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Zheng K, Li B, Lu H, Wang H, Liu J, Yan B, Friston KJ, Wu Y, Liu J, Zhang X, Liu M, Li L, Qin J, Chen B, Hu D, Li L. Aberrant temporal-spatial complexity of intrinsic fluctuations in major depression. Eur Arch Psychiatry Clin Neurosci 2023; 273:169-181. [PMID: 35419632 DOI: 10.1007/s00406-022-01403-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Accepted: 03/25/2022] [Indexed: 11/28/2022]
Abstract
Accumulating evidence suggests that the brain is highly dynamic; thus, investigation of brain dynamics especially in brain connectivity would provide crucial information that stationary functional connectivity could miss. This study investigated temporal expressions of spatial modes within the default mode network (DMN), salience network (SN) and cognitive control network (CCN) using a reliable data-driven co-activation pattern (CAP) analysis in two independent data sets. We found enhanced CAP-to-CAP transitions of the SN in patients with MDD. Results suggested enhanced flexibility of this network in the patients. By contrast, we also found reduced spatial consistency and persistence of the DMN in the patients, indicating reduced variability and stability in individuals with MDD. In addition, the patients were characterized by prominent activation of mPFC. Moreover, further correlation analysis revealed that persistence and transitions of RCCN were associated with the severity of depression. Our findings suggest that functional connectivity in the patients may not be simply attenuated or potentiated, but just alternating faster or slower among more complex patterns. The aberrant temporal-spatial complexity of intrinsic fluctuations reflects functional diaschisis of resting-state networks as characteristic of patients with MDD.
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Affiliation(s)
- Kaizhong Zheng
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China
| | - Baojuan Li
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Hongbing Lu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Huaning Wang
- Xijing Hospital, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Jin Liu
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Baoyu Yan
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Karl J Friston
- The Wellcome Department of Imaging Neuroscience, Institute of Neurology, University College London, 12 Queen Square, London, WC1N 3AR, UK
| | - Yuxia Wu
- Department of Information and Communication Engineering, Xi'an Jiaotong University, Xi'an, 710032, China
| | - Jian Liu
- Network Center, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Xi Zhang
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Mengwan Liu
- School of Biomedical Engineering, Fourth Military Medical University, Xi'an, 710032, Shaanxi, China
| | - Liang Li
- Institute of Military Cognition and Brain Sciences, Academy of Military Medical Sciences, 27 Tai-Ping Road, Beijing, 100850, China
| | - Jian Qin
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China
| | - Badong Chen
- Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, Shaanxi, China.
| | - Dewen Hu
- Department of Intelligence Science and Technology, College of Intelligence Science and Technology, National University of Defense Technology, Changsha, 410073, China.
| | - Lingjiang Li
- Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China.
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Fernandez Z, Scheel N, Baker JH, Zhu DC. Functional connectivity of cortical resting-state networks is differentially affected by rest conditions. Brain Res 2022; 1796:148081. [PMID: 36100086 DOI: 10.1016/j.brainres.2022.148081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 08/23/2022] [Accepted: 09/06/2022] [Indexed: 11/16/2022]
Abstract
Optimal conditions for resting-state functional magnetic resonance imaging (rs-fMRI) are still highly debated. Here, we comprehensively assessed the effects of various rest conditions on all cortical resting-state networks (RSNs) defined by an established atlas. Twenty-two healthy college students (22 ± 4 years old, 12 females) were scanned on a GE 3T MRI scanner. Rs-fMRI datasets were collected under four different conditions for each subject: (1) eyes open in dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), (3) eyes closed while remembering four numbers through the scan session (Eyes-Closed-Number) and (4) asked to watch a movie (Movie). We completed a thorough examination of the 17 functional RSNs defined by Yeo and colleagues. Importantly, the movie led to changes in global connectivity and should be avoided as a rest condition. Conversely, there were no significant connectivity differences between conditions within the frontoparietal control and limbic networks and the following subnetworks as defined by Yeo et al.: default-B, dorsal-attention-B and salience/ventral-attention-B. These were not even significant when compared to the highly stimulative Movie condition. A significant difference was not found between Eyes-Closed and Eyes-Closed-Number conditions in whole-brain, within-network and between-network comparisons. When considering other rest conditions, however, we observed connectivity changes in some subnetworks, including those of the default-mode network. Overall, we found conditions with more external stimulation led to more changes in functional connectivity during rs-fMRI. In conclusion, the comprehensive results of our study can aid in choosing rest conditions for the study of overall and specific functional networks.
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Affiliation(s)
- Zachary Fernandez
- Department of Radiology, Michigan State University, USA; Neuroscience Program, Michigan State University, USA; Cognitive Imaging Research Center, Michigan State University, USA
| | - Norman Scheel
- Department of Radiology, Michigan State University, USA; Cognitive Imaging Research Center, Michigan State University, USA
| | - Joshua H Baker
- Department of Radiology, Michigan State University, USA; Neuroscience Program, Michigan State University, USA; College of Osteopathic Medicine, Michigan State University, USA; Cognitive Imaging Research Center, Michigan State University, USA
| | - David C Zhu
- Department of Radiology, Michigan State University, USA; Neuroscience Program, Michigan State University, USA; Cognitive Imaging Research Center, Michigan State University, USA.
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Easy to interpret coordinate based meta-analysis of neuroimaging studies: Analysis of brain coordinates (ABC). J Neurosci Methods 2022; 372:109556. [PMID: 35271873 DOI: 10.1016/j.jneumeth.2022.109556] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 02/09/2022] [Accepted: 03/04/2022] [Indexed: 11/20/2022]
Abstract
BACKGROUND Functional MRI and voxel-based morphometry are important in neuroscience. They are technically challenging with no globally optimal analysis method, and the multiple approaches have been shown to produce different results. It is useful to be able to meta-analyse results from such studies that tested a similar hypothesis potentially using different analysis methods. The aim is to identify replicable results and infer hypothesis specific effects. Coordinate based meta-analysis (CBMA) offers this, but the multiple algorithms can produce different results, making interpretation conditional on the algorithm. NEW METHOD Here a new model based CBMA algorithm, Analysis of Brain Coordinates (ABC), is presented. ABC aims to be simple to understand by avoiding empirical elements where possible and by using a simple to interpret statistical threshold, which relates to the primary aim of detecting replicable effects. RESULTS ABC is compared to both the most used and the most recently developed CBMA algorithms, by reproducing a published meta-analysis of localised grey matter changes in schizophrenia. There are some differences in results and the type of data that can be analysed, which are related to the algorithm specifics. COMPARISON TO OTHER METHODS Compared to other algorithms ABC eliminates empirical elements where possible and uses a simple to interpret statistical threshold. CONCLUSIONS There may be no optimal way to meta-analyse neuroimaging studies using CBMA. However, by eliminating some empirical elements and relating the statistical threshold directly to the aim of finding replicable effects, ABC makes the impact of the algorithm on any conclusion easier to understand.
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Dockès J, Poldrack RA, Primet R, Gözükan H, Yarkoni T, Suchanek F, Thirion B, Varoquaux G. NeuroQuery, comprehensive meta-analysis of human brain mapping. eLife 2020; 9:53385. [PMID: 32129761 PMCID: PMC7164961 DOI: 10.7554/elife.53385] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2019] [Accepted: 03/03/2020] [Indexed: 11/13/2022] Open
Abstract
Reaching a global view of brain organization requires assembling evidence on widely different mental processes and mechanisms. The variety of human neuroscience concepts and terminology poses a fundamental challenge to relating brain imaging results across the scientific literature. Existing meta-analysis methods perform statistical tests on sets of publications associated with a particular concept. Thus, large-scale meta-analyses only tackle single terms that occur frequently. We propose a new paradigm, focusing on prediction rather than inference. Our multivariate model predicts the spatial distribution of neurological observations, given text describing an experiment, cognitive process, or disease. This approach handles text of arbitrary length and terms that are too rare for standard meta-analysis. We capture the relationships and neural correlates of 7547 neuroscience terms across 13 459 neuroimaging publications. The resulting meta-analytic tool, neuroquery.org, can ground hypothesis generation and data-analysis priors on a comprehensive view of published findings on the brain.
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Affiliation(s)
- Jérôme Dockès
- Inria, CEA, Université Paris-Saclay, Essonne, France
| | | | | | | | - Tal Yarkoni
- University of Texas at Austin, Austin, United States
| | | | | | - Gaël Varoquaux
- Inria, CEA, Université Paris-Saclay, Essonne, France.,Montréal Neurological Institute, McGill University, Montreal, Canada
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Tench CR, Tanasescu R, Constantinescu CS, Cottam WJ, Auer DP. Coordinate based meta-analysis of networks in neuroimaging studies. Neuroimage 2019; 205:116259. [PMID: 31626896 DOI: 10.1016/j.neuroimage.2019.116259] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 09/03/2019] [Accepted: 10/07/2019] [Indexed: 10/25/2022] Open
Abstract
Meta-analysis of summary results from published neuroimaging studies independently testing a common hypothesis is performed using coordinate based meta-analysis (CBMA), which tests for consistent activation (in the case of functional MRI studies) of the same anatomical regions. Using just the reported coordinates it is also possible to meta-analyse coactivated regions to reveal a network-like structure of coordinate clusters (network nodes) distributed at the coactivated locations and a measure of the coactivation strength (network edges), which is determined by the presence/absence of reported activation. Here a new coordinate-based method to estimate a network of coactivations is detailed, which utilises the Z score accompanying each reported. Coordinate based meta-analysis of networks (CBMAN) assumes that if the activation pattern reported by independent studies is truly consistent, then the relative magnitude of these Z scores might also be consistent. It is hypothesised that this is detectable as Z score covariance between coactivated regions provided the within study variances are small. Advantages of using the Z scores instead of coordinates to measure coactivation strength are that censoring by the significance thresholds can be considered, and that using a continuous measure rather than a dichotomous one can increase statistical power. CBMAN uses maximum likelihood estimation to fit multivariate normal distributions to the standardised Z scores, and the covariances are considered as edges of a network of coactivated clusters (nodes). Here it is validated by numerical simulation and demonstrated on real data used previously to demonstrate CBMA. Software to perform CBMAN is freely available.
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Affiliation(s)
- C R Tench
- Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK; NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK.
| | - Radu Tanasescu
- Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK.
| | - C S Constantinescu
- Division of Clinical Neurosciences, Clinical Neurology, University of Nottingham, Queen's Medical Centre, Nottingham, UK.
| | - W J Cottam
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK; Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK; Division of Clinical Neuroscience, Radiological Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.
| | - D P Auer
- NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK; Arthritis Research UK Pain Centre, University of Nottingham, Nottingham, UK; Division of Clinical Neuroscience, Radiological Sciences, University of Nottingham, Queen's Medical Centre, Nottingham, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK.
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6
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Luo X, Wei Y. Nonparametric Bayesian learning of heterogeneous dynamic transcription factor networks. Ann Appl Stat 2018. [DOI: 10.1214/17-aoas1129] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
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Samartsidis P, Montagna S, Nichols TE, Johnson TD. The coordinate-based meta-analysis of neuroimaging data. Stat Sci 2017; 32:580-599. [PMID: 29545671 DOI: 10.1214/17-sts624] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Neuroimaging meta-analysis is an area of growing interest in statistics. The special characteristics of neuroimaging data render classical meta-analysis methods inapplicable and therefore new methods have been developed. We review existing methodologies, explaining the benefits and drawbacks of each. A demonstration on a real dataset of emotion studies is included. We discuss some still-open problems in the field to highlight the need for future research.
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Affiliation(s)
- Pantelis Samartsidis
- MRC Biostatistics Unit, University Forvie Site, Robinson Way, Cambridge CB2 0SR, UK
| | - Silvia Montagna
- School of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury, CT2 7FS
| | | | - Timothy D Johnson
- Biostatistics Department, University of Michigan, Ann Arbor, MI 48109, USA
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ANIMA: A data-sharing initiative for neuroimaging meta-analyses. Neuroimage 2015; 124:1245-1253. [PMID: 26231246 DOI: 10.1016/j.neuroimage.2015.07.060] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2015] [Revised: 06/25/2015] [Accepted: 07/22/2015] [Indexed: 01/13/2023] Open
Abstract
Meta-analytic techniques allow cognitive neuroscientists to pool large amounts of data across many individual task-based functional neuroimaging experiments. These methods have been aided by the introduction of online databases such as Brainmap.org or Neurosynth.org, which collate peak activation coordinates obtained from thousands of published studies. Findings from meta-analytic studies typically include brain regions which are consistently activated across studies for specific contrasts, investigating cognitive or clinical hypotheses. These regions can be subsequently used as the basis for seed-based connectivity analysis, or formally compared to neuroimaging data in order to help interpret new findings. To facilitate such approaches, we have developed a new online repository of meta-analytic neuroimaging results, named the Archive of Neuroimaging Meta-analyses (ANIMA). The ANIMA platform consists of an intuitive online interface for querying, downloading, and contributing data from published meta-analytic studies. Additionally, to aid the process of organizing, visualizing, and working with these data, we present an open-source desktop application called Volume Viewer. Volume Viewer allows users to easily arrange imaging data into composite stacks, and save these sessions as individual files, which can also be uploaded to the ANIMA database. The application also allows users to perform basic functions, such as computing conjunctions between images, or extracting regions-of-interest or peak coordinates for further analysis. The introduction of this new resource will enhance the ability of researchers to both share their findings and incorporate existing meta-analytic results into their own research.
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