Idiosynchrony: From shared responses to individual differences during naturalistic neuroimaging

Two ongoing movements in human cognitive neuroscience have researchers shifting focus from group-level inferences to characterizing single subjects, and complementing tightly controlled tasks with rich, dynamic paradigms such as movies and stories. Yet relatively little work combines these two, perhaps because traditional analysis approaches for naturalistic imaging data are geared toward detecting shared responses rather than between-subject variability. Here, we review recent work using naturalistic stimuli to study individual differences, and advance a framework for detecting structure in idiosyncratic patterns of brain activity, or "idiosynchrony". Specifically, we outline the emerging technique of inter-subject representational similarity analysis (IS-RSA), including its theoretical motivation and an empirical demonstration of how it recovers brain-behavior relationships during movie watching using data from the Human Connectome Project. We also consider how stimulus choice may affect the individual signal and discuss areas for future research. We argue that naturalistic neuroimaging paradigms have the potential to reveal meaningful individual differences above and beyond those observed during traditional tasks or at rest.

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

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

[3]  Samuel A. Nastase,et al.  Measuring shared responses across subjects using intersubject correlation , 2019, bioRxiv.

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

[5]  M. Breakspear,et al.  Distinct neurobiological signatures of brain connectivity in depression subtypes during natural viewing of emotionally salient films , 2016, Psychological Medicine.

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

[7]  Joseph JaJa,et al.  Comparing Functional Connectivity Matrices: A Geometry-Aware Approach applied to Participant Identification , 2019, NeuroImage.

[8]  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 .

[9]  O. Bertrand,et al.  Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.

[10]  Steen Moeller,et al.  ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging , 2014, NeuroImage.

[11]  Steen Moeller,et al.  Tradeoffs in pushing the spatial resolution of fMRI for the 7T Human Connectome Project , 2017, NeuroImage.

[12]  Jun Li,et al.  Brain spontaneous functional connectivity and intelligence , 2008, NeuroImage.

[13]  M. Seghier,et al.  Interpreting and Utilising Intersubject Variability in Brain Function , 2018, Trends in Cognitive Sciences.

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

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

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

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

[18]  R. Hari,et al.  Emotions promote social interaction by synchronizing brain activity across individuals , 2012, Proceedings of the National Academy of Sciences.

[19]  Uri Hasson,et al.  Engaged listeners: shared neural processing of powerful political speeches. , 2015, Social cognitive and affective neuroscience.

[20]  R Todd Constable,et al.  Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative , 2018, Nature Communications.

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

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

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

[24]  Alberto Bizzi,et al.  Disentangling subgroups of participants recruiting shared as well as different brain regions for the execution of the verb generation task: A data-driven fMRI study , 2017, Cortex.

[25]  Olaf Sporns,et al.  Towards a new approach to reveal dynamical organization of the brain using topological data analysis , 2018, Nature Communications.

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

[27]  Monica D. Rosenberg,et al.  Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence , 2019, NeuroImage.

[28]  Waltz,et al.  Descriptor : An open resource for transdiagnostic research in pediatric mental health and learning disorders , 2019 .

[29]  Mikko Sams,et al.  Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity , 2012, Brain Connect..

[30]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[31]  Iiro P. Jääskeläinen,et al.  Brain hemodynamic activity during viewing and re-viewing of comedy movies explained by experienced humor , 2016, Scientific Reports.

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

[33]  Mikko Sams,et al.  Emotional speech synchronizes brains across listeners and engages large-scale dynamic brain networks , 2014, NeuroImage.

[34]  Adam M. Kleinbaum,et al.  Similar neural responses predict friendship , 2018, Nature Communications.

[35]  Luke J. Chang,et al.  Endogenous variation in ventromedial prefrontal cortex state dynamics during naturalistic viewing reflects affective experience , 2018, Science Advances.

[36]  Paul A. Taylor,et al.  Untangling the relatedness among correlations, Part II: Inter-subject correlation group analysis through linear mixed-effects modeling , 2017, NeuroImage.

[37]  Steven L. Small,et al.  Interpretation-mediated changes in neural activity during language comprehension , 2011, NeuroImage.

[38]  John S. Johnson,et al.  Audience preferences are predicted by temporal reliability of neural processing , 2014, Nature Communications.

[39]  Michael Breakspear,et al.  Naturalistic Stimuli in Neuroscience: Critically Acclaimed , 2019, Trends in Cognitive Sciences.

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

[41]  Viviana Betti,et al.  Natural Scenes Viewing Alters the Dynamics of Functional Connectivity in the Human Brain , 2013, Neuron.

[42]  S. Zeki,et al.  Functional brain mapping during free viewing of natural scenes , 2004, Human brain mapping.

[43]  Mikko Sams,et al.  Naturalistic fMRI Mapping Reveals Superior Temporal Sulcus as the Hub for the Distributed Brain Network for Social Perception , 2012, Front. Hum. Neurosci..

[44]  Koene R. A. Van Dijk,et al.  Less head motion during MRI under task than resting-state conditions , 2017, NeuroImage.

[45]  David A. Leopold,et al.  Functional MRI mapping of dynamic visual features during natural viewing in the macaque , 2015, NeuroImage.

[46]  F. Heider,et al.  An experimental study of apparent behavior , 1944 .

[47]  Feilong Ma,et al.  Reliable individual differences in fine-grained cortical functional architecture , 2018, NeuroImage.

[48]  John P. A. Ioannidis,et al.  Exploration, Inference, and Prediction in Neuroscience and Biomedicine , 2019, Trends in Neurosciences.

[49]  Michael W. Cole,et al.  Global Connectivity of Prefrontal Cortex Predicts Cognitive Control and Intelligence , 2012, The Journal of Neuroscience.

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

[51]  Luke J. Chang,et al.  The computational and neural substrates of moral strategies in social decision-making , 2019, Nature Communications.

[52]  Yang Hu,et al.  Individualized psychiatric imaging based on inter-subject neural synchronization in movie watching , 2020, NeuroImage.

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

[54]  F. Castellanos,et al.  Movies in the magnet: Naturalistic paradigms in developmental functional neuroimaging , 2018, Developmental Cognitive Neuroscience.

[55]  Michelle Hampson,et al.  Connectivity–behavior analysis reveals that functional connectivity between left BA39 and Broca's area varies with reading ability , 2006, NeuroImage.

[56]  Ludovica Griffanti,et al.  Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers , 2014, NeuroImage.

[57]  Damien A. Fair,et al.  Behavioral interventions for reducing head motion during MRI scans in children , 2018, NeuroImage.

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

[59]  Mikko Sams,et al.  A drama movie activates brains of holistic and analytical thinkers differentially , 2018, Social cognitive and affective neuroscience.

[60]  Frank Van Overwalle,et al.  Social cognition and the cerebellum: A meta-analysis of over 350 fMRI studies , 2014, NeuroImage.

[61]  Christine C. Guo,et al.  Out-of-sync: disrupted neural activity in emotional circuitry during film viewing in melancholic depression , 2015, Scientific Reports.

[62]  Gang Chen,et al.  Inter-subject synchrony as an index of functional specialization in early childhood , 2018, Scientific Reports.

[63]  Jukka-Pekka Kauppi,et al.  Inter-Subject Correlation in fMRI: Method Validation against Stimulus-Model Based Analysis , 2012, PloS one.

[64]  Mikko Sams,et al.  Inferior parietal lobule and early visual areas support elicitation of individualized meanings during narrative listening , 2018 .

[65]  Bryan R. Conroy,et al.  A Common, High-Dimensional Model of the Representational Space in Human Ventral Temporal Cortex , 2011, Neuron.

[66]  Uri Hasson,et al.  Shared and idiosyncratic cortical activation patterns in autism revealed under continuous real‐life viewing conditions , 2009, Autism research : official journal of the International Society for Autism Research.

[67]  Thomas A. W. Bolton,et al.  Brain dynamics in ASD during movie‐watching show idiosyncratic functional integration and segregation , 2018, Human brain mapping.

[68]  Emily S. Finn,et al.  Individual variation in functional brain connectivity: implications for personalized approaches to psychiatric disease , 2016, Dialogues in clinical neuroscience.

[69]  Monica D. Rosenberg,et al.  Relationships between depressive symptoms and brain responses during emotional movie viewing emerge in adolescence , 2020, NeuroImage.

[70]  Jouko Lampinen,et al.  Stimulus-Related Independent Component and Voxel-Wise Analysis of Human Brain Activity during Free Viewing of a Feature Film , 2012, PloS one.

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

[72]  P. Bartolomeo,et al.  Botallo's error, or the quandaries of the universality assumption , 2017, Cortex.

[73]  Annchen R. Knodt,et al.  The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences , 2017, Behavior Research Methods.

[74]  T. Insel,et al.  Toward the future of psychiatric diagnosis: the seven pillars of RDoC , 2013, BMC Medicine.

[75]  Christopher J. Honey,et al.  Same Story , Different Story : The Neural Representation of Interpretive Frameworks , 2017 .

[76]  Nikolaus Kriegeskorte,et al.  Frontiers in Systems Neuroscience Systems Neuroscience , 2022 .

[77]  Riitta Hari,et al.  Aberrant Cortical Integration in First-Episode Psychosis During Natural Audiovisual Processing , 2018, Biological Psychiatry.

[78]  Jari Saramäki,et al.  Reorganization of functionally connected brain subnetworks in high‐functioning autism , 2015, Human brain mapping.

[79]  Tamara Vanderwal,et al.  Shared understanding of narratives is correlated with shared neural responses , 2019, NeuroImage.

[80]  Luke J. Chang,et al.  Intersubject representational similarity analysis reveals individual variations in affective experience when watching erotic movies , 2019, NeuroImage.

[81]  Po-Hsuan Chen,et al.  A Reduced-Dimension fMRI Shared Response Model , 2015, NIPS.

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

[83]  Mikko Sams,et al.  Synchronous brain activity across individuals underlies shared psychological perspectives , 2014, NeuroImage.

[84]  N. Kriegeskorte,et al.  Author ' s personal copy Representational geometry : integrating cognition , computation , and the brain , 2013 .

[85]  Luke J. Chang,et al.  Intersubject representational similarity analysis reveals individual variations in affective experience when watching erotic movies , 2020, NeuroImage.

[86]  Gang Chen,et al.  Untangling the relatedness among correlations, part III: Inter-subject correlation analysis through Bayesian multilevel modeling for naturalistic scanning , 2019, NeuroImage.

[87]  Joseph JáJá,et al.  Brain dynamics and temporal trajectories during task and naturalistic processing , 2018, NeuroImage.

[88]  Vince D Calhoun,et al.  Interparticipant correlations: A model free FMRI analysis technique , 2007, Human brain mapping.

[89]  R. Saxe,et al.  Development of the social brain from age three to twelve years , 2018, Nature Communications.

[90]  Jukka-Pekka Kauppi,et al.  Inter-subject correlation of temporoparietal junction activity is associated with conflict patterns during flexible decision-making , 2019, Neuroscience Research.

[91]  César Caballero-Gaudes,et al.  Imaging the spontaneous flow of thought: Distinct periods of cognition contribute to dynamic functional connectivity during rest , 2019 .