlation analysis was used to analyze relations between abnormal network metrics and clinical characteristics (BIS total scores, CCS abuse duration, and mean CCS dose) in CCSD. A ten-fold cross-validation LR classifier was used to classify CCSD users and HC subjects. RESULTS The CCSD group showed significantly abnormal nodes and connections in the right posterior cingulate, right middle insula, bilateral prefrontal cortex, parietal lobe, temporal lobe, occipital lobe, and cerebellum. Furthermore, higher characteristic path length and lower clustering coefficient (Cp), global efficiency, and local efficiency (Eloc) were observed in the global topologies in CCSD. The abnormal global properties (Cp and Eloc) and node properties of the prefrontal cortex were significantly correlated with clinical characteristics (BIS-11 scores, CCS abuse duration) in CCSD. The LR classifier models demonstrated accuracy, sensitivity, specificity, precision, and AUC of 82.5%, 82.5%, 82.5%, 76.8%, and 82.5%. CONCLUSIONS These data demonstrate that abnormal functional connectome may be closely linked to clinical characteristics in CCSD. Functional connectome-based biomarkers can be a powerful tool for personalized diagnosis of CCSD in the future.
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25
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Wang R, Lin J, Sun C, Hu B, Liu X, Geng D, Li Y, Yang L. Topological reorganization of brain functional networks in patients with mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes. NEUROIMAGE-CLINICAL 2020; 28:102480. [PMID: 33395972 PMCID: PMC7645289 DOI: 10.1016/j.nicl.2020.102480] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 10/16/2020] [Accepted: 10/19/2020] [Indexed: 12/30/2022]
Abstract
MELAS patients showed topological reorganization of brain functional network. Network abnormalities in MELAS patients may be affected by stroke-like lesions. Graph theory based on rs-fMRI may be used for monitoring the status of MELAS.
Mitochondrial encephalomyopathy with lactic acidosis and stroke‐like episodes (MELAS) is a rare maternally inherited genetic disease; however, little is known about its underlying brain basis. Furthermore, the topological organization of brain functional network in MELAS has not been explored. Here, 45 patients with MELAS (22 at acute stage, 23 at chronic stage) and 22 normal controls were studied using resting- state functional magnetic resonance imaging and graph theory analysis approaches. Topological properties of brain functional networks including global and nodal metrics, rich club organization and modularity were analyzed. At the global level, MELAS patients exhibited reduced clustering coefficient, normalized clustering coefficient, normalized characteristic path length and local network efficiency compared with the controls. At the nodal level, several nodes with abnormal degree centrality and nodal efficiency were detected in MELAS patients, and the distribution of these nodes was partly consistent with the stroke-like lesions. For rich club organization, rich club nodes were reorganized and the connections among them were decreased in MELAS patients. Modularity analysis revealed that MELAS patents had altered intra- or inter-modular connections in default mode network, fronto-parietal network, sensorimotor network, occipital network and cerebellum network. Notably, the patients at acute stage showed more obvious changes in these topological properties than the patients at chronic stage. These findings indicated that MELAS patients, particularly those at acute stage, exhibited topological reorganization of the whole-brain functional network. This study may help us to understand the neuropathological mechanisms of MELAS.
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Affiliation(s)
- Rong Wang
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Shanghai Institution of Medical Imaging, Shanghai 200032, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China
| | - Jie Lin
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Chong Sun
- Department of Neurology, Huashan Hospital, Fudan University, Shanghai 200040, China
| | - Bin Hu
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China
| | - Xueling Liu
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China
| | - Daoying Geng
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Shanghai Institution of Medical Imaging, Shanghai 200032, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China
| | - Yuxin Li
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China.
| | - Liqin Yang
- Department of Radiology, HuaShan Hospital, Fudan University, Shanghai 200040, China; Institute of Functional and Molecular Medical Imaging, Fudan University, Shanghai 200040, China.
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26
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A common variant in OXTR rs53576 impacts topological patterns of brain functional networks. Eur Child Adolesc Psychiatry 2020; 29:993-1002. [PMID: 31587084 DOI: 10.1007/s00787-019-01414-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 09/29/2019] [Indexed: 01/03/2023]
Abstract
A common variant (rs53576, G/A) in the oxytocin receptor (OXTR) gene is associated with individual differences in social behavior and may increase the risk for neuropsychiatric disorders characterized by social impairment, especially autism. Although recent functional magnetic resonance imaging (fMRI) studies have identified functional connectivity alteration in some brain regions in risk A allele carriers, it is currently unknown whether this dysfunctional connectivity causes disruption of the topological properties of brain functional networks. We applied a graph-theoretical analysis to investigate the topological properties of brain networks derived from resting-state fMRI in relation to AA homozygotes versus G allele carriers in 290 cognitive normal young adults. We found both AA homozygotes and G allele carriers demonstrated small-world properties; however, male AA homozygotes showed lower normalized clustering coefficient, small-worldness, and local efficiency compared with male G allele carriers, no differences survived after Bonferroni correction; and the inter-group differences of all three metrics exhibited an allele-load-dependent trend (AA < AG < GG), indicating a randomization shift of their brain functional networks. No significant results were observed in any global measures in female AA homozygotes as compared to female G allele carriers. Our results suggested that the topological patterns of brain functional networks were altered in OXTR rs53576 male homozygotes for the risk A allele compared with male G allele carriers, providing evidence for the disruption of integrity in large-scale intrinsic brain networks in a sex-dimorphic manner.
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Pegg EJ, Taylor JR, Keller SS, Mohanraj R. Interictal structural and functional connectivity in idiopathic generalized epilepsy: A systematic review of graph theoretical studies. Epilepsy Behav 2020; 106:107013. [PMID: 32193094 DOI: 10.1016/j.yebeh.2020.107013] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Revised: 02/22/2020] [Accepted: 02/28/2020] [Indexed: 12/18/2022]
Abstract
The evaluation of the role of anomalous neuronal networks in epilepsy using a graph theoretical approach is of growing research interest. There is currently no consensus on optimal methods for performing network analysis, and it is possible that variations in study methodology account for diverging findings. This review focuses on global functional and structural interictal network characteristics in people with idiopathic generalized epilepsy (IGE) with the aim of appraising the methodological approaches used and assessing for meaningful consensus. Thirteen studies were included in the review. Data were heterogenous and not suitable for meta-analysis. Overall, there is a suggestion that the cerebral neuronal networks of people with IGE have different global structural and functional characteristics to people without epilepsy. However, the nature of the aberrations is inconsistent with some studies demonstrating a more regular network configuration in IGE, and some, a more random topology. There is greater consistency when different data modalities and connectivity subtypes are compared separately, with a tendency towards increased small-worldness of networks in functional electroencephalography/magnetoencephalography (EEG/MEG) studies and decreased small-worldness of networks in structural studies. Prominent variation in study design at all stages is likely to have contributed to differences in study outcomes. Despite increasing literature surrounding neuronal network analysis, systematic methodological studies are lacking. Absence of consensus in this area significantly limits comparison of results from different studies, and the ability to draw firm conclusions about network characteristics in IGE.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, United Kingdom; Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom.
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom; Manchester Academic Health Sciences Centre, United Kingdom
| | - Simon S Keller
- Department of Molecular and Clinical Pharmacology, Institute of Translational Medicine, University of Liverpool, United Kingdom; The Walton Centre NHS Foundation Trust, Liverpool, United Kingdom
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, United Kingdom; Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
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28
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Wang S, Li Y, Qiu S, Zhang C, Wang G, Xian J, Li T, He H. Reorganization of rich-clubs in functional brain networks during propofol-induced unconsciousness and natural sleep. NEUROIMAGE-CLINICAL 2020; 25:102188. [PMID: 32018124 PMCID: PMC6997627 DOI: 10.1016/j.nicl.2020.102188] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 12/31/2019] [Accepted: 01/18/2020] [Indexed: 12/20/2022]
Abstract
BACKGROUND General anesthesia (GA) provides an invaluable experimental tool to understand the essential neural mechanisms underlying consciousness. Previous neuroimaging studies have shown the functional integration and segregation of brain functional networks during anesthetic-induced alteration of consciousness. However, the organization pattern of hubs in functional brain networks remains unclear. Moreover, comparisons with the well-characterized physiological unconsciousness can help us understand the neural mechanisms of anesthetic-induced unconsciousness. METHODS Resting-state functional magnetic resonance imaging was performed during wakefulness, mild propofol-induced sedation (m-PIS), and deep PIS (d-PIS) with clinical unconsciousness on 8 healthy volunteers and wakefulness and natural sleep on 9 age- and sex-matched healthy volunteers. Large-scale functional brain networks of each volunteer were constructed based on 160 regions of interest. Then, rich-club organizations in brain functional networks and nodal properties (nodal strength and efficiency) were assessed and analyzed among the different states and groups. RESULTS Rich-clubs in the functional brain networks were reorganized during alteration of consciousness induced by propofol. Firstly, rich-club nodes were switched from the posterior cingulate cortex (PCC), angular gyrus, and anterior and middle insula to the inferior parietal lobule (IPL), inferior parietal sulcus (IPS), and cerebellum. When sedation was deepened to unconsciousness, the rich-club nodes were switched to the occipital and angular gyrus. These results suggest that the rich-club nodes were switched among the high-order cognitive function networks (default mode network [DMN] and fronto-parietal network [FPN]), sensory networks (occipital network [ON]), and cerebellum network (CN) from consciousness (wakefulness) to propofol-induced unconsciousness. At the same time, compared with wakefulness, local connections were switched to rich-club connections during propofol-induced unconsciousness, suggesting a strengthening of the overall information commutation of networks. Nodal efficiency of the anterior and middle insula and ventral frontal cortex was significantly decreased. Additionally, from wakefulness to natural sleep, a similar pattern of rich-club reorganization with propofol-induced unconsciousness was observed: rich-club nodes were switched from the DMN (including precuneus and PCC) to the sensorimotor network (SMN, including part of the frontal and temporal gyrus). Compared with natural sleep, nodal efficiency of the insula, frontal gyrus, PCC, and cerebellum significantly decreased during propofol-induced unconsciousness. CONCLUSIONS Our study demonstrated that the rich-club reorganization in functional brain networks is characterized by switching of rich-club nodes between the high-order cognitive and sensory and motor networks during propofol-induced alteration of consciousness and natural sleep. These findings will help understand the common neurological mechanism of pharmacological and physiological unconsciousness.
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Affiliation(s)
- Shengpei Wang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China
| | - Yun Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Shuang Qiu
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Chuncheng Zhang
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Guyan Wang
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Junfang Xian
- Department of Radiology, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Tianzuo Li
- Department of Anesthesia, Beijing Tongren Hospital, Capital Medical University, Beijing, China; Beijing Shijitan Hospital, Capital Medical University, Beijing, China.
| | - Huiguang He
- Research Center for Brain-inspired Intelligence and National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Beijing, China.
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29
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A robust swarm intelligence-based feature selection model for neuro-fuzzy recognition of mild cognitive impairment from resting-state fMRI. Inf Sci (N Y) 2019. [DOI: 10.1016/j.ins.2019.07.026] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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30
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Farooq H, Chen Y, Georgiou TT, Tannenbaum A, Lenglet C. Network curvature as a hallmark of brain structural connectivity. Nat Commun 2019; 10:4937. [PMID: 31666510 PMCID: PMC6821808 DOI: 10.1038/s41467-019-12915-x] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 10/03/2019] [Indexed: 12/14/2022] Open
Abstract
Although brain functionality is often remarkably robust to lesions and other insults, it may be fragile when these take place in specific locations. Previous attempts to quantify robustness and fragility sought to understand how the functional connectivity of brain networks is affected by structural changes, using either model-based predictions or empirical studies of the effects of lesions. We advance a geometric viewpoint relying on a notion of network curvature, the so-called Ollivier-Ricci curvature. This approach has been proposed to assess financial market robustness and to differentiate biological networks of cancer cells from healthy ones. Here, we apply curvature-based measures to brain structural networks to identify robust and fragile brain regions in healthy subjects. We show that curvature can also be used to track changes in brain connectivity related to age and autism spectrum disorder (ASD), and we obtain results that are in agreement with previous MRI studies. The brain can often continue to function despite lesions in many areas, but damage to particular locations may have serious effects. Here, the authors use the concept of Ollivier-Ricci curvature to investigate the robustness of brain networks.
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Affiliation(s)
- Hamza Farooq
- Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA.
| | - Yongxin Chen
- School of Aerospace Engineering, Georgia Institute of Technology, Atlanta, GA, USA
| | - Tryphon T Georgiou
- Department of Mechanical and Aerospace Engineering, University of California, Irvine, CA, USA
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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31
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Khosla M, Jamison K, Kuceyeski A, Sabuncu MR. Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction. Neuroimage 2019; 199:651-662. [PMID: 31220576 DOI: 10.1016/j.neuroimage.2019.06.012] [Citation(s) in RCA: 58] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2019] [Revised: 05/30/2019] [Accepted: 06/04/2019] [Indexed: 11/16/2022] Open
Abstract
The specificity and sensitivity of resting state functional MRI (rs-fMRI) measurements depend on preprocessing choices, such as the parcellation scheme used to define regions of interest (ROIs). In this study, we critically evaluate the effect of brain parcellations on machine learning models applied to rs-fMRI data. Our experiments reveal an intriguing trend: On average, models with stochastic parcellations consistently perform as well as models with widely used atlases at the same spatial scale. We thus propose an ensemble learning strategy to combine the predictions from models trained on connectivity data extracted using different (e.g., stochastic) parcellations. We further present an implementation of our ensemble learning strategy with a novel 3D Convolutional Neural Network (CNN) approach. The proposed CNN approach takes advantage of the full-resolution 3D spatial structure of rs-fMRI data and fits non-linear predictive models. Our ensemble CNN framework overcomes the limitations of traditional machine learning models for connectomes that often rely on region-based summary statistics and/or linear models. We showcase our approach on a classification (autism patients versus healthy controls) and a regression problem (prediction of subject's age), and report promising results.
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Affiliation(s)
- Meenakshi Khosla
- School of Electrical and Computer Engineering, Cornell University, USA
| | | | - Amy Kuceyeski
- Radiology, Weill Cornell Medical College, USA; Brain and Mind Research Institute, Weill Cornell Medical College, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, USA; Nancy E. & Peter C. Meinig School of Biomedical Engineering, Cornell University, USA.
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32
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Towards artificial intelligence in mental health by improving schizophrenia prediction with multiple brain parcellation ensemble-learning. NPJ SCHIZOPHRENIA 2019; 5:2. [PMID: 30659193 PMCID: PMC6386753 DOI: 10.1038/s41537-018-0070-8] [Citation(s) in RCA: 47] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 12/06/2018] [Indexed: 12/16/2022]
Abstract
In the literature, there are substantial machine learning attempts to classify schizophrenia based on alterations in resting-state (RS) brain patterns using functional magnetic resonance imaging (fMRI). Most earlier studies modelled patients undergoing treatment, entailing confounding with drug effects on brain activity, and making them less applicable to real-world diagnosis at the point of first medical contact. Further, most studies with classification accuracies >80% are based on small sample datasets, which may be insufficient to capture the heterogeneity of schizophrenia, limiting generalization to unseen cases. In this study, we used RS fMRI data collected from a cohort of antipsychotic drug treatment-naive patients meeting DSM IV criteria for schizophrenia (N = 81) as well as age- and sex-matched healthy controls (N = 93). We present an ensemble model -- EMPaSchiz (read as ‘Emphasis’; standing for ‘Ensemble algorithm with Multiple Parcellations for Schizophrenia prediction’) that stacks predictions from several ‘single-source’ models, each based on features of regional activity and functional connectivity, over a range of different a priori parcellation schemes. EMPaSchiz yielded a classification accuracy of 87% (vs. chance accuracy of 53%), which out-performs earlier machine learning models built for diagnosing schizophrenia using RS fMRI measures modelled on large samples (N > 100). To our knowledge, EMPaSchiz is first to be reported that has been trained and validated exclusively on data from drug-naive patients diagnosed with schizophrenia. The method relies on a single modality of MRI acquisition and can be readily scaled-up without needing to rebuild parcellation maps from incoming training images.
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33
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Yin W, Chen MH, Hung SC, Baluyot KR, Li T, Lin W. Brain functional development separates into three distinct time periods in the first two years of life. Neuroimage 2019; 189:715-726. [PMID: 30641240 DOI: 10.1016/j.neuroimage.2019.01.025] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2018] [Revised: 12/25/2018] [Accepted: 01/09/2019] [Indexed: 12/14/2022] Open
Abstract
Recently, resting functional MRI has provided invaluable insight into the brain developmental processes of early infancy and childhood. A common feature of previous functional development studies is the use of age to separate subjects into different cohorts for group comparisons. However, functional maturation paces vary tremendously from subject to subject. Since this is particularly true for the first years of life, an alternative to physical age alone is needed for cluster analysis. Here, a data-driven approach based on individual brain functional connectivity was employed to cluster typically developing children who were longitudinally imaged using MRI without sedation for the first two years of life. Specifically, three time periods were determined based on the distinction of brain functional connectivity patterns, including 0-1 month (group 1), 2-7 months (group 2), and 8-24 (group 3) of age, respectively. From groups 1 to 2, connection density increased by almost two-fold, local efficacy (LE) is significantly improved, and there was no change in global efficiency (GE). From groups 2 to 3, connection density increased slightly, LE showed no change, and a significant increase in GE were observed. Furthermore, 27 core brain regions were identified which yielded clustering results that resemble those obtained using all brain regions. These core regions were largely associated with the motor, visual and language functional domains as well as regions associated with higher order cognitive functional domains. Both visual and language functional domains exhibited a persistent and significant increase within domain connection from groups 1 to 3, while no changes were observed for the motor domain. In contrast, while a reduction of inter-domain connection was the general developmental pattern, the motor domain exhibited an interesting "V" shape pattern in its relationship to visual and language associated areas, showing a decrease from groups 1 to 2, followed by an increase from groups 2 to 3. In summary, our results offer new insights into functional brain development and identify 27 core brain regions critically important for early brain development.
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Affiliation(s)
- Weiyan Yin
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Meng-Hsiang Chen
- Department of Diagnostic Radiology, Kaohsiung Chang Gung Memorial Hospital, Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Sheng-Che Hung
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kristine R Baluyot
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Tengfei Li
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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34
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Carey D, Nolan H, Kenny RA, Meaney J. Cortical covariance networks in ageing: Cross-sectional data from the Irish Longitudinal Study on Ageing (TILDA). Neuropsychologia 2019; 122:51-61. [DOI: 10.1016/j.neuropsychologia.2018.11.013] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2018] [Revised: 11/24/2018] [Accepted: 11/26/2018] [Indexed: 01/06/2023]
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35
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Bernier M, Cunnane SC, Whittingstall K. The morphology of the human cerebrovascular system. Hum Brain Mapp 2018; 39:4962-4975. [PMID: 30265762 DOI: 10.1002/hbm.24337] [Citation(s) in RCA: 56] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/02/2018] [Accepted: 07/19/2018] [Indexed: 12/13/2022] Open
Abstract
While several methodologies exist for quantifying gray and white matter properties in humans, relatively little is known regarding the spatial organization and the intersubject variability of cerebral vessels. To resolve this, we developed a fast, open-source processing algorithm using advanced vessel segmentation schemes and iterative nonlinear registration to isolate, extract, and quantify cerebral vessels in susceptibility weighting imaging (SWI) and time-of-flight angiography (TOF-MRA) datasets acquired in a large cohort (n = 42) of healthy individuals. From this, whole-brain venous and arterial probabilistic maps were generated along with the computation of regional densities and diameters within regions based on popular anatomical and functional atlases. The results show that cerebral vasculature is highly heterogeneous, displaying disproportionally large vessel densities in brain areas such as the anterior and posterior cingulate, cuneus, precuneus, parahippocampus, insula, and temporal gyri. On average, venous densities were slightly higher and less variable across subjects than arterial. Moreover, regional variations in both venous and arterial density were significantly correlated to cortical thickness (R = 0.42). This publicly available new atlas of the human cerebrovascular system provides a first step toward quantifying morphological changes in the diseased brain and serving as a potential regression tool in fMRI analysis.
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Affiliation(s)
- Michaël Bernier
- Department of Nuclear Medicine and Radiobiology, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Stephen C Cunnane
- Department of Medicine, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Department of Pharmacology and Physiology, Université de Sherbrooke, Sherbrooke, Québec, Canada.,Research Center on Aging, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kevin Whittingstall
- Department of Radiology, Université de Sherbrooke, Sherbrooke, Québec, Canada.,CR-CHUS, Université de Sherbrooke, Sherbrooke, Québec, Canada
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36
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Wang WW, Lu YC, Tang WJ, Zhang JH, Sun HP, Feng XY, Liu HQ. Small-worldness of brain networks after brachial plexus injury: A resting-state functional magnetic resonance imaging study. Neural Regen Res 2018; 13:1061-1065. [PMID: 29926834 PMCID: PMC6022461 DOI: 10.4103/1673-5374.233450] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/02/2018] [Indexed: 12/18/2022] Open
Abstract
Research on brain function after brachial plexus injury focuses on local cortical functional reorganization, and few studies have focused on brain networks after brachial plexus injury. Changes in brain networks may help understanding of brain plasticity at the global level. We hypothesized that topology of the global cerebral resting-state functional network changes after unilateral brachial plexus injury. Thus, in this cross-sectional study, we recruited eight male patients with unilateral brachial plexus injury (right handedness, mean age of 27.9 ± 5.4 years old) and eight male healthy controls (right handedness, mean age of 28.6 ± 3.2). After acquiring and preprocessing resting-state magnetic resonance imaging data, the cerebrum was divided into 90 regions and Pearson's correlation coefficient calculated between regions. These correlation matrices were then converted into a binary matrix with affixed sparsity values of 0.1-0.46. Under sparsity conditions, both groups satisfied this small-world property. The clustering coefficient was markedly lower, while average shortest path remarkably higher in patients compared with healthy controls. These findings confirm that cerebral functional networks in patients still show small-world characteristics, which are highly effective in information transmission in the brain, as well as normal controls. Alternatively, varied small-worldness suggests that capacity of information transmission and integration in different brain regions in brachial plexus injury patients is damaged.
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Affiliation(s)
- Wei-Wei Wang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ye-Chen Lu
- Department of Hand Surgery, Huashan Hospital, Fudan University, Shanghai, China
| | - Wei-Jun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Jun-Hai Zhang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hua-Ping Sun
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Xiao-Yuan Feng
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Han-Qiu Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
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37
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Cassidy B, Bowman FD, Rae C, Solo V. On the Reliability of Individual Brain Activity Networks. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:649-662. [PMID: 29408792 DOI: 10.1109/tmi.2017.2774364] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH2 network construction method outperforms other approaches at most data spatiotemporal resolutions.
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38
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Alemi R, Batouli SAH, Behzad E, Ebrahimpoor M, Oghabian MA. Not single brain areas but a network is involved in language: Applications in presurgical planning. Clin Neurol Neurosurg 2018; 165:116-128. [PMID: 29334640 DOI: 10.1016/j.clineuro.2018.01.009] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2017] [Revised: 01/03/2018] [Accepted: 01/08/2018] [Indexed: 01/22/2023]
Abstract
OBJECTIVES Language is an important human function, and is a determinant of the quality of life. In conditions such as brain lesions, disruption of the language function may occur, and lesion resection is a solution for that. Presurgical planning to determine the language-related brain areas would enhance the chances of language preservation after the operation; however, availability of a normative language template is essential. PATIENTS AND METHODS In this study, using data from 60 young individuals who were meticulously checked for mental and physical health, and using fMRI and robust imaging and data analysis methods, functional brain maps for the language production, perception and semantic were produced. RESULTS The obtained templates showed that the language function should be considered as the product of the collaboration of a network of brain regions, instead of considering only few brain areas to be involved in that. CONCLUSION This study has important clinical applications, and extends our knowledge on the neuroanatomy of the language function.
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Affiliation(s)
- Razieh Alemi
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Department of Otorhinolaryngology, Faculty of Medicine, McGill University, Canada
| | - Seyed Amir Hossein Batouli
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran; Neuroimaging and Analysis Group, Tehran University of Medical Sciences, Tehran, Iran
| | - Ebrahim Behzad
- Department of Neuroscience and Addiction Studies, School of Advanced Technologies in Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Mitra Ebrahimpoor
- Neuroimaging and Analysis Group, Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammad Ali Oghabian
- Neuroimaging and Analysis Group, Tehran University of Medical Sciences, Tehran, Iran; Medical Physics and Biomedical Engineering Department, Tehran University of Medical Sciences, Tehran, Iran.
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Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, Smid J, Nitrini R, Buchpiguel CA, Busatto GF. Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases. Neuroimage Clin 2017; 17:628-641. [PMID: 29234599 PMCID: PMC5716956 DOI: 10.1016/j.nicl.2017.10.026] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 10/12/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). METHODS We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. RESULTS Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. CONCLUSION The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.
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Key Words
- 18F-FDG-PET, 18F-Fluorodeoxyglucose-Positron Emission Tomography
- AAL, Automated Anatomical Labeling (atlas)
- AD, Alzheimer's Disease
- Alzheimer's Disease
- BA, Brodmann's Area
- Brain atlas
- GM, Gray Matter
- MKL, Multiple Kernel Learning
- MKL-ROI, MKL based on regions of interest
- ML, Machine Learning
- MRI
- Multiple kernel learning
- NF, number of features
- NSR, Number of Selected Regions
- PET
- PVE, Partial Volume Effects
- ROI, Region of Interest
- SPECT
- SVM, Support Vector Machine
- T1-MRI, T1-weighted Magnetic Resonance Imaging
- TN, True Negative (specificity - proportion of healthy controls correctly classified)
- TP, True Positive (sensitivity - proportion of patients correctly classified)
- rAUC, Ratio between negative and positive Area Under Curve
- rCBF-SPECT, Regional Cerebral Blood Flow
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Affiliation(s)
- Jane Maryam Rondina
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK.
| | - Luiz Kobuti Ferreira
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Fabio Luis de Souza Duran
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Rodrigo Kubo
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Carla Rachel Ono
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Claudia Costa Leite
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil; Department of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Jerusa Smid
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | - Ricardo Nitrini
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | | | - Geraldo F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil; Department and Institute of Psychiatry, University of São Paulo, São Paulo, Brazil
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40
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Do We Understand the Relationship between Affective Computing, Emotion and Context-Awareness? MACHINES 2017. [DOI: 10.3390/machines5030016] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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41
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Bardella G, Bifone A, Gabrielli A, Gozzi A, Squartini T. Hierarchical organization of functional connectivity in the mouse brain: a complex network approach. Sci Rep 2016; 6:32060. [PMID: 27534708 PMCID: PMC4989195 DOI: 10.1038/srep32060] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2016] [Accepted: 07/26/2016] [Indexed: 01/04/2023] Open
Abstract
This paper represents a contribution to the study of the brain functional connectivity from the perspective of complex networks theory. More specifically, we apply graph theoretical analyses to provide evidence of the modular structure of the mouse brain and to shed light on its hierarchical organization. We propose a novel percolation analysis and we apply our approach to the analysis of a resting-state functional MRI data set from 41 mice. This approach reveals a robust hierarchical structure of modules persistent across different subjects. Importantly, we test this approach against a statistical benchmark (or null model) which constrains only the distributions of empirical correlations. Our results unambiguously show that the hierarchical character of the mouse brain modular structure is not trivially encoded into this lower-order constraint. Finally, we investigate the modular structure of the mouse brain by computing the Minimal Spanning Forest, a technique that identifies subnetworks characterized by the strongest internal correlations. This approach represents a faster alternative to other community detection methods and provides a means to rank modules on the basis of the strength of their internal edges.
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Affiliation(s)
- Giampiero Bardella
- Istituto dei Sistemi Complessi ISC-CNR, Università “Sapienza” di Roma, P.le A. Moro 5, 00185 Rome, Italy
| | - Angelo Bifone
- Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, C.so Bettini 31, I-38068 Rovereto (TN), Italy
| | - Andrea Gabrielli
- Istituto dei Sistemi Complessi ISC-CNR, Università “Sapienza” di Roma, P.le A. Moro 5, 00185 Rome, Italy
- IMT School for Advanced Studies Lucca, P.zza S. Ponziano 6, 55100 Lucca, Italy
| | - Alessandro Gozzi
- Istituto Italiano di Tecnologia, Center for Neuroscience and Cognitive Systems, C.so Bettini 31, I-38068 Rovereto (TN), Italy
| | - Tiziano Squartini
- Istituto dei Sistemi Complessi ISC-CNR, Università “Sapienza” di Roma, P.le A. Moro 5, 00185 Rome, Italy
- IMT School for Advanced Studies Lucca, P.zza S. Ponziano 6, 55100 Lucca, Italy
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42
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Weighted Node Network Voronoi Diagram and its application to optimization of chain stores layout. INT J MACH LEARN CYB 2016. [DOI: 10.1007/s13042-015-0491-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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43
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Papo D, Zanin M, Martínez JH, Buldú JM. Beware of the Small-World Neuroscientist! Front Hum Neurosci 2016; 10:96. [PMID: 27014027 PMCID: PMC4781830 DOI: 10.3389/fnhum.2016.00096] [Citation(s) in RCA: 40] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2016] [Accepted: 02/22/2016] [Indexed: 12/31/2022] Open
Affiliation(s)
- David Papo
- Laboratory of Biological Networks, Center for Biomedical Technology and GISC, Universidad Politécnica de MadridMadrid, Spain
| | - Massimiliano Zanin
- Departamento de Engenharia Electrotecnica, Faculdade de Ciencias e Tecnologia, Universidade Nova de LisboaLisboa, Portugal
- Innaxis Foundation and Research InstituteMadrid, Spain
| | | | - Javier M. Buldú
- Laboratory of Biological Networks, Center for Biomedical Technology and GISC, Universidad Politécnica de MadridMadrid, Spain
- Complex Systems Group and GISC, Universidad Rey Juan CarlosMóstoles, Spain
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44
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Wang J, Lu M, Fan Y, Wen X, Zhang R, Wang B, Ma Q, Song Z, He Y, Wang J, Huang R. Exploring brain functional plasticity in world class gymnasts: a network analysis. Brain Struct Funct 2015; 221:3503-19. [DOI: 10.1007/s00429-015-1116-6] [Citation(s) in RCA: 35] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2015] [Accepted: 09/16/2015] [Indexed: 12/14/2022]
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45
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: the workflows, methods, and platforms. Brain Inform 2015; 2:181-195. [PMID: 27747508 PMCID: PMC4737665 DOI: 10.1007/s40708-015-0020-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2015] [Accepted: 08/20/2015] [Indexed: 12/20/2022] Open
Abstract
The last two decades have witnessed the explosive growth in the development and use of noninvasive neuroimaging technologies that advance the research on human brain under normal and pathological conditions. Multimodal neuroimaging has become a major driver of current neuroimaging research due to the recognition of the clinical benefits of multimodal data, and the better access to hybrid devices. Multimodal neuroimaging computing is very challenging, and requires sophisticated computing to address the variations in spatiotemporal resolution and merge the biophysical/biochemical information. We review the current workflows and methods for multimodal neuroimaging computing, and also demonstrate how to conduct research using the established neuroimaging computing packages and platforms.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- School of IT, The University of Sydney, Sydney, Australia
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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46
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Liu S, Cai W, Liu S, Zhang F, Fulham M, Feng D, Pujol S, Kikinis R. Multimodal neuroimaging computing: a review of the applications in neuropsychiatric disorders. Brain Inform 2015; 2:167-180. [PMID: 27747507 PMCID: PMC4737664 DOI: 10.1007/s40708-015-0019-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2015] [Accepted: 08/08/2015] [Indexed: 12/20/2022] Open
Abstract
Multimodal neuroimaging is increasingly used in neuroscience research, as it overcomes the limitations of individual modalities. One of the most important applications of multimodal neuroimaging is the provision of vital diagnostic data for neuropsychiatric disorders. Multimodal neuroimaging computing enables the visualization and quantitative analysis of the alterations in brain structure and function, and has reshaped how neuroscience research is carried out. Research in this area is growing exponentially, and so it is an appropriate time to review the current and future development of this emerging area. Hence, in this paper, we review the recent advances in multimodal neuroimaging (MRI, PET) and electrophysiological (EEG, MEG) technologies, and their applications to the neuropsychiatric disorders. We also outline some future directions for multimodal neuroimaging where researchers will design more advanced methods and models for neuropsychiatric research.
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Affiliation(s)
- Sidong Liu
- School of IT, The University of Sydney, Sydney, Australia.
| | - Weidong Cai
- School of IT, The University of Sydney, Sydney, Australia
| | - Siqi Liu
- School of IT, The University of Sydney, Sydney, Australia
| | - Fan Zhang
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Michael Fulham
- Department of PET and Nuclear Medicine, Royal Prince Alfred Hospital, and the Sydney Medical School, The University of Sydney, Sydney, Australia
| | - Dagan Feng
- School of IT, The University of Sydney, Sydney, Australia
- Med-X Research Institute, Shanghai Jiao Tong University, Shanghai, China
| | - Sonia Pujol
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Surgical Planning Laboratory, Harvard Medical School, Boston, USA
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