Multisite reliability of MR-based functional connectivity

Abstract Recent years have witnessed an increasing number of multisite MRI functional connectivity (fcMRI) studies. While multisite studies provide an efficient way to accelerate data collection and increase sample sizes, especially for rare clinical populations, any effects of site or MRI scanner could ultimately limit power and weaken results. Little data exists on the stability of functional connectivity measurements across sites and sessions. In this study, we assess the influence of site and session on resting state functional connectivity measurements in a healthy cohort of traveling subjects (8 subjects scanned twice at each of 8 sites) scanned as part of the North American Prodrome Longitudinal Study (NAPLS). Reliability was investigated in three types of connectivity analyses: (1) seed‐based connectivity with posterior cingulate cortex (PCC), right motor cortex (RMC), and left thalamus (LT) as seeds; (2) the intrinsic connectivity distribution (ICD), a voxel‐wise connectivity measure; and (3) matrix connectivity, a whole‐brain, atlas‐based approach to assessing connectivity between nodes. Contributions to variability in connectivity due to subject, site, and day‐of‐scan were quantified and used to assess between‐session (test‐retest) reliability in accordance with Generalizability Theory. Overall, no major site, scanner manufacturer, or day‐of‐scan effects were found for the univariate connectivity analyses; instead, subject effects dominated relative to the other measured factors. However, summaries of voxel‐wise connectivity were found to be sensitive to site and scanner manufacturer effects. For all connectivity measures, although subject variance was three times the site variance, the residual represented 60–80% of the variance, indicating that connectivity differed greatly from scan to scan independent of any of the measured factors (i.e., subject, site, and day‐of‐scan). Thus, for a single 5 min scan, reliability across connectivity measures was poor (ICC=0.07–0.17), but increased with increasing scan duration (ICC=0.21–0.36 at 25 min). The limited effects of site and scanner manufacturer support the use of multisite studies, such as NAPLS, as a viable means of collecting data on rare populations and increasing power in univariate functional connectivity studies. However, the results indicate that aggregation of fcMRI data across longer scan durations is necessary to increase the reliability of connectivity estimates at the single‐subject level. HighlightsfcMRI (seed, matrix, ICD) is stable across 8 sites in a Traveling Subjects dataset.No major site, scanner manufacturer, or day‐of‐scan effects were found (GLM).No outlier sites were found (leave‐one‐site‐out analysis of variance).Reliability substantially improves when averaging data over multiple days.Data can be combined across sites to increase power without impacting reliability.

[1]  A. Hayes Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression-Based Approach , 2013 .

[2]  David T. Jones,et al.  Non-Stationarity in the “Resting Brain’s” Modular Architecture , 2012, PloS one.

[3]  Alan C. Evans,et al.  Enhancement of MR Images Using Registration for Signal Averaging , 1998, Journal of Computer Assisted Tomography.

[4]  Dustin Scheinost,et al.  Coupled Intrinsic Connectivity Distribution Analysis: A Method for Exploratory Connectivity Analysis of Paired fMRI Data , 2014, PloS one.

[5]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[6]  Amy M. Jimenez,et al.  Developmental disruptions in neural connectivity in the pathophysiology of schizophrenia , 2008, Development and Psychopathology.

[7]  Carl D. Hacker,et al.  Resting state network estimation in individual subjects , 2013, NeuroImage.

[8]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[9]  Nobuhiko Hata,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 , 2014, Lecture Notes in Computer Science.

[10]  Robert T. Schultz,et al.  Integrated Intensity and Point-Feature Nonrigid Registration , 2004, MICCAI.

[11]  Samuel M. McClure,et al.  BOLD Responses Reflecting Dopaminergic Signals in the Human Ventral Tegmental Area , 2008, Science.

[12]  Noreen M. Webb,et al.  Generalizability Theory: Overview , 2005 .

[13]  Donald B. Rubin,et al.  The Dependability of Behavioral Measurements: Theory of Generalizability for Scores and Profiles. , 1974 .

[14]  G. Pearlson Multisite collaborations and large databases in psychiatric neuroimaging: advantages, problems, and challenges. , 2009, Schizophrenia bulletin.

[15]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[16]  D. Schacter,et al.  The Brain's Default Network , 2008, Annals of the New York Academy of Sciences.

[17]  Dustin Scheinost,et al.  The impact of image smoothness on intrinsic functional connectivity and head motion confounds , 2014, NeuroImage.

[18]  Essa Yacoub,et al.  The rapid development of high speed, resolution and precision in fMRI , 2012, NeuroImage.

[19]  R. Hoffman,et al.  Book Review: Neural Network Models of Schizophrenia , 2001 .

[20]  Eva Nick,et al.  The dependability of behavioral measurements: theory of generalizability for scores and profiles , 1973 .

[21]  F. Collins,et al.  Policy: NIH plans to enhance reproducibility , 2014, Nature.

[22]  S. Debener,et al.  Default-mode brain dysfunction in mental disorders: A systematic review , 2009, Neuroscience & Biobehavioral Reviews.

[23]  Andrew Rutherford,et al.  ANOVA and ANCOVA: A GLM Approach , 2011 .

[24]  E. Bullmore,et al.  Functional Connectivity and Brain Networks in Schizophrenia , 2010, The Journal of Neuroscience.

[25]  D. Yurgelun-Todd,et al.  Reproducibility of Single-Subject Functional Connectivity Measurements , 2011, American Journal of Neuroradiology.

[26]  Kawin Setsompop,et al.  Ultra-fast MRI of the human brain with simultaneous multi-slice imaging. , 2013, Journal of magnetic resonance.

[27]  Dustin Scheinost,et al.  Alterations in Anatomical Covariance in the Prematurely Born , 2015, Cerebral cortex.

[28]  Mert R. Sabuncu,et al.  The influence of head motion on intrinsic functional connectivity MRI , 2012, NeuroImage.

[29]  Stephen M Smith,et al.  Fast robust automated brain extraction , 2002, Human brain mapping.

[30]  Mary E. Meyerand,et al.  The effect of scan length on the reliability of resting-state fMRI connectivity estimates , 2013, NeuroImage.

[31]  David T. Jones,et al.  Nonstationarity in the ‘resting brain's’ modular architecture , 2012, Alzheimer's & Dementia.

[32]  Gregory G. Brown,et al.  r Human Brain Mapping 29:958–972 (2008) r Test–Retest and Between-Site Reliability in a Multicenter fMRI Study , 2022 .

[33]  Keith A. Johnson,et al.  Cortical Hubs Revealed by Intrinsic Functional Connectivity: Mapping, Assessment of Stability, and Relation to Alzheimer's Disease , 2009, The Journal of Neuroscience.

[34]  D. Spencer,et al.  Repetition time in echo planar functional MRI , 2001, Magnetic resonance in medicine.

[35]  Olivia K. Faull,et al.  Physiological Noise in Brainstem fMRI , 2013, Front. Hum. Neurosci..

[36]  Veronique Bohbot,et al.  Test-retest resting-state fMRI in healthy elderly persons with a family history of Alzheimer’s disease , 2015, Scientific Data.

[37]  Andreas Heinz,et al.  Test–retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures , 2012, NeuroImage.

[38]  Simon Schwab,et al.  Functional connectivity in BOLD and CBF data: Similarity and reliability of resting brain networks , 2015, NeuroImage.

[39]  R. Shavelson,et al.  Sampling Variability of Performance Assessments. , 1993 .

[40]  Michael B. Miller,et al.  How reliable are the results from functional magnetic resonance imaging? , 2010, Annals of the New York Academy of Sciences.

[41]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[42]  Jonathan D. Power,et al.  The Development of Human Functional Brain Networks , 2010, Neuron.

[43]  Walter Schneider,et al.  Identifying the brain's most globally connected regions , 2010, NeuroImage.

[44]  Yong He,et al.  Graph Theoretical Analysis of Functional Brain Networks: Test-Retest Evaluation on Short- and Long-Term Resting-State Functional MRI Data , 2011, PloS one.

[45]  M. V. D. Heuvel,et al.  Brain Networks in Schizophrenia , 2014, Neuropsychology Review.

[46]  David A. Leopold,et al.  Dynamic functional connectivity: Promise, issues, and interpretations , 2013, NeuroImage.

[47]  Richard Frayne,et al.  Reliability of neuroanatomical measurements in a multisite longitudinal study of youth at risk for psychosis , 2014, Human brain mapping.

[48]  David B. Keator,et al.  A National Human Neuroimaging Collaboratory Enabled by the Biomedical Informatics Research Network (BIRN) , 2008, IEEE Transactions on Information Technology in Biomedicine.

[49]  Dustin Scheinost,et al.  The intrinsic connectivity distribution: A novel contrast measure reflecting voxel level functional connectivity , 2012, NeuroImage.

[50]  Kuncheng Li,et al.  Reliability correction for functional connectivity: Theory and implementation , 2015, Human brain mapping.

[51]  Paul J. Laurienti,et al.  Neuroinformatics Original Research Article Materials and Methods Study Participants , 2022 .

[52]  Michele T. Diaz,et al.  Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies , 2012, Journal of magnetic resonance imaging : JMRI.

[53]  John O. Willis,et al.  Wechsler Abbreviated Scale of Intelligence , 2014 .

[54]  Jessica A. Turner,et al.  Multisite reliability of cognitive BOLD data , 2011, NeuroImage.

[55]  Xenophon Papademetris,et al.  Groupwise whole-brain parcellation from resting-state fMRI data for network node identification , 2013, NeuroImage.

[56]  Theo G. M. van Erp,et al.  Reliability of functional magnetic resonance imaging activation during working memory in a multi-site study: Analysis from the North American Prodrome Longitudinal Study , 2014, NeuroImage.

[57]  F. Collins,et al.  NIH plans to enhance reproducibility , 2014 .

[58]  Thomas E. Nichols,et al.  Functional connectomics from resting-state fMRI , 2013, Trends in Cognitive Sciences.

[59]  Rex E. Jung,et al.  A Baseline for the Multivariate Comparison of Resting-State Networks , 2011, Front. Syst. Neurosci..

[60]  Karl J. Friston Functional and effective connectivity in neuroimaging: A synthesis , 1994 .

[61]  A. Toga,et al.  Multisite neuroimaging trials , 2009, Current opinion in neurology.

[62]  Larry Davidson,et al.  Instrument for the Assessment of Prodromal Symptoms and States , 2001 .

[63]  Bing Chen,et al.  An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.

[64]  Dustin Scheinost,et al.  Unified Framework for Development, Deployment and Robust Testing of Neuroimaging Algorithms , 2011, Neuroinformatics.

[65]  Aysenil Belger,et al.  Reliability of an fMRI paradigm for emotional processing in a multisite longitudinal study , 2015, Human brain mapping.

[66]  B. Biswal,et al.  The resting brain: unconstrained yet reliable. , 2009, Cerebral cortex.

[67]  John D. Storey A direct approach to false discovery rates , 2002 .

[68]  Evan M. Gordon,et al.  Functional System and Areal Organization of a Highly Sampled Individual Human Brain , 2015, Neuron.

[69]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[70]  Simon B. Eickhoff,et al.  An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data , 2013, NeuroImage.

[71]  M. Chun,et al.  Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity , 2015, Nature Neuroscience.

[72]  Xi-Nian Zuo,et al.  Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach , 2010, NeuroImage.

[73]  D. Cicchetti,et al.  Developing criteria for establishing interrater reliability of specific items: applications to assessment of adaptive behavior. , 1981, American journal of mental deficiency.

[74]  Graeme D. Jackson,et al.  Cortical and thalamic resting-state functional connectivity is altered in childhood absence epilepsy , 2012, Epilepsy Research.

[75]  Nigel O'Brian,et al.  Generalizability Theory I , 2003 .

[76]  Dustin Scheinost,et al.  Cerebral Lateralization is Protective in the Very Prematurely Born. , 2015, Cerebral cortex.

[77]  Antonio Napolitano,et al.  Test-retest reliability of graph metrics of resting state MRI functional brain networks: A review , 2015, Journal of Neuroscience Methods.

[78]  J M Taveras,et al.  Magnetic Resonance in Medicine , 1991, The Western journal of medicine.

[79]  Dardo Tomasi,et al.  Gender differences in brain functional connectivity density , 2012, Human brain mapping.

[80]  Jessica A. Turner,et al.  Exploration of scanning effects in multi-site structural MRI studies , 2014, Journal of Neuroscience Methods.

[81]  Tyrone D. Cannon,et al.  North American Prodrome Longitudinal Study: a collaborative multisite approach to prodromal schizophrenia research. , 2007, Schizophrenia bulletin.

[82]  B. Biswal,et al.  Functional connectivity in the motor cortex of resting human brain using echo‐planar mri , 1995, Magnetic resonance in medicine.

[83]  Xi-Nian Zuo,et al.  Short-term test–retest reliability of resting state fMRI metrics in children with and without attention-deficit/hyperactivity disorder , 2015, Developmental Cognitive Neuroscience.

[84]  L. Shah,et al.  Reliability and reproducibility of individual differences in functional connectivity acquired during task and resting state , 2016, Brain and behavior.

[85]  Dustin Scheinost,et al.  Potential Use and Challenges of Functional Connectivity Mapping in Intractable Epilepsy , 2013, Front. Neurol..