1
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Ma L, Braun SE, Steinberg JL, Bjork JM, Martin CE, Keen Ii LD, Moeller FG. Effect of scanning duration and sample size on reliability in resting state fMRI dynamic causal modeling analysis. Neuroimage 2024; 292:120604. [PMID: 38604537 DOI: 10.1016/j.neuroimage.2024.120604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/31/2024] [Accepted: 04/07/2024] [Indexed: 04/13/2024] Open
Abstract
Despite its widespread use, resting-state functional magnetic resonance imaging (rsfMRI) has been criticized for low test-retest reliability. To improve reliability, researchers have recommended using extended scanning durations, increased sample size, and advanced brain connectivity techniques. However, longer scanning runs and larger sample sizes may come with practical challenges and burdens, especially in rare populations. Here we tested if an advanced brain connectivity technique, dynamic causal modeling (DCM), can improve reliability of fMRI effective connectivity (EC) metrics to acceptable levels without extremely long run durations or extremely large samples. Specifically, we employed DCM for EC analysis on rsfMRI data from the Human Connectome Project. To avoid bias, we assessed four distinct DCMs and gradually increased sample sizes in a randomized manner across ten permutations. We employed pseudo true positive and pseudo false positive rates to assess the efficacy of shorter run durations (3.6, 7.2, 10.8, 14.4 min) in replicating the outcomes of the longest scanning duration (28.8 min) when the sample size was fixed at the largest (n = 160 subjects). Similarly, we assessed the efficacy of smaller sample sizes (n = 10, 20, …, 150 subjects) in replicating the outcomes of the largest sample (n = 160 subjects) when the scanning duration was fixed at the longest (28.8 min). Our results revealed that the pseudo false positive rate was below 0.05 for all the analyses. After the scanning duration reached 10.8 min, which yielded a pseudo true positive rate of 92%, further extensions in run time showed no improvements in pseudo true positive rate. Expanding the sample size led to enhanced pseudo true positive rate outcomes, with a plateau at n = 70 subjects for the targeted top one-half of the largest ECs in the reference sample, regardless of whether the longest run duration (28.8 min) or the viable run duration (10.8 min) was employed. Encouragingly, smaller sample sizes exhibited pseudo true positive rates of approximately 80% for n = 20, and 90% for n = 40 subjects. These data suggest that advanced DCM analysis may be a viable option to attain reliable metrics of EC when larger sample sizes or run times are not feasible.
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Affiliation(s)
- Liangsuo Ma
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA.
| | | | - Joel L Steinberg
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - James M Bjork
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA
| | - Caitlin E Martin
- Institute for Drug and Alcohol Studies, USA; Department of Obstetrics and Gynecology, USA
| | - Larry D Keen Ii
- Department of Psychology, Virginia State University, Petersburg, VA, USA
| | - F Gerard Moeller
- Institute for Drug and Alcohol Studies, USA; Department of Psychiatry, USA; Department of Neurology, USA; Department of Pharmacology and Toxicology, Virginia Commonwealth University, Richmond, VA, USA
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2
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Spencer D, Yue YR, Bolin D, Ryan S, Mejia AF. Spatial Bayesian GLM on the cortical surface produces reliable task activations in individuals and groups. Neuroimage 2022; 249:118908. [PMID: 35032660 PMCID: PMC9006342 DOI: 10.1016/j.neuroimage.2022.118908] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 12/19/2021] [Accepted: 01/12/2022] [Indexed: 11/24/2022] Open
Abstract
The general linear model (GLM) is a widely popular and convenient tool for estimating the functional brain response and identifying areas of significant activation during a task or stimulus. However, the classical GLM is based on a massive univariate approach that does not explicitly leverage the similarity of activation patterns among neighboring brain locations. As a result, it tends to produce noisy estimates and be underpowered to detect significant activations, particularly in individual subjects and small groups. A recently proposed alternative, a cortical surface-based spatial Bayesian GLM, leverages spatial dependencies among neighboring cortical vertices to produce more accurate estimates and areas of functional activation. The spatial Bayesian GLM can be applied to individual and group-level analysis. In this study, we assess the reliability and power of individual and group-average measures of task activation produced via the surface-based spatial Bayesian GLM. We analyze motor task data from 45 subjects in the Human Connectome Project (HCP) and HCP Retest datasets. We also extend the model to multi-run analysis and employ subject-specific cortical surfaces rather than surfaces inflated to a sphere for more accurate distance-based modeling. Results show that the surface-based spatial Bayesian GLM produces highly reliable activations in individual subjects and is powerful enough to detect trait-like functional topologies. Additionally, spatial Bayesian modeling enhances reliability of group-level analysis even in moderately sized samples (n = 45). Notably, the power of the spatial Bayesian GLM to detect activations above a scientifically meaningful effect size is nearly invariant to sample size, exhibiting high power even in small samples (n = 10). The spatial Bayesian GLM is computationally efficient in individuals and groups and is convenient to implement with the open-source BayesfMRI R package.
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Affiliation(s)
- Daniel Spencer
- Department of Statistics, Indiana University, Myles Brand Hall E104 901 E. 10th Street Bloomington, IN, 47408, USA.
| | - Yu Ryan Yue
- Paul H. Chook Department of Information Systems and Statistics, Baruch College, The City University of New York, New York, NY, 10010, USA
| | - David Bolin
- CEMSE Division, King Abdullah University of Science and Technology, Thuwal, Makkah Province, 23955-6900, Saudi Arabia
| | - Sarah Ryan
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Amanda F Mejia
- Department of Statistics, Indiana University, Myles Brand Hall E104 901 E. 10th Street Bloomington, IN, 47408, USA
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3
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Duda M, Koutra D, Sripada C. Validating dynamicity in resting state fMRI with activation-informed temporal segmentation. Hum Brain Mapp 2021; 42:5718-5735. [PMID: 34510647 PMCID: PMC8559473 DOI: 10.1002/hbm.25649] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 08/11/2021] [Accepted: 08/24/2021] [Indexed: 12/18/2022] Open
Abstract
Confirming the presence (or absence) of dynamic functional connectivity (dFC) states during rest is an important open question in the field of cognitive neuroscience. The prevailing dFC framework aims to identify dynamics directly from connectivity estimates with a sliding window approach, however this method suffers from several drawbacks including sensitivity to window size and poor test-retest reliability. We hypothesize that time-varying changes in functional connectivity are mirrored by significant temporal changes in functional activation, and that this coupling can be leveraged to study dFC without the need for a predefined sliding window. Here, we introduce a data-driven dFC framework, which involves informed segmentation of fMRI time series at candidate FC state transition points estimated from changes in whole-brain functional activation, rather than a fixed-length sliding window. We show our approach reliably identifies true cognitive state change points when applied on block-design working memory task data and outperforms the standard sliding window approach in both accuracy and computational efficiency in this context. When applied to data from four resting state fMRI scanning sessions, our method consistently recovers five reliable FC states, and subject-specific features derived from these states show significant correlation with behavioral phenotypes of interest (cognitive ability, personality). Overall, these results suggest abrupt whole-brain changes in activation can be used as a marker for changes in connectivity states and provides new evidence for the existence of time-varying FC in rest.
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Affiliation(s)
- Marlena Duda
- Department of Computational Medicine and BioinformaticsUniversity of MichiganAnn ArborMichiganUSA
| | - Danai Koutra
- Department of Computer Science and EngineeringUniversity of MichiganAnn ArborMichiganUSA
| | - Chandra Sripada
- Department of PsychiatryUniversity of MichiganAnn ArborMichiganUSA
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4
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Sun J, He L, Chen Q, Yang W, Wei D, Qiu J. The bright side and dark side of daydreaming predict creativity together through brain functional connectivity. Hum Brain Mapp 2021; 43:902-914. [PMID: 34676650 PMCID: PMC8764487 DOI: 10.1002/hbm.25693] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 10/03/2021] [Accepted: 10/12/2021] [Indexed: 01/02/2023] Open
Abstract
Daydreaming and creativity have similar cognitive processes and neural basis. However, few empirical studies have examined the relationship between daydreaming and creativity using cognitive neuroscience methods. The present study explored the relationship between different types of daydreaming and creativity and their common neural basis. The behavioral results revealed that positive constructive daydreaming is positively related to creativity, while poor attentional control is negatively related to it. Machine learning framework was adopted to examine the predictive effect of daydreaming-related brain functional connectivity (FC) on creativity. The results demonstrated that task FCs related to positive constructive daydreaming and task FCs related to poor attentional control both predicted an individual's creativity score successfully. In addition, task FCs combining the positive constructive daydreaming and poor attentional control also had significant predictive effect on creativity score. Furthermore, predictive analysis based on resting-state FCs showed similar patterns. Both of the subscale-related FCs and combined FCs had significant predictive effect on creativity score. Further analysis showed the task and the resting-state FCs both mainly located in the default mode network, central executive network, salience network, and attention network. These results showed that daydreaming was closely related to creativity, as they shared common FC basis.
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Affiliation(s)
- Jiangzhou Sun
- Center for Studies of Education and Psychology of Ethnic Minorities in Southwest China of Southwest UniversityChongqingChina
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Li He
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Qunlin Chen
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Wenjing Yang
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Dongtao Wei
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
| | - Jiang Qiu
- Key Laboratory of Cognition and Personality (SWU), Ministry of EducationChongqingChina
- Faculty of PsychologySouthwest UniversityChongqingChina
- Southwest University Branch, Collaborative Innovation Center of Assessment Toward Basic Education Quality at Beijing Normal UniversityBeijingChina
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5
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Tait L, Özkan A, Szul MJ, Zhang J. A systematic evaluation of source reconstruction of resting MEG of the human brain with a new high-resolution atlas: Performance, precision, and parcellation. Hum Brain Mapp 2021; 42:4685-4707. [PMID: 34219311 PMCID: PMC8410546 DOI: 10.1002/hbm.25578] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 06/09/2021] [Accepted: 06/12/2021] [Indexed: 12/21/2022] Open
Abstract
Noninvasive functional neuroimaging of the human brain can give crucial insight into the mechanisms that underpin healthy cognition and neurological disorders. Magnetoencephalography (MEG) measures extracranial magnetic fields originating from neuronal activity with high temporal resolution, but requires source reconstruction to make neuroanatomical inferences from these signals. Many source reconstruction algorithms are available, and have been widely evaluated in the context of localizing task-evoked activities. However, no consensus yet exists on the optimum algorithm for resting-state data. Here, we evaluated the performance of six commonly-used source reconstruction algorithms based on minimum-norm and beamforming estimates. Using human resting-state MEG, we compared the algorithms using quantitative metrics, including resolution properties of inverse solutions and explained variance in sensor-level data. Next, we proposed a data-driven approach to reduce the atlas from the Human Connectome Project's multi-modal parcellation of the human cortex based on metrics such as MEG signal-to-noise-ratio and resting-state functional connectivity gradients. This procedure produced a reduced cortical atlas with 230 regions, optimized to match the spatial resolution and the rank of MEG data from the current generation of MEG scanners. Our results show that there is no "one size fits all" algorithm, and make recommendations on the appropriate algorithms depending on the data and aimed analyses. Our comprehensive comparisons and recommendations can serve as a guide for choosing appropriate methodologies in future studies of resting-state MEG.
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Affiliation(s)
- Luke Tait
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Ayşegül Özkan
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Maciej J. Szul
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
| | - Jiaxiang Zhang
- Cardiff University Brain Research Imaging CentreCardiff UniversityCardiff
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6
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Sareen E, Zahar S, Ville DVD, Gupta A, Griffa A, Amico E. Exploring MEG brain fingerprints: Evaluation, pitfalls, and interpretations. Neuroimage 2021; 240:118331. [PMID: 34237444 DOI: 10.1016/j.neuroimage.2021.118331] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Revised: 06/22/2021] [Accepted: 07/01/2021] [Indexed: 12/16/2022] Open
Abstract
Individual characterization of subjects based on their functional connectome (FC), termed "FC fingerprinting", has become a highly sought-after goal in contemporary neuroscience research. Recent functional magnetic resonance imaging (fMRI) studies have demonstrated unique characterization and accurate identification of individuals as an accomplished task. However, FC fingerprinting in magnetoencephalography (MEG) data is still widely unexplored. Here, we study resting-state MEG data from the Human Connectome Project to assess the MEG FC fingerprinting and its relationship with several factors including amplitude- and phase-coupling functional connectivity measures, spatial leakage correction, frequency bands, and behavioral significance. To this end, we first employ two identification scoring methods, differential identifiability and success rate, to provide quantitative fingerprint scores for each FC measurement. Secondly, we explore the edgewise and nodal MEG fingerprinting patterns across the different frequency bands (delta, theta, alpha, beta, and gamma). Finally, we investigate the cross-modality fingerprinting patterns obtained from MEG and fMRI recordings from the same subjects. We assess the behavioral significance of FC across connectivity measures and imaging modalities using partial least square correlation analyses. Our results suggest that fingerprinting performance is heavily dependent on the functional connectivity measure, frequency band, identification scoring method, and spatial leakage correction. We report higher MEG fingerprinting performances in phase-coupling methods, central frequency bands (alpha and beta), and in the visual, frontoparietal, dorsal-attention, and default-mode networks. Furthermore, cross-modality comparisons reveal a certain degree of spatial concordance in fingerprinting patterns between the MEG and fMRI data, especially in the visual system. Finally, the multivariate correlation analyses show that MEG connectomes have strong behavioral significance, which however depends on the considered connectivity measure and temporal scale. This comprehensive, albeit preliminary investigation of MEG connectome test-retest identifiability offers a first characterization of MEG fingerprinting in relation to different methodological and electrophysiological factors and contributes to the understanding of fingerprinting cross-modal relationships. We hope that this first investigation will contribute to setting the grounds for MEG connectome identification.
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Affiliation(s)
- Ekansh Sareen
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Sélima Zahar
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland
| | - Dimitri Van De Ville
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland
| | - Anubha Gupta
- Signal Processing and Biomedical Imaging, Dept. of Electronics and Communication Engineering, IIIT-Delhi, New Delhi, India
| | - Alessandra Griffa
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Clinical Neurosciences, Division of Neurology, Geneva University Hospitals and Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Enrico Amico
- Institute of Bioengineering, Center for Neuroprosthetics, École Polytechnique Fédérale De Lausanne (EPFL), Geneva, Switzerland; Department of Radiology and Medical Informatics, University of Geneva (UNIGE), Geneva, Switzerland.
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Taxali A, Angstadt M, Rutherford S, Sripada C. Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling. Cereb Cortex 2021; 31:2822-2833. [PMID: 33447841 PMCID: PMC8599720 DOI: 10.1093/cercor/bhaa390] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 11/08/2020] [Accepted: 11/08/2020] [Indexed: 08/17/2023] Open
Abstract
Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.
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Affiliation(s)
- Aman Taxali
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Mike Angstadt
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Saige Rutherford
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
| | - Chandra Sripada
- Department of Psychiatry, University of Michigan, Ann Arbor, MI 48109, USA
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Kang J, Jeong S, Pae C, Park H. Bayesian estimation of maximum entropy model for individualized energy landscape analysis of brain state dynamics. Hum Brain Mapp 2021; 42:3411-3428. [PMID: 33934421 PMCID: PMC8249903 DOI: 10.1002/hbm.25442] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 03/25/2021] [Accepted: 03/29/2021] [Indexed: 11/24/2022] Open
Abstract
The pairwise maximum entropy model (MEM) for resting state functional MRI (rsfMRI) has been used to generate energy landscape of brain states and to explore nonlinear brain state dynamics. Researches using MEM, however, has mostly been restricted to fixed‐effect group‐level analyses, using concatenated time series across individuals, due to the need for large samples in the parameter estimation of MEM. To mitigate the small sample problem in analyzing energy landscapes for individuals, we propose a Bayesian estimation of individual MEM using variational Bayes approximation (BMEM). We evaluated the performances of BMEM with respect to sample sizes and prior information using simulation. BMEM showed advantages over conventional maximum likelihood estimation in reliably estimating model parameters for individuals with small sample data, particularly utilizing the empirical priors derived from group data. We then analyzed individual rsfMRI of the Human Connectome Project to show the usefulness of MEM in differentiating individuals and in exploring neural correlates for human behavior. MEM and its energy landscape properties showed high subject specificity comparable to that of functional connectivity. Canonical correlation analysis identified canonical variables for MEM highly associated with cognitive scores. Inter‐individual variations of cognitive scores were also reflected in energy landscape properties such as energies, occupation times, and basin sizes at local minima. We conclude that BMEM provides an efficient method to characterize dynamic properties of individuals using energy landscape analysis of individual brain states.
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Affiliation(s)
- Jiyoung Kang
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Seok‐Oh Jeong
- Department of StatisticsHankuk University of Foreign StudiesYong‐In, SeoulSouth Korea
| | - Chongwon Pae
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
| | - Hae‐Jeong Park
- Center for Systems and Translational Brain ScienceInstitute of Human Complexity and Systems Science, Yonsei UniversitySeoulSouth Korea
- Department of Nuclear Medicine, PsychiatryYonsei University College of MedicineSeoulSouth Korea
- Graduate School of Medical Science, Brain Korea 21 ProjectYonsei University College of MedicineSeoulSouth Korea
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Baker BH, Lugo-Candelas C, Wu H, Laue HE, Boivin A, Gillet V, Aw N, Rahman T, Lepage JF, Whittingstall K, Bellenger JP, Posner J, Takser L, Baccarelli AA. Association of Prenatal Acetaminophen Exposure Measured in Meconium With Risk of Attention-Deficit/Hyperactivity Disorder Mediated by Frontoparietal Network Brain Connectivity. JAMA Pediatr 2020; 174:1073-1081. [PMID: 32986124 PMCID: PMC7522774 DOI: 10.1001/jamapediatrics.2020.3080] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Accepted: 06/06/2020] [Indexed: 12/19/2022]
Abstract
Importance Despite evidence of an association between prenatal acetaminophen exposure and attention-deficit/hyperactivity disorder (ADHD) in offspring, the drug is not contraindicated during pregnancy, possibly because prior studies have relied on maternal self-report, failed to quantify acetaminophen dose, and lacked mechanistic insight. Objective To examine the association between prenatal acetaminophen exposure measured in meconium (hereinafter referred to as meconium acetaminophen) and ADHD in children aged 6 to 7 years, along with the potential for mediation by functional brain connectivity. Design, Setting, and Participants This prospective birth cohort study from the Centre Hospitalier Université de Sherbrooke in Sherbrooke, Québec, Canada, included 394 eligible children, of whom 345 had meconium samples collected at delivery and information on ADHD diagnosis. Mothers were enrolled from September 25, 2007, to September 10, 2009, at their first prenatal care visit or delivery and were followed up when children were aged 6 to 7 years. When children were aged 9 to 11 years, resting-state brain connectivity was assessed with magnetic resonance imaging. Data for the present study were collected from September 25, 2007, to January 18, 2020, and analyzed from January 7, 2019, to January 22, 2020. Exposures Acetaminophen levels measured in meconium. Main Outcomes and Measures Physician diagnosis of ADHD was determined at follow-up when children were aged 6 to 7 years or from medical records. Resting-state brain connectivity was assessed with magnetic resonance imaging; attention problems and hyperactivity were assessed with the Behavioral Assessment System for Children Parent Report Scale. Associations between meconium acetaminophen levels and outcomes were estimated with linear and logistic regressions weighted on the inverse probability of treatment to account for potential confounders. Causal mediation analysis was used to test for mediation of the association between prenatal acetaminophen exposure and hyperactivity by resting-state brain connectivity. Results Among the 345 children included in the analysis (177 boys [51.3%]; mean [SD] age, 6.58 [0.54] years), acetaminophen was detected in 199 meconium samples (57.7%), and ADHD was diagnosed in 33 children (9.6%). Compared with no acetaminophen, detection of acetaminophen in meconium was associated with increased odds of ADHD (odds ratio [OR], 2.43; 95% CI, 1.41-4.21). A dose-response association was detected; each doubling of exposure increased the odds of ADHD by 10% (OR, 1.10; 95% CI, 1.02-1.19). Children with acetaminophen detected in meconium showed increased negative connectivity between frontoparietal and default mode network nodes to clusters in the sensorimotor cortices, which mediated an indirect effect on increased child hyperactivity (14%; 95% CI, 1%-26%). Conclusions and Relevance Together with the multitude of other cohort studies showing adverse neurodevelopment associated with prenatal acetaminophen exposure, this work suggests caution should be used in administering acetaminophen during pregnancy. Research into alternative pain management strategies for pregnant women could be beneficial.
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Affiliation(s)
- Brennan H. Baker
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York
| | - Claudia Lugo-Candelas
- Department of Psychiatry, Columbia University Medical Center, New York, New York
- New York State Psychiatric Institute, New York, New York
| | - Haotian Wu
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York
| | - Hannah E. Laue
- Department of Epidemiology, Geisel School of Medicine at Dartmouth College, Hanover, New Hampshire
| | - Amélie Boivin
- Departement de Pédiatrie, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Virginie Gillet
- Departement de Pédiatrie, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Natalie Aw
- New York State Psychiatric Institute, New York, New York
| | - Tonima Rahman
- New York State Psychiatric Institute, New York, New York
| | - Jean-François Lepage
- Departement de Pédiatrie, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kevin Whittingstall
- Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Department of Diagnostic Radiology, Faculty of Medicine and Health Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Jean-Philippe Bellenger
- Department of Chemistry, Faculty of Sciences, University of Sherbrooke, Sherbrooke, Québec, Canada
| | - Jonathan Posner
- Department of Psychiatry, Columbia University Medical Center, New York, New York
- New York State Psychiatric Institute, New York, New York
- Sackler Institute for Developmental Psychobiology, Columbia University Medical Center, New York, New York
| | - Larissa Takser
- Departement de Pédiatrie, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
- Departement de Psychiatrie, Faculté de Médecine et des Sciences de la Santé, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Andrea A. Baccarelli
- Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York
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Han S, Cui Q, Wang X, Li L, Li D, He Z, Guo X, Fan Y, Guo J, Sheng W, Lu F, Chen H. Resting state functional network switching rate is differently altered in bipolar disorder and major depressive disorder. Hum Brain Mapp 2020; 41:3295-3304. [PMID: 32400932 PMCID: PMC7375077 DOI: 10.1002/hbm.25017] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2019] [Revised: 03/20/2020] [Accepted: 04/11/2020] [Indexed: 12/24/2022] Open
Abstract
The clinical misdiagnosis ratio of bipolar disorder (BD) patients to major depressive disorder (MDD) patients is high. Recent findings hypothesize that the ability to flexibly recruit functional neural networks is differently altered in BD and MDD patients. This study aimed to explore distinct aberrance of network flexibility during dynamic networks configuration in BD and MDD patients. Resting state functional magnetic resonance imaging of 40 BD patients, 61 MDD patients, and 61 matched healthy controls were recruited. Dynamic functional connectivity matrices for each subject were constructed with a sliding window method. Then, network switching rate of each node was calculated and compared among the three groups. BD and MDD patients shared decreased network switching rate of regions including left precuneus, bilateral parahippocampal gyrus, and bilateral dorsal medial prefrontal cortex. Apart from these regions, MDD patients presented specially decreased network switching rate in the bilateral anterior insula, left amygdala, and left striatum. Taken together, BD and MDD patients shared decreased network switching rate of key hubs in default mode network and MDD patients presented specially decreased switching rate in salience network and striatum. We found shared and distinct aberrance of network flexibility which revealed altered adaptive functions during dynamic networks configuration of BD and MDD.
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Affiliation(s)
- Shaoqiang Han
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Qian Cui
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
- School of Public Affairs and Administration, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiao Wang
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Liang Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Di Li
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Zongling He
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Xiaonan Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Yun‐Shuang Fan
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Jing Guo
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Wei Sheng
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Fengmei Lu
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
| | - Huafu Chen
- The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of ChinaChengduChina
- MOE Key Lab for NeuroinformationHigh‐Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, University of Electronic Science and Technology of ChinaChengduChina
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11
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Gesierich B, Tuladhar AM, ter Telgte A, Wiegertjes K, Konieczny MJ, Finsterwalder S, Hübner M, Pirpamer L, Koini M, Abdulkadir A, Franzmeier N, Norris DG, Marques JP, zu Eulenburg P, Ewers M, Schmidt R, de Leeuw F, Duering M. Alterations and test-retest reliability of functional connectivity network measures in cerebral small vessel disease. Hum Brain Mapp 2020; 41:2629-2641. [PMID: 32087047 PMCID: PMC7294060 DOI: 10.1002/hbm.24967] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2019] [Revised: 01/30/2020] [Accepted: 02/13/2020] [Indexed: 12/19/2022] Open
Abstract
While structural network analysis consolidated the hypothesis of cerebral small vessel disease (SVD) being a disconnection syndrome, little is known about functional changes on the level of brain networks. In patients with genetically defined SVD (CADASIL, n = 41) and sporadic SVD (n = 46), we independently tested the hypothesis that functional networks change with SVD burden and mediate the effect of disease burden on cognitive performance, in particular slowing of processing speed. We further determined test-retest reliability of functional network measures in sporadic SVD patients participating in a high-frequency (monthly) serial imaging study (RUN DMC-InTENse, median: 8 MRIs per participant). Functional networks for the whole brain and major subsystems (i.e., default mode network, DMN; fronto-parietal task control network, FPCN; visual network, VN; hand somatosensory-motor network, HSMN) were constructed based on resting-state multi-band functional MRI. In CADASIL, global efficiency (a graph metric capturing network integration) of the DMN was lower in patients with high disease burden (standardized beta = -.44; p [corrected] = .035) and mediated the negative effect of disease burden on processing speed (indirect path: std. beta = -.20, p = .047; direct path: std. beta = -.19, p = .25; total effect: std. beta = -.39, p = .02). The corresponding analyses in sporadic SVD showed no effect. Intraclass correlations in the high-frequency serial MRI dataset of the sporadic SVD patients revealed poor test-retest reliability and analysis of individual variability suggested an influence of age, but not disease burden, on global efficiency. In conclusion, our results suggest that changes in functional connectivity networks mediate the effect of SVD-related brain damage on cognitive deficits. However, limited reliability of functional network measures, possibly due to age-related comorbidities, impedes the analysis in elderly SVD patients.
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Affiliation(s)
- Benno Gesierich
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Anil Man Tuladhar
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Annemieke ter Telgte
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Kim Wiegertjes
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marek J. Konieczny
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Sofia Finsterwalder
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Mathias Hübner
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - Lukas Pirpamer
- Department of NeurologyMedical University of GrazGrazAustria
| | - Marisa Koini
- Department of NeurologyMedical University of GrazGrazAustria
| | - Ahmed Abdulkadir
- University Hospital of Old Age Psychiatry, Universitäre Psychiatrische Dienste (UPD) BernUniversity of BernBernSwitzerland
| | - Nicolai Franzmeier
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | - David G. Norris
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - José P. Marques
- Donders Institute for Brain, Cognition, and BehaviorRadboud UniversityNijmegenThe Netherlands
| | - Peter zu Eulenburg
- German Center for Vertigo and Balance DisordersUniversity HospitalMunichGermany
| | - Michael Ewers
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
| | | | - Frank‐Erik de Leeuw
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD)University HospitalMunichGermany
- Department of Neurology, Donders Institute for Brain, Cognition and BehaviourRadboud University Medical CenterNijmegenThe Netherlands
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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12
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Abstract
A major goal of neuroimaging studies is to develop predictive models to analyze the relationship between whole brain functional connectivity patterns and behavioural traits. However, there is no single widely-accepted standard pipeline for analyzing functional connectivity. The common procedure for designing functional connectivity based predictive models entails three main steps: parcellating the brain, estimating the interaction between defined parcels, and lastly, using these integrated associations between brain parcels as features fed to a classifier for predicting non-imaging variables e.g., behavioural traits, demographics, emotional measures, etc. There are also additional considerations when using correlation-based measures of functional connectivity, resulting in three supplementary steps: utilising Riemannian geometry tangent space parameterization to preserve the geometry of functional connectivity; penalizing the connectivity estimates with shrinkage approaches to handle challenges related to short time-series (and noisy) data; and removing confounding variables from brain-behaviour data. These six steps are contingent on each-other, and to optimise a general framework one should ideally examine these various methods simultaneously. In this paper, we investigated strengths and short-comings, both independently and jointly, of the following measures: parcellation techniques of four kinds (categorized further depending upon number of parcels), five measures of functional connectivity, the decision of staying in the ambient space of connectivity matrices or in tangent space, the choice of applying shrinkage estimators, six alternative techniques for handling confounds and finally four novel classifiers/predictors. For performance evaluation, we have selected two of the largest datasets, UK Biobank and the Human Connectome Project resting state fMRI data, and have run more than 9000 different pipeline variants on a total of ∼14000 individuals to determine the optimum pipeline. For independent performance validation, we have run some best-performing pipeline variants on ABIDE and ACPI datasets (∼1000 subjects) to evaluate the generalisability of proposed network modelling methods.
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Affiliation(s)
- Usama Pervaiz
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom.
| | - Diego Vidaurre
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom; Department of Clinical Medicine, Aarhus University, Denmark
| | - Mark W Woolrich
- Oxford Centre for Human Brain Activity (OHBA), Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, OX3 7JX, United Kingdom
| | - Stephen M Smith
- Oxford Centre for Functional MRI of the Brain (FMRIB), Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, OX3 9DU, United Kingdom
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13
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Siless V, Hubbard NA, Jones R, Wang J, Lo N, Bauer CCC, Goncalves M, Frosch I, Norton D, Vergara G, Conroy K, De Souza FV, Rosso IM, Wickham AH, Cosby EA, Pinaire M, Hirshfeld-Becker D, Pizzagalli DA, Henin A, Hofmann SG, Auerbach RP, Ghosh S, Gabrieli J, Whitfield-Gabrieli S, Yendiki A. Image acquisition and quality assurance in the Boston Adolescent Neuroimaging of Depression and Anxiety study. Neuroimage Clin 2020; 26:102242. [PMID: 32339824 PMCID: PMC7184183 DOI: 10.1016/j.nicl.2020.102242] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2019] [Revised: 02/19/2020] [Accepted: 03/10/2020] [Indexed: 12/18/2022]
Abstract
The Connectomes Related to Human Diseases (CRHD) initiative was developed with the Human Connectome Project (HCP) to provide high-resolution, open-access, multi-modal MRI data to better understand the neural correlates of human disease. Here, we present an introduction to a CRHD project, the Boston Adolescent Neuroimaging of Depression and Anxiety (BANDA) study, which is collecting multimodal neuroimaging, clinical, and neuropsychological data from 225 adolescents (ages 14-17), 150 of whom are expected to have a diagnosis of depression and/or anxiety. Our transdiagnostic recruitment approach samples the full spectrum of depressed/anxious symptoms and their comorbidity, consistent with NIMH Research Domain Criteria (RDoC). We focused on an age range that is critical for brain development and for the onset of mental illness. This project sought to harmonize imaging sequences, hardware, and functional tasks with other HCP studies, although some changes were made to canonical HCP methods to accommodate our study population and questions. We present a thorough overview of our imaging sequences, hardware, and scanning protocol. We detail similarities and differences between this study and other HCP studies. We evaluate structural-, diffusion-, and functional-image-quality measures that may be influenced by clinical factors (e.g., disorder, symptomatology). Signal-to-noise and motion estimates from the first 140 adolescents suggest minimal influence of clinical factors on image quality. We anticipate enrollment of an additional 85 participants, most of whom are expected to have a diagnosis of anxiety and/or depression. Clinical and neuropsychological data from the first 140 participants are currently freely available through the National Institute of Mental Health Data Archive (NDA).
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Affiliation(s)
- Viviana Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicholas A Hubbard
- Massachusetts Institute of Technology, Cambridge, MA, United States; University of Nebraska, Lincoln, Lincoln, NE, United States
| | - Robert Jones
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Jonathan Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | - Nicole Lo
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Clemens C C Bauer
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | | | - Isabelle Frosch
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Daniel Norton
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | - Isabelle M Rosso
- McLean Hospital, Belmont, MA, United States; Harvard Medical School, Boston, MA, United States
| | | | | | | | | | | | - Aude Henin
- Massachusetts General Hospital, Boston, MA, United States
| | | | | | - Satrajit Ghosh
- Harvard Medical School, Boston, MA, United States; Massachusetts Institute of Technology, Cambridge, MA, United States
| | - John Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Susan Whitfield-Gabrieli
- Massachusetts Institute of Technology, Cambridge, MA, United States; Northeastern University, Department of Psychology, Boston, MA, United States
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
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14
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Gotts SJ, Gilmore AW, Martin A. Brain networks, dimensionality, and global signal averaging in resting-state fMRI: Hierarchical network structure results in low-dimensional spatiotemporal dynamics. Neuroimage 2020; 205:116289. [PMID: 31629827 PMCID: PMC6919311 DOI: 10.1016/j.neuroimage.2019.116289] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Revised: 10/12/2019] [Accepted: 10/16/2019] [Indexed: 12/11/2022] Open
Abstract
One of the most controversial practices in resting-state fMRI functional connectivity studies is whether or not to regress out the global average brain signal (GS) during artifact removal. Some groups have argued that it is absolutely essential to regress out the GS in order to fully remove head motion, respiration, and other global imaging artifacts. Others have argued that removing the GS distorts the resulting correlation matrices and inappropriately alters the results of group comparisons and relationships to behavior. At the core of this argument is the assessment of dimensionality in terms of the number of brain networks with uncorrelated time series. If the dimensionality is high, then the distortions due to GS removal could be effectively negligible. In the current paper, we examine the dimensionality of resting-state fMRI data using principal component analyses (PCA) and network clustering analyses. In two independent datasets (Set 1: N = 62, Set 2: N = 32), scree plots of the eigenvalues level off at or prior to 10 principal components, with prominent elbows at 3 and 7 components. While network clustering analyses have previously demonstrated that numerous networks can be distinguished with high thresholding of the voxel-wise correlation matrices, lower thresholding reveals a lower-dimensional hierarchical structure, with the first prominent branch at 2 networks (corresponding to the previously described "task-positive"/"task-negative" distinction) and further stable subdivisions at 4, 7 and 17. Since inter-correlated time series within these larger branches do not cancel to zero when averaged, the hierarchical nature of the correlation structure results in low effective dimensionality. Consistent with this, partial correlation analyses revealed that network-specific variance remains present in the GS at each level of the hierarchy, accounting for at least 14-18% of the overall GS variance in each dataset. These results demonstrate that GS regression is expected to remove substantial portions of network-specific brain signals along with artifacts, not simply whole-brain signals corresponding to arousal levels. We highlight alternative means of controlling for residual global artifacts when not removing the GS.
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Affiliation(s)
- Stephen J Gotts
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Adrian W Gilmore
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Alex Martin
- Section on Cognitive Neuropsychology, Laboratory of Brain and Cognition, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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15
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Cai X, Xie D, Madsen KH, Wang Y, Bögemann SA, Cheung EFC, Møller A, Chan RCK. Generalizability of machine learning for classification of schizophrenia based on resting-state functional MRI data. Hum Brain Mapp 2020; 41:172-184. [PMID: 31571320 PMCID: PMC7268030 DOI: 10.1002/hbm.24797] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2019] [Revised: 08/19/2019] [Accepted: 09/04/2019] [Indexed: 12/11/2022] Open
Abstract
Machine learning has increasingly been applied to classification of schizophrenia in neuroimaging research. However, direct replication studies and studies seeking to investigate generalizability are scarce. To address these issues, we assessed within-site and between-site generalizability of a machine learning classification framework which achieved excellent performance in a previous study using two independent resting-state functional magnetic resonance imaging data sets collected from different sites and scanners. We established within-site generalizability of the classification framework in the main data set using cross-validation. Then, we trained a model in the main data set and investigated between-site generalization in the validated data set using external validation. Finally, recognizing the poor between-site generalization performance, we updated the unsupervised algorithm to investigate if transfer learning using additional unlabeled data were able to improve between-site classification performance. Cross-validation showed that the published classification procedure achieved an accuracy of 0.73 using majority voting across all selected components. External validation found a classification accuracy of 0.55 (not significant) and 0.70 (significant) using the direct and transfer learning procedures, respectively. The failure of direct generalization from one site to another demonstrates the limitation of within-site cross-validation and points toward the need to incorporate efforts to facilitate application of machine learning across multiple data sets. The improvement in performance with transfer learning highlights the importance of taking into account the properties of data when constructing predictive models across samples and sites. Our findings suggest that machine learning classification result based on a single study should be interpreted cautiously.
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Affiliation(s)
- Xin‐Lu Cai
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Dong‐Jie Xie
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Hangzhou College of Preschool Teacher EducationZhejiang Normal UniversityHangzhouChina
| | - Kristoffer H. Madsen
- Sino‐Danish Center for Education and ResearchBeijingChina
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital HvidovreCopenhagenDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - Yong‐Ming Wang
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | - Sophie Alida Bögemann
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
| | | | - Arne Møller
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of Nuclear Medicine and PET CentreAarhus University HospitalAarhusDenmark
| | - Raymond C. K. Chan
- Neuropsychology and Applied Cognitive Neuroscience Laboratory, CAS Key Laboratory of Mental HealthInstitute of PsychologyBeijingChina
- Sino‐Danish College, University of Chinese Academy of SciencesBeijingChina
- Sino‐Danish Center for Education and ResearchBeijingChina
- Department of PsychologyUniversity of Chinese Academy of SciencesBeijingChina
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16
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Afyouni S, Smith SM, Nichols TE. Effective degrees of freedom of the Pearson's correlation coefficient under autocorrelation. Neuroimage 2019; 199:609-625. [PMID: 31158478 PMCID: PMC6693558 DOI: 10.1016/j.neuroimage.2019.05.011] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2018] [Revised: 05/02/2019] [Accepted: 05/06/2019] [Indexed: 12/13/2022] Open
Abstract
The dependence between pairs of time series is commonly quantified by Pearson's correlation. However, if the time series are themselves dependent (i.e. exhibit temporal autocorrelation), the effective degrees of freedom (EDF) are reduced, the standard error of the sample correlation coefficient is biased, and Fisher's transformation fails to stabilise the variance. Since fMRI time series are notoriously autocorrelated, the issue of biased standard errors - before or after Fisher's transformation - becomes vital in individual-level analysis of resting-state functional connectivity (rsFC) and must be addressed anytime a standardised Z-score is computed. We find that the severity of autocorrelation is highly dependent on spatial characteristics of brain regions, such as the size of regions of interest and the spatial location of those regions. We further show that the available EDF estimators make restrictive assumptions that are not supported by the data, resulting in biased rsFC inferences that lead to distorted topological descriptions of the connectome on the individual level. We propose a practical "xDF" method that accounts not only for distinct autocorrelation in each time series, but instantaneous and lagged cross-correlation. We find the xDF correction varies substantially over node pairs, indicating the limitations of global EDF corrections used previously. In addition to extensive synthetic and real data validations, we investigate the impact of this correction on rsFC measures in data from the Young Adult Human Connectome Project, showing that accounting for autocorrelation dramatically changes fundamental graph theoretical measures relative to no correction.
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Affiliation(s)
- Soroosh Afyouni
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK.
| | - Stephen M Smith
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK; The Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK.
| | - Thomas E Nichols
- Oxford Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Population Health, University of Oxford, UK; The Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK; Department of Statistics, University of Warwick, UK.
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17
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Ji G, Chen X, Bai T, Wang L, Wei Q, Gao Y, Tao L, He K, Li D, Dong Y, Hu P, Yu F, Zhu C, Tian Y, Yu Y, Wang K. Classification of schizophrenia by intersubject correlation in functional connectome. Hum Brain Mapp 2019; 40:2347-2357. [PMID: 30663853 PMCID: PMC6865403 DOI: 10.1002/hbm.24527] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2018] [Revised: 12/07/2018] [Accepted: 01/08/2019] [Indexed: 01/16/2023] Open
Abstract
Functional connectomes have been suggested as fingerprinting for individual identification. Accordingly, we hypothesized that subjects in the same phenotypic group have similar functional connectome features, which could help to discriminate schizophrenia (SCH) patients from healthy controls (HCs) and from depression patients. To this end, we included resting-state functional magnetic resonance imaging data of SCH, depression patients, and HCs from three centers. We first investigated the characteristics of connectome similarity between individuals, and found higher similarity between subjects belonging to the same group (i.e., SCH-SCH) than different groups (i.e., HC-SCH). These findings suggest that the average connectome within group (termed as group-specific functional connectome [GFC]) may help in individual classification. Consistently, significant accuracy (75-77%) and area under curve (81-86%) were found in discriminating SCH from HC or depression patients by GFC-based leave-one-out cross-validation. Cross-center classification further suggests a good generalizability of the GFC classification. We additionally included normal aging data (255 young and 242 old subjects with different scanning sequences) to show factors could be improved for better classification performance, and the findings emphasized the importance of increasing sample size but not temporal resolution during scanning. In conclusion, our findings suggest that the average functional connectome across subjects contained group-specific biological features and may be helpful in clinical diagnosis for schizophrenia.
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Affiliation(s)
- Gong‐Jun Ji
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Xingui Chen
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Tongjian Bai
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Lu Wang
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Qiang Wei
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yaxiang Gao
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Longxiang Tao
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kongliang He
- Anhui Mental Health CenterHefeiChina
- The Fourth People's Hospital of HefeiHefeiChina
| | - Dandan Li
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yi Dong
- Anhui Mental Health CenterHefeiChina
- The Second Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Panpan Hu
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Fengqiong Yu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Chunyan Zhu
- Department of Medical PsychologyChaohu Clinical Medical College, Anhui Medical UniversityHefeiChina
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
| | - Yanghua Tian
- Laboratory of Cognitive NeuropsychologyCollaborative Innovation Centre of Neuropsychiatric Disorder and Mental HealthHefeiChina
- Anhui Province Key Laboratory of Cognition and Neuropsychiatric DisordersHefeiChina
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Yongqiang Yu
- Department of RadiologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
| | - Kai Wang
- Department of NeurologyThe First Affiliated Hospital of Anhui Medical UniversityHefeiChina
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18
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Jing R, Li P, Ding Z, Lin X, Zhao R, Shi L, Yan H, Liao J, Zhuo C, Lu L, Fan Y. Machine learning identifies unaffected first-degree relatives with functional network patterns and cognitive impairment similar to those of schizophrenia patients. Hum Brain Mapp 2019; 40:3930-3939. [PMID: 31148311 DOI: 10.1002/hbm.24678] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Revised: 05/17/2019] [Accepted: 05/20/2019] [Indexed: 12/19/2022] Open
Abstract
Schizophrenia (SCZ) patients and their unaffected first-degree relatives (FDRs) share similar functional neuroanatomy. However, it remains largely unknown to what extent unaffected FDRs with functional neuroanatomy patterns similar to patients can be identified at an individual level. In this study, we used a multivariate pattern classification method to learn informative large-scale functional networks (FNs) and build classifiers to distinguish 32 patients from 30 healthy controls and to classify 34 FDRs as with or without FNs similar to patients. Four informative FNs-the cerebellum, default mode network (DMN), ventral frontotemporal network, and posterior DMN with parahippocampal gyrus-were identified based on a training cohort and pattern classifiers built upon these FNs achieved a correct classification rate of 83.9% (sensitivity 87.5%, specificity 80.0%, and area under the receiver operating characteristic curve [AUC] 0.914) estimated based on leave-one-out cross-validation for the training cohort and a correct classification rate of 77.5% (sensitivity 72.5%, specificity 82.5%, and AUC 0.811) for an independent validation cohort. The classification scores of the FDRs and patients were negatively correlated with their measures of cognitive function. FDRs identified by the classifiers as having SCZ patterns were similar to the patients, but significantly different from the controls and FDRs with normal patterns in terms of their cognitive measures. These results demonstrate that the pattern classifiers built upon the informative FNs can serve as biomarkers for quantifying brain alterations in SCZ and help to identify FDRs with FN patterns and cognitive impairment similar to those of SCZ patients.
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Affiliation(s)
- Rixing Jing
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Peng Li
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Zengbo Ding
- National Institute on Drug Dependence and Beijing Key laboratory of Drug Dependence, Peking University, Beijing, China
| | - Xiao Lin
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Rongjiang Zhao
- Department of Alcohol and Drug Dependence, Beijing Hui-Long-Guan Hospital, Peking University, Beijing, China
| | - Le Shi
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Hao Yan
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Jinmin Liao
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
| | - Chuanjun Zhuo
- Tianjin Mental Health Center, Nankai University Affiliated Tianjin Anding Hospital, Tianjin, China
- Department of Psychiatry, Tianjin Medical University, Tianjin, China
| | - Lin Lu
- Institute of Mental Health, National Clinical Research Center for Mental Disorders, Key Laboratory of Mental Health and Peking University Sixth Hospital, Peking University, Beijing, China
- National Institute on Drug Dependence and Beijing Key laboratory of Drug Dependence, Peking University, Beijing, China
- Peking-Tsinghua Center for Life Sciences and PKU-IDG/McGovern Institute for Brain Research, Peking University, Beijing, China
| | - Yong Fan
- Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
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19
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Elliott ML, Knodt AR, Cooke M, Kim MJ, Melzer TR, Keenan R, Ireland D, Ramrakha S, Poulton R, Caspi A, Moffitt TE, Hariri AR. General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks. Neuroimage 2019; 189:516-532. [PMID: 30708106 PMCID: PMC6462481 DOI: 10.1016/j.neuroimage.2019.01.068] [Citation(s) in RCA: 142] [Impact Index Per Article: 28.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2018] [Revised: 01/22/2019] [Accepted: 01/27/2019] [Indexed: 01/15/2023] Open
Abstract
Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displayed higher estimates of heritability than resting-state functional connectivity. We also found that predictions of cognitive ability from GFC generalized across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.
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Affiliation(s)
- Maxwell L Elliott
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA.
| | - Annchen R Knodt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - Megan Cooke
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
| | - M Justin Kim
- Department of Psychology, University of Hawaii at Manoa, Honolulu, HI, 96822, USA
| | - Tracy R Melzer
- New Zealand Brain Research Institute, Christchurch, New Zealand; Department of Medicine, University of Otago, Christchurch, New Zealand
| | - Ross Keenan
- New Zealand Brain Research Institute, Christchurch, New Zealand; Christchurch Radiology Group, Christchurch, New Zealand
| | - David Ireland
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Sandhya Ramrakha
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Richie Poulton
- Dunedin Multidisciplinary Health and Development Research Unit, Department of Psychology, University of Otago, 163 Union St E, Dunedin, 9016, New Zealand
| | - Avshalom Caspi
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Terrie E Moffitt
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA; Social, Genetic, & Developmental Psychiatry Research Centre, Institute of Psychiatry, Psychology, & Neuroscience, King's College London, De Crespigny Park, Denmark Hill, London, SE5 8AF, UK; Department of Psychiatry & Behavioral Sciences, Duke University School of Medicine, Durham, NC, 27708, USA; Center for Genomic and Computational Biology, Duke University, Box 90338, Durham, NC, 27708, USA
| | - Ahmad R Hariri
- Department of Psychology & Neuroscience, Duke University, Box 104410, Durham, NC, 27708, USA
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20
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Abstract
Recent advances in parallel imaging and simultaneous multi-slice techniques have permitted whole-brain fMRI acquisitions at sub-second sampling intervals, without significantly sacrificing the spatial coverage and resolution. Apart from probing brain function at finer temporal scales, faster sampling rates may potentially lead to enhanced functional sensitivity, owing possibly to both cleaner neural representations (due to less aliased physiological noise) and additional statistical benefits (due to more degrees of freedom for a fixed scan duration). Accompanying these intriguing aspects of fast acquisitions, however, confusion has also arisen regarding (1) how to preprocess/analyze these fast fMRI data, and (2) what exactly is the extent of benefits with fast acquisitions, i.e., how fast is fast enough for a specific research aim? The first question is motivated by the altered spectral distribution and noise characteristics at short sampling intervals, while the second question seeks to reconcile the complicated trade-offs between the functional contrast-to-noise ratio and the effective degrees of freedom. Although there have been recent efforts to empirically approach different aspects of these two questions, in this work we discuss, from a theoretical perspective accompanied by some illustrative, proof-of-concept experimental in vivo human fMRI data, a few considerations that are rarely mentioned, yet are important for both preprocessing and optimizing statistical inferences for studies that employ acquisitions with sub-second sampling intervals. Several summary recommendations include concerns regarding advisability of relying on low-pass filtering to de-noise physiological contributions, employment of statistical models with sufficient complexity to account for the substantially increased serial correlation, and cautions regarding using rapid sampling to enhance functional sensitivity given that different analysis models may associate with distinct trade-offs between contrast-to-noise ratios and the effective degrees of freedom. As an example, we demonstrate that as TR shortens, the intrinsic differences in how noise is accommodated in general linear models and Pearson correlation analyses (assuming Gaussian distributed stochastic signals and noise) can result in quite different outcomes, either gaining or losing statistical power.
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Affiliation(s)
- Jingyuan E Chen
- Department of Radiology, Stanford University, Stanford, CA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA.
| | - Jonathan R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA; Department of Radiology, Harvard Medical School, Boston, MA, USA; Harvard-Massachusetts Institute of Technology Division of Health Sciences and Technology, Cambridge, MA, USA
| | - Saskia Bollmann
- Centre for Advanced Imaging, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Gary H Glover
- Department of Radiology, Stanford University, Stanford, CA, USA
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21
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Xie H, Zheng CY, Handwerker DA, Bandettini PA, Calhoun VD, Mitra S, Gonzalez-Castillo J. Efficacy of different dynamic functional connectivity methods to capture cognitively relevant information. Neuroimage 2019; 188:502-514. [PMID: 30576850 PMCID: PMC6401299 DOI: 10.1016/j.neuroimage.2018.12.037] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2018] [Revised: 12/13/2018] [Accepted: 12/17/2018] [Indexed: 10/27/2022] Open
Abstract
Given the dynamic nature of the human brain, there has been an increasing interest in investigating short-term temporal changes in functional connectivity, also known as dynamic functional connectivity (dFC), i.e., the time-varying inter-regional statistical dependence of blood oxygenation level-dependent (BOLD) signal within the constraints of a single scan. Numerous methodologies have been proposed to characterize dFC during rest and task, but few studies have compared them in terms of their efficacy to capture behavioral and clinically relevant dynamics. This is mostly due to lack of a well-defined ground truth, especially for rest scans. In this study, with a multitask dataset (rest, memory, video, and math) serving as ground truth, we investigated the efficacy of several dFC estimation techniques at capturing cognitively relevant dFC modulation induced by external tasks. We evaluated two framewise methods (dFC estimates for a single time point): dynamic conditional correlation (DCC) and jackknife correlation (JC); and five window-based methods: sliding window correlation (SWC), sliding window correlation with L1-regularization (SWC_L1), a combination of DCC and SWC called moving average DCC (DCC_MA), multiplication of temporal derivatives (MTD), and a variant of jackknife correlation called delete-d jackknife correlation (dJC). The efficacy is defined as each dFC metric's ability to successfully subdivide multitask scans into cognitively homogenous segments (even if those segments are not temporally continuous). We found that all window-based dFC methods performed well for commonly used window lengths (WL ≥ 30sec), with sliding window methods (SWC, SWC_L1) as well as the hybrid DCC_MA approach performing slightly better. For shorter window lengths (WL ≤ 15sec), DCC_MA and dJC produced the best results. Neither framewise method (i.e., DCC and JC) led to dFC estimates with high accuracy.
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Affiliation(s)
- Hua Xie
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA; Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA.
| | - Charles Y Zheng
- Machine Learning Team, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Daniel A Handwerker
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Peter A Bandettini
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA; Functional MRI Facility, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Vince D Calhoun
- The Mind Research Network, Albuquerque, NM, USA; Department of Electrical and Computer Engineering, University of New Mexico, Albuquerque, NM, USA
| | - Sunanda Mitra
- Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA
| | - Javier Gonzalez-Castillo
- Section on Functional Imaging Methods, National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
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22
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Aydogan DB, Shi Y. Tracking and validation techniques for topographically organized tractography. Neuroimage 2018; 181:64-84. [PMID: 29986834 PMCID: PMC6139055 DOI: 10.1016/j.neuroimage.2018.06.071] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 05/18/2018] [Accepted: 06/26/2018] [Indexed: 12/22/2022] Open
Abstract
Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.
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Affiliation(s)
- Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
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23
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Abstract
Sparse graphical models are frequently used to explore both static and dynamic functional brain networks from neuroimaging data. However, the practical performance of the models has not been studied in detail for brain networks. In this work, we have two objectives. First, we compare several sparse graphical model estimation procedures and several selection criteria under various experimental settings, such as different dimensions, sample sizes, types of data, and sparsity levels of the true model structures. We discuss in detail the superiority and deficiency of each combination. Second, in the same simulation study, we show the impact of autocorrelation and whitening on the estimation of functional brain networks. We apply the methods to a resting-state functional magnetic resonance imaging (fMRI) data set. Our results show that the best sparse graphical model, in terms of detection of true connections and having few false-positive connections, is the smoothly clipped absolute deviation (SCAD) estimating method in combination with the Bayesian information criterion (BIC) and cross-validation (CV) selection method. In addition, the presence of autocorrelation in the data adversely affects the estimation of networks but can be helped by using the CV selection method. These results question the validity of a number of fMRI studies where inferior graphical model techniques have been used to estimate brain networks.
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Affiliation(s)
- Yunan Zhu
- 1 Department of Mathematical and Statistical Sciences, University of Alberta , Edmonton, Canada
| | - Ivor Cribben
- 2 Department of Finance and Statistical Analysis, Alberta School of Business, University of Alberta , Edmonton, Canada
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24
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Abstract
There is intense interest in fMRI research on whole-brain functional connectivity, and however, two fundamental issues are still unresolved: the impact of spatiotemporal data resolution (spatial parcellation and temporal sampling) and the impact of the network construction method on the reliability of functional brain networks. In particular, the impact of spatiotemporal data resolution on the resulting connectivity findings has not been sufficiently investigated. In fact, a number of studies have already observed that functional networks often give different conclusions across different parcellation scales. If the interpretations from functional networks are inconsistent across spatiotemporal scales, then the whole validity of the functional network paradigm is called into question. This paper investigates the consistency of resting state network structure when using different temporal sampling or spatial parcellation, or different methods for constructing the networks. To pursue this, we develop a novel network comparison framework based on persistent homology from a topological data analysis. We use the new network comparison tools to characterize the spatial and temporal scales under which consistent functional networks can be constructed. The methods are illustrated on Human Connectome Project data, showing that the DISCOH2 network construction method outperforms other approaches at most data spatiotemporal resolutions.
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25
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Mueller S, Wang D, Fox MD, Pan R, Lu J, Li K, Sun W, Buckner RL, Liu H. Reliability correction for functional connectivity: Theory and implementation. Hum Brain Mapp 2015; 36:4664-80. [PMID: 26493163 PMCID: PMC4803495 DOI: 10.1002/hbm.22947] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2015] [Revised: 07/18/2015] [Accepted: 08/06/2015] [Indexed: 01/15/2023] Open
Abstract
Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe's contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity.
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Affiliation(s)
- Sophia Mueller
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
- Department of Psychology and Center for Brain ScienceHarvard UniversityCambridgeMassachusetts
- Institute of Clinical Radiology, Ludwig Maximilians University MunichMunichGermany
| | - Danhong Wang
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
| | - Michael D. Fox
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
- Department of NeurologyBerenson‐Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center and Harvard Medical SchoolBostonMassachusetts
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusetts
| | - Ruiqi Pan
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
- Department of RadiologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Jie Lu
- Department of RadiologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Kuncheng Li
- Department of RadiologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Wei Sun
- Department of NeurologyXuanwu Hospital, Capital Medical UniversityBeijingChina
| | - Randy L. Buckner
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
- Department of Psychology and Center for Brain ScienceHarvard UniversityCambridgeMassachusetts
- Department of PsychiatryMassachusetts General HospitalBostonMassachusetts
| | - Hesheng Liu
- Department of RadiologyAthinoula a. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCharlestownMassachusetts
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26
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Wang Y, Nelissen N, Adamczuk K, De Weer AS, Vandenbulcke M, Sunaert S, Vandenberghe R, Dupont P. Reproducibility and robustness of graph measures of the associative-semantic network. PLoS One 2014; 9:e115215. [PMID: 25500823 PMCID: PMC4264875 DOI: 10.1371/journal.pone.0115215] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2014] [Accepted: 11/19/2014] [Indexed: 01/25/2023] Open
Abstract
Graph analysis is a promising tool to quantify brain connectivity. However, an essential requirement is that the graph measures are reproducible and robust. We have studied the reproducibility and robustness of various graph measures in group based and in individual binary and weighted networks derived from a task fMRI experiment during explicit associative-semantic processing of words and pictures. The nodes of the network were defined using an independent study and the connectivity was based on the partial correlation of the time series between any pair of nodes. The results showed that in case of binary networks, global graph measures exhibit a good reproducibility and robustness for networks which are not too sparse and these figures of merit depend on the graph measure and on the density of the network. Furthermore, group based binary networks should be derived from groups of sufficient size and the lower the density the more subjects are required to obtain robust values. Local graph measures are very variable in terms of reproducibility and should be interpreted with care. For weighted networks, we found good reproducibility (average test-retest variability <5% and ICC values >0.4) when using subject specific networks and this will allow us to relate network properties to individual subject information.
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Affiliation(s)
- Yu Wang
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Natalie Nelissen
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Katarzyna Adamczuk
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - An-Sofie De Weer
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
| | - Mathieu Vandenbulcke
- Psychiatry Department, University Hospitals Leuven, Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Institute for Neuroscience and Disease, Leuven, Belgium
| | - Stefan Sunaert
- Medical Imaging Research Center (MIRC), University of Leuven and University Hospitals Leuven, Leuven, Belgium
- Radiology Department, University Hospitals Leuven, Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Institute for Neuroscience and Disease, Leuven, Belgium
| | - Rik Vandenberghe
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Neurology Department, University Hospitals Leuven, Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Institute for Neuroscience and Disease, Leuven, Belgium
| | - Patrick Dupont
- Laboratory for Cognitive Neurology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Medical Imaging Research Center (MIRC), University of Leuven and University Hospitals Leuven, Leuven, Belgium
- Alzheimer Research Centre KU Leuven, Leuven Institute for Neuroscience and Disease, Leuven, Belgium
- * E-mail:
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27
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Abstract
There is accumulating evidence that the neurobiology of autism spectrum disorders (ASD) is linked to atypical neural communication and connectivity. This body of work emphasizes the need to characterize the function of multiple regions that comprise neural circuits rather than focusing on singular regions as contributing to deficits in ASD. Multimodal neuroimaging - the formal combination of multiple functional and structural measures of the brain - is extremely promising as an approach to understanding neural deficits in ASD. This review provides an overview of the multimodal imaging approach, and then provides a snapshot of how multimodal imaging has been applied in the study of ASD to date. This body of work is separated into two categories: one concerning whole brain connectomics and the other focused on characterizing neural circuits implicated as altered in ASD. We end this review by highlighting emerging themes from the existing body of literature, and new resources that will likely influence future multimodal imaging studies.
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Affiliation(s)
- Benjamin E Yerys
- Center for Autism Research, The Children's Hospital of Philadelphia, 3535 Market Street, Ste 860, Philadelphia, PA, 19104, USA,
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28
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Marcus DS, Harms MP, Snyder AZ, Jenkinson M, Wilson JA, Glasser MF, Barch DM, Archie KA, Burgess GC, Ramaratnam M, Hodge M, Horton W, Herrick R, Olsen T, McKay M, House M, Hileman M, Reid E, Harwell J, Coalson T, Schindler J, Elam JS, Curtiss SW, Van Essen DC. Human Connectome Project informatics: quality control, database services, and data visualization. Neuroimage 2013; 80:202-19. [PMID: 23707591 DOI: 10.1016/j.neuroimage.2013.05.077] [Citation(s) in RCA: 258] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2013] [Revised: 05/01/2013] [Accepted: 05/13/2013] [Indexed: 11/17/2022] Open
Abstract
The Human Connectome Project (HCP) has developed protocols, standard operating and quality control procedures, and a suite of informatics tools to enable high throughput data collection, data sharing, automated data processing and analysis, and data mining and visualization. Quality control procedures include methods to maintain data collection consistency over time, to measure head motion, and to establish quantitative modality-specific overall quality assessments. Database services developed as customizations of the XNAT imaging informatics platform support both internal daily operations and open access data sharing. The Connectome Workbench visualization environment enables user interaction with HCP data and is increasingly integrated with the HCP's database services. Here we describe the current state of these procedures and tools and their application in the ongoing HCP study.
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Affiliation(s)
- Daniel S Marcus
- Department of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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