How tasks change whole-brain functional organization to reveal brain-phenotype relationships

Functional connectivity (FC) calculated from task fMRI data better reveals brain-phenotype relationships than rest-based FC, but how tasks have this effect is unknown. In over 700 individuals performing 7 tasks, we use psychophysiological interaction (PPI) and predictive modeling analyses to demonstrate that task-induced changes in FC successfully predict phenotype, and these changes are not simply driven by task activation. Activation, however, is useful for prediction only if the in-scanner task is related to the predicted phenotype. Given this evidence that tasks change patterns of FC independent of activation to amplify brain-phenotype relationships, we develop and apply an inter-subject PPI analysis to further characterize these predictive FC changes. We find that task-induced consistency of FC patterns across individuals is useful for prediction—to a point; these results suggest that tasks improve FC-based prediction performance by de-noising the BOLD signal, revealing meaningful individual differences in brain functional organization. Together, these findings demonstrate that, when it comes to the effects of in-scanner tasks on the brain, focal activation is only the tip of the iceberg, and they offer a framework to best leverage both task activation and FC to reveal the neural bases of complex human traits, symptoms, and behaviors.

[1]  Dustin Scheinost,et al.  Resting-state functional connectivity predicts neuroticism and extraversion in novel individuals , 2018, Social cognitive and affective neuroscience.

[2]  R. Gur,et al.  Development of Abbreviated Nine-Item Forms of the Raven’s Standard Progressive Matrices Test , 2012, Assessment.

[3]  M. Lindquist,et al.  An fMRI-based neurologic signature of physical pain. , 2013, The New England journal of medicine.

[4]  Xin Di,et al.  Toward Task Connectomics: Examining Whole-Brain Task Modulated Connectivity in Different Task Domains , 2018, Cerebral cortex.

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

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

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

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

[9]  Rainer Goebel,et al.  Information-based functional brain mapping. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Dustin Scheinost,et al.  Task-induced brain state manipulation improves prediction of individual traits , 2018, Nature Communications.

[11]  Soon Chun Siong,et al.  Comparison of block and event‐related fMRI designs in evaluating the word‐frequency effect , 2003, Human brain mapping.

[12]  A. Newell You can't play 20 questions with nature and win : projective comments on the papers of this symposium , 1973 .

[13]  Wei Gao,et al.  Task‐related modulation of functional connectivity variability and its behavioral correlations , 2015, Human brain mapping.

[14]  Kristina M. Visscher,et al.  A Core System for the Implementation of Task Sets , 2006, Neuron.

[15]  B T Thomas Yeo,et al.  Reconfigurable task-dependent functional coupling modes cluster around a core functional architecture , 2014, Philosophical Transactions of the Royal Society B: Biological Sciences.

[16]  Michael W. Cole,et al.  Task activations produce spurious but systematic inflation of task functional connectivity estimates , 2018, NeuroImage.

[17]  Mert R. Sabuncu,et al.  Global signal regression strengthens association between resting-state functional connectivity and behavior , 2019, NeuroImage.

[18]  Amin Karbasi,et al.  There is no single functional atlas even for a single individual: Functional parcel definitions change with task , 2019, NeuroImage.

[19]  Roland Eils,et al.  Complex heatmaps reveal patterns and correlations in multidimensional genomic data , 2016, Bioinform..

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

[21]  C. Sripada,et al.  Toward a “treadmill test” for cognition: Improved prediction of general cognitive ability from the task activated brain , 2020, Human brain mapping.

[22]  Dustin Scheinost,et al.  Influences on the Test–Retest Reliability of Functional Connectivity MRI and its Relationship with Behavioral Utility , 2017, Cerebral cortex.

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

[24]  P. Mahadevan,et al.  An overview , 2007, Journal of Biosciences.

[25]  Nicola Filippini,et al.  Large-scale intrinsic connectivity is consistent across varying task demands , 2019, PloS one.

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

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

[28]  Bharat B. Biswal,et al.  Psychophysiological Interactions in a Visual Checkerboard Task: Reproducibility, Reliability, and the Effects of Deconvolution , 2017, bioRxiv.

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

[30]  Fenna M. Krienen,et al.  Opportunities and limitations of intrinsic functional connectivity MRI , 2013, Nature Neuroscience.

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

[32]  Sean M. Polyn,et al.  Beyond mind-reading: multi-voxel pattern analysis of fMRI data , 2006, Trends in Cognitive Sciences.

[33]  M. Posner,et al.  The attention system of the human brain. , 1990, Annual review of neuroscience.

[34]  Jason S. Nomi,et al.  Correspondence between evoked and intrinsic functional brain network configurations , 2017, Human brain mapping.

[35]  Dustin Scheinost,et al.  Can brain state be manipulated to emphasize individual differences in functional connectivity? , 2017, NeuroImage.

[36]  Paola Galdi,et al.  Resting-State Functional Brain Connectivity Best Predicts the Personality Dimension of Openness to Experience , 2018, Personality Neuroscience.

[37]  Olaf Sporns,et al.  High-amplitude co-fluctuations in cortical activity drive functional connectivity , 2019 .

[38]  Dustin Scheinost,et al.  Regions and Connections: Complementary Approaches to Characterize Brain Organization and Function , 2019, The Neuroscientist : a review journal bringing neurobiology, neurology and psychiatry.

[39]  Stephen M. Smith,et al.  Permutation inference for the general linear model , 2014, NeuroImage.

[40]  Dustin Scheinost,et al.  Combining multiple connectomes improves predictive modeling of phenotypic measures , 2019, NeuroImage.

[41]  Dimitri Van De Ville,et al.  Disentangling dynamic networks: Separated and joint expressions of functional connectivity patterns in time , 2014, Human brain mapping.

[42]  Ahmad R. Hariri,et al.  General functional connectivity: Shared features of resting-state and task fMRI drive reliable and heritable individual differences in functional brain networks , 2018, NeuroImage.

[43]  André Zugman,et al.  Commentary: Functional connectome fingerprint: identifying individuals using patterns of brain connectivity , 2017, Front. Hum. Neurosci..

[44]  Timothy O. Laumann,et al.  Functional Brain Networks Are Dominated by Stable Group and Individual Factors, Not Cognitive or Daily Variation , 2018, Neuron.

[45]  Mark W. Woolrich,et al.  Advances in functional and structural MR image analysis and implementation as FSL , 2004, NeuroImage.

[46]  Stephen M. Smith,et al.  Multi-level block permutation , 2015, NeuroImage.

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

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

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

[50]  Roland Eils,et al.  circlize implements and enhances circular visualization in R , 2014, Bioinform..

[51]  Jacob Cohen Statistical Power Analysis for the Behavioral Sciences , 1969, The SAGE Encyclopedia of Research Design.

[52]  Karl J. Friston,et al.  Stochastic Designs in Event-Related fMRI , 1999, NeuroImage.

[53]  N. Turk-Browne Functional Interactions as Big Data in the Human Brain , 2013, Science.

[54]  O. Sporns,et al.  The economy of brain network organization , 2012, Nature Reviews Neuroscience.

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

[56]  Thomas T. Liu,et al.  The global signal in fMRI: Nuisance or Information? , 2017, NeuroImage.

[57]  Matthias S. Treder,et al.  Activity and Connectivity Differences Underlying Inhibitory Control Across the Adult Life Span , 2018, The Journal of Neuroscience.

[58]  Sterling C. Johnson,et al.  A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches , 2012, NeuroImage.

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

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

[61]  Dustin Scheinost,et al.  Ten simple rules for predictive modeling of individual differences in neuroimaging , 2019, NeuroImage.

[62]  Jessica R. Cohen The behavioral and cognitive relevance of time-varying, dynamic changes in functional connectivity , 2017, NeuroImage.

[63]  Danielle S. Bassett,et al.  Cognitive Network Neuroscience , 2015, Journal of Cognitive Neuroscience.

[64]  Yizhen Zhang,et al.  Task-Evoked Functional Connectivity Does Not Explain Functional Connectivity Differences Between Rest and Task Conditions , 2018, bioRxiv.

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

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