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Sheng D, Pu W, Linli Z, Tian GL, Guo S, Fei Y. Aberrant global and local dynamic properties in schizophrenia with instantaneous phase method based on Hilbert transform. Psychol Med 2023; 53:2125-2135. [PMID: 34588010 DOI: 10.1017/s0033291721003895] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
BACKGROUND Emerging functional imaging studies suggest that schizophrenia is associated with aberrant spatiotemporal interaction which may result in aberrant global and local dynamic properties. METHODS We investigated the dynamic functional connectivity (FC) by using instantaneous phase method based on Hilbert transform to detect abnormal spatiotemporal interaction in schizophrenia. Based on resting-state functional magnetic resonance imaging, two independent datasets were included, with 114 subjects from COBRE [51 schizophrenia patients (SZ) and 63 healthy controls (HCs)] and 96 from OpenfMRI (36 SZ and 60 HCs). Phase differences and instantaneous coupling matrices were firstly calculated at all time points by extracting instantaneous parameters. Global [global synchrony and intertemporal closeness (ITC)] and local dynamic features [strength of FC (sFC) and variability of FC (vFC)] were compared between two groups. Support vector machine (SVM) was used to estimate the ability to discriminate two groups by using all aberrant features. RESULTS We found SZ had lower global synchrony and ITC than HCs on both datasets. Furthermore, SZ had a significant decrease in sFC but an increase in vFC, which were mainly located at prefrontal cortex, anterior cingulate cortex, temporal cortex and visual cortex or temporal cortex and hippocampus, forming significant dynamic subnetworks. SVM analysis revealed a high degree of balanced accuracy (85.75%) on the basis of all aberrant dynamic features. CONCLUSIONS SZ has worse overall spatiotemporal stability and extensive FC subnetwork lesions compared to HCs, which to some extent elucidates the pathophysiological mechanism of schizophrenia, providing insight into time-variation properties of patients with other mental illnesses.
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Affiliation(s)
- Dan Sheng
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Weidan Pu
- Medical Psychological Center, the Second Xiangya Hospital, Central South University, Changsha, Hunan, PR China
- China National Clinical Research Center for Mental Health Disorders, Changsha, PR China
- College of Mechatronics and Automation, National University of Defense Technology, Changsha, PR China
| | - Zeqiang Linli
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Guo-Liang Tian
- Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen, PR China
| | - Shuixia Guo
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
- Key Laboratory of Applied Statistics and Data Science, Hunan Normal University, College of Hunan Province, Changsha, PR China
| | - Yu Fei
- School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, PR China
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2
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Shi Y, Zeng W. The integrative functional connectivity analysis between seafarer’s brain networks using functional magnetic resonance imaging data of different states. Front Neurosci 2022; 16:1008652. [DOI: 10.3389/fnins.2022.1008652] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Accepted: 09/30/2022] [Indexed: 11/13/2022] Open
Abstract
The particularity of seafarers’ occupation makes their brain functional activities vulnerable to the influence of working environments, which leads to abnormal functional connectivities (FCs) between brain networks. To further investigate the influences of maritime environments on the seafarers’ functional brain networks, the functional magnetic resonance imaging (fMRI) datasets of 33 seafarers before and after sailing were used to study FCs among the functional brain networks in this paper. On the basis of making full use of the intrinsic prior information from fMRI data, six resting-state brain functional networks of seafarers before and after sailing were obtained by using group independent component analysis with intrinsic reference, and then the differences between the static and dynamic FCs among these six brain networks of seafarers before and after sailing were, respectively, analyzed from both group and individual levels. Subsequently, the potential dynamic functional connectivity states of seafarers before and after sailing were extracted by using the affine propagation clustering algorithm and the probabilities of state transition between them were obtained simultaneously. The results show that the dynamic FCs among large-scale brain networks have significant difference seafarers before and after sailing both at the group level and individual level, while the static FCs between them varies only at the individual level. This suggests that the maritime environments can indeed affect the brain functional activity of seafarers in real time, and the degree of influence is different for different subjects, which is of a great significance to explore the neural changes of seafarer’s brain functional network.
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3
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Lee JJ, Lee S, Lee DH, Woo CW. Functional brain reconfiguration during sustained pain. eLife 2022; 11:e74463. [PMID: 36173388 PMCID: PMC9522250 DOI: 10.7554/elife.74463] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 09/09/2022] [Indexed: 11/13/2022] Open
Abstract
Pain is constructed through complex interactions among multiple brain systems, but it remains unclear how functional brain networks are reconfigured over time while experiencing pain. Here, we investigated the time-varying changes in the functional brain networks during 20 min capsaicin-induced sustained orofacial pain. In the early stage, the orofacial areas of the primary somatomotor cortex were separated from other areas of the somatosensory cortex and integrated with subcortical and frontoparietal regions, constituting an extended brain network of sustained pain. As pain decreased over time, the subcortical and frontoparietal regions were separated from this brain network and connected to multiple cerebellar regions. Machine-learning models based on these network features showed significant predictions of changes in pain experience across two independent datasets (n = 48 and 74). This study provides new insights into how multiple brain systems dynamically interact to construct and modulate pain experience, advancing our mechanistic understanding of sustained pain.
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Affiliation(s)
- Jae-Joong Lee
- Center for Neuroscience Imaging Research, Institute for Basic ScienceSuwonRepublic of Korea
- Department of Biomedical Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea
| | - Sungwoo Lee
- Center for Neuroscience Imaging Research, Institute for Basic ScienceSuwonRepublic of Korea
- Department of Biomedical Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan UniversitySuwonRepublic of Korea
| | - Dong Hee Lee
- Center for Neuroscience Imaging Research, Institute for Basic ScienceSuwonRepublic of Korea
- Department of Biomedical Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan UniversitySuwonRepublic of Korea
| | - Choong-Wan Woo
- Center for Neuroscience Imaging Research, Institute for Basic ScienceSuwonRepublic of Korea
- Department of Biomedical Engineering, Sungkyunkwan UniversitySuwonRepublic of Korea
- Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan UniversitySuwonRepublic of Korea
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4
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Fauchon C, Kim JA, El-Sayed R, Osborne NR, Rogachov A, Cheng JC, Hemington KS, Bosma RL, Dunkley BT, Oh J, Bhatia A, Inman RD, Davis KD. A Hidden Markov Model reveals magnetoencephalography spectral frequency-specific abnormalities of brain state power and phase-coupling in neuropathic pain. Commun Biol 2022; 5:1000. [PMID: 36131088 PMCID: PMC9492713 DOI: 10.1038/s42003-022-03967-9] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 09/08/2022] [Indexed: 11/09/2022] Open
Abstract
Neuronal populations in the brain are engaged in a temporally coordinated manner at rest. Here we show that spontaneous transitions between large-scale resting-state networks are altered in chronic neuropathic pain. We applied an approach based on the Hidden Markov Model to magnetoencephalography data to describe how the brain moves from one activity state to another. This identified 12 fast transient (~80 ms) brain states including the sensorimotor, ascending nociceptive pathway, salience, visual, and default mode networks. Compared to healthy controls, we found that people with neuropathic pain exhibited abnormal alpha power in the right ascending nociceptive pathway state, but higher power and coherence in the sensorimotor network state in the beta band, and shorter time intervals between visits of the sensorimotor network, indicating more active time in this state. Conversely, the neuropathic pain group showed lower coherence and spent less time in the frontal attentional state. Therefore, this study reveals a temporal imbalance and dysregulation of spectral frequency-specific brain microstates in patients with neuropathic pain. These findings can potentially impact the development of a mechanism-based therapeutic approach by identifying brain targets to stimulate using neuromodulation to modify abnormal activity and to restore effective neuronal synchrony between brain states.
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Affiliation(s)
- Camille Fauchon
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada
| | - Junseok A Kim
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Rima El-Sayed
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Natalie R Osborne
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Anton Rogachov
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Joshua C Cheng
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Kasey S Hemington
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Rachael L Bosma
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada
| | - Benjamin T Dunkley
- Neurosciences & Mental Health Program, The Hospital for Sick Children Research Institute, Toronto, ON, M5G 0A4, Canada.,Diagnostic Imaging, The Hospital for Sick Children, Toronto, ON, M5G 0A4, Canada.,Department of Medical Imaging, University of Toronto, Toronto, ON, M5T 1W7, Canada
| | - Jiwon Oh
- Div of Neurology, Dept of Medicine, St. Michael's Hospital, Toronto, ON, M5B 1W8, Canada
| | - Anuj Bhatia
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada.,Department of Anesthesia and Pain Medicine, Toronto Western Hospital, and University of Toronto, Toronto, ON, M5T 2S8, Canada
| | - Robert D Inman
- Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada.,Division of Immunology, University of Toronto, Toronto, ON, M5S 1A8, Canada
| | - Karen Deborah Davis
- Division of Brain, Imaging, and Behaviour, Krembil Brain Institute, University Health Network, Toronto, ON, M5T 2S8, Canada. .,Institute of Medical Science, University of Toronto, Toronto, ON, M5S 1A8, Canada. .,Department of Surgery, University of Toronto, Toronto, ON, M5T 1P5, Canada.
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5
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Bian L, Cui T, Thomas Yeo BT, Fornito A, Razi A, Keith J. Identification of community structure-based brain states and transitions using functional MRI. Neuroimage 2021; 244:118635. [PMID: 34624503 PMCID: PMC8905300 DOI: 10.1016/j.neuroimage.2021.118635] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 09/29/2021] [Accepted: 10/04/2021] [Indexed: 11/14/2022] Open
Abstract
Community-based detection of discrete brain states using stochastic latent block model. Bayesian change-point detection and model selection via posterior predictive discrepancy. Markov chain Monte Carlo methods for estimation of community memberships. Distinctive brain states for varying task demands in working memory task fMRI.
Brain function relies on a precisely coordinated and dynamic balance between the functional integration and segregation of distinct networks. Characterizing the way in which brain regions reconfigure their interactions to give rise to distinct but hidden brain states remains an open challenge. In this paper, we propose a Bayesian method for characterizing community structure-based latent brain states and showcase a novel strategy based on posterior predictive discrepancy using the latent block model to detect transitions between community structures in blood oxygen level-dependent (BOLD) time series. The set of estimated parameters in the model includes a latent label vector that assigns network nodes to communities, and also block model parameters that reflect the weighted connectivity within and between communities. Besides extensive in-silico model evaluation, we also provide empirical validation (and replication) using the Human Connectome Project (HCP) dataset of 100 healthy adults. Our results obtained through an analysis of task-fMRI data during working memory performance show appropriate lags between external task demands and change-points between brain states, with distinctive community patterns distinguishing fixation, low-demand and high-demand task conditions.
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Affiliation(s)
- Lingbin Bian
- School of Mathematics, Monash University, Australia; Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Australia.
| | - Tiangang Cui
- School of Mathematics, Monash University, Australia
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Australia; Monash Biomedical Imaging, Monash University, Australia
| | - Adeel Razi
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Australia; Monash Biomedical Imaging, Monash University, Australia; Wellcome Centre for Human Neuroimaging, University College London, United Kingdom; CIFAR Azrieli Global Scholars Program, CIFAR, Toronto, Canada.
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6
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Llano DA, Ma C, Di Fabrizio U, Taheri A, Stebbings KA, Yudintsev G, Xiao G, Kenyon RV, Berger-Wolf TY. A novel dynamic network imaging analysis method reveals aging-related fragmentation of cortical networks in mouse. Netw Neurosci 2021; 5:569-590. [PMID: 34189378 PMCID: PMC8233117 DOI: 10.1162/netn_a_00191] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2020] [Accepted: 03/11/2021] [Indexed: 12/12/2022] Open
Abstract
Network analysis of large-scale neuroimaging data is a particularly challenging computational problem. Here, we adapt a novel analytical tool, the community dynamic inference method (CommDy), for brain imaging data from young and aged mice. CommDy, which was inspired by social network theory, has been successfully used in other domains in biology; this report represents its first use in neuroscience. We used CommDy to investigate aging-related changes in network metrics in the auditory and motor cortices by using flavoprotein autofluorescence imaging in brain slices and in vivo. We observed that auditory cortical networks in slices taken from aged brains were highly fragmented compared to networks observed in young animals. CommDy network metrics were then used to build a random-forests classifier based on NMDA receptor blockade data, which successfully reproduced the aging findings, suggesting that the excitatory cortical connections may be altered during aging. A similar aging-related decline in network connectivity was also observed in spontaneous activity in the awake motor cortex, suggesting that the findings in the auditory cortex reflect general mechanisms during aging. These data suggest that CommDy provides a new dynamic network analytical tool to study the brain and that aging is associated with fragmentation of intracortical networks.
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Affiliation(s)
- Daniel A. Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Chihua Ma
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Umberto Di Fabrizio
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Aynaz Taheri
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Kevin A. Stebbings
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Georgiy Yudintsev
- Neuroscience Program, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Gang Xiao
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Beckman Institute for Advanced Science and Technology, Urbana, IL, USA
| | - Robert V. Kenyon
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - Tanya Y. Berger-Wolf
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
- Current affiliation: Department of Computer Science and Engineering, The Ohio State University, Columbus, OH, USA
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7
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Finn ES, Rosenberg MD. Beyond fingerprinting: Choosing predictive connectomes over reliable connectomes. Neuroimage 2021; 239:118254. [PMID: 34118397 DOI: 10.1016/j.neuroimage.2021.118254] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 05/25/2021] [Accepted: 06/07/2021] [Indexed: 12/20/2022] Open
Abstract
Recent years have seen a surge of research on variability in functional brain connectivity within and between individuals, with encouraging progress toward understanding the consequences of this variability for cognition and behavior. At the same time, well-founded concerns over rigor and reproducibility in psychology and neuroscience have led many to question whether functional connectivity is sufficiently reliable, and call for methods to improve its reliability. The thesis of this opinion piece is that when studying variability in functional connectivity-both across individuals and within individuals over time-we should use behavior prediction as our benchmark rather than optimize reliability for its own sake. We discuss theoretical and empirical evidence to compel this perspective, both when the goal is to study stable, trait-level differences between people, as well as when the goal is to study state-related changes within individuals. We hope that this piece will be useful to the neuroimaging community as we continue efforts to characterize inter- and intra-subject variability in brain function and build predictive models with an eye toward eventual real-world applications.
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Affiliation(s)
- Emily S Finn
- Department of Psychological and Brain Sciences, Dartmouth College, United States.
| | - Monica D Rosenberg
- Department of Psychology, University of Chicago, United States; Neuroscience Institute, University of Chicago, United States.
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8
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Ting CM, Samdin SB, Tang M, Ombao H. Detecting Dynamic Community Structure in Functional Brain Networks Across Individuals: A Multilayer Approach. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:468-480. [PMID: 33044929 DOI: 10.1109/tmi.2020.3030047] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
OBJECTIVE We present a unified statistical framework for characterizing community structure of brain functional networks that captures variation across individuals and evolution over time. Existing methods for community detection focus only on single-subject analysis of dynamic networks; while recent extensions to multiple-subjects analysis are limited to static networks. METHOD To overcome these limitations, we propose a multi-subject, Markov-switching stochastic block model (MSS-SBM) to identify state-related changes in brain community organization over a group of individuals. We first formulate a multilayer extension of SBM to describe the time-dependent, multi-subject brain networks. We develop a novel procedure for fitting the multilayer SBM that builds on multislice modularity maximization which can uncover a common community partition of all layers (subjects) simultaneously. By augmenting with a dynamic Markov switching process, our proposed method is able to capture a set of distinct, recurring temporal states with respect to inter-community interactions over subjects and the change points between them. RESULTS Simulation shows accurate community recovery and tracking of dynamic community regimes over multilayer networks by the MSS-SBM. Application to task fMRI reveals meaningful non-assortative brain community motifs, e.g., core-periphery structure at the group level, that are associated with language comprehension and motor functions suggesting their putative role in complex information integration. Our approach detected dynamic reconfiguration of modular connectivity elicited by varying task demands and identified unique profiles of intra and inter-community connectivity across different task conditions. CONCLUSION The proposed multilayer network representation provides a principled way of detecting synchronous, dynamic modularity in brain networks across subjects.
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9
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Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain network cluster structure. Neuroimage 2020; 220:116611. [PMID: 32058004 DOI: 10.1016/j.neuroimage.2020.116611] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 01/31/2020] [Accepted: 02/04/2020] [Indexed: 12/12/2022] Open
Abstract
There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.
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10
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Pavlović DM, Guillaume BR, Afyouni S, Nichols TE. Multi‐subject stochastic blockmodels with mixed effects for adaptive analysis of individual differences in human brain network cluster structure. STAT NEERL 2020. [DOI: 10.1111/stan.12219] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Dragana M. Pavlović
- Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population HealthUniversity of Oxford Oxford UK
| | - Bryan R.L. Guillaume
- Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population HealthUniversity of Oxford Oxford UK
| | - Soroosh Afyouni
- Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population HealthUniversity of Oxford Oxford UK
| | - Thomas E. Nichols
- Oxford Big Data Institute Li Ka Shing Centre for Health Information and Discovery Nuffield Department of Population HealthUniversity of Oxford Oxford UK
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11
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Multivariate graph learning for detecting aberrant connectivity of dynamic brain networks in autism. Med Image Anal 2019; 56:11-25. [DOI: 10.1016/j.media.2019.05.007] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2018] [Revised: 04/01/2019] [Accepted: 05/24/2019] [Indexed: 01/24/2023]
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12
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Zhou Q, Zhang L, Feng J, Lo CYZ. Tracking the Main States of Dynamic Functional Connectivity in Resting State. Front Neurosci 2019; 13:685. [PMID: 31338016 PMCID: PMC6629909 DOI: 10.3389/fnins.2019.00685] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2019] [Accepted: 06/17/2019] [Indexed: 01/22/2023] Open
Abstract
Dynamical changes have recently been tracked in functional connectivity (FC) calculated from resting-state functional magnetic resonance imaging (R-fMRI), when a person is conscious but not carrying out a directed task during scanning. Diverse dynamical FC states (dFC) are believed to represent different internal states of the brain, in terms of brain-regional interactions. In this paper, we propose a novel protocol, the signed community clustering with the optimized modularity by two-step procedures, to track dynamical whole brain functional connectivity (dWFC) states. This protocol is assumption free without a priori threshold for the number of clusters. By applying our method on sliding window based dWFC’s with automated anatomical labeling 2 (AAL2), three main dWFC states were extracted from R-fMRI datasets in Human Connectome Project, that are independent on window size. Through extracting the FC features of these states, we found the functional links in state 1 (WFC-C1) mainly involved visual, somatomotor, attention and cerebellar (posterior lobe) modules. State 2 (WFC-C2) was similar to WFC-C1, but more FC’s linking limbic, default mode, and frontoparietal modules and less linking the cerebellum, sensory and attention modules. State 3 had more FC’s linking default mode, limbic, and cerebellum, compared to WFC-C1 and WFC-C2. With tests of robustness and stability, our work provides a solid, hypothesis-free tool to detect dWFC states for the possibility of tracking rapid dynamical change in FCs among large data sets.
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Affiliation(s)
- Qunjie Zhou
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China
| | - Lu Zhang
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Jianfeng Feng
- Shanghai Center for Mathematical Sciences, Fudan University, Shanghai, China.,Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China.,Oxford Centre for Computational Neuroscience, Oxford, United Kingdom.,Department of Computer Science, University of Warwick, Coventry, United Kingdom
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
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13
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Davis KD, Cheng JC. Differentiating trait pain from state pain: a window into brain mechanisms underlying how we experience and cope with pain. Pain Rep 2019; 4:e735. [PMID: 31579845 PMCID: PMC6727997 DOI: 10.1097/pr9.0000000000000735] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2018] [Revised: 02/07/2019] [Accepted: 02/22/2019] [Indexed: 11/25/2022] Open
Abstract
Across various biological and psychological attributes, individuals have a set point around which they can fluctuate transiently into various states. However, if one remains in a different state other than their set point for a considerable period (eg, induced by a disease), this different state can be considered to be a new set point that also has associated surrounding states. This concept is instructive for understanding chronic pain, where an individual's set point may maladaptively shift such that they become stuck at a new set point of pain (trait pain), from which pain can fluctuate on different timescales (ie, pain states). Here, we discuss the importance of considering trait and state pains in neuroimaging studies of brain structure and function to gain an understanding of not only an individual's current pain state but also more broadly to their trait pain, which may be more reflective of their general condition.
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Affiliation(s)
- Karen D. Davis
- Department of Surgery and Institute of Medical Science, University of Toronto, Toronto, ON, Canada
- Krembil Brain Institute, Krembil Research Institute, Toronto Western Hospital, University Health Network, Toronto, ON, Canada
| | - Joshua C. Cheng
- Stony Brook University School of Medicine, Stony Brook, NY, USA
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14
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Necka EA, Lee IS, Kucyi A, Cheng JC, Yu Q, Atlas LY. Applications of dynamic functional connectivity to pain and its modulation. Pain Rep 2019; 4:e752. [PMID: 31579848 PMCID: PMC6728009 DOI: 10.1097/pr9.0000000000000752] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Revised: 03/21/2019] [Accepted: 04/07/2019] [Indexed: 12/30/2022] Open
Abstract
Since early work attempting to characterize the brain's role in pain, it has been clear that pain is not generated by a specific brain region, but rather by coordinated activity across a network of brain regions, the "neuromatrix." The advent of noninvasive whole-brain neuroimaging, including functional magnetic resonance imaging, has provided insight on coordinated activity in the pain neuromatrix and how correlations in activity between regions, referred to as "functional connectivity," contribute to pain and its modulation. Initial functional connectivity investigations assumed interregion connectivity remained stable over time, and measured variability across individuals. However, new dynamic functional connectivity (dFC) methods allow researchers to measure how connectivity changes over time within individuals, permitting insights on the dynamic reorganization of the pain neuromatrix in humans. We review how dFC methods have been applied to pain, and insights afforded on how brain connectivity varies across time, either spontaneously or as a function of psychological states, cognitive demands, or the external environment. Specifically, we review psychophysiological interaction, dynamic causal modeling, state-based dynamic community structure, and sliding-window analyses and their use in human functional neuroimaging of acute pain, chronic pain, and pain modulation. We also discuss promising uses of dFC analyses for the investigation of chronic pain conditions and predicting pain treatment efficacy and the relationship between state- and trait-based pain measures. Throughout this review, we provide information regarding the advantages and shortcomings of each approach, and highlight potential future applications of these methodologies for better understanding the brain processes associated with pain.
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Affiliation(s)
- Elizabeth A. Necka
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - In-Seon Lee
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - Aaron Kucyi
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Joshua C. Cheng
- School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Qingbao Yu
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
| | - Lauren Y. Atlas
- Division of Intramural Research, National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, MD, USA
- Division of Intramural Research, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
- Division of Intramural Research, National Insitute of Mental Health, Bethesda, MD, USA
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15
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Funke T, Becker T. Stochastic block models: A comparison of variants and inference methods. PLoS One 2019; 14:e0215296. [PMID: 31013290 PMCID: PMC6478296 DOI: 10.1371/journal.pone.0215296] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 03/30/2019] [Indexed: 11/19/2022] Open
Abstract
Finding communities in complex networks is a challenging task and one promising approach is the Stochastic Block Model (SBM). But the influences from various fields led to a diversity of variants and inference methods. Therefore, a comparison of the existing techniques and an independent analysis of their capabilities and weaknesses is needed. As a first step, we review the development of different SBM variants such as the degree-corrected SBM of Karrer and Newman or Peixoto's hierarchical SBM. Beside stating all these variants in a uniform notation, we show the reasons for their development. Knowing the variants, we discuss a variety of approaches to infer the optimal partition like the Metropolis-Hastings algorithm. We perform our analysis based on our extension of the Girvan-Newman test and the Lancichinetti-Fortunato-Radicchi benchmark as well as a selection of some real world networks. Using these results, we give some guidance to the challenging task of selecting an inference method and SBM variant. In addition, we give a simple heuristic to determine the number of steps for the Metropolis-Hastings algorithms that lack a usual stop criterion. With our comparison, we hope to guide researches in the field of SBM and highlight the problem of existing techniques to focus future research. Finally, by making our code freely available, we want to promote a faster development, integration and exchange of new ideas.
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Affiliation(s)
- Thorben Funke
- Production Systems and Logistic Systems, BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Bremen, Germany
- Faculty of Production Engineering, University of Bremen, Bremen, Bremen, Germany
| | - Till Becker
- Production Systems and Logistic Systems, BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen, Bremen, Bremen, Germany
- Faculty of Business Studies, University of Applied Sciences Emden/Leer, Emden, Lower Saxony, Germany
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16
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Moningka H, Lichenstein S, Worhunsky PD, DeVito EE, Scheinost D, Yip SW. Can neuroimaging help combat the opioid epidemic? A systematic review of clinical and pharmacological challenge fMRI studies with recommendations for future research. Neuropsychopharmacology 2019; 44:259-273. [PMID: 30283002 PMCID: PMC6300537 DOI: 10.1038/s41386-018-0232-4] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2018] [Revised: 09/11/2018] [Accepted: 09/18/2018] [Indexed: 02/04/2023]
Abstract
The current opioid epidemic is an urgent public health problem, with enormous individual, societal, and healthcare costs. Despite effective, evidence-based treatments, there is significant individual variability in treatment responses and relapse rates are high. In addition, the neurobiology of opioid-use disorder (OUD) and its treatment is not well understood. This review synthesizes published fMRI literature relevant to OUD, with an emphasis on findings related to opioid medications and treatment, and proposes areas for further research. We conducted a systematic literature review of Medline and Psychinfo to identify (i) fMRI studies comparing OUD and control participants; (ii) studies related to medication, treatment, abstinence or withdrawal effects in OUD; and (iii) studies involving manipulation of the opioid system in healthy individuals. Following application of exclusionary criteria (e.g., insufficient sample size), 45 studies were retained comprising data from ~1400 individuals. We found convergent evidence that individuals with OUD display widespread heightened neural activation to heroin cues. This pattern is potentiated by heroin, attenuated by medication-assisted treatments for opioids, predicts treatment response, and is reduced following extended abstinence. Nonetheless, there is a paucity of literature examining neural characteristics of OUD and its treatment. We discuss limitations of extant research and identify critical areas for future neuroimaging studies, including the urgent need for studies examining prescription opioid users, assessing sex differences and utilizing a wider range of clinically relevant task-based fMRI paradigms across different stages of addiction.
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Affiliation(s)
- Hestia Moningka
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Sarah Lichenstein
- Yale School of Medicine, Radiology and Bioimaging Sciences, New Haven, CT, 06510, USA
| | - Patrick D Worhunsky
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Elise E DeVito
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA
| | - Dustin Scheinost
- Yale School of Medicine, Radiology and Bioimaging Sciences, New Haven, CT, 06510, USA
| | - Sarah W Yip
- Department of Psychiatry, Yale School of Medicine, New Haven, CT, 06510, USA.
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17
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Ma C, Pellolio F, Llano DA, Stebbings KA, Kenyon RV, Marai GE. RemBrain: Exploring Dynamic Biospatial Networks with Mosaic Matrices and Mirror Glyphs. J Imaging Sci Technol 2018; 61. [PMID: 30505140 DOI: 10.2352/j.imagingsci.technol.2017.61.6.060404] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/01/2022]
Abstract
We introduce a web-based visual comparison approach for the systematic exploration of dynamic activation networks across biological datasets. Understanding the dynamics of such networks in the context of demographic factors like age is a fundamental problem in computational systems biology and neuroscience. We design visual encodings for the dynamic and community characteristics of these temporal networks. Our multi-scale approach blends nested mosaic matrices that capture temporal characteristics of the data, spatial views of the network data, Kiviat diagrams and mirror glyphs that detail the temporal behavior and community assignment of specific nodes. A top design specifically targeted at pairwise visual comparison further supports the comparative analysis of multiple dataset activations. We demonstrate the effectiveness of this approach through a case study on mouse brain network data. Domain expert feedback indicates this approach can help identify trends and anomalies in the data.
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Affiliation(s)
- Chihua Ma
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | | | - Daniel A Llano
- Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | | | - Robert V Kenyon
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
| | - G Elisabeta Marai
- Department of Computer Science, University of Illinois at Chicago, Chicago, IL, USA
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18
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Sizemore AE, Bassett DS. Dynamic graph metrics: Tutorial, toolbox, and tale. Neuroimage 2018; 180:417-427. [PMID: 28698107 PMCID: PMC5758445 DOI: 10.1016/j.neuroimage.2017.06.081] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2017] [Revised: 05/24/2017] [Accepted: 06/29/2017] [Indexed: 11/23/2022] Open
Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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19
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Abstract
The central nervous system is composed of many individual units - from cells to areas - that are connected with one another in a complex pattern of functional interactions that supports perception, action, and cognition. One natural and parsimonious representation of such a system is a graph in which nodes (units) are connected by edges (interactions). While applicable across spatiotemporal scales, species, and cohorts, the traditional graph approach is unable to address the complexity of time-varying connectivity patterns that may be critically important for an understanding of emotional and cognitive state, task-switching, adaptation and development, or aging and disease progression. Here we survey a set of tools from applied mathematics that offer measures to characterize dynamic graphs. Along with this survey, we offer suggestions for visualization and a publicly-available MATLAB toolbox to facilitate the application of these metrics to existing or yet-to-be acquired neuroimaging data. We illustrate the toolbox by applying it to a previously published data set of time-varying functional graphs, but note that the tools can also be applied to time-varying structural graphs or to other sorts of relational data entirely. Our aim is to provide the neuroimaging community with a useful set of tools, and an intuition regarding how to use them, for addressing emerging questions that hinge on accurate and creative analyses of dynamic graphs.
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Affiliation(s)
- Ann E Sizemore
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, 19104, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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20
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Du Y, Fu Z, Calhoun VD. Classification and Prediction of Brain Disorders Using Functional Connectivity: Promising but Challenging. Front Neurosci 2018; 12:525. [PMID: 30127711 PMCID: PMC6088208 DOI: 10.3389/fnins.2018.00525] [Citation(s) in RCA: 172] [Impact Index Per Article: 28.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2018] [Accepted: 07/12/2018] [Indexed: 12/13/2022] Open
Abstract
Brain functional imaging data, especially functional magnetic resonance imaging (fMRI) data, have been employed to reflect functional integration of the brain. Alteration in brain functional connectivity (FC) is expected to provide potential biomarkers for classifying or predicting brain disorders. In this paper, we present a comprehensive review in order to provide guidance about the available brain FC measures and typical classification strategies. We survey the state-of-the-art FC analysis methods including widely used static functional connectivity (SFC) and more recently proposed dynamic functional connectivity (DFC). Temporal correlations among regions of interest (ROIs), data-driven spatial network and functional network connectivity (FNC) are often computed to reflect SFC from different angles. SFC can be extended to DFC using a sliding-window framework, and intrinsic connectivity states along the time-varying connectivity patterns are typically extracted using clustering or decomposition approaches. We also briefly summarize window-less DFC approaches. Subsequently, we highlight various strategies for feature selection including the filter, wrapper and embedded methods. In terms of model building, we include traditional classifiers as well as more recently applied deep learning methods. Moreover, we review representative applications with remarkable classification accuracy for psychosis and mood disorders, neurodevelopmental disorder, and neurological disorders using fMRI data. Schizophrenia, bipolar disorder, autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), Alzheimer's disease and mild cognitive impairment (MCI) are discussed. Finally, challenges in the field are pointed out with respect to the inaccurate diagnosis labeling, the abundant number of possible features and the difficulty in validation. Some suggestions for future work are also provided.
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Affiliation(s)
- Yuhui Du
- The Mind Research Network, Albuquerque, NM, United States
- School of Computer & Information Technology, Shanxi University, Taiyuan, China
| | - Zening Fu
- The Mind Research Network, Albuquerque, NM, United States
| | - Vince D. Calhoun
- The Mind Research Network, Albuquerque, NM, United States
- Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, United States
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21
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Hasson U, Egidi G, Marelli M, Willems RM. Grounding the neurobiology of language in first principles: The necessity of non-language-centric explanations for language comprehension. Cognition 2018; 180:135-157. [PMID: 30053570 PMCID: PMC6145924 DOI: 10.1016/j.cognition.2018.06.018] [Citation(s) in RCA: 76] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Revised: 06/05/2018] [Accepted: 06/24/2018] [Indexed: 12/26/2022]
Abstract
Recent decades have ushered in tremendous progress in understanding the neural basis of language. Most of our current knowledge on language and the brain, however, is derived from lab-based experiments that are far removed from everyday language use, and that are inspired by questions originating in linguistic and psycholinguistic contexts. In this paper we argue that in order to make progress, the field needs to shift its focus to understanding the neurobiology of naturalistic language comprehension. We present here a new conceptual framework for understanding the neurobiological organization of language comprehension. This framework is non-language-centered in the computational/neurobiological constructs it identifies, and focuses strongly on context. Our core arguments address three general issues: (i) the difficulty in extending language-centric explanations to discourse; (ii) the necessity of taking context as a serious topic of study, modeling it formally and acknowledging the limitations on external validity when studying language comprehension outside context; and (iii) the tenuous status of the language network as an explanatory construct. We argue that adopting this framework means that neurobiological studies of language will be less focused on identifying correlations between brain activity patterns and mechanisms postulated by psycholinguistic theories. Instead, they will be less self-referential and increasingly more inclined towards integration of language with other cognitive systems, ultimately doing more justice to the neurobiological organization of language and how it supports language as it is used in everyday life.
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Affiliation(s)
- Uri Hasson
- Center for Mind/Brain Sciences, The University of Trento, Trento, Italy; Center for Practical Wisdom, The University of Chicago, Chicago, IL, United States.
| | - Giovanna Egidi
- Center for Mind/Brain Sciences, The University of Trento, Trento, Italy
| | - Marco Marelli
- Department of Psychology, University of Milano-Bicocca, Milano, Italy; NeuroMI - Milan Center for Neuroscience, Milano, Italy
| | - Roel M Willems
- Centre for Language Studies & Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands; Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
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22
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Taya F, Dimitriadis SI, Dragomir A, Lim J, Sun Y, Wong KF, Thakor NV, Bezerianos A. Fronto-Parietal Subnetworks Flexibility Compensates For Cognitive Decline Due To Mental Fatigue. Hum Brain Mapp 2018; 39:3528-3545. [PMID: 29691949 DOI: 10.1002/hbm.24192] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2017] [Revised: 03/29/2018] [Accepted: 04/05/2018] [Indexed: 12/22/2022] Open
Abstract
Fronto-parietal subnetworks were revealed to compensate for cognitive decline due to mental fatigue by community structure analysis. Here, we investigate changes in topology of subnetworks of resting-state fMRI networks due to mental fatigue induced by prolonged performance of a cognitively demanding task, and their associations with cognitive decline. As it is well established that brain networks have modular organization, community structure analyses can provide valuable information about mesoscale network organization and serve as a bridge between standard fMRI approaches and brain connectomics that quantify the topology of whole brain networks. We developed inter- and intramodule network metrics to quantify topological characteristics of subnetworks, based on our hypothesis that mental fatigue would impact on functional relationships of subnetworks. Functional networks were constructed with wavelet correlation and a data-driven thresholding scheme based on orthogonal minimum spanning trees, which allowed detection of communities with weak connections. A change from pre- to posttask runs was found for the intermodule density between the frontal and the temporal subnetworks. Seven inter- or intramodule network metrics, mostly at the frontal or the parietal subnetworks, showed significant predictive power of individual cognitive decline, while the network metrics for the whole network were less effective in the predictions. Our results suggest that the control-type fronto-parietal networks have a flexible topological architecture to compensate for declining cognitive ability due to mental fatigue. This community structure analysis provides valuable insight into connectivity dynamics under different cognitive states including mental fatigue.
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Affiliation(s)
- Fumihiko Taya
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore.,Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Stavros I Dimitriadis
- Institute of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, United Kingdom.,Cardiff University Brain Research Imaging Center (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.,Neuroinformatics Group, (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Andrei Dragomir
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Julian Lim
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Neuroscience and Behavioral Disorders Program, Duke-NUS Graduate Medical School, Singapore
| | - Yu Sun
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Kian Foong Wong
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore
| | - Nitish V Thakor
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,Department of Electrical & Computer Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, National University of Singapore, Singapore.,Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, Singapore.,School of Medicine, University of Patras, Greece
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23
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Moayedi M, Salomons TV, Atlas LY. Pain Neuroimaging in Humans: A Primer for Beginners and Non-Imagers. THE JOURNAL OF PAIN 2018; 19:961.e1-961.e21. [PMID: 29608974 PMCID: PMC6192705 DOI: 10.1016/j.jpain.2018.03.011] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/15/2017] [Revised: 02/22/2018] [Accepted: 03/19/2018] [Indexed: 01/06/2023]
Abstract
Human pain neuroimaging has exploded in the past 2 decades. During this time, the broader neuroimaging community has continued to investigate and refine methods. Another key to progress is exchange with clinicians and pain scientists working with other model systems and approaches. These collaborative efforts require that non-imagers be able to evaluate and assess the evidence provided in these reports. Likewise, new trainees must design rigorous and reliable pain imaging experiments. In this article we provide a guideline for designing, reading, evaluating, analyzing, and reporting results of a pain neuroimaging experiment, with a focus on functional and structural magnetic resonance imaging. We focus in particular on considerations that are unique to neuroimaging studies of pain in humans, including study design and analysis, inferences that can be drawn from these studies, and the strengths and limitations of the approach.
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Affiliation(s)
- Massieh Moayedi
- Faculty of Dentistry, University of Toronto, Toronto, Ontario, Canada; University of Toronto Centre for the Study of Pain, University of Toronto, Toronto, Ontario, Canada; Department of Dentistry, Mount Sinai Hospital, Toronto, Ontario, Canada.
| | - Tim V Salomons
- School of Psychology and Clinical Language Science, University of Reading, Reading, UK; Centre for Integrated Neuroscience and Neurodynamics, University of Reading, Reading, UK
| | - Lauren Y Atlas
- National Center for Complementary and Integrative Health, National Institutes of Health, Bethesda, Maryland; National Institute on Drug Abuse, National Institutes of Health, Bethesda, Maryland
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24
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Abstract
The network architecture of the human brain has become a feature of increasing interest to the neuroscientific community, largely because of its potential to illuminate human cognition, its variation over development and aging, and its alteration in disease or injury. Traditional tools and approaches to study this architecture have largely focused on single scales-of topology, time, and space. Expanding beyond this narrow view, we focus this review on pertinent questions and novel methodological advances for the multi-scale brain. We separate our exposition into content related to multi-scale topological structure, multi-scale temporal structure, and multi-scale spatial structure. In each case, we recount empirical evidence for such structures, survey network-based methodological approaches to reveal these structures, and outline current frontiers and open questions. Although predominantly peppered with examples from human neuroimaging, we hope that this account will offer an accessible guide to any neuroscientist aiming to measure, characterize, and understand the full richness of the brain's multiscale network structure-irrespective of species, imaging modality, or spatial resolution.
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Affiliation(s)
- Richard F Betzel
- School of Engineering and Applied Science, Department of Bioengineering, USA
| | - Danielle S Bassett
- School of Engineering and Applied Science, Department of Bioengineering, USA; Department of Electrical and Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104, USA.
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25
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Taghia J, Ryali S, Chen T, Supekar K, Cai W, Menon V. Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI. Neuroimage 2017; 155:271-290. [PMID: 28267626 PMCID: PMC5536190 DOI: 10.1016/j.neuroimage.2017.02.083] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2016] [Revised: 02/16/2017] [Accepted: 02/27/2017] [Indexed: 12/18/2022] Open
Abstract
There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity.
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Affiliation(s)
- Jalil Taghia
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA.
| | - Srikanth Ryali
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA
| | - Tianwen Chen
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA
| | - Kaustubh Supekar
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA
| | - Weidong Cai
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA
| | - Vinod Menon
- Department of Psychiatry & Behavioral Sciences Stanford University, School of Medicine, Stanford, CA 94305, USA; Department of Neurology & Neurological Sciences, School of Medicine, Stanford, CA 94305, USA; Stanford Neurosciences Institute Stanford University, School of Medicine, Stanford, CA 94305, USA.
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26
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Chai LR, Khambhati AN, Ciric R, Moore TM, Gur RC, Gur RE, Satterthwaite TD, Bassett DS. Evolution of brain network dynamics in neurodevelopment. Netw Neurosci 2017; 1:14-30. [PMID: 30793068 PMCID: PMC6330215 DOI: 10.1162/netn_a_00001] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Accepted: 10/20/2016] [Indexed: 01/08/2023] Open
Abstract
Cognitive function evolves significantly over development, enabling flexible control of human behavior. Yet, how these functions are instantiated in spatially distributed and dynamically interacting networks, or graphs, that change in structure from childhood to adolescence is far from understood. Here we applied a novel machine-learning method to track continuously overlapping and time-varying subgraphs in the brain at rest within a sample of 200 healthy youth (ages 8-11 and 19-22) drawn from the Philadelphia Neurodevelopmental Cohort. We uncovered a set of subgraphs that capture surprisingly integrated and dynamically changing interactions among known cognitive systems. We observed that subgraphs that were highly expressed were especially transient, flexibly switching between high and low expression over time. This transience was particularly salient in a subgraph predominantly linking frontoparietal regions of the executive system, which increases in both expression and flexibility from childhood to young adulthood. Collectively, these results suggest that healthy development is accompanied by an increasing precedence of executive networks and a greater switching of the regions and interactions subserving these networks.
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Affiliation(s)
- Lucy R. Chai
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ankit N. Khambhati
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Rastko Ciric
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Tyler M. Moore
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Ruben C. Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Raquel E. Gur
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Theodore D. Satterthwaite
- Brain Behavior Laboratory, Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104 USA
| | - Danielle S. Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104 USA
- Department of Electrical & Systems Engineering, University of Pennsylvania, Philadelphia, PA 19104 USA
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27
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Abnormal dynamics of cortical resting state functional connectivity in chronic headache patients. Magn Reson Imaging 2017; 36:56-67. [DOI: 10.1016/j.mri.2016.10.015] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Revised: 10/09/2016] [Accepted: 10/10/2016] [Indexed: 01/07/2023]
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28
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Hirayama JI, Hyvärinen A, Kiviniemi V, Kawanabe M, Yamashita O. Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis. PLoS One 2016; 11:e0168180. [PMID: 28002474 PMCID: PMC5176286 DOI: 10.1371/journal.pone.0168180] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2016] [Accepted: 11/28/2016] [Indexed: 11/18/2022] Open
Abstract
Characterizing the variability of resting-state functional brain connectivity across subjects and/or over time has recently attracted much attention. Principal component analysis (PCA) serves as a fundamental statistical technique for such analyses. However, performing PCA on high-dimensional connectivity matrices yields complicated “eigenconnectivity” patterns, for which systematic interpretation is a challenging issue. Here, we overcome this issue with a novel constrained PCA method for connectivity matrices by extending the idea of the previously proposed orthogonal connectivity factorization method. Our new method, modular connectivity factorization (MCF), explicitly introduces the modularity of brain networks as a parametric constraint on eigenconnectivity matrices. In particular, MCF analyzes the variability in both intra- and inter-module connectivities, simultaneously finding network modules in a principled, data-driven manner. The parametric constraint provides a compact module-based visualization scheme with which the result can be intuitively interpreted. We develop an optimization algorithm to solve the constrained PCA problem and validate our method in simulation studies and with a resting-state functional connectivity MRI dataset of 986 subjects. The results show that the proposed MCF method successfully reveals the underlying modular eigenconnectivity patterns in more general situations and is a promising alternative to existing methods.
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Affiliation(s)
- Jun-ichiro Hirayama
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- RIKEN Center for Advanced Integrated Intelligence Research, Japan
- * E-mail:
| | - Aapo Hyvärinen
- Department of Computer Science/HIIT, University of Helsinki, Helsinki, Finland
- Gatsby Computational Neuroscience Unit, University College London, London, United Kingdom
| | - Vesa Kiviniemi
- Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
| | - Motoaki Kawanabe
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- RIKEN Center for Advanced Integrated Intelligence Research, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institute International (ATR), Kyoto, Japan
- RIKEN Center for Advanced Integrated Intelligence Research, Japan
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29
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Dynamic network interactions supporting internally-oriented cognition. Curr Opin Neurobiol 2016; 40:86-93. [DOI: 10.1016/j.conb.2016.06.014] [Citation(s) in RCA: 103] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 06/10/2016] [Accepted: 06/22/2016] [Indexed: 11/21/2022]
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30
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Monti RP, Lorenz R, Braga RM, Anagnostopoulos C, Leech R, Montana G. Real-time estimation of dynamic functional connectivity networks. Hum Brain Mapp 2016; 38:202-220. [PMID: 27600689 DOI: 10.1002/hbm.23355] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2016] [Revised: 07/28/2016] [Accepted: 08/10/2016] [Indexed: 11/09/2022] Open
Abstract
Two novel and exciting avenues of neuroscientific research involve the study of task-driven dynamic reconfigurations of functional connectivity networks and the study of functional connectivity in real-time. While the former is a well-established field within neuroscience and has received considerable attention in recent years, the latter remains in its infancy. To date, the vast majority of real-time fMRI studies have focused on a single brain region at a time. This is due in part to the many challenges faced when estimating dynamic functional connectivity networks in real-time. In this work, we propose a novel methodology with which to accurately track changes in time-varying functional connectivity networks in real-time. The proposed method is shown to perform competitively when compared to state-of-the-art offline algorithms using both synthetic as well as real-time fMRI data. The proposed method is applied to motor task data from the Human Connectome Project as well as to data obtained from a visuospatial attention task. We demonstrate that the algorithm is able to accurately estimate task-related changes in network structure in real-time. Hum Brain Mapp 38:202-220, 2017. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Ricardo Pio Monti
- Department of Mathematics, Imperial College London, London, United Kingdom
| | - Romy Lorenz
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, the Division of Brain Sciences, Imperial College London, London, United Kingdom.,Department of Bioengineering, Imperial College London, London, United Kingdom
| | - Rodrigo M Braga
- Department of Mathematics, Imperial College London, London, United Kingdom.,Center for Brain Science, Harvard University, Cambridge, Massachusetts
| | | | - Robert Leech
- The Computational, Cognitive and Clinical Neuroimaging Laboratory, the Division of Brain Sciences, Imperial College London, London, United Kingdom
| | - Giovanni Montana
- Department of Mathematics, Imperial College London, London, United Kingdom.,Department of Biomedical Engineering, King's College London, London, United Kingdom
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31
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Isolating biomarkers for symptomatic states: considering symptom-substrate chronometry. Mol Psychiatry 2016; 21:1180-7. [PMID: 27240533 PMCID: PMC5114713 DOI: 10.1038/mp.2016.83] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2015] [Revised: 03/22/2016] [Accepted: 04/18/2016] [Indexed: 12/12/2022]
Abstract
A long-standing goal of psychopathology research is to develop objective markers of symptomatic states, yet progress has been far slower than expected. Although prior reviews have attributed this state of affairs to diagnostic heterogeneity, symptom comorbidity and phenotypic complexity, little attention has been paid to the implications of intra-individual symptom dynamics and inter-relatedness for biomarker study designs. In this critical review, we consider the impact of short-term symptom fluctuations on widely used study designs that regress the 'average level' of a given symptom against biological data collected at a single time point, and summarize findings from ambulatory assessment studies suggesting that such designs may be sub-optimal to detect symptom-substrate relationships. Although such designs have a crucial role in advancing our understanding of biological substrates related to more stable, longer-term changes (for example, gray matter thinning during a depressive episode), they may be less optimal for the detection of symptoms that exhibit high frequency fluctuations, are susceptible to common reporting biases, or may be heavily influenced by the presence of other symptoms. We propose that a greater emphasis on intra-individual symptom chronometry may be useful for identifying subgroups of patients with common, proximal pathological indicators. Taken together, these three recent developments in the areas of symptom conceptualization and measurement raise important considerations for future studies attempting to identify reliable biomarkers in psychiatry.
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32
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Taya F, de Souza J, Thakor NV, Bezerianos A. Comparison method for community detection on brain networks from neuroimaging data. APPLIED NETWORK SCIENCE 2016; 1:8. [PMID: 30533500 PMCID: PMC6245170 DOI: 10.1007/s41109-016-0007-y] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2016] [Accepted: 06/12/2016] [Indexed: 06/09/2023]
Abstract
The brain is a complex system consisting of regions dedicated to different brain functions, and higher cognitive functions are realized via information flow between distant brain areas communicating with each other. As such, it is natural to shift towards brain network analysis from mapping of brain functions, for deeper understanding of the brain system. The graph theoretical network metrics measure global or local properties of network topology, but they do not provide any information about the intermediate scale of the network. Community structure analysis is a useful approach to investigate the mesoscale organization of brain network. However, the community detection schemes are yet to be established. In this paper, we propose a method to compare different community detection schemes for neuroimaging data from multiple subjects. To the best of our knowledge, our method is the first attempt to evaluate community detection from multiple-subject data without "ground truth" community and any assumptions about the original network features. To show its feasibility, three community detection algorithms and three different brain atlases were examined using resting-state fMRI functional networks. As it is crucial to find a single group-based community structure as a representative for a group of subjects to allow discussion about brain areas and connections in different conditions on common ground, a number of community detection schemes based on different approaches have been proposed. A non-parametric permutation test on similarity between group-based community structures and individual community structures was used to determine which algorithm or atlas provided the best representative structure of the group. The Normalized Mutual Information (NMI) was computed to measure the similarity between the community structures. We also discuss further issues on community detection using the proposed method.
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Affiliation(s)
- Fumihiko Taya
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore
- Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore
| | - Joshua de Souza
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore
| | - Nitish V. Thakor
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore
- Department of Electrical & Computer Engineering, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, National University of Singapore, Singapore, Singapore
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD USA
| | - Anastasios Bezerianos
- Singapore Institute for Neurotechnology (SINAPSE), Centre for Life Sciences, National University of Singapore, 28 Medical Drive, #05-Cor, 117456 Singapore, Singapore
- School of Medicine, University of Patras, Patras, Greece
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33
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Shine JM, Koyejo O, Bell PT, Gorgolewski KJ, Gilat M, Poldrack RA. Estimation of dynamic functional connectivity using Multiplication of Temporal Derivatives. Neuroimage 2015; 122:399-407. [DOI: 10.1016/j.neuroimage.2015.07.064] [Citation(s) in RCA: 132] [Impact Index Per Article: 14.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2015] [Revised: 05/01/2015] [Accepted: 07/23/2015] [Indexed: 11/29/2022] Open
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34
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Chen JE, Glover GH. Functional Magnetic Resonance Imaging Methods. Neuropsychol Rev 2015; 25:289-313. [PMID: 26248581 PMCID: PMC4565730 DOI: 10.1007/s11065-015-9294-9] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2015] [Accepted: 07/28/2015] [Indexed: 12/11/2022]
Abstract
Since its inception in 1992, Functional Magnetic Resonance Imaging (fMRI) has become an indispensible tool for studying cognition in both the healthy and dysfunctional brain. FMRI monitors changes in the oxygenation of brain tissue resulting from altered metabolism consequent to a task-based evoked neural response or from spontaneous fluctuations in neural activity in the absence of conscious mentation (the "resting state"). Task-based studies have revealed neural correlates of a large number of important cognitive processes, while fMRI studies performed in the resting state have demonstrated brain-wide networks that result from brain regions with synchronized, apparently spontaneous activity. In this article, we review the methods used to acquire and analyze fMRI signals.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Department of Electrical Engineering, Stanford University, Stanford, CA, 94305, USA,
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