Can brain state be manipulated to emphasize individual differences in functional connectivity?

&NA; While neuroimaging studies typically collapse data from many subjects, brain functional organization varies between individuals, and characterizing this variability is crucial for relating brain activity to behavioral phenotypes. Rest has become the default state for probing individual differences, chiefly because it is easy to acquire and a supposed neutral backdrop. However, the assumption that rest is the optimal condition for individual differences research is largely untested. In fact, other brain states may afford a better ratio of within‐ to between‐subject variability, facilitating biomarker discovery. Depending on the trait or behavior under study, certain tasks may bring out meaningful idiosyncrasies across subjects, essentially enhancing the individual signal in networks of interest beyond what can be measured at rest. Here, we review theoretical considerations and existing work on how brain state influences individual differences in functional connectivity, present some preliminary analyses of within‐ and between‐subject variability across conditions using data from the Human Connectome Project, and outline questions for future study. HighlightsRest is the default for studying individual differences in functional connectivity.But certain tasks may improve the ratio of within‐ to between‐subject variability.We review work on how scan condition influences individual differences.Preliminary results using HCP data show individual differences change with task.Using certain tasks over rest may improve sensitivity of imaging‐based biomarkers.

[1]  Jessica A. Turner,et al.  Sharing the wealth: Neuroimaging data repositories , 2016, NeuroImage.

[2]  Satrajit S. Ghosh,et al.  Prediction as a Humanitarian and Pragmatic Contribution from Human Cognitive Neuroscience , 2015, Neuron.

[3]  M. Yücel,et al.  Sex differences in the neural correlates of emotion: Evidence from neuroimaging , 2011, Biological Psychology.

[4]  Hanna Damasio,et al.  Sexual dimorphism and asymmetries in the gray–white composition of the human cerebrum , 2003, NeuroImage.

[5]  R. Cameron Craddock,et al.  Clinical applications of the functional connectome , 2013, NeuroImage.

[6]  Jason B. Mattingley,et al.  Functional brain networks related to individual differences in human intelligence at rest , 2016, Scientific Reports.

[7]  M. V. D. Heuvel,et al.  Exploring the brain network: A review on resting-state fMRI functional connectivity , 2010, European Neuropsychopharmacology.

[8]  Theo G. M. van Erp,et al.  Multisite reliability of MR-based functional connectivity , 2017, NeuroImage.

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

[10]  R. Malach,et al.  Intersubject Synchronization of Cortical Activity During Natural Vision , 2004, Science.

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

[12]  Jonathan D. Power,et al.  Intrinsic and Task-Evoked Network Architectures of the Human Brain , 2014, Neuron.

[13]  Ifat Levy,et al.  Neural Correlates of Decision-Making Under Ambiguity and Conflict , 2015, Front. Behav. Neurosci..

[14]  R. Adolphs,et al.  Building a Science of Individual Differences from fMRI , 2016, Trends in Cognitive Sciences.

[15]  N. Makris,et al.  Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. , 2001, Cerebral cortex.

[16]  Efstathios D. Gennatas,et al.  Linked Sex Differences in Cognition and Functional Connectivity in Youth. , 2015, Cerebral cortex.

[17]  L. Nummenmaa,et al.  The brains of high functioning autistic individuals do not synchronize with those of others☆ , 2013, NeuroImage: Clinical.

[18]  B. Harrison,et al.  Modulation of Brain Resting-State Networks by Sad Mood Induction , 2008, PloS one.

[19]  Jonathan D. Power,et al.  Recent progress and outstanding issues in motion correction in resting state fMRI , 2015, NeuroImage.

[20]  D. Dunstan,et al.  Providing NHS staff with height-adjustable workstations and behaviour change strategies to reduce workplace sitting time: protocol for the Stand More AT (SMArT) Work cluster randomised controlled trial , 2015, BMC Public Health.

[21]  Mark W. Woolrich,et al.  Resting-state fMRI in the Human Connectome Project , 2013, NeuroImage.

[22]  Scott T. Grafton,et al.  Dynamic reconfiguration of human brain networks during learning , 2010, Proceedings of the National Academy of Sciences.

[23]  Evan M. Gordon,et al.  Individual-specific features of brain systems identified with resting state functional correlations , 2017, NeuroImage.

[24]  J. Dubois,et al.  Brain Age: A State-Of-Mind? On the Stability of Functional Connectivity across Behavioral States , 2016, The Journal of Neuroscience.

[25]  Erica J. Ho,et al.  Imaging the “At-Risk” Brain: Future Directions , 2016, Journal of the International Neuropsychological Society.

[26]  Miao‐kun Sun,et al.  Trends in cognitive sciences , 2012 .

[27]  Dimitri Van De Ville,et al.  Decoding brain states from fMRI connectivity graphs , 2011, NeuroImage.

[28]  B. Turetsky,et al.  An fMRI Study of Sex Differences in Regional Activation to a Verbal and a Spatial Task , 2000, Brain and Language.

[29]  M. Schölvinck,et al.  Tracking brain arousal fluctuations with fMRI , 2016, Proceedings of the National Academy of Sciences.

[30]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

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

[32]  Dustin Scheinost,et al.  Sex differences in normal age trajectories of functional brain networks , 2015, Human brain mapping.

[33]  Jr. William Rush Dunton,et al.  THE AMERICAN JOURNAL OF PSYCHIATRY , 1944 .

[34]  Steen Moeller,et al.  Pushing spatial and temporal resolution for functional and diffusion MRI in the Human Connectome Project , 2013, NeuroImage.

[35]  Xi-Nian Zuo,et al.  Assessing Variations in Areal Organization for the Intrinsic Brain: From Fingerprints to Reliability , 2016, bioRxiv.

[36]  M. Fox,et al.  Individual Variability in Functional Connectivity Architecture of the Human Brain , 2013, Neuron.

[37]  Karl J. Friston,et al.  Cerebral Asymmetry and the Effects of Sex and Handedness on Brain Structure: A Voxel-Based Morphometric Analysis of 465 Normal Adult Human Brains , 2001, NeuroImage.

[38]  R. Cameron Craddock,et al.  Data-Driven Phenotypic Categorization for Neurobiological Analyses: Beyond DSM-5 Labels , 2016 .

[39]  R. Whelan,et al.  When Optimism Hurts: Inflated Predictions in Psychiatric Neuroimaging , 2014, Biological Psychiatry.

[40]  Guanghua Xiao,et al.  Alterations in resting functional connectivity due to recent motor task , 2013, NeuroImage.

[41]  Russell A. Poldrack,et al.  Scanning the Horizon: Future challenges for neuroimaging research , 2016 .

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

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

[44]  R. Kahn,et al.  Efficiency of Functional Brain Networks and Intellectual Performance , 2009, The Journal of Neuroscience.

[45]  Y. Liu,et al.  Resting-State Functional Connectivity Predicts Impulsivity in Economic Decision-Making , 2013, The Journal of Neuroscience.

[46]  Jonathan D. Power,et al.  Multi-task connectivity reveals flexible hubs for adaptive task control , 2013, Nature Neuroscience.

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

[48]  Linda Geerligs,et al.  State and Trait Components of Functional Connectivity: Individual Differences Vary with Mental State , 2015, The Journal of Neuroscience.

[49]  Jonathan D. Power,et al.  Studying Brain Organization via Spontaneous fMRI Signal , 2014, Neuron.

[50]  Stephan Hamann,et al.  Sex differences in brain activation to emotional stimuli: A meta-analysis of neuroimaging studies , 2012, Neuropsychologia.

[51]  Mark A. Elliott,et al.  Impact of in-scanner head motion on multiple measures of functional connectivity: Relevance for studies of neurodevelopment in youth , 2012, NeuroImage.

[52]  Arno Villringer,et al.  The LIFE-Adult-Study: objectives and design of a population-based cohort study with 10,000 deeply phenotyped adults in Germany , 2015, BMC Public Health.

[53]  Paul C Fletcher,et al.  Does the brain have a baseline? Why we should be resisting a rest. , 2007, NeuroImage.

[54]  Tamara Vanderwal,et al.  Inscapes: A movie paradigm to improve compliance in functional magnetic resonance imaging , 2015, NeuroImage.

[55]  Scott T. Grafton,et al.  Individual Variability in Brain Activity: A Nuisance or an Opportunity? , 2008, Brain Imaging and Behavior.

[56]  Shannon M. Pruden,et al.  A Meta-analysis of Neuroimaging Studies , 2015 .

[57]  Thomas E. Nichols,et al.  A positive-negative mode of population covariation links brain connectivity, demographics and behavior , 2015, Nature Neuroscience.

[58]  Jessica F. Cantlon,et al.  Neural Activity during Natural Viewing of Sesame Street Statistically Predicts Test Scores in Early Childhood , 2013, PLoS biology.

[59]  B. Biswal,et al.  Characterizing variation in the functional connectome: promise and pitfalls , 2012, Trends in Cognitive Sciences.

[60]  Nikos Makris,et al.  Sex differences in prefrontal cortical brain activity during fMRI of auditory verbal working memory. , 2005, Neuropsychology.

[61]  Mark Jenkinson,et al.  The minimal preprocessing pipelines for the Human Connectome Project , 2013, NeuroImage.

[62]  Daniel P. Kennedy,et al.  Idiosyncratic Brain Activation Patterns Are Associated with Poor Social Comprehension in Autism , 2015, The Journal of Neuroscience.

[63]  Steven C. R. Williams,et al.  Measuring fMRI reliability with the intra-class correlation coefficient , 2009, NeuroImage.

[64]  M. Chun,et al.  A neuromarker of sustained attention from whole-brain functional connectivity , 2015, Nature Neuroscience.

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

[66]  E. Bullmore,et al.  Endogenous Human Brain Dynamics Recover Slowly Following Cognitive Effort , 2008, PloS one.

[67]  Niall W. Duncan,et al.  Overview of potential procedural and participant-related confounds for neuroimaging of the resting state. , 2013, Journal of psychiatry & neuroscience : JPN.

[68]  Daniel S. Margulies,et al.  A Correspondence between Individual Differences in the Brain's Intrinsic Functional Architecture and the Content and Form of Self-Generated Thoughts , 2014, PloS one.

[69]  T. Insel,et al.  Wesleyan University From the SelectedWorks of Charles A . Sanislow , Ph . D . 2010 Research Domain Criteria ( RDoC ) : Toward a New Classification Framework for Research on Mental Disorders , 2018 .

[70]  David H. Zald,et al.  Sex-related differences in amygdala functional connectivity during resting conditions , 2006, NeuroImage.

[71]  R. Cameron Craddock,et al.  Individual differences in functional connectivity during naturalistic viewing conditions , 2016, NeuroImage.

[72]  M. Greicius Resting-state functional connectivity in neuropsychiatric disorders , 2008, Current opinion in neurology.

[73]  Olaf Sporns,et al.  Dynamic fluctuations coincide with periods of high and low modularity in resting-state functional brain networks , 2015, NeuroImage.

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

[75]  Eleanor A. Maguire,et al.  Spontaneous mentalizing during an interactive real world task: An fMRI study , 2006, Neuropsychologia.

[76]  Janice Chen,et al.  Dynamic reconfiguration of the default mode network during narrative comprehension , 2016, Nature Communications.

[77]  Catie Chang,et al.  Time–frequency dynamics of resting-state brain connectivity measured with fMRI , 2010, NeuroImage.

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

[79]  M. Greicius,et al.  Decoding subject-driven cognitive states with whole-brain connectivity patterns. , 2012, Cerebral cortex.

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

[81]  K. Christoff,et al.  Experience sampling during fMRI reveals default network and executive system contributions to mind wandering , 2009, Proceedings of the National Academy of Sciences.

[82]  R. Poldrack,et al.  Temporal metastates are associated with differential patterns of time-resolved connectivity, network topology, and attention , 2016, Proceedings of the National Academy of Sciences.

[83]  Marek McGann,et al.  How Mean is the Mean? , 2013, Front. Psychol..

[84]  Ulla Richardson,et al.  Print-specific multimodal brain activation in kindergarten improves prediction of reading skills in second grade , 2011, NeuroImage.

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

[86]  D. Heeger,et al.  Reliability of cortical activity during natural stimulation , 2010, Trends in Cognitive Sciences.

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

[88]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[89]  Thomas T. Liu,et al.  Noise contributions to the fMRI signal: An overview , 2016, NeuroImage.

[90]  H. Laufs,et al.  Decoding Wakefulness Levels from Typical fMRI Resting-State Data Reveals Reliable Drifts between Wakefulness and Sleep , 2014, Neuron.

[91]  R. Cameron Craddock,et al.  Individual differences in functional connectivity during naturalistic viewing conditions , 2016 .

[92]  Vivek Prabhakaran,et al.  The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated , 2013, NeuroImage.

[93]  Dustin Scheinost,et al.  Using connectome-based predictive modeling to predict individual behavior from brain connectivity , 2017, Nature Protocols.

[94]  X. Zuo,et al.  Test-retest reliabilities of resting-state FMRI measurements in human brain functional connectomics: A systems neuroscience perspective , 2014, Neuroscience & Biobehavioral Reviews.

[95]  Markus Heinrichs,et al.  The neural correlates of sex differences in emotional reactivity and emotion regulation , 2009, Human brain mapping.

[96]  Raag D. Airan,et al.  Factors affecting characterization and localization of interindividual differences in functional connectivity using MRI , 2016, Human brain mapping.

[97]  Rainer Goebel,et al.  Measuring structural–functional correspondence: Spatial variability of specialised brain regions after macro-anatomical alignment , 2012, NeuroImage.

[98]  H. Chernoff,et al.  Why significant variables aren’t automatically good predictors , 2015, Proceedings of the National Academy of Sciences.

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

[100]  Christian Windischberger,et al.  Toward discovery science of human brain function , 2010, Proceedings of the National Academy of Sciences.

[101]  Tyrone D. Cannon,et al.  Predicting risky choices from brain activity patterns , 2014, Proceedings of the National Academy of Sciences.

[102]  Thomas Wolbers,et al.  Hippocampus activity differentiates good from poor learners of a novel lexicon , 2005, NeuroImage.

[103]  Abraham Z. Snyder,et al.  Function in the human connectome: Task-fMRI and individual differences in behavior , 2013, NeuroImage.

[104]  Laura C. Buchanan,et al.  Tracking ongoing cognition in individuals using brief, whole-brain functional connectivity patterns , 2015, Proceedings of the National Academy of Sciences.