Variance decomposition for single-subject task-based fMRI activity estimates across many sessions

&NA; Here we report an exploratory within‐subject variance decomposition analysis conducted on a task‐based fMRI dataset with an unusually large number of repeated measures (i.e., 500 trials in each of three different subjects) distributed across 100 functional scans and 9 to 10 different sessions. Within‐subject variance was segregated into four primary components: variance across‐sessions, variance across‐runs within a session, variance across‐blocks within a run, and residual measurement/modeling error. Our results reveal inhomogeneous and distinct spatial distributions of these variance components across significantly active voxels in grey matter. Measurement error is dominant across the whole brain. Detailed evaluation of the remaining three components shows that across‐session variance is the second largest contributor to total variance in occipital cortex, while across‐runs variance is the second dominant source for the rest of the brain. Network‐specific analysis revealed that across‐block variance contributes more to total variance in higher‐order cognitive networks than in somatosensory cortex. Moreover, in some higher‐order cognitive networks across‐block variance can exceed across‐session variance. These results help us better understand the temporal (i.e., across blocks, runs and sessions) and spatial distributions (i.e., across different networks) of within‐subject natural variability in estimates of task responses in fMRI. They also suggest that different brain regions will show different natural levels of test‐retest reliability even in the absence of residual artifacts and sufficiently high contrast‐to‐noise measurements. Further confirmation with a larger sample of subjects and other tasks is necessary to ensure generality of these results. HighlightsWithin‐subject variance of activity estimates was decomposed into four components.Measurement error is the dominant source of within‐subject variance across the brain.Across‐session var. was next highest contributor in occipital cortex (task target).Across‐block exceeded across‐session variance in higher‐order cognitive networks.

[1]  Thomas M. Talavage,et al.  Reproducibility of fMRI activations associated with auditory sentence comprehension , 2011, NeuroImage.

[2]  Farshad Moradi,et al.  Luminance contrast of a visual stimulus modulates the BOLD response more than the cerebral blood flow response in the human brain , 2013, NeuroImage.

[3]  M. Fox,et al.  Resting-state spontaneous fluctuations in brain activity: a new paradigm for presurgical planning using fMRI. , 2009, Academic radiology.

[4]  Maximilian Reiser,et al.  Effects of aging on default mode network activity in resting state fMRI: Does the method of analysis matter? , 2010, NeuroImage.

[5]  Gary H. Glover,et al.  Reducing interscanner variability of activation in a multicenter fMRI study: Controlling for signal-to-fluctuation-noise-ratio (SFNR) differences , 2006, NeuroImage.

[6]  Jiachen Zhuo,et al.  Functional Neuroimaging: Fundamental Principles and Clinical Applications , 2015, The neuroradiology journal.

[7]  Jessica A. Turner,et al.  Behavioral Interpretations of Intrinsic Connectivity Networks , 2011, Journal of Cognitive Neuroscience.

[8]  L. Cohen,et al.  Neuroplasticity Subserving Motor Skill Learning , 2011, Neuron.

[9]  J. Driver,et al.  Modulation of visual processing by attention and emotion: windows on causal interactions between human brain regions , 2007, Philosophical Transactions of the Royal Society B: Biological Sciences.

[10]  Mark D'Esposito,et al.  Variation of BOLD hemodynamic responses across subjects and brain regions and their effects on statistical analyses , 2004, NeuroImage.

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

[12]  Michael S. Beauchamp,et al.  FMRI group analysis combining effect estimates and their variances , 2012, NeuroImage.

[13]  S. Shergill,et al.  Aging effects on functional auditory and visual processing using fMRI with variable sensory loading , 2013, Cortex.

[14]  Denise C Park,et al.  Investigation and validation of intersite fMRI studies using the same imaging hardware , 2008, Journal of magnetic resonance imaging : JMRI.

[15]  Angela R Laird,et al.  Brainmap taxonomy of experimental design: Description and evaluation , 2005, Human brain mapping.

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

[17]  Erika Skoe,et al.  Neural Processing of Speech Sounds in ASD and First-Degree Relatives , 2010, Journal of Autism and Developmental Disorders.

[18]  Noah D. Brenowitz,et al.  Whole-brain, time-locked activation with simple tasks revealed using massive averaging and model-free analysis , 2012, Proceedings of the National Academy of Sciences.

[19]  Mark W. Woolrich,et al.  Multilevel linear modelling for FMRI group analysis using Bayesian inference , 2004, NeuroImage.

[20]  G. Fesl,et al.  Reproducibility of activation in four motor paradigms , 2006, Journal of Neurology.

[21]  Nanyin Zhang,et al.  Influence of gradient acoustic noise on fMRI response in the human visual cortex , 2005, Magnetic resonance in medicine.

[22]  G H Glover,et al.  Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR , 2000, Magnetic resonance in medicine.

[23]  Baxter P. Rogers,et al.  Unsupervised Spatiotemporal Analysis of FMRI Data Using Graph-Based Visualizations of Self-Organizing Maps , 2013, IEEE Transactions on Biomedical Engineering.

[24]  Ian Marshall,et al.  Functional Magnetic Resonance Imaging (fMRI) reproducibility and variance components across visits and scanning sites with a finger tapping task , 2010, NeuroImage.

[25]  Peter Kirsch,et al.  Test–retest reliability of evoked BOLD signals from a cognitive–emotive fMRI test battery , 2012, NeuroImage.

[26]  Nadim Joni Shah,et al.  Nicotine Effects on Brain Function during a Visual Oddball Task: A Comparison between Conventional and EEG-informed fMRI Analysis , 2012, Journal of Cognitive Neuroscience.

[27]  A. Kelly,et al.  Human functional neuroimaging of brain changes associated with practice. , 2005, Cerebral cortex.

[28]  L. Jäncke,et al.  Attention Modulates the Blood Oxygen Level Dependent Response in the Primary Visual Cortex measured with Functional Magnetic Resonance Imaging , 1999, Naturwissenschaften.

[29]  Ann S. Choe,et al.  Reproducibility and Temporal Structure in Weekly Resting-State fMRI over a Period of 3.5 Years , 2015, PloS one.

[30]  V D Calhoun,et al.  Spatial and temporal independent component analysis of functional MRI data containing a pair of task‐related waveforms , 2001, Human brain mapping.

[31]  Hang Joon Jo,et al.  Mapping sources of correlation in resting state FMRI, with artifact detection and removal , 2010, NeuroImage.

[32]  Peter A. Bandettini,et al.  The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration , 2008, NeuroImage.

[33]  John Suckling,et al.  Components of variance in a multicentre functional MRI study and implications for calculation of statistical power , 2008, Human brain mapping.

[34]  G. Vandewalle,et al.  Neuroimaging, cognition, light and circadian rhythms , 2014, Front. Syst. Neurosci..

[35]  Oliver Speck,et al.  Quantitative assessment of visual cortex function with fMRI at 7 Tesla—test–retest variability , 2015, Front. Hum. Neurosci..

[36]  Alan C. Evans,et al.  A general statistical analysis for fMRI data , 2000, NeuroImage.

[37]  Wolfgang Viechtbauer,et al.  Conducting Meta-Analyses in R with the metafor Package , 2010 .

[38]  Ernst Nennig,et al.  Localizing and lateralizing language in patients with brain tumors: feasibility of routine preoperative functional MR imaging in 81 consecutive patients. , 2007, Radiology.

[39]  S. Drummond,et al.  Associations between circadian activity rhythms and functional brain abnormalities among euthymic bipolar patients: a preliminary study. , 2014, Journal of affective disorders.

[40]  Justin L. Gardner,et al.  Modulation of Visual Responses by Gaze Direction in Human Visual Cortex , 2013, The Journal of Neuroscience.

[41]  Laura C. Buchanan,et al.  The spatial structure of resting state connectivity stability on the scale of minutes , 2014, Frontiers in Neuroscience.

[42]  Yufeng Zang,et al.  Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes , 2013, NeuroImage.

[43]  Giuseppe Sartori,et al.  How to Avoid the Fallacies of Cognitive Subtraction in Brain Imaging , 2000, Brain and Language.

[44]  Rasmus M. Birn,et al.  The role of physiological noise in resting-state functional connectivity , 2012, NeuroImage.

[45]  R W Cox,et al.  AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. , 1996, Computers and biomedical research, an international journal.

[46]  J. Hirsch,et al.  An Integrated Functional Magnetic Resonance Imaging Procedure for Preoperative Mapping of Cortical Areas Associated with Tactile, Motor, Language, and Visual Functions , 2000, Neurosurgery.

[47]  Randy L. Gollub,et al.  Multi-site characterization of an fMRI working memory paradigm: Reliability of activation indices , 2010, NeuroImage.

[48]  Philippe Peigneux,et al.  Pushing the Limits: Chronotype and Time of Day Modulate Working Memory-Dependent Cerebral Activity , 2015, Front. Neurol..

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

[50]  Evan M. Gordon,et al.  Long-term neural and physiological phenotyping of a single human , 2015, Nature Communications.

[51]  Nick F. Ramsey,et al.  Test–retest variability underlying fMRI measurements , 2012, NeuroImage.

[52]  Karl J. Friston,et al.  Variability in fMRI: An Examination of Intersession Differences , 2000, NeuroImage.

[53]  Kâmil Uludağ,et al.  Transient and sustained BOLD responses to sustained visual stimulation. , 2008, Magnetic resonance imaging.

[54]  Javier Gonzalez-Castillo,et al.  Task Dependence, Tissue Specificity, and Spatial Distribution of Widespread Activations in Large Single-Subject Functional MRI Datasets at 7T. , 2015, Cerebral cortex.

[55]  Karl J. Friston,et al.  The Trouble with Cognitive Subtraction , 1996, NeuroImage.

[56]  Spyros Konstantopoulos,et al.  Fixed effects and variance components estimation in three‐level meta‐analysis , 2011, Research synthesis methods.

[57]  M. Fukunaga,et al.  Sources of functional magnetic resonance imaging signal fluctuations in the human brain at rest: a 7 T study. , 2009, Magnetic resonance imaging.

[58]  C. Siedentopf,et al.  Caffeine and cognition in functional magnetic resonance imaging. , 2010, Journal of Alzheimer's disease : JAD.

[59]  M Tynan R Stevens,et al.  Improving fMRI reliability in presurgical mapping for brain tumours , 2015, Journal of Neurology, Neurosurgery & Psychiatry.

[60]  L. Jäncke,et al.  Calibrated LCD/TFT stimulus presentation for visual psychophysics in fMRI , 2002, Journal of Neuroscience Methods.

[61]  N Jon Shah,et al.  Assessment of reliability in functional imaging studies , 2003, Journal of magnetic resonance imaging : JMRI.

[62]  David J. McGonigle,et al.  Test–retest reliability in fMRI: Or how I learned to stop worrying and love the variability , 2012, NeuroImage.

[63]  Thomas T. Liu,et al.  Caffeine alters the temporal dynamics of the visual BOLD response , 2004, NeuroImage.

[64]  Marina Schmid,et al.  An Introduction To The Event Related Potential Technique , 2016 .

[65]  Abraham Z. Snyder,et al.  Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion , 2012, NeuroImage.

[66]  Steen Moeller,et al.  The Human Connectome Project: A data acquisition perspective , 2012, NeuroImage.

[67]  N. Jon Shah,et al.  Direction and magnitude of nicotine effects on the fMRI BOLD response are related to nicotine effects on behavioral performance , 2010, Psychopharmacology.