1
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Chen Y, Lin SC, Zhou Y, Carmichael O, Müller HG, Wang JL. Gradient synchronization for multivariate functional data, with application to brain connectivity. J R Stat Soc Series B Stat Methodol 2024; 86:694-713. [PMID: 39005888 PMCID: PMC11239314 DOI: 10.1093/jrsssb/qkad140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 12/05/2023] [Accepted: 12/07/2023] [Indexed: 07/16/2024]
Abstract
Quantifying the association between components of multivariate random curves is of general interest and is a ubiquitous and basic problem that can be addressed with functional data analysis. An important application is the problem of assessing functional connectivity based on functional magnetic resonance imaging (fMRI), where one aims to determine the similarity of fMRI time courses that are recorded on anatomically separated brain regions. In the functional brain connectivity literature, the static temporal Pearson correlation has been the prevailing measure for functional connectivity. However, recent research has revealed temporally changing patterns of functional connectivity, leading to the study of dynamic functional connectivity. This motivates new similarity measures for pairs of random curves that reflect the dynamic features of functional similarity. Specifically, we introduce gradient synchronization measures in a general setting. These similarity measures are based on the concordance and discordance of the gradients between paired smooth random functions. Asymptotic normality of the proposed estimates is obtained under regularity conditions. We illustrate the proposed synchronization measures via simulations and an application to resting-state fMRI signals from the Alzheimer's Disease Neuroimaging Initiative and they are found to improve discrimination between subjects with different disease status.
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Affiliation(s)
- Yaqing Chen
- Department of Statistics, Rutgers University, New Brunswick, New Jersey, USA
| | - Shu-Chin Lin
- Department of Statistics, University of California, Davis, Davis, California, USA
| | - Yang Zhou
- Department of Statistics, University of California, Davis, Davis, California, USA
| | - Owen Carmichael
- Pennington Biomedical Research Center, Louisiana State University, Baton Rouge, Louisiana, USA
| | - Hans-Georg Müller
- Department of Statistics, University of California, Davis, Davis, California, USA
| | - Jane-Ling Wang
- Department of Statistics, University of California, Davis, Davis, California, USA
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2
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Chauhan N, Choi BJ. Classification of Alzheimer's Disease Using Maximal Information Coefficient-Based Functional Connectivity with an Extreme Learning Machine. Brain Sci 2023; 13:1046. [PMID: 37508978 PMCID: PMC10377329 DOI: 10.3390/brainsci13071046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 07/03/2023] [Accepted: 07/05/2023] [Indexed: 07/30/2023] Open
Abstract
Alzheimer's disease (AD) is a progressive chronic illness that leads to cognitive decline and dementia. Neuroimaging technologies, such as functional magnetic resonance imaging (fMRI), and deep learning approaches offer promising avenues for AD classification. In this study, we investigate the use of fMRI-based functional connectivity (FC) measures, including the Pearson correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC), combined with extreme learning machines (ELM) for AD classification. Our findings demonstrate that employing non-linear techniques, such as MIC and eMIC, as features for classification yields accurate results. Specifically, eMIC-based features achieve a high accuracy of 94% for classifying cognitively normal (CN) and mild cognitive impairment (MCI) individuals, outperforming PCC (81%) and MIC (85%). For MCI and AD classification, MIC achieves higher accuracy (81%) compared to PCC (58%) and eMIC (78%). In CN and AD classification, eMIC exhibits the best accuracy of 95% compared to MIC (90%) and PCC (87%). These results underscore the effectiveness of fMRI-based features derived from non-linear techniques in accurately differentiating AD and MCI individuals from CN individuals, emphasizing the potential of neuroimaging and machine learning methods for improving AD diagnosis and classification.
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Affiliation(s)
- Nishant Chauhan
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
| | - Byung-Jae Choi
- Department of Electronic Engineering, Daegu University, Gyeongsan 38453, Republic of Korea
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3
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Sun H, Lin F, Wu X, Zhang T, Li J. Normalized mutual information of fNIRS signals as a measure for accessing typical and atypical brain activity. JOURNAL OF BIOPHOTONICS 2023; 16:e202200369. [PMID: 36808258 DOI: 10.1002/jbio.202200369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 01/24/2023] [Accepted: 02/10/2023] [Indexed: 06/07/2023]
Abstract
Normalized mutual information (NMI) can be used to detect statistical correlations between time series. We showed possibility of using NMI to quantify synchronicity of information transmission in different brain regions, thus to characterize functional connections, and ultimately analyze differences in physiological states of brain. Resting-state brain signals were recorded from bilateral temporal lobes by functional near-infrared spectroscopy (fNIRS) in 19 young healthy (YH) adults, 25 children with autism spectrum disorder (ASD), and 22 children with typical development (TD). Using NMI of the fNIRS signals, common information volume was assessed for each of three groups. Results showed that mutual information of children with ASD was significantly smaller than that of TD children, while mutual information of YH adults was slightly larger than that of TD children. This study may suggest that NMI could be a measure for assessing brain activity with different development states.
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Affiliation(s)
- Huiwen Sun
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Fang Lin
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Xiaoyin Wu
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Tingzhen Zhang
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
| | - Jun Li
- South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou, China
- Key Lab for Behavioral Economic Science & Technology, South China Normal University, Guangzhou, China
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4
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Fagerholm ED, Dezhina Z, Moran RJ, Turkheimer FE, Leech R. A primer on entropy in neuroscience. Neurosci Biobehav Rev 2023; 146:105070. [PMID: 36736445 DOI: 10.1016/j.neubiorev.2023.105070] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 01/16/2023] [Accepted: 01/29/2023] [Indexed: 02/04/2023]
Abstract
Entropy is not just a property of a system - it is a property of a system and an observer. Specifically, entropy is a measure of the amount of hidden information in a system that arises due to an observer's limitations. Here we provide an account of entropy from first principles in statistical mechanics with the aid of toy models of neural systems. Specifically, we describe the distinction between micro and macrostates in the context of simplified binary-state neurons and the characteristics of entropy required to capture an associated measure of hidden information. We discuss the origin of the mathematical form of entropy via the indistinguishable re-arrangements of discrete-state neurons and show the way in which the arguments are extended into a phase space description for continuous large-scale neural systems. Finally, we show the ways in which limitations in neuroimaging resolution, as represented by coarse graining operations in phase space, lead to an increase in entropy in time as per the second law of thermodynamics. It is our hope that this primer will support the increasing number of studies that use entropy as a way of characterising neuroimaging timeseries and of making inferences about brain states.
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Affiliation(s)
- Erik D Fagerholm
- Department of Neuroimaging, King's College London, United Kingdom.
| | - Zalina Dezhina
- Department of Neuroimaging, King's College London, United Kingdom
| | - Rosalyn J Moran
- Department of Neuroimaging, King's College London, United Kingdom
| | | | - Robert Leech
- Department of Neuroimaging, King's College London, United Kingdom
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5
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Du X, Wei X, Ding H, Yu Y, Xie Y, Ji Y, Zhang Y, Chai C, Liang M, Li J, Zhuo C, Yu C, Qin W. Unraveling schizophrenia replicable functional connectivity disruption patterns across sites. Hum Brain Mapp 2022; 44:156-169. [PMID: 36222054 PMCID: PMC9783440 DOI: 10.1002/hbm.26108] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 09/19/2022] [Accepted: 09/20/2022] [Indexed: 02/05/2023] Open
Abstract
Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal-based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site-effect correction methods in removing the site-related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site-related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo-connectivity within sensory areas (Net1), hypo-connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper-connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set (p < .05). Finally, the three subnet-specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out-of-sample public data set (Z = -1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet-specific composite FC measures might be replicable neuroimaging markers for schizophrenia.
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Affiliation(s)
- Xiaotong Du
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Xiaotong Wei
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Hao Ding
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina,School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Ying Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yingying Xie
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yi Ji
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Yu Zhang
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Chao Chai
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
| | - Meng Liang
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina,School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Jie Li
- Department of Psychiatry Functional Neuroimaging LaboratoryTianjin Mental Health Center, Tianjin Anding HospitalTianjinChina
| | - Chuanjun Zhuo
- Department of Psychiatry Functional Neuroimaging LaboratoryTianjin Mental Health Center, Tianjin Anding HospitalTianjinChina
| | - Chunshui Yu
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina,School of Medical ImagingTianjin Medical UniversityTianjinChina
| | - Wen Qin
- Department of RadiologyTianjin Medical University General HospitalTianjinChina,Tianjin Key Lab of Functional ImagingTianjin Medical University General HospitalTianjinChina
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6
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Andrea B, Atiqah A, Gianluca E. Reproducible Inter-Personal Brain Coupling Measurements in Hyperscanning Settings With functional Near Infra-Red Spectroscopy. Neuroinformatics 2022; 20:665-675. [PMID: 34716564 DOI: 10.1007/s12021-021-09551-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/21/2021] [Indexed: 12/31/2022]
Abstract
Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen's d, the power analysis indicated that a high sample size ([Formula: see text]) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.
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Affiliation(s)
- Bizzego Andrea
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy
| | - Azhari Atiqah
- Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore, Singapore
| | - Esposito Gianluca
- Department of Psychology and Cognitive Science, University of Trento, Trento, Italy. .,Psychology Program, School of Social Sciences, Nanyang Technological University, Singapore, Singapore. .,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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7
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Deng S, Franklin CG, O'Boyle M, Zhang W, Heyl BL, Jerabek PA, Lu H, Fox PT. Hemodynamic and metabolic correspondence of resting-state voxel-based physiological metrics in healthy adults. Neuroimage 2022; 250:118923. [PMID: 35066157 PMCID: PMC9201851 DOI: 10.1016/j.neuroimage.2022.118923] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2021] [Revised: 01/07/2022] [Accepted: 01/11/2022] [Indexed: 12/18/2022] Open
Abstract
Voxel-based physiological (VBP) variables derived from blood oxygen level dependent (BOLD) fMRI time-course variations include: amplitude of low frequency fluctuations (ALFF), fractional amplitude of low frequency fluctuations (fALFF) and regional homogeneity (ReHo). Although these BOLD-derived variables can detect between-group (e.g. disease vs control) spatial pattern differences, physiological interpretations are not well established. The primary objective of this study was to quantify spatial correspondences between BOLD VBP variables and PET measurements of cerebral metabolic rate and hemodynamics, being well-validated physiological standards. To this end, quantitative, whole-brain PET images of metabolic rate of glucose (MRGlu; 18FDG) and oxygen (MRO2; 15OO), blood flow (BF; H215O) and blood volume (BV; C15O) were obtained in 16 healthy controls. In the same subjects, BOLD time-courses were obtained for computation of ALFF, fALFF and ReHo images. PET variables were compared pair-wise with BOLD variables. In group-averaged, across-region analyses, ALFF corresponded significantly only with BV (R = 0.64; p < 0.0001). fALFF corresponded most strongly with MRGlu (R = 0.79; p < 0.0001), but also significantly (p < 0.0001) with MRO2 (R = 0.68), BF (R = 0.68) and BV (R=0.68). ReHo performed similarly to fALFF, with significant strong correspondence (p < 0.0001) with MRGlu (R = 0.78), MRO2 (R = 0.54), and, but less strongly with BF (R = 0.50) and BV (R=0.50). Mutual information analyses further clarified these physiological interpretations. When conditioned by BV, ALFF retained no significant MRGlu, MRO2 or BF information. When conditioned by MRGlu, fALFF and ReHo retained no significant MRO2, BF or BV information. Of concern, however, the strength of PET-BOLD correspondences varied markedly by brain region, which calls for future investigation on physiological interpretations at a regional and per-subject basis.
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Affiliation(s)
- Shengwen Deng
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, USA
| | - Crystal G Franklin
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Michael O'Boyle
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Wei Zhang
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Betty L Heyl
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Paul A Jerabek
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, MD, USA
| | - Peter T Fox
- Research Imaging Institute, University of Texas Health Science Center at San Antonio, 7703 Floyd Curl Drive, San Antonio, TX 78229, USA; Glenn Biggs Institute for Alzheimer's & Neurodegenerative Disorders, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, TX, USA; South Texas Veterans Health Care System, San Antonio, TX, USA.
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8
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Kocaoglu B, Alexander WH. Degeneracy measures in biologically plausible random Boolean networks. BMC Bioinformatics 2022; 23:71. [PMID: 35164672 PMCID: PMC8845291 DOI: 10.1186/s12859-022-04601-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Accepted: 01/31/2022] [Indexed: 11/10/2022] Open
Abstract
Background Degeneracy—the ability of structurally different elements to perform similar functions—is a property of many biological systems. Highly degenerate systems show resilience to perturbations and damage because the system can compensate for compromised function due to reconfiguration of the underlying network dynamics. Degeneracy thus suggests how biological systems can thrive despite changes to internal and external demands. Although degeneracy is a feature of network topologies and seems to be implicated in a wide variety of biological processes, research on degeneracy in biological networks is mostly limited to weighted networks. In this study, we test an information theoretic definition of degeneracy on random Boolean networks, frequently used to model gene regulatory networks. Random Boolean networks are discrete dynamical systems with binary connectivity and thus, these networks are well-suited for tracing information flow and the causal effects. By generating networks with random binary wiring diagrams, we test the effects of systematic lesioning of connections and perturbations of the network nodes on the degeneracy measure. Results Our analysis shows that degeneracy, on average, is the highest in networks in which ~ 20% of the connections are lesioned while 50% of the nodes are perturbed. Moreover, our results for the networks with no lesions and the fully-lesioned networks are comparable to the degeneracy measures from weighted networks, thus we show that the degeneracy measure is applicable to different networks. Conclusions Such a generalized applicability implies that degeneracy measures may be a useful tool for investigating a wide range of biological networks and, therefore, can be used to make predictions about the variety of systems’ ability to recover function. Supplementary Information The online version contains supplementary material available at 10.1186/s12859-022-04601-5.
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Affiliation(s)
- Basak Kocaoglu
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA. .,The Brain Institute, Florida Atlantic University, Jupiter, FL, 33431, USA.
| | - William H Alexander
- Center for Complex Systems and Brain Sciences, Florida Atlantic University, Boca Raton, FL, USA.,Department of Psychology, Florida Atlantic University, Boca Raton, FL, USA.,The Brain Institute, Florida Atlantic University, Jupiter, FL, 33431, USA
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9
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Chu Y, Wang G, Cao L, Qiao L, Liu M. Multi-Scale Graph Representation Learning for Autism Identification With Functional MRI. Front Neuroinform 2022; 15:802305. [PMID: 35095453 PMCID: PMC8792610 DOI: 10.3389/fninf.2021.802305] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Accepted: 12/06/2021] [Indexed: 11/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) has been widely used for the early diagnosis of autism spectrum disorder (ASD). With rs-fMRI, the functional connectivity networks (FCNs) are usually constructed for representing each subject, with each element representing the pairwise relationship between brain region-of-interests (ROIs). Previous studies often first extract handcrafted network features (such as node degree and clustering coefficient) from FCNs and then construct a prediction model for ASD diagnosis, which largely requires expert knowledge. Graph convolutional networks (GCNs) have recently been employed to jointly perform FCNs feature extraction and ASD identification in a data-driven manner. However, existing studies tend to focus on the single-scale topology of FCNs by using one single atlas for ROI partition, thus ignoring potential complementary topology information of FCNs at different spatial scales. In this paper, we develop a multi-scale graph representation learning (MGRL) framework for rs-fMRI based ASD diagnosis. The MGRL consists of three major components: (1) multi-scale FCNs construction using multiple brain atlases for ROI partition, (2) FCNs representation learning via multi-scale GCNs, and (3) multi-scale feature fusion and classification for ASD diagnosis. The proposed MGRL is evaluated on 184 subjects from the public Autism Brain Imaging Data Exchange (ABIDE) database with rs-fMRI scans. Experimental results suggest the efficacy of our MGRL in FCN feature extraction and ASD identification, compared with several state-of-the-art methods.
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Affiliation(s)
- Ying Chu
- School of Mathematics Science, Liaocheng University, Liaocheng, China
- Department of Information Science and Technology, Taishan University, Taian, China
| | - Guangyu Wang
- School of Mathematics Science, Liaocheng University, Liaocheng, China
| | - Liang Cao
- Taian Tumor Prevention and Treatment Hospital, Taian, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng, China
- *Correspondence: Lishan Qiao
| | - Mingxia Liu
- Department of Information Science and Technology, Taishan University, Taian, China
- Mingxia Liu
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10
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Sun L, Xue Y, Zhang Y, Qiao L, Zhang L, Liu M. Estimating sparse functional connectivity networks via hyperparameter-free learning model. Artif Intell Med 2020; 111:102004. [PMID: 33461688 DOI: 10.1016/j.artmed.2020.102004] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 10/14/2020] [Accepted: 12/15/2020] [Indexed: 12/11/2022]
Abstract
Functional connectivity networks (FCNs) provide a potential way for understanding the brain organizational patterns and diagnosing neurological diseases. Currently, researchers have proposed many methods for FCN construction, among which the most classic example is Pearson's correlation (PC). Despite its simplicity and popularity, PC always results in dense FCNs, and thus a thresholding strategy is usually needed in practice to sparsify the estimated FCNs prior to the network analysis, which undoubtedly causes the problem of threshold parameter selection. As an alternative to PC, sparse representation (SR) can directly generate sparse FCNs due to the l1 regularizer in the estimation model. However, similar to the thresholding scheme used in PC, it is also challenging to determine suitable values for the regularization parameter in SR. To circumvent the difficulty of parameter selection involved in these traditional methods, we propose a hyperparameter-free method for FCN construction based on the global representation among fMRI time courses. Interestingly, the proposed method can automatically generate sparse FCNs, without any thresholding or regularization parameters. To verify the effectiveness of the proposed method, we conduct experiments to identify subjects with mild cognitive impairment (MCI) and Autism spectrum disorder (ASD) from normal controls (NCs) based on the estimated FCNs. Experimental results on two benchmark databases demonstrate that the achieved classification performance of our proposed scheme is comparable to four conventional methods.
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Affiliation(s)
- Lei Sun
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Yanfang Xue
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Yining Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Lishan Qiao
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China
| | - Limei Zhang
- School of Mathematics Science, Liaocheng University, Liaocheng 252000, China.
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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11
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Kottaram A, Johnston LA, Tian Y, Ganella EP, Laskaris L, Cocchi L, McGorry P, Pantelis C, Kotagiri R, Cropley V, Zalesky A. Predicting individual improvement in schizophrenia symptom severity at 1-year follow-up: Comparison of connectomic, structural, and clinical predictors. Hum Brain Mapp 2020; 41:3342-3357. [PMID: 32469448 PMCID: PMC7375115 DOI: 10.1002/hbm.25020] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2019] [Revised: 01/13/2020] [Accepted: 04/13/2020] [Indexed: 12/25/2022] Open
Abstract
In a machine learning setting, this study aims to compare the prognostic utility of connectomic, brain structural, and clinical/demographic predictors of individual change in symptom severity in individuals with schizophrenia. Symptom severity at baseline and 1-year follow-up was assessed in 30 individuals with a schizophrenia-spectrum disorder using the Brief Psychiatric Rating Scale. Structural and functional neuroimaging was acquired in all individuals at baseline. Machine learning classifiers were trained to predict whether individuals improved or worsened with respect to positive, negative, and overall symptom severity. Classifiers were trained using various combinations of predictors, including regional cortical thickness and gray matter volume, static and dynamic resting-state connectivity, and/or baseline clinical and demographic variables. Relative change in overall symptom severity between baseline and 1-year follow-up varied markedly among individuals (interquartile range: 55%). Dynamic resting-state connectivity measured within the default-mode network was the most accurate single predictor of change in positive (accuracy: 87%), negative (83%), and overall symptom severity (77%) at follow-up. Incorporating predictors based on regional cortical thickness, gray matter volume, and baseline clinical variables did not markedly improve prediction accuracy and the prognostic utility of these predictors in isolation was moderate (<70%). Worsening negative symptoms at 1-year follow-up were predicted by hyper-connectivity and hypo-dynamism within the default-mode network at baseline assessment, while hypo-connectivity and hyper-dynamism predicted worsening positive symptoms. Given the modest sample size investigated, we recommend giving precedence to the relative ranking of the predictors investigated in this study, rather than the prediction accuracy estimates.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Melbourne, Victoria, Australia
| | - Ye Tian
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia
| | - Liliana Laskaris
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Patrick McGorry
- Orygen, Parkville, Victoria, Australia.,Centre for Youth Mental Health, The University of Melbourne, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia.,Centre for Neural Engineering, Department of Electrical and Electronic Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,North Western Mental Health, Melbourne Health, Parkville, Victoria, Australia.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Vanessa Cropley
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Melbourne, Victoria, Australia.,Centre for Mental Health, Faculty of Health, Arts and Design, School of Health Sciences, Swinburne University, Hawthorn, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
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12
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D'Cruz J, Hefner M, Ledbetter C, Frilot C, Howard B, Zhu P, Riel-Romero R, Notarianni C, Toledo EG, Nanda A, Sun H. Focal epilepsy caused by single cerebral cavernous malformation (CCM) is associated with regional and global resting state functional connectivity (FC) disruption. NEUROIMAGE-CLINICAL 2019; 24:102072. [PMID: 31734529 PMCID: PMC6854067 DOI: 10.1016/j.nicl.2019.102072] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 10/09/2019] [Accepted: 11/04/2019] [Indexed: 12/17/2022]
Abstract
To our knowledge, this is the first study to report resting state functional connectivity (FC) abnormalities associated with focal epilepsy caused by a single cerebral cavernous malformation (CCM). We show, by comparing to the data acquired from the age and gender matched control group, that this type of focal epilepsy is associated with the disruption of the normal regional and global FC. The disruption includes a decrease in the coactivation between the region surrounding the CCM lesion, i.e., the lesional region, and its homotopic counterpart, a reduction in FC between the lesional region and the rest of the brain, and decreased FC among the default mode network (DMN). These changes may be alleviated or reversed after the surgical resection of the CCM and the epileptogenic zone has successfully stopped recurrent seizures. Finally, the severity of the FC disruption in the brain tissue adjacent to the CCM may be used to delineate the epileptogenic zone and to aid the surgical resection.
Epilepsy, including the type with focal onset, is increasingly viewed as a disorder of the brain network. Here we employed the functional connectivity (FC) metrics estimated from the resting state functional MRI (rsfMRI) to investigate the changes of brain network associated with focal epilepsy caused by single cerebral cavernous malformation (CCM). Eight CCM subjects and 21 age and gender matched controls were enrolled in the study. Seven of 8 CCM subjects underwent surgical resection of the CCM and became seizure free and 4 of the surgical subjects underwent a repeat rsfMRI study. We showed that there was both regional and global disruption of the FC values among the CCM subjects including decreased in homotopic FC (HFC) and global FC (GFC) in the regions of interest (ROIs) where the CCMs were located. There was also the disruption of the default mode network (DMN) especially the FC between the middle prefrontal cortex (MPFC) and the right lateral parietal cortex (LPR) among these individuals. We observed the trend of alleviation of these disruptions after the individual has become seizure free from the surgical resection of the CCM. Using a voxel-based approach, we found the disruption of the HFC and GFC in the brain tissue immediately adjacent to the CCM and the severity of the disruption appeared inversely proportional to the distance of the brain tissue to the lesion. Our findings confirm the disruption of normal brain networks from focal epilepsy, a process that may be reversible with successful surgical treatments rendering patients seizure free. Some voxel-based metrics may help identify the epileptogenic zone and guide the surgical resection.
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Affiliation(s)
- Jason D'Cruz
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Matthew Hefner
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Christina Ledbetter
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Clifton Frilot
- School of Allied Health Professions, Department of Rehabilitation Sciences, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Brady Howard
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Peimin Zhu
- Department of Neurology, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Rosario Riel-Romero
- Department of Neurology, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Christina Notarianni
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Eduardo Gonzalez Toledo
- Department of Radiology, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States
| | - Anil Nanda
- Department of Neurosurgery, Robert Wood Johnson Medical School, New Brunswick, NJ 08903, United States
| | - Hai Sun
- Department of Neurosurgery, Louisiana State Unversity Health Science Center, Shreveport, LA 71103, United States.
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13
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Kottaram A, Johnston LA, Cocchi L, Ganella EP, Everall I, Pantelis C, Kotagiri R, Zalesky A. Brain network dynamics in schizophrenia: Reduced dynamism of the default mode network. Hum Brain Mapp 2019; 40:2212-2228. [PMID: 30664285 DOI: 10.1002/hbm.24519] [Citation(s) in RCA: 51] [Impact Index Per Article: 10.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 12/06/2018] [Accepted: 12/26/2018] [Indexed: 02/03/2023] Open
Abstract
Complex human behavior emerges from dynamic patterns of neural activity that transiently synchronize between distributed brain networks. This study aims to model the dynamics of neural activity in individuals with schizophrenia and to investigate whether the attributes of these dynamics associate with the disorder's behavioral and cognitive deficits. A hidden Markov model (HMM) was inferred from resting-state functional magnetic resonance imaging (fMRI) data that was temporally concatenated across individuals with schizophrenia (n = 41) and healthy comparison individuals (n = 41). Under the HMM, fluctuations in fMRI activity within 14 canonical resting-state networks were described using a repertoire of 12 brain states. The proportion of time spent in each state and the mean length of visits to each state were compared between groups, and canonical correlation analysis was used to test for associations between these state descriptors and symptom severity. Individuals with schizophrenia activated default mode and executive networks for a significantly shorter proportion of the 8-min acquisition than healthy comparison individuals. While the default mode was activated less frequently in schizophrenia, the duration of each activation was on average 4-5 s longer than the comparison group. Severity of positive symptoms was associated with a longer proportion of time spent in states characterized by inactive default mode and executive networks, together with heightened activity in sensory networks. Furthermore, classifiers trained on the state descriptors predicted individual diagnostic status with an accuracy of 76-85%.
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Affiliation(s)
- Akhil Kottaram
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia
| | - Leigh A Johnston
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Brain Centre Imaging Unit, The University of Melbourne, Victoria, Australia
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Eleni P Ganella
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia
| | - Ian Everall
- Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Psychology and Neuroscience, Institute of Psychiatry, Kings College London, London, United Kingdom.,South London and Maudsley NHS Foundation Trust, Bethlem Royal Hospital, Beckenham, United Kingdom.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia
| | - Christos Pantelis
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia.,Department of Psychiatry, The University of Melbourne, Victoria, Australia.,Schizophrenia Research Group, Cooperative Research Centre for Mental Health, Carlton, Victoria, Australia.,Florey Institute for Neurosciences and Mental Health, Parkville, Victoria, Australia.,Department of Electrical and Electronic Engineering, Centre for Neural Engineering, The University of Melbourne, Victoria, Australia.,North Western Mental Health, Melbourne Health, Victoria, Australia
| | - Ramamohanarao Kotagiri
- Department of Computing and Information Systems, The University of Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Victoria, Australia.,Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, Australia
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14
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Liu R, Vlachos I. Mutual information in the frequency domain for the study of biological systems. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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15
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Yu M, Dai Z, Tang X, Wang X, Zhang X, Sha W, Yao S, Shu N, Wang X, Yang J, Zhang X, Zhang X, He Y, Zhang Z. Convergence and Divergence of Brain Network Dysfunction in Deficit and Non-deficit Schizophrenia. Schizophr Bull 2017; 43:1315-1328. [PMID: 29036672 PMCID: PMC5737538 DOI: 10.1093/schbul/sbx014] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/17/2023]
Abstract
Deficit schizophrenia (DS), characterized by primary and enduring negative symptoms, has been considered as a pathophysiologically distinct schizophrenic subgroup. Neuroimaging characteristics of DS, especially functional brain network architecture, remain largely unknown. Resting-state functional magnetic resonance imaging and graph theory approaches were employed to investigate the topological organization of whole-brain functional networks of 114 male participants including 33 DS, 41 non-deficit schizophrenia (NDS) and 40 healthy controls (HCs). At the whole-brain level, both the NDS and DS group exhibited lower local efficiency (Eloc) than the HC group, implying the reduction of local specialization of brain information processing (reduced functional segregation). The DS, but not NDS group, exhibited enhanced parallel information transfer (enhanced functional integration) as determined by smaller characteristic path length (Lp) and higher global efficiency (Eglob). The Lp and Eglob presented significant correlations with Brief Psychiatric Rating Scale (BPRS) total score in the DS group. At the nodal level, both the NDS and DS groups showed higher functional connectivity in the inferior frontal gyrus and hippocampus, and lower connectivity in the visual areas and striatum than the controls. The DS group exhibited higher nodal connectivity in the right inferior temporal gyrus than the NDS and HC group. The diminished expression of Scale for the Assessment of Negative Symptoms (SANS) subfactors negatively correlated with nodal connectivity of right putamen, while asociality/amotivation positively correlated with right hippocampus across whole patients. We highlighted the convergence and divergence of brain functional network dysfunctions in patients with DS and NDS, which provides crucial insights into pathophysiological mechanisms of the 2 schizophrenic subtypes.
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Affiliation(s)
- Miao Yu
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou, China,State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xiaowei Tang
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Xiang Wang
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Xiaobin Zhang
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Weiwei Sha
- Department of Psychiatry, Wutaishan Hospital of Yangzhou, Yangzhou, China
| | - Shuqiao Yao
- Medical Psychological Institute of the Second Xiangya Hospital, Central South University, Changsha, China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Jiaying Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China
| | - Xiangyang Zhang
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX
| | - Xiangrong Zhang
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China,Department of Geriatric Psychiatry, Nanjing Brain Hospital Affiliated to Nanjing Medical University, Nanjing, China,To whom correspondence should be addressed; Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, No.87 DingJiaQiao Road, Nanjing 210009, China; tel: 0086-25-822906586, fax:0086-25-83719457, e-mail:
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
| | - Zhijun Zhang
- Department of Neuropsychiatry, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, China,Beijing Institute for Brain Disorders, Beijing Anding Hospital, Capital Medical University, Beijing, China
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16
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Pang R, Zhan Y, Zhang Y, Guo R, Wang J, Guo X, Liu Y, Wang Z, Li K. Aberrant Functional Connectivity Architecture in Participants with Chronic Insomnia Disorder Accompanying Cognitive Dysfunction: A Whole-Brain, Data-Driven Analysis. Front Neurosci 2017; 11:259. [PMID: 28553199 PMCID: PMC5425485 DOI: 10.3389/fnins.2017.00259] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2016] [Accepted: 04/21/2017] [Indexed: 12/14/2022] Open
Abstract
Objectives: Although it is widely observed that chronic insomnia disorder (CID) is associated with cognitive impairment, the neurobiological mechanisms underlying this remain unclear. Prior neuroimaging studies have confirmed that a close correlation exists between functional connectivity and cognitive impairment. Based on this observation, in this study we used resting-state functional magnetic resonance imaging (rs-fMRI) to study the relationship between whole brain functional connectivity and cognitive function in CID. Methods: We included 39 patients with CID and 28 age-, gender-, and education-matched healthy controls (HC). Abnormalities in functional connectivity were identified by comparing the correlation coefficients for each pair of 116 brain regions between CID and HC. Results: Cognitive impairment was associated with reduced subjective insomnia scores after controlling for age, gender, and educational effects. Compared with HC, patients with CID had larger negative correlations within the task-negative network [medial prefrontal cortex (mPFC), precuneus, inferior temporal gyrus, cerebellum, and superior parietal gyrus], and between two intrinsic anti-correlation networks (mPFC and middle temporal gyrus; supplementary motor area and cerebellum). Patients with CID also had decreased positive correlations within the default mode network (DMN), and between the cerebellum and DMN, which mainly comprises the mPFC and posterior cingulated cortex. There were positive correlations of decreased positive connectivity with subjective sleep scores and MMSE scores, and increased negative correlations between the task-negative-network and MMSE scores in CID. Conclusions: Using rs-fMRI, our results support previous observations of cortical disconnection in CID in the prefrontal and DMN networks. Moreover, abnormal correlations within the task-negative network, and between two intrinsically anti-correlation networks, might be important neurobiological indicators of CID and associated cognitive impairment.
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Affiliation(s)
- Ran Pang
- Department of Radiology, Xuanwu Hospital, Capital Medical UniversityBeijing, China.,Department of Radiology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China
| | - Yafeng Zhan
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,School of Biomedical Engineering, Southern Medical UniversityGuangzhou, China
| | - Yunling Zhang
- Department of Neurology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China
| | - Rongjuan Guo
- Department of Neurology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China
| | - Jialin Wang
- Department of Neurology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China
| | - Xiao Guo
- Department of Neurology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China.,School of Clinical Medicine, Beijing University of Chinese MedicineBeijing, China
| | - Yong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of SciencesBeijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of SciencesBeijing, China.,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of SciencesBeijing, China.,University of Chinese Academy of SciencesBeijing, China
| | - Zhiqun Wang
- Department of Radiology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China
| | - Kuncheng Li
- Department of Radiology, Xuanwu Hospital, Capital Medical UniversityBeijing, China.,Department of Radiology, Dongfang Hospital, Beijing University of Chinese MedicineBeijing, China.,Beijing Key Laboratory of Magnetic Resonance Imaging and Brain InformaticsBeijing, China
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17
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Goelman G, Dan R, Růžička F, Bezdicek O, Růžička E, Roth J, Vymazal J, Jech R. Frequency-phase analysis of resting-state functional MRI. Sci Rep 2017; 7:43743. [PMID: 28272522 PMCID: PMC5341062 DOI: 10.1038/srep43743] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Accepted: 01/30/2017] [Indexed: 12/14/2022] Open
Abstract
We describe an analysis method that characterizes the correlation between coupled time-series functions by their frequencies and phases. It provides a unified framework for simultaneous assessment of frequency and latency of a coupled time-series. The analysis is demonstrated on resting-state functional MRI data of 34 healthy subjects. Interactions between fMRI time-series are represented by cross-correlation (with time-lag) functions. A general linear model is used on the cross-correlation functions to obtain the frequencies and phase-differences of the original time-series. We define symmetric, antisymmetric and asymmetric cross-correlation functions that correspond respectively to in-phase, 90° out-of-phase and any phase difference between a pair of time-series, where the last two were never introduced before. Seed maps of the motor system were calculated to demonstrate the strength and capabilities of the analysis. Unique types of functional connections, their dominant frequencies and phase-differences have been identified. The relation between phase-differences and time-delays is shown. The phase-differences are speculated to inform transfer-time and/or to reflect a difference in the hemodynamic response between regions that are modulated by neurotransmitters concentration. The analysis can be used with any coupled functions in many disciplines including electrophysiology, EEG or MEG in neuroscience.
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Affiliation(s)
- Gadi Goelman
- MRI Lab, The Human Biology Research Center, Department of Medical Biophysics, Hadassah Hebrew University Medical Center, Jerusalem, Israel
| | - Rotem Dan
- MRI Lab, The Human Biology Research Center, Department of Medical Biophysics, Hadassah Hebrew University Medical Center, Jerusalem, Israel.,Edmond and Lily Safra Center for Brain Sciences (ELSC), The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Filip Růžička
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University in Prague, Prague, Czech Republic
| | - Ondrej Bezdicek
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University in Prague, Prague, Czech Republic
| | - Evžen Růžička
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University in Prague, Prague, Czech Republic
| | - Jan Roth
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University in Prague, Prague, Czech Republic
| | - Josef Vymazal
- Department of Radiology, Na Homolce Hospital, Prague, Czech Republic
| | - Robert Jech
- Department of Neurology and Center of Clinical Neuroscience, First Faculty of Medicine and General University Hospital, Charles University in Prague, Prague, Czech Republic
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18
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Ince RA, Giordano BL, Kayser C, Rousselet GA, Gross J, Schyns PG. A statistical framework for neuroimaging data analysis based on mutual information estimated via a gaussian copula. Hum Brain Mapp 2017; 38:1541-1573. [PMID: 27860095 PMCID: PMC5324576 DOI: 10.1002/hbm.23471] [Citation(s) in RCA: 144] [Impact Index Per Article: 20.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Revised: 10/25/2016] [Accepted: 11/07/2016] [Indexed: 12/17/2022] Open
Abstract
We begin by reviewing the statistical framework of information theory as applicable to neuroimaging data analysis. A major factor hindering wider adoption of this framework in neuroimaging is the difficulty of estimating information theoretic quantities in practice. We present a novel estimation technique that combines the statistical theory of copulas with the closed form solution for the entropy of Gaussian variables. This results in a general, computationally efficient, flexible, and robust multivariate statistical framework that provides effect sizes on a common meaningful scale, allows for unified treatment of discrete, continuous, unidimensional and multidimensional variables, and enables direct comparisons of representations from behavioral and brain responses across any recording modality. We validate the use of this estimate as a statistical test within a neuroimaging context, considering both discrete stimulus classes and continuous stimulus features. We also present examples of analyses facilitated by these developments, including application of multivariate analyses to MEG planar magnetic field gradients, and pairwise temporal interactions in evoked EEG responses. We show the benefit of considering the instantaneous temporal derivative together with the raw values of M/EEG signals as a multivariate response, how we can separately quantify modulations of amplitude and direction for vector quantities, and how we can measure the emergence of novel information over time in evoked responses. Open-source Matlab and Python code implementing the new methods accompanies this article. Hum Brain Mapp 38:1541-1573, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Robin A.A. Ince
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Bruno L. Giordano
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Christoph Kayser
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | | | - Joachim Gross
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
| | - Philippe G. Schyns
- Institute of Neuroscience and Psychology, University of GlasgowGlasgowUnited Kingdom
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19
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Li R, Lai Y, Zhang Y, Yao L, Wu X. Classification of Cognitive Level of Patients with Leukoaraiosis on the Basis of Linear and Non-Linear Functional Connectivity. Front Neurol 2017; 8:2. [PMID: 28154549 PMCID: PMC5243822 DOI: 10.3389/fneur.2017.00002] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2016] [Accepted: 01/04/2017] [Indexed: 11/18/2022] Open
Abstract
Leukoaraiosis (LA) describes diffuse white matter abnormalities apparent in computed tomography (CT) or magnetic resonance (MR) brain scans. Patients with LA generally show varying degrees of cognitive impairment, which can be classified as cognitively normal (CN), mild cognitive impairment (MCI), and dementia. However, a consistent relationship between the degree of LA and the level of cognitive impairment has not yet been established. We used functional magnetic resonance imaging (fMRI) to explore possible neuroimaging biomarkers for classification of cognitive level in LA. Functional connectivity (FC) between brain regions was calculated using Pearson’s correlation coefficient (PCC), maximal information coefficient (MIC), and extended maximal information coefficient (eMIC). Next, FCs with high discriminative power for different cognitive levels in LA were used as features for classification based on support vector machine. CN and MCI were classified with accuracies of 75.0, 61.9, and 91.1% based on features from PCC, MIC, and eMIC, respectively. MCI and dementia were classified with accuracies of 80.1, 86.2, and 87.4% based on features from PCC, MIC, and eMIC, respectively. CN and dementia were classified with accuracies of 80.1, 89.9, and 94.4% based on features from PCC, MIC, and eMIC, respectively. Our results suggest that features extracted from fMRI were efficient for classification of cognitive impairment level in LA, especially, when features were based on a non-linear method (eMIC).
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Affiliation(s)
- Ranran Li
- College of Information Science and Technology, Beijing Normal University , Beijing , China
| | - Youzhi Lai
- College of Information Science and Technology, Beijing Normal University , Beijing , China
| | - Yumei Zhang
- Neurology Department, Beijing Tiantan Hospital Affiliated with Capital Medical University , Beijing , China
| | - Li Yao
- College of Information Science and Technology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xia Wu
- College of Information Science and Technology, Beijing Normal University, Beijing, China; State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
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20
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Peters H, Riedl V, Manoliu A, Scherr M, Schwerthöffer D, Zimmer C, Förstl H, Bäuml J, Sorg C, Koch K. Changes in extra-striatal functional connectivity in patients with schizophrenia in a psychotic episode. Br J Psychiatry 2017; 210:75-82. [PMID: 26892851 DOI: 10.1192/bjp.bp.114.151928] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2014] [Revised: 12/15/2014] [Accepted: 05/27/2015] [Indexed: 11/23/2022]
Abstract
BACKGROUND In patients with schizophrenia in a psychotic episode, intra-striatal intrinsic connectivity is increased in the putamen but not ventral striatum. Furthermore, multimodal changes have been observed in the anterior insula that interact extensively with the putamen. AIMS We hypothesised that during psychosis, putamen extra-striatal functional connectivity is altered with both the anterior insula and areas normally connected with the ventral striatum (i.e. altered functional connectivity distinctiveness of putamen and ventral striatum). METHOD We acquired resting-state functional magnetic resonance images from 21 patients with schizophrenia in a psychotic episode and 42 controls. RESULTS Patients had decreased functional connectivity: the putamen with right anterior insula and dorsal prefrontal cortex, the ventral striatum with left anterior insula. Decreased functional connectivity between putamen and right anterior insula was specifically associated with patients' hallucinations. Functional connectivity distinctiveness was impaired only for the putamen. CONCLUSIONS Results indicate aberrant extra-striatal connectivity during psychosis and a relationship between reduced putamen-right anterior insula connectivity and hallucinations. Data suggest that altered intrinsic connectivity links striatal and insular pathophysiology in psychosis.
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Affiliation(s)
- Henning Peters
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Valentin Riedl
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Andrei Manoliu
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Martin Scherr
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Dirk Schwerthöffer
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Claus Zimmer
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Hans Förstl
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Josef Bäuml
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Christian Sorg
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
| | - Kathrin Koch
- Henning Peters, MD, PhD, Department of Psychiatry and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Valentin Riedl, MD, PhD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Andrei Manoliu, MD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany and Department of Radiology, University Hospital Zürich, Rämistrasse 100, 8091 Zürich, Switzerland; Martin Scherr, MD, Dirk Schwerthöffer, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Claus Zimmer, MD, Department of Neuroradiology, Technische Universität München, Munich, Germany; Hans Förstl, MD, Josef Baüml, MD, Department of Psychiatry, Technische Universität München, Munich, Germany; Christian Sorg, MD, Department of Psychiatry, Department of Neuroradiology, Department of Nuclear Medicine and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany; Kathrin Koch, PhD, Department of Neuroradiology and TUM-Neuroimaging Center Klinikum rechts der Isar, Technische Universität München, Munich, Germany
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Zhou D, Gozolchiani A, Ashkenazy Y, Havlin S. Teleconnection Paths via Climate Network Direct Link Detection. PHYSICAL REVIEW LETTERS 2015; 115:268501. [PMID: 26765033 DOI: 10.1103/physrevlett.115.268501] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2015] [Indexed: 06/05/2023]
Abstract
Teleconnections describe remote connections (typically thousands of kilometers) of the climate system. These are of great importance in climate dynamics as they reflect the transportation of energy and climate change on global scales (like the El Niño phenomenon). Yet, the path of influence propagation between such remote regions, and weighting associated with different paths, are only partially known. Here we propose a systematic climate network approach to find and quantify the optimal paths between remotely distant interacting locations. Specifically, we separate the correlations between two grid points into direct and indirect components, where the optimal path is found based on a minimal total cost function of the direct links. We demonstrate our method using near surface air temperature reanalysis data, on identifying cross-latitude teleconnections and their corresponding optimal paths. The proposed method may be used to quantify and improve our understanding regarding the emergence of climate patterns on global scales.
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Affiliation(s)
- Dong Zhou
- Department of Physics, Bar-Ilan University, Ramat Gan 52900, Israel
| | - Avi Gozolchiani
- Department of Solar Energy and Environmental Physics, BIDR, Ben-Gurion University, Midreshet Ben-Gurion 84990, Israel
| | - Yosef Ashkenazy
- Department of Solar Energy and Environmental Physics, BIDR, Ben-Gurion University, Midreshet Ben-Gurion 84990, Israel
| | - Shlomo Havlin
- Department of Physics, Bar-Ilan University, Ramat Gan 52900, Israel
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22
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Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci 2015; 9:386. [PMID: 26175682 PMCID: PMC4485071 DOI: 10.3389/fnhum.2015.00386] [Citation(s) in RCA: 526] [Impact Index Per Article: 58.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2015] [Accepted: 06/16/2015] [Indexed: 01/19/2023] Open
Abstract
Recent studies have suggested that the brain’s structural and functional networks (i.e., connectomics) can be constructed by various imaging technologies (e.g., EEG/MEG; structural, diffusion and functional MRI) and further characterized by graph theory. Given the huge complexity of network construction, analysis and statistics, toolboxes incorporating these functions are largely lacking. Here, we developed the GRaph thEoreTical Network Analysis (GRETNA) toolbox for imaging connectomics. The GRETNA contains several key features as follows: (i) an open-source, Matlab-based, cross-platform (Windows and UNIX OS) package with a graphical user interface (GUI); (ii) allowing topological analyses of global and local network properties with parallel computing ability, independent of imaging modality and species; (iii) providing flexible manipulations in several key steps during network construction and analysis, which include network node definition, network connectivity processing, network type selection and choice of thresholding procedure; (iv) allowing statistical comparisons of global, nodal and connectional network metrics and assessments of relationship between these network metrics and clinical or behavioral variables of interest; and (v) including functionality in image preprocessing and network construction based on resting-state functional MRI (R-fMRI) data. After applying the GRETNA to a publicly released R-fMRI dataset of 54 healthy young adults, we demonstrated that human brain functional networks exhibit efficient small-world, assortative, hierarchical and modular organizations and possess highly connected hubs and that these findings are robust against different analytical strategies. With these efforts, we anticipate that GRETNA will accelerate imaging connectomics in an easy, quick and flexible manner. GRETNA is freely available on the NITRC website.1
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Affiliation(s)
- Jinhui Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Cognition and Brain Disorders, Hangzhou Normal University Hangzhou, China ; Zhejiang Key Laboratory for Research in Assessment of Cognitive Impairments Hangzhou, China
| | - Xindi Wang
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Mingrui Xia
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Xuhong Liao
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China
| | - Alan Evans
- McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; McConnell Brain Imaging Center, Montreal Neurological Institute, McGill University Montreal, QC, Canada
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Aberrant Functional Connectivity Architecture in Alzheimer's Disease and Mild Cognitive Impairment: A Whole-Brain, Data-Driven Analysis. BIOMED RESEARCH INTERNATIONAL 2015; 2015:495375. [PMID: 26167487 PMCID: PMC4475740 DOI: 10.1155/2015/495375] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2014] [Accepted: 03/31/2015] [Indexed: 11/17/2022]
Abstract
The purpose of our study was to investigate whether the whole-brain functional connectivity pattern exhibits disease severity-related alterations in patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI). Resting-state functional magnetic resonance imaging data were acquired in 27 MCI subjects, 35 AD patients, and 27 age- and gender-matched subjects with normal cognition (NC). Interregional functional connectivity was assessed based on a predefined template which parcellated the brain into 90 regions. Altered whole-brain functional connectivity patterns were identified via connectivity comparisons between the AD and NC subjects. Finally, the relationship between functional connectivity strength and cognitive ability according to the mini-mental state examination (MMSE) was evaluated in the MCI and AD groups. Compared with the NC group, the AD group exhibited decreased functional connectivities throughout the brain. The most significantly affected regions included several important nodes of the default mode network and the temporal lobe. Moreover, changes in functional connectivity strength exhibited significant associations with disease severity-related alterations in the AD and MCI groups. The present study provides novel evidence and will facilitate meta-analysis of whole-brain analyses in AD and MCI, which will be critical to better understand the neural basis of AD.
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24
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Fornito A, Bullmore ET. Connectomics: a new paradigm for understanding brain disease. Eur Neuropsychopharmacol 2015; 25:733-48. [PMID: 24726580 DOI: 10.1016/j.euroneuro.2014.02.011] [Citation(s) in RCA: 154] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2013] [Revised: 01/20/2014] [Accepted: 02/12/2014] [Indexed: 12/18/2022]
Abstract
In recent years, pathophysiological models of brain disorders have shifted from an emphasis on understanding pathology in specific brain regions to characterizing disturbances of interconnected neural systems. This shift has paralleled rapid advances in connectomics, a field concerned with comprehensively mapping the neural elements and inter-connections that constitute the brain. Magnetic resonance imaging (MRI) has played a central role in these efforts, as it allows relatively cost-effective in vivo assessment of the macro-scale architecture of brain network connectivity. In this paper, we provide a brief introduction to some of the basic concepts in the field and review how recent developments in imaging connectomics are yielding new insights into brain disease, with a particular focus on Alzheimer's disease and schizophrenia. Specifically, we consider how research into circuit-level, connectome-wide and topological changes is stimulating the development of new aetiopathological theories and biomarkers with potential for clinical translation. The findings highlight the advantage of conceptualizing brain disease as a result of disturbances in an interconnected complex system, rather than discrete pathology in isolated sub-sets of brain regions.
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Affiliation(s)
- Alex Fornito
- Monash Clinical and Imaging Neuroscience, School of Psychology and Psychiatry & Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton 3168, Victoria, Australia.
| | - Edward T Bullmore
- Monash Clinical and Imaging Neuroscience, School of Psychology and Psychiatry & Monash Biomedical Imaging, Monash University, 770 Blackburn Rd, Clayton 3168, Victoria, Australia; Brain Mapping Unit, Department of Psychiatry, and Behavioural and Clinical Neuroscience Institute, University of Cambridge, Cambridge, UK; GlaxoSmithKline, ImmunoPsychiatry, Alternative Discovery & Development, Stevenage, UK; Cambridgeshire & Peterborough NHS Foundation Trust, Cambridge, UK
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25
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Zhou Y, Fan L, Qiu C, Jiang T. Prefrontal cortex and the dysconnectivity hypothesis of schizophrenia. Neurosci Bull 2015; 31:207-19. [PMID: 25761914 DOI: 10.1007/s12264-014-1502-8] [Citation(s) in RCA: 126] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2014] [Accepted: 11/20/2014] [Indexed: 12/15/2022] Open
Abstract
Schizophrenia is hypothesized to arise from disrupted brain connectivity. This "dysconnectivity hypothesis" has generated interest in discovering whether there is anatomical and functional dysconnectivity between the prefrontal cortex (PFC) and other brain regions, and how this dysconnectivity is linked to the impaired cognitive functions and aberrant behaviors of schizophrenia. Critical advances in neuroimaging technologies, including diffusion tensor imaging (DTI) and functional magnetic resonance imaging (fMRI), make it possible to explore these issues. DTI affords the possibility to explore anatomical connectivity in the human brain in vivo and fMRI can be used to make inferences about functional connections between brain regions. In this review, we present major advances in the understanding of PFC anatomical and functional dysconnectivity and their implications in schizophrenia. We then briefly discuss future prospects that need to be explored in order to move beyond simple mapping of connectivity changes to elucidate the neuronal mechanisms underlying schizophrenia.
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Affiliation(s)
- Yuan Zhou
- Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China
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Sasai S, Homae F, Watanabe H, Sasaki AT, Tanabe HC, Sadato N, Taga G. Frequency-specific network topologies in the resting human brain. Front Hum Neurosci 2014; 8:1022. [PMID: 25566037 PMCID: PMC4273625 DOI: 10.3389/fnhum.2014.01022] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Accepted: 12/04/2014] [Indexed: 11/23/2022] Open
Abstract
A community is a set of nodes with dense inter-connections, while there are sparse connections between different communities. A hub is a highly connected node with high centrality. It has been shown that both “communities” and “hubs” exist simultaneously in the brain's functional connectivity network (FCN), as estimated by correlations among low-frequency spontaneous fluctuations in functional magnetic resonance imaging (fMRI) signal changes (0.01–0.10 Hz). This indicates that the brain has a spatial organization that promotes both segregation and integration of information. Here, we demonstrate that frequency-specific network topologies that characterize segregation and integration also exist within this frequency range. In investigating the coherence spectrum among 87 brain regions, we found that two frequency bands, 0.01–0.03 Hz (very low frequency [VLF] band) and 0.07–0.09 Hz (low frequency [LF] band), mainly contributed to functional connectivity. Comparing graph theoretical indices for the VLF and LF bands revealed that the network in the former had a higher capacity for information segregation between identified communities than the latter. Hubs in the VLF band were mainly located within the anterior cingulate cortices, whereas those in the LF band were located in the posterior cingulate cortices and thalamus. Thus, depending on the timescale of brain activity, at least two distinct network topologies contributed to information segregation and integration. This suggests that the brain intrinsically has timescale-dependent functional organizations.
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Affiliation(s)
- Shuntaro Sasai
- Graduate School of Education, University of Tokyo Tokyo, Japan ; Department of Psychiatry, University of Wisconsin - Madison Madison, WI, USA
| | - Fumitaka Homae
- Department of Language Sciences, Tokyo Metropolitan University Tokyo, Japan
| | - Hama Watanabe
- Graduate School of Education, University of Tokyo Tokyo, Japan
| | - Akihiro T Sasaki
- Pathophysiological and Health Science Team, Imaging Application Group, Division of Bio-function Dynamics Imaging, RIKEN Center for Life Science Technologies Kobe, Japan ; Department of Physiology, Osaka City University Graduate School of Medicine Osaka, Japan
| | - Hiroki C Tanabe
- Graduate School of Environmental Studies, Nagoya University Nagoya, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, Department of Cerebral Research, National Institute for Physiological Sciences Okazaki, Japan ; Department of Physiological Sciences, SOKENDAI (The Graduate University for Advanced Studies) Okazaki, Japan
| | - Gentaro Taga
- Graduate School of Education, University of Tokyo Tokyo, Japan
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Barkhof F, Haller S, Rombouts SARB. Resting-state functional MR imaging: a new window to the brain. Radiology 2014; 272:29-49. [PMID: 24956047 DOI: 10.1148/radiol.14132388] [Citation(s) in RCA: 263] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Resting-state (RS) functional magnetic resonance (MR) imaging constitutes a novel paradigm that examines spontaneous brain function by using blood oxygen level-dependent contrast in the absence of a task. Spatially distributed networks of temporal synchronization can be detected that can characterize RS networks (RSNs). With a short acquisition time of less than 10 minutes, RS functional MR imaging can be applied in special populations such as children and patients with dementia. Some RSNs are already present in utero, while others mature in childhood. Around 10 major RSNs are consistently found in adults, but their exact spatial extent and strength of coherence are affected by physiologic parameters and drugs. Though the acquisition and analysis methods are still evolving, new disease insights are emerging in a variety of neurologic and psychiatric disorders. The default mode network is affected in Alzheimer disease and various other diseases of cognitive impairment. Alterations in RSNs have been identified in many diseases, in the absence of evident structural modifications, indicating a high sensitivity of the method. Moreover, there is evidence of correlation between RSN alterations and disease progression and severity. However, different diseases often affect the same RSN, illustrating the limited specificity of the findings. This suggests that neurologic and psychiatric diseases are characterized by altered interactions between RSNs and therefore the whole brain should be examined as an integral network (with subnetworks), for example, using graph analysis. A challenge for clinical applications of RS functional MR imaging is the potentially confounding effect of aging, concomitant vascular diseases, or medication on the neurovascular coupling and consequently the functional MR imaging response. Current investigation combines RS functional MR imaging and other methods such as electroencephalography or magnetoencephalography to better understand the vascular and neuronal contributions to alterations in functional connectivity.
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Affiliation(s)
- Frederik Barkhof
- From the Department of Radiology and Nuclear Medicine, Neuroscience Campus Amsterdam, VU University Medical Centre, PO Box 7057, 1007 MB Amsterdam, the Netherlands (F.B.); Service neuro-diagnostique et neuro-interventionnel DISIM, University Hospitals of Geneva, Geneva, Switzerland (S.H.); and Department of Radiology, Leiden University Medical Center and Institute of Psychology, Leiden University, Leiden, the Netherlands (S.A.R.B.R.)
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Eyes-Open/Eyes-Closed Dataset Sharing for Reproducibility Evaluation of Resting State fMRI Data Analysis Methods. Neuroinformatics 2013; 11:469-76. [DOI: 10.1007/s12021-013-9187-0] [Citation(s) in RCA: 58] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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29
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Tung KC, Uh J, Mao D, Xu F, Xiao G, Lu H. Alterations in resting functional connectivity due to recent motor task. Neuroimage 2013; 78:316-24. [PMID: 23583747 DOI: 10.1016/j.neuroimage.2013.04.006] [Citation(s) in RCA: 52] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2012] [Revised: 03/29/2013] [Accepted: 04/01/2013] [Indexed: 01/17/2023] Open
Abstract
The impact of recent experiences of task performance on resting functional connectivity MRI (fcMRI) has important implications for the design of many neuroimaging studies, because, if an effect is present, the fcMRI scan then must be performed before any evoked fMRI or after a time gap to allow it to dissipate. The present study aims to determine the effect of simple button presses, which are used in many cognitive fMRI tasks as a response recording method, on later acquired fcMRI data. Human volunteers were subject to a 23-minute button press motor task. Their resting-state brain activity before and after the task was assessed with fcMRI. It was found that, compared to the pre-task resting period, the post-task resting fcMRI revealed a significantly higher (p=0.002, N=24) cross correlation coefficient (CC) between left and right motor cortices. These changes were not present in sham control studies that matched the paradigm timing but had no actual task. The amplitude of fcMRI signal fluctuation (AF) also demonstrated an increase in the post-task period compared to pre-task. These changes were observed using both the right-hand-only task and the two-hand task. Study of the recovery time course of these effects revealed that the CC changes lasted for about 5 min while the AF change lasted for at least 15 min. Finally, voxelwise analysis revealed that the pre/post-task differences were also observed in several other brain regions, including the auditory cortex, visual areas, and the thalamus. Our data suggest that the recent performance of the simple button press task can result in elevated fcMRI CC and AF in relevant brain networks and that fcMRI scan should be performed either before evoked fMRI or after a sufficient time gap following fMRI.
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Affiliation(s)
- Kuang-Chi Tung
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
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30
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Simpson SL, Bowman FD, Laurienti PJ. Analyzing complex functional brain networks: Fusing statistics and network science to understand the brain *†. STATISTICS SURVEYS 2013; 7:1-36. [PMID: 25309643 DOI: 10.1214/13-ss103] [Citation(s) in RCA: 77] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
Complex functional brain network analyses have exploded over the last decade, gaining traction due to their profound clinical implications. The application of network science (an interdisciplinary offshoot of graph theory) has facilitated these analyses and enabled examining the brain as an integrated system that produces complex behaviors. While the field of statistics has been integral in advancing activation analyses and some connectivity analyses in functional neuroimaging research, it has yet to play a commensurate role in complex network analyses. Fusing novel statistical methods with network-based functional neuroimage analysis will engender powerful analytical tools that will aid in our understanding of normal brain function as well as alterations due to various brain disorders. Here we survey widely used statistical and network science tools for analyzing fMRI network data and discuss the challenges faced in filling some of the remaining methodological gaps. When applied and interpreted correctly, the fusion of network scientific and statistical methods has a chance to revolutionize the understanding of brain function.
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Affiliation(s)
- Sean L Simpson
- Department of Biostatistical Sciences, Wake Forest School of Medicine, Winston-Salem, NC
| | - F DuBois Bowman
- Department of Biostatistics and Bioinformatics, The Rollins School of Public Health, Emory University, Atlanta, GA
| | - Paul J Laurienti
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC
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Zhao X, Liu Y, Wang X, Liu B, Xi Q, Guo Q, Jiang H, Jiang T, Wang P. Disrupted small-world brain networks in moderate Alzheimer's disease: a resting-state FMRI study. PLoS One 2012; 7:e33540. [PMID: 22457774 PMCID: PMC3311642 DOI: 10.1371/journal.pone.0033540] [Citation(s) in RCA: 158] [Impact Index Per Article: 13.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2011] [Accepted: 02/10/2012] [Indexed: 01/06/2023] Open
Abstract
The small-world organization has been hypothesized to reflect a balance between local processing and global integration in the human brain. Previous multimodal imaging studies have consistently demonstrated that the topological architecture of the brain network is disrupted in Alzheimer's disease (AD). However, these studies have reported inconsistent results regarding the topological properties of brain alterations in AD. One potential explanation for these inconsistent results lies with the diverse homogeneity and distinct progressive stages of the AD involved in these studies, which are thought to be critical factors that might affect the results. We investigated the topological properties of brain functional networks derived from resting functional magnetic resonance imaging (fMRI) of carefully selected moderate AD patients and normal controls (NCs). Our results showed that the topological properties were found to be disrupted in AD patients, which showing increased local efficiency but decreased global efficiency. We found that the altered brain regions are mainly located in the default mode network, the temporal lobe and certain subcortical regions that are closely associated with the neuropathological changes in AD. Of note, our exploratory study revealed that the ApoE genotype modulates brain network properties, especially in AD patients.
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Affiliation(s)
- Xiaohu Zhao
- Imaging Department, TongJi University, TongJi Hospital Shanghai, China
| | - Yong Liu
- LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences, Beijing, China
| | - Xiangbin Wang
- Imaging Department, TongJi University, TongJi Hospital Shanghai, China
| | - Bing Liu
- LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences, Beijing, China
| | - Qian Xi
- Imaging Department, TongJi University, TongJi Hospital Shanghai, China
| | - Qihao Guo
- State Key Laboratory of Medical Neurobiology, Department of Neurology, Institute of Neurology, Huashan Hospital, Shanghai Medical College, Fudan University, Shanghai, China
| | - Hong Jiang
- Imaging Department, TongJi University, TongJi Hospital Shanghai, China
| | - Tianzi Jiang
- LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, the Chinese Academy of Sciences, Beijing, China
- Key Laboratory for NeuroInformation of Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
- The Queensland Brain Institute, The University of Queensland, Brisbane, Australia
| | - Peijun Wang
- Imaging Department, TongJi University, TongJi Hospital Shanghai, China
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Shafi MM, Westover MB, Fox MD, Pascual-Leone A. Exploration and modulation of brain network interactions with noninvasive brain stimulation in combination with neuroimaging. Eur J Neurosci 2012; 35:805-25. [PMID: 22429242 PMCID: PMC3313459 DOI: 10.1111/j.1460-9568.2012.08035.x] [Citation(s) in RCA: 116] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/30/2023]
Abstract
Much recent work in systems neuroscience has focused on how dynamic interactions between different cortical regions underlie complex brain functions such as motor coordination, language and emotional regulation. Various studies using neuroimaging and neurophysiologic techniques have suggested that in many neuropsychiatric disorders, these dynamic brain networks are dysregulated. Here we review the utility of combined noninvasive brain stimulation and neuroimaging approaches towards greater understanding of dynamic brain networks in health and disease. Brain stimulation techniques, such as transcranial magnetic stimulation and transcranial direct current stimulation, use electromagnetic principles to alter brain activity noninvasively, and induce focal but also network effects beyond the stimulation site. When combined with brain imaging techniques such as functional magnetic resonance imaging, positron emission tomography and electroencephalography, these brain stimulation techniques enable a causal assessment of the interaction between different network components, and their respective functional roles. The same techniques can also be applied to explore hypotheses regarding the changes in functional connectivity that occur during task performance and in various disease states such as stroke, depression and schizophrenia. Finally, in diseases characterized by pathologic alterations in either the excitability within a single region or in the activity of distributed networks, such techniques provide a potential mechanism to alter cortical network function and architectures in a beneficial manner.
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Affiliation(s)
- Mouhsin M. Shafi
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - M. Brandon Westover
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Michael D. Fox
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Division of Cognitive Neurology, Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, USA
- Department of Neurology, Harvard Medical School, Boston, MA, USA
- Institut Universitari de Neurorehabilitació Guttmann, Universidad Autónoma de Barcelona, Badalona, Spain
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33
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Fornito A, Zalesky A, Pantelis C, Bullmore ET. Schizophrenia, neuroimaging and connectomics. Neuroimage 2012; 62:2296-314. [PMID: 22387165 DOI: 10.1016/j.neuroimage.2011.12.090] [Citation(s) in RCA: 540] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2011] [Revised: 11/15/2011] [Accepted: 12/15/2011] [Indexed: 10/28/2022] Open
Abstract
Schizophrenia is frequently characterized as a disorder of brain connectivity. Neuroimaging has played a central role in supporting this view, with nearly two decades of research providing abundant evidence of structural and functional connectivity abnormalities in the disorder. In recent years, our understanding of how schizophrenia affects brain networks has been greatly advanced by attempts to map the complete set of inter-regional interactions comprising the brain's intricate web of connectivity; i.e., the human connectome. Imaging connectomics refers to the use of neuroimaging techniques to generate these maps which, combined with the application of graph theoretic methods, has enabled relatively comprehensive mapping of brain network connectivity and topology in unprecedented detail. Here, we review the application of these techniques to the study of schizophrenia, focusing principally on magnetic resonance imaging (MRI) research, while drawing attention to key methodological issues in the field. The published findings suggest that schizophrenia is associated with a widespread and possibly context-independent functional connectivity deficit, upon which are superimposed more circumscribed, context-dependent alterations associated with transient states of hyper- and/or hypo-connectivity. In some cases, these changes in inter-regional functional coupling dynamics can be related to measures of intra-regional dysfunction. Topological disturbances of functional brain networks in schizophrenia point to reduced local network connectivity and modular structure, as well as increased global integration and network robustness. Some, but not all, of these functional abnormalities appear to have an anatomical basis, though the relationship between the two is complex. By comprehensively mapping connectomic disturbances in patients with schizophrenia across the entire brain, this work has provided important insights into the highly distributed character of neural abnormalities in the disorder, and the potential functional consequences that these disturbances entail.
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Affiliation(s)
- Alex Fornito
- Melbourne Neuropsychiatry Centre, The University of Melbourne and Melbourne Health, Melbourne, Australia.
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Guerrero-Pedraza A, McKenna PJ, Gomar JJ, Sarró S, Salvador R, Amann B, Carrión MI, Landin-Romero R, Blanch J, Pomarol-Clotet E. First-episode psychosis is characterized by failure of deactivation but not by hypo- or hyperfrontality. Psychol Med 2012; 42:73-84. [PMID: 21733286 DOI: 10.1017/s0033291711001073] [Citation(s) in RCA: 41] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
BACKGROUND It is not known whether first-episode psychosis is characterized by the same prefrontal cortex functional imaging abnormalities as chronic schizophrenia. METHOD Thirty patients with a first episode of non-affective functional psychosis and 28 healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back working memory task. Voxel-based analyses of brain activations and deactivations were carried out and compared between groups. The connectivity of regions of significant difference between the patients and controls was also examined. RESULTS The first-episode patients did not show significant prefrontal hypo- or hyperactivation compared to controls. However, they showed failure of deactivation in the medial frontal cortex. This area showed high levels of connectivity with the posterior cingulate gyrus/precuneus and parts of the parietal cortex bilaterally. Failure of deactivation was significantly greater in first-episode patients who had or went on to acquire a DSM-IV diagnosis of schizophrenia than in those who did not, and in those who met RDC criteria for schizophrenia compared to those who did not. CONCLUSIONS First-episode psychosis is not characterized by hypo- or hyperfrontality but instead by a failure of deactivation in the medial frontal cortex. The location and connectivity of this area suggest that it is part of the default mode network. The failure of deactivation seems to be particularly marked in first-episode patients who have, or progress to, schizophrenia.
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Gómez-Verdejo V, Martínez-Ramón M, Florensa-Vila J, Oliviero A. Analysis of fMRI time series with mutual information. Med Image Anal 2011; 16:451-8. [PMID: 22155195 DOI: 10.1016/j.media.2011.11.002] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2011] [Revised: 11/07/2011] [Accepted: 11/08/2011] [Indexed: 11/18/2022]
Abstract
Neuroimaging plays a fundamental role in the study of human cognitive neuroscience. Functional magnetic resonance imaging (fMRI), based on the Blood Oxygenation Level Dependent signal, is currently considered as a standard technique for a system level understanding of the human brain. The problem of identifying regionally specific effects in neuroimaging data is usually solved by applying Statistical Parametric Mapping (SPM). Here, a mutual information (MI) criterion is used to identify regionally specific effects produced by a task. In particular, two MI estimators are presented for its use in fMRI data. The first one uses a Parzen probability density estimation, and the second one is based on a K Nearest Neighbours (KNN) estimation. Additionally, a statistical measure has been introduced to automatically detect the voxels which are relevant to the fMRI task. Experiments demonstrate the advantages of MI estimators over SPM maps; firstly, providing more significant differences between relevant and irrelevant voxels; secondly, presenting more focalized activation; and, thirdly, detecting small areas related to the task. These findings, and the improved performance of KNN MI estimator in multisubject and multistimuli studies, make the proposed methods a good alternative to SPM.
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Affiliation(s)
- Vanessa Gómez-Verdejo
- Departamento de Teoría de la Señal y Comunicaciones, Universidad Carlos III de Madrid, Leganés, Madrid, Spain.
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36
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Ferguson MA, Anderson JS. Dynamical stability of intrinsic connectivity networks. Neuroimage 2011; 59:4022-31. [PMID: 22056459 DOI: 10.1016/j.neuroimage.2011.10.062] [Citation(s) in RCA: 11] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2011] [Revised: 10/17/2011] [Accepted: 10/18/2011] [Indexed: 10/15/2022] Open
Abstract
Functional connectivity MRI (fcMRI) has become a widely used technique in recent years for measuring the static correlation of activity between cortical regions. Using a publicly available resting state dataset (n = 961 subjects), we obtained high spatial-resolution maps of functional connectivity between a lattice of 7266 regions covering the gray matter. Average whole brain functional correlations were calculated, with high reproducibility within the dataset and across sites. Since correlation measures not only represent pairwise connectivity information, but also shared inputs from other brain regions, we approximate pairwise connection strength by representing each region as a linear combination of the others by performing a Cholesky decomposition of the pairwise correlation matrix. We then used this weighted connection strength between regions to iterate relative brain activity in discrete temporal steps, beginning both with random initial conditions, and with initial conditions reflecting intrinsic connectivity networks using each region as a seed. In whole brain simulations based on weighted connectivity from healthy adult subjects (mean age 27.3), there was consistent convergence to one of two inverted states, one representing high activity in the default mode network, the other representing low relative activity in the default mode network. Metastable intermediate states in our simulation corresponded to combinations of characterized functional networks. Convergence to a final state was slowest for initial conditions on the borders of the default mode network.
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Affiliation(s)
- Michael A Ferguson
- Department of Bioengineering, University of Utah, 72 S Central Campus Drive, Rm 2750, Salt Lake City, UT 84112, USA
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37
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Besserve M, Martinerie J, Garnero L. Improving quantification of functional networks with EEG inverse problem: Evidence from a decoding point of view. Neuroimage 2011; 55:1536-47. [DOI: 10.1016/j.neuroimage.2011.01.056] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2010] [Revised: 12/20/2010] [Accepted: 01/20/2011] [Indexed: 11/16/2022] Open
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Garg R, Cecchi GA, Rao AR. Full-brain auto-regressive modeling (FARM) using fMRI. Neuroimage 2011; 58:416-41. [PMID: 21439388 DOI: 10.1016/j.neuroimage.2011.02.074] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2010] [Revised: 02/10/2011] [Accepted: 02/27/2011] [Indexed: 10/18/2022] Open
Abstract
In order to fully uncover the information potentially available in the fMRI signal, we model it as a multivariate auto-regressive process. To infer the model, we do not apply any form of clustering or dimensionality reduction, and solve the problem of under-determinacy using sparse regression. We find that only a few small clusters (with average size of 3-4 voxels) are useful in predicting the activity of other voxels, and demonstrate remarkable consistency within a subject as well as across multiple subjects. Moreover, we find that: (a) the areas that can predict activity of other voxels are consistent with previous results related to networks activated by the specific somatosensory task, as well as networks related to the default mode activity; (b) there is a global dynamical state dominated by two prominent (although not unique) streams, originating in the posterior parietal cortex and the posterior cingulate/precuneus cortex; (c) these streams span default mode and task-specific networks, and interact in several regions, notably the insula; and (d) the posterior cingulate is a central node of the default mode network, in terms of its ability to determine the future evolution of the rest of the nodes.
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Affiliation(s)
- Rahul Garg
- Computational Biology Center, IBM T.J. Watson Research Center, Yorktown Heights, NY 10598, USA.
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39
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Salvador R, Sarró S, Gomar JJ, Ortiz-Gil J, Vila F, Capdevila A, Bullmore E, McKenna PJ, Pomarol-Clotet E. Overall brain connectivity maps show cortico-subcortical abnormalities in schizophrenia. Hum Brain Mapp 2010; 31:2003-14. [PMID: 20225222 PMCID: PMC6870792 DOI: 10.1002/hbm.20993] [Citation(s) in RCA: 113] [Impact Index Per Article: 8.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2009] [Revised: 11/27/2009] [Accepted: 12/17/2009] [Indexed: 11/08/2022] Open
Abstract
Abnormal interactions between areas of the brain have been pointed as possible causes for schizophrenia. However, the nature of these disturbances and the anatomical location of the regions involved are still unclear. Here, we describe a method to estimate maps of net levels of connectivity in the resting brain, and we apply it to look for differential patterns of connectivity in schizophrenia. This method uses partial coherences as a basic measure of covariability, and it minimises the effect of major physiological noise. When overall (net) connectivity maps of a sample of 40 patients with schizophrenia were compared with the maps from a matched sample of 40 controls, a single area of abnormality was found. It is an area of patient hyper-connectivity and is located frontally, in medial and orbital structures, clearly overlapping the anterior node of the default mode network (DMN). When this area is used as a region of interest in a second-level analysis, it shows functional hyper-connections with several cortical and subcortical structures. Interestingly, the most significant abnormality is found with the caudate, which has a bilateral pattern of abnormality, pointing to a possible DMN-striatum deviant relation in schizophrenia. However, hyper-connectivity observed with other regions (right hippocampus and amygdala, and other cortical structures) suggests a more pervasive alteration of brain connectivity in this disease.
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Affiliation(s)
- Raymond Salvador
- Benito Menni C.A.S.M.-CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain.
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40
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Salvador R, Anguera M, Gomar JJ, Bullmore ET, Pomarol-Clotet E. Conditional mutual information maps as descriptors of net connectivity levels in the brain. Front Neuroinform 2010; 4:115. [PMID: 21151357 PMCID: PMC2995463 DOI: 10.3389/fninf.2010.00115] [Citation(s) in RCA: 31] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2010] [Accepted: 10/15/2010] [Indexed: 12/03/2022] Open
Abstract
There is a growing interest in finding ways to summarize the local connectivity properties of the brain through single brain maps. Here we propose a method based on the conditional mutual information (CMI) in the frequency domain. CMI maps quantify the amount of non-redundant covariability between each site and all others in the rest of the brain, partialling out the joint variability due to gross physiological noise. Average maps from a sample of 45 healthy individuals scanned in the resting state show a clear and symmetric pattern of connectivity maxima in several regions of cortex, including prefrontal, orbitofrontal, lateral–parietal, and midline default mode network components; and in subcortical nuclei, including the amygdala, thalamus, and basal ganglia. Such cortical and subcortical hotspots of functional connectivity were more clearly evident at lower frequencies (0.02–0.1 Hz) than at higher frequencies (0.1–0.2 Hz) of endogenous oscillation. CMI mapping can also be easily applied to perform group analyses. This is exemplified by exploring effects of normal aging on CMI in a sample of healthy controls and by investigating correlations between CMI and positive psychotic symptom scores in a sample of 40 schizophrenic patients. Both the normative aging and schizophrenia studies reveal functional connectivity trends that converge with reported findings from other studies, thus giving further support to the validity of the proposed method.
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Affiliation(s)
- Raymond Salvador
- Unitat de Recerca, Benito Menni C.A.S.M. - CIBERSAM Sant Boi de Llobregat, Barcelona, Spain
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41
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Motor network degeneration in amyotrophic lateral sclerosis: a structural and functional connectivity study. PLoS One 2010; 5:e13664. [PMID: 21060689 PMCID: PMC2965124 DOI: 10.1371/journal.pone.0013664] [Citation(s) in RCA: 132] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2010] [Accepted: 09/24/2010] [Indexed: 01/21/2023] Open
Abstract
BACKGROUND Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease characterised by motor neuron degeneration. How this disease affects the central motor network is largely unknown. Here, we combined for the first time structural and functional imaging measures on the motor network in patients with ALS and healthy controls. METHODOLOGY/PRINCIPAL FINDINGS Structural measures included whole brain cortical thickness and diffusion tensor imaging (DTI) of crucial motor tracts. These structural measures were combined with functional connectivity analysis of the motor network based on resting state fMRI. Focal cortical thinning was observed in the primary motor area in patients with ALS compared to controls and was found to correlate with disease progression. DTI revealed reduced FA values in the corpus callosum and in the rostral part of the corticospinal tract. Overall functional organisation of the motor network was unchanged in patients with ALS compared to healthy controls, however the level of functional connectedness was significantly correlated with disease progression rate. Patients with increased connectedness appear to have a more progressive disease course. CONCLUSIONS/SIGNIFICANCE We demonstrate structural motor network deterioration in ALS with preserved functional connectivity measures. The positive correlation between functional connectedness of the motor network and disease progression rate could suggest spread of disease along functional connections of the motor network.
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Benjaminsson S, Fransson P, Lansner A. A novel model-free data analysis technique based on clustering in a mutual information space: application to resting-state FMRI. Front Syst Neurosci 2010; 4. [PMID: 20721313 PMCID: PMC2922939 DOI: 10.3389/fnsys.2010.00034] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2009] [Accepted: 06/18/2010] [Indexed: 11/26/2022] Open
Abstract
Non-parametric data-driven analysis techniques can be used to study datasets with few assumptions about the data and underlying experiment. Variations of independent component analysis (ICA) have been the methods mostly used on fMRI data, e.g., in finding resting-state networks thought to reflect the connectivity of the brain. Here we present a novel data analysis technique and demonstrate it on resting-state fMRI data. It is a generic method with few underlying assumptions about the data. The results are built from the statistical relations between all input voxels, resulting in a whole-brain analysis on a voxel level. It has good scalability properties and the parallel implementation is capable of handling large datasets and databases. From the mutual information between the activities of the voxels over time, a distance matrix is created for all voxels in the input space. Multidimensional scaling is used to put the voxels in a lower-dimensional space reflecting the dependency relations based on the distance matrix. By performing clustering in this space we can find the strong statistical regularities in the data, which for the resting-state data turns out to be the resting-state networks. The decomposition is performed in the last step of the algorithm and is computationally simple. This opens up for rapid analysis and visualization of the data on different spatial levels, as well as automatically finding a suitable number of decomposition components.
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43
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Zhou Y, Wang K, Liu Y, Song M, Song SW, Jiang T. Spontaneous brain activity observed with functional magnetic resonance imaging as a potential biomarker in neuropsychiatric disorders. Cogn Neurodyn 2010; 4:275-94. [PMID: 22132039 DOI: 10.1007/s11571-010-9126-9] [Citation(s) in RCA: 41] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2010] [Revised: 07/18/2010] [Accepted: 07/21/2010] [Indexed: 12/01/2022] Open
Abstract
As functional magnetic resonance imaging (fMRI) studies have yielded increasing amounts of information about the brain's spontaneous activity, they have revealed fMRI's potential to locate changes in brain hemodynamics that are associated with neuropsychiatric disorders. In this paper, we review studies that support the notion that changes in brain spontaneous activity observed by fMRI can be used as potential biomarkers for diagnosis and treatment evaluation in neuropsychiatric disorders. We first review the methods used to study spontaneous activity from the perspectives of (1) the properties of local spontaneous activity, (2) the spatial pattern of spontaneous activity, and (3) the topological properties of brain networks. We also summarize the major findings associated with major neuropsychiatric disorders obtained using these methods. Then we review the pilot studies that have used spontaneous activity to discriminate patients from normal controls. Finally, we discuss current challenges and potential research directions to further elucidate the clinical use of spontaneous brain activity in neuropsychiatric disorders.
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Öngür D, Lundy M, Greenhouse I, Shinn AK, Menon V, Cohen BM, Renshaw PF. Default mode network abnormalities in bipolar disorder and schizophrenia. Psychiatry Res 2010; 183:59-68. [PMID: 20553873 PMCID: PMC2902695 DOI: 10.1016/j.pscychresns.2010.04.008] [Citation(s) in RCA: 327] [Impact Index Per Article: 23.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/15/2009] [Revised: 04/06/2010] [Accepted: 04/10/2010] [Indexed: 01/22/2023]
Abstract
The default-mode network (DMN) consists of a set of brain areas preferentially activated during internally focused tasks. We used functional magnetic resonance imaging (fMRI) to study the DMN in bipolar mania and acute schizophrenia. Participants comprised 17 patients with bipolar disorder (BD), 14 patients with schizophrenia (SZ) and 15 normal controls (NC), who underwent 10-min resting fMRI scans. The DMN was extracted using independent component analysis and template-matching; spatial extent and timecourse were examined. Both patient groups showed reduced DMN connectivity in the medial prefrontal cortex (mPFC) (BD: x=-2, y=54, z=-12; SZ: x=-2, y=22, z=18). BD subjects showed abnormal recruitment of parietal cortex (correlated with mania severity) while SZ subjects showed greater recruitment of the frontopolar cortex/basal ganglia. Both groups had significantly higher frequency fluctuations than controls. We found ventral mPFC abnormalities in BD and dorsal mPFC abnormalities in SZ. The higher frequency of BOLD signal oscillations observed in patients suggests abnormal functional organization of circuits in both disorders. Further studies are needed to determine how these abnormalities are related to specific symptoms of each condition.
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Affiliation(s)
- Dost Öngür
- McLean Hospital, Belmont, MA and Harvard Medical School, Boston, MA
| | - Miriam Lundy
- Yale University School of Nursing, New Haven, CT
| | - Ian Greenhouse
- Department of Neuroscience, University of California San Diego, San Diego, CA
| | - Ann K. Shinn
- McLean Hospital, Belmont, MA and Harvard Medical School, Boston, MA
| | - Vinod Menon
- Department of Psychiatry and Behavioral Sciences and Program in Neuroscience, Stanford University School of Medicine, Stanford, CA
| | - Bruce M. Cohen
- McLean Hospital, Belmont, MA and Harvard Medical School, Boston, MA
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45
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Fox MD, Greicius M. Clinical applications of resting state functional connectivity. Front Syst Neurosci 2010; 4:19. [PMID: 20592951 PMCID: PMC2893721 DOI: 10.3389/fnsys.2010.00019] [Citation(s) in RCA: 573] [Impact Index Per Article: 40.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2010] [Accepted: 05/11/2010] [Indexed: 12/14/2022] Open
Abstract
During resting conditions the brain remains functionally and metabolically active. One manifestation of this activity that has become an important research tool is spontaneous fluctuations in the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI). The identification of correlation patterns in these spontaneous fluctuations has been termed resting state functional connectivity (fcMRI) and has the potential to greatly increase the translation of fMRI into clinical care. In this article we review the advantages of the resting state signal for clinical applications including detailed discussion of signal to noise considerations. We include guidelines for performing resting state research on clinical populations, outline the different areas for clinical application, and identify important barriers to be addressed to facilitate the translation of resting state fcMRI into the clinical realm.
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Affiliation(s)
- Michael D Fox
- Partners Neurology Residency, Massachusetts General Hospital, Brigham and Women's Hospital, Harvard Medical School Boston, MA, USA
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Cole DM, Smith SM, Beckmann CF. Advances and pitfalls in the analysis and interpretation of resting-state FMRI data. Front Syst Neurosci 2010; 4:8. [PMID: 20407579 PMCID: PMC2854531 DOI: 10.3389/fnsys.2010.00008] [Citation(s) in RCA: 492] [Impact Index Per Article: 35.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2010] [Accepted: 03/17/2010] [Indexed: 12/16/2022] Open
Abstract
The last 15 years have witnessed a steady increase in the number of resting-state functional neuroimaging studies. The connectivity patterns of multiple functional, distributed, large-scale networks of brain dynamics have been recognised for their potential as useful tools in the domain of systems and other neurosciences. The application of functional connectivity methods to areas such as cognitive psychology, clinical diagnosis and treatment progression has yielded promising preliminary results, but is yet to be fully realised. This is due, in part, to an array of methodological and interpretative issues that remain to be resolved. We here present a review of the methods most commonly applied in this rapidly advancing field, such as seed-based correlation analysis and independent component analysis, along with examples of their use at the individual subject and group analysis levels and a discussion of practical and theoretical issues arising from this data ‘explosion’. We describe the similarities and differences across these varied statistical approaches to processing resting-state functional magnetic resonance imaging signals, and conclude that further technical optimisation and experimental refinement is required in order to fully delineate and characterise the gross complexity of the human neural functional architecture.
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Affiliation(s)
- David M Cole
- Department of Clinical Neuroscience, Imperial College London London, UK
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van den Heuvel MP, Mandl RCW, Kahn RS, Hulshoff Pol HE. Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp 2009; 30:3127-41. [PMID: 19235882 DOI: 10.1002/hbm.20737] [Citation(s) in RCA: 744] [Impact Index Per Article: 49.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
Abstract
During rest, multiple cortical brain regions are functionally linked forming resting-state networks. This high level of functional connectivity within resting-state networks suggests the existence of direct neuroanatomical connections between these functionally linked brain regions to facilitate the ongoing interregional neuronal communication. White matter tracts are the structural highways of our brain, enabling information to travel quickly from one brain region to another region. In this study, we examined both the functional and structural connections of the human brain in a group of 26 healthy subjects, combining 3 Tesla resting-state functional magnetic resonance imaging time-series with diffusion tensor imaging scans. Nine consistently found functionally linked resting-state networks were retrieved from the resting-state data. The diffusion tensor imaging scans were used to reconstruct the white matter pathways between the functionally linked brain areas of these resting-state networks. Our results show that well-known anatomical white matter tracts interconnect at least eight of the nine commonly found resting-state networks, including the default mode network, the core network, primary motor and visual network, and two lateralized parietal-frontal networks. Our results suggest that the functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain.
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Ferrarini L, Veer IM, Baerends E, van Tol MJ, Renken RJ, van der Wee NJA, Veltman DJ, Aleman A, Zitman FG, Penninx BWJH, van Buchem MA, Reiber JHC, Rombouts SARB, Milles J. Hierarchical functional modularity in the resting-state human brain. Hum Brain Mapp 2009; 30:2220-31. [PMID: 18830955 PMCID: PMC6871119 DOI: 10.1002/hbm.20663] [Citation(s) in RCA: 140] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2008] [Revised: 07/25/2008] [Accepted: 08/12/2008] [Indexed: 11/11/2022] Open
Abstract
Functional magnetic resonance imaging (fMRI) studies have shown that anatomically distinct brain regions are functionally connected during the resting state. Basic topological properties in the brain functional connectivity (BFC) map have highlighted the BFC's small-world topology. Modularity, a more advanced topological property, has been hypothesized to be evolutionary advantageous, contributing to adaptive aspects of anatomical and functional brain connectivity. However, current definitions of modularity for complex networks focus on nonoverlapping clusters, and are seriously limited by disregarding inclusive relationships. Therefore, BFC's modularity has been mainly qualitatively investigated. Here, we introduce a new definition of modularity, based on a recently improved clustering measurement, which overcomes limitations of previous definitions, and apply it to the study of BFC in resting state fMRI of 53 healthy subjects. Results show hierarchical functional modularity in the brain.
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Affiliation(s)
- Luca Ferrarini
- Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
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Microstructural organization of the cingulum tract and the level of default mode functional connectivity. J Neurosci 2008; 28:10844-51. [PMID: 18945892 DOI: 10.1523/jneurosci.2964-08.2008] [Citation(s) in RCA: 262] [Impact Index Per Article: 16.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
The default mode network is a functionally connected network of brain regions that show highly synchronized intrinsic neuronal activation during rest. However, less is known about the structural connections of this network, which could play an important role in the observed functional connectivity patterns. In this study, we examined the microstructural organization of the cingulum tract in relation to the level of resting-state default mode functional synchronization. Resting-state functional magnetic resonance imaging and diffusion tensor imaging data of 45 healthy subjects were acquired on a 3 tesla scanner. Both structural and functional connectivity of the default mode network were examined. In all subjects, the cingulum tract was identified from the total collection of reconstructed tracts to interconnect the precuneus/posterior cingulate cortex and medial frontal cortex, key regions of the default mode network. A significant positive correlation was found between the average fractional anisotropy value of the cingulum tract and the level of functional connectivity between the precuneus/posterior cingulate cortex and medial frontal cortex. Our results suggest a direct relationship between the structural and functional connectivity measures of the default mode network and contribute to the understanding of default mode network connectivity.
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