Bidirectional signal exchanges and their mechanisms during joint attention interaction – A hyperscanning fMRI study

Social interactions are essential to our daily life. We tested the hypothesis that social interactions during joint attention (JA) require bidirectional communication, each with a different mechanism. We used a novel multivariate functional connectivity analysis, which enables obtaining directed pathways between four regions at each time-frequency point, with hyper-scanning MRI data of real-time JA interaction. Constructing multiple "4-regional directed pathways" and counting the number of times, regions engaged in feedforward or feedback processes in the 'sender' or the 'receiver brains, we obtained the following. (1) There were more regions in feedforward than in feedback processes (125 versus 99). (2) The right hemisphere was more involved in feedforward (74 versus 33), while the left hemisphere in feedback (66 versus 51). (3) The dmPFC was more engaged in feedforward (73 versus 44) while the TPJ in both (49 versus 45). (4) The dmPFC was more involved in the sending processes (i.e. initiation of feedforward and feedback) while the TPJ in the receiving processes. (5) JA interaction was involved with high MRI frequencies (0.04-0.1 Hz), while continues interactions by low MRI frequencies (0.01-0.04 Hz). (6) Initiation and responding to JA (i.e. IJA and RJA) evolved with composite neural systems: similar systems for pathways that included the dmPFC, vmPFC and the STS, and different systems for pathways that included the TPJ, vmPFC, PCC and the STS. These findings have important consequences in the basic understanding of social interaction and could help in diagnose and follow-up of social impairments.

[1]  Nadim Joni Shah,et al.  Minds Made for Sharing: Initiating Joint Attention Recruits Reward-related Neurocircuitry , 2010, Journal of Cognitive Neuroscience.

[2]  L. Astolfi,et al.  Social neuroscience and hyperscanning techniques: Past, present and future , 2014, Neuroscience & Biobehavioral Reviews.

[3]  M. Raichle,et al.  Lag threads organize the brain’s intrinsic activity , 2015, Proceedings of the National Academy of Sciences.

[4]  F. Overwalle.  Social cognition and the brain: a meta-analysis. , 2009 .

[5]  Mark W. Woolrich,et al.  Network modelling methods for FMRI , 2011, NeuroImage.

[6]  Susan L. Whitfield-Gabrieli,et al.  Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks , 2012, Brain Connect..

[7]  Gadi Goelman,et al.  Multiple‐region directed functional connectivity based on phase delays , 2017, Human brain mapping.

[8]  Kevin A. Pelphrey,et al.  Social, reward, and attention brain networks are involved when online bids for joint attention are met with congruent versus incongruent responses , 2013, Social neuroscience.

[9]  Abraham Z. Snyder,et al.  The Lag Structure of Intrinsic Activity is Focally Altered in High Functioning Adults with Autism , 2015, Cerebral cortex.

[10]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[11]  Dost Öngür,et al.  Anticorrelations in resting state networks without global signal regression , 2012, NeuroImage.

[12]  M. Fox,et al.  The global signal and observed anticorrelated resting state brain networks. , 2009, Journal of neurophysiology.

[13]  A Donner,et al.  Construction of confidence limits about effect measures: A general approach , 2008, Statistics in medicine.

[14]  Koji Shimada,et al.  Neural substrates of shared attention as social memory: A hyperscanning functional magnetic resonance imaging study , 2016, NeuroImage.

[15]  Kwanguk Kim,et al.  Joint Attention, Social-Cognition, and Recognition Memory in Adults , 2012, Front. Hum. Neurosci..

[16]  R. Newcombe.  Propagating Imprecision: Combining Confidence Intervals from Independent Sources , 2011 .

[17]  C. Torrence,et al.  A Practical Guide to Wavelet Analysis. , 1998 .

[18]  R. Saxe,et al.  Look at this: the neural correlates of initiating and responding to bids for joint attention , 2012, Front. Hum. Neurosci..

[19]  Michela Balconi,et al.  Brains in Competition: Improved Cognitive Performance and Inter-Brain Coupling by Hyperscanning Paradigm with Functional Near-Infrared Spectroscopy , 2017, Front. Behav. Neurosci..

[20]  Gadi Goelman,et al.  Frequency-phase analysis of resting-state functional MRI , 2017, Scientific reports.

[21]  Wanqing Li,et al.  The default mode network and social understanding of others: what do brain connectivity studies tell us , 2014, Front. Hum. Neurosci..

[22]  Peter Kirsch,et al.  State-Dependent Cross-Brain Information Flow in Borderline Personality Disorder , 2017, JAMA psychiatry.

[23]  K. Vogeley,et al.  Toward a second-person neuroscience 1 , 2013, Behavioral and Brain Sciences.

[24]  P. Mundy,et al.  A review of joint attention and social‐cognitive brain systems in typical development and autism spectrum disorder , 2018, The European journal of neuroscience.

[25]  M. Raichle,et al.  Lag structure in resting-state fMRI. , 2014, Journal of neurophysiology.

[26]  Kevin Murphy,et al.  The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? , 2009, NeuroImage.

[27]  Cornelis J. Stam,et al.  Go with the flow: Use of a directed phase lag index (dPLI) to characterize patterns of phase relations in a large-scale model of brain dynamics , 2012, NeuroImage.

[28]  Angela R. Laird,et al.  Characterization of the temporo-parietal junction by combining data-driven parcellation, complementary connectivity analyses, and functional decoding , 2013, NeuroImage.

[29]  Alexis T Baria,et al.  Anatomical and Functional Assemblies of Brain BOLD Oscillations , 2011, The Journal of Neuroscience.

[30]  M. Fukunaga,et al.  Negative BOLD-fMRI Signals in Large Cerebral Veins , 2011, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[31]  J. Perner,et al.  Neuroscience and Biobehavioral Reviews Fractionating Theory of Mind: a Meta-analysis of Functional Brain Imaging Studies , 2022 .

[32]  Timothy O. Laumann,et al.  Methods to detect, characterize, and remove motion artifact in resting state fMRI , 2014, NeuroImage.

[33]  Rupert Lanzenberger,et al.  Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies , 2009, NeuroImage.

[34]  N. Logothetis,et al.  The Amplitude and Timing of the BOLD Signal Reflects the Relationship between Local Field Potential Power at Different Frequencies , 2012, The Journal of Neuroscience.

[35]  Marleen B. Schippers,et al.  Mapping the information flow from one brain to another during gestural communication , 2010, Proceedings of the National Academy of Sciences.

[36]  Gadi Goelman,et al.  Maximizing Negative Correlations in Resting-State Functional Connectivity MRI by Time-Lag , 2014, PloS one.

[37]  Hong-Ye Gao,et al.  Wavelet analysis [for signal processing] , 1996 .

[38]  R. Hari,et al.  Centrality of Social Interaction in Human Brain Function , 2015, Neuron.

[39]  Nathan Caruana,et al.  A frontotemporoparietal network common to initiating and responding to joint attention bids , 2014, NeuroImage.

[40]  J. Mattingley,et al.  Understanding the minds of others: A neuroimaging meta-analysis , 2016, Neuroscience & Biobehavioral Reviews.

[41]  Shen Liu,et al.  Interactive Brain Activity: Review and Progress on EEG-Based Hyperscanning in Social Interactions , 2018, Front. Psychol..

[42]  V. Calhoun,et al.  Information flow between interacting human brains: Identification, validation, and relationship to social expertise , 2015, Proceedings of the National Academy of Sciences.

[43]  M. Raichle,et al.  Human cortical–hippocampal dialogue in wake and slow-wave sleep , 2016, Proceedings of the National Academy of Sciences.