Estimation and validation of individualized dynamic brain models with resting state fMRI

A key challenge for neuroscience is to develop generative, causal models of the human nervous system in an individualized, data-driven manner. Previous initiatives have either constructed biologically-plausible models that are not constrained by individual-level human brain activity or used data-driven statistical characterizations of individuals that are not mechanistic. We aim to bridge this gap through the development of a new modeling approach termed Mesoscale Individualized Neurodynamic (MINDy) modeling, wherein we fit nonlinear dynamical systems models directly to human brain imaging data. The MINDy framework is able to produce these data-driven network models for hundreds to thousands of interacting brain regions in just 1-3 minutes per subject. We demonstrate that the models are valid, reliable, and robust. We show that MINDy models are predictive of individualized patterns of resting-state brain dynamical activity. Furthermore, MINDy is better able to uncover the mechanisms underlying individual differences in resting state activity than functional connectivity methods.

[1]  Danielle S. Bassett,et al.  Personalized Neuroscience: Common and Individual-Specific Features in Functional Brain Networks , 2018, Neuron.

[2]  Rainer Goebel,et al.  The identification of interacting networks in the brain using fMRI: Model selection, causality and deconvolution , 2011, NeuroImage.

[3]  M. Breakspear.  Dynamic models of large-scale brain activity , 2017, Nature Neuroscience.

[4]  Gilles Faÿ,et al.  Características inmunológicas claves en la fisiopatología de la sepsis. Infectio , 2009 .

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

[6]  Justin L. Vincent,et al.  Intrinsic Fluctuations within Cortical Systems Account for Intertrial Variability in Human Behavior , 2007, Neuron.

[7]  Y. Yamaguchi,et al.  Brain/MINDS: A Japanese National Brain Project for Marmoset Neuroscience , 2016, Neuron.

[8]  Schreiber,et al.  Measuring information transfer , 2000, Physical review letters.

[9]  Jonathan R. Polimeni,et al.  Relative latency and temporal variability of hemodynamic responses at the human primary visual cortex , 2018, NeuroImage.

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

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

[13]  Viktor K. Jirsa,et al.  An automated pipeline for constructing personalized virtual brains from multimodal neuroimaging data , 2015, NeuroImage.

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

[15]  John D. Murray,et al.  Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics , 2019, Neuron.

[16]  Karl J. Friston,et al.  Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri Nonlinear Dynamic Causal Models for Fmri , 2022 .

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

[18]  P. Holland,et al.  Robust regression using iteratively reweighted least-squares , 1977 .

[19]  M. D’Esposito,et al.  The variability of human BOLD hemodynamic responses , 1998, NeuroImage.

[20]  Viktor K. Jirsa,et al.  The Virtual Epileptic Patient: Individualized whole-brain models of epilepsy spread , 2017, NeuroImage.

[21]  Gustavo Deco,et al.  Functional connectivity dynamics: Modeling the switching behavior of the resting state , 2015, NeuroImage.

[22]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[23]  G. Deco,et al.  Emerging concepts for the dynamical organization of resting-state activity in the brain , 2010, Nature Reviews Neuroscience.

[24]  Aileen Schroeter,et al.  The hemodynamic response to somatosensory stimulation in mice depends on the anesthetic used: Implications on analysis of mouse fMRI data , 2015, NeuroImage.

[25]  Matthew F. Singh,et al.  A simple transfer function for nonlinear dendritic integration , 2015, Front. Comput. Neurosci..

[26]  Sepideh Sadaghiani,et al.  Ongoing dynamics in large-scale functional connectivity predict perception , 2015, Proceedings of the National Academy of Sciences.

[27]  R. Engle.  Dynamic Conditional Correlation , 2002 .

[28]  Joachim M. Buhmann,et al.  Regression DCM for fMRI , 2017, NeuroImage.

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

[30]  M. B. Nebel,et al.  Quantifying the reliability of image replication studies: The image intraclass correlation coefficient (I2C2) , 2013, Cognitive, affective & behavioral neuroscience.

[31]  Kaustubh Supekar,et al.  Multivariate dynamical systems models for estimating causal interactions in fMRI , 2011, NeuroImage.

[32]  Andrew W. Kraft,et al.  Spontaneous Infra-slow Brain Activity Has Unique Spatiotemporal Dynamics and Laminar Structure , 2018, Neuron.

[33]  R. Turner,et al.  Event-Related fMRI: Characterizing Differential Responses , 1998, NeuroImage.

[34]  H. Markram.  The Blue Brain Project , 2006, Nature Reviews Neuroscience.

[35]  O Sporns,et al.  Predicting human resting-state functional connectivity from structural connectivity , 2009, Proceedings of the National Academy of Sciences.

[36]  Enzo Tagliazucchi,et al.  Propagated infra-slow intrinsic brain activity reorganizes across wake and slow wave sleep , 2015, eLife.

[37]  Thomas T. Liu,et al.  A component based noise correction method (CompCor) for BOLD and perfusion based fMRI , 2007, NeuroImage.

[38]  Karl J. Friston,et al.  The influence of spontaneous activity on stimulus processing in primary visual cortex , 2012, NeuroImage.

[39]  A. R. McIntosh,et al.  The effects of physiologically plausible connectivity structure on local and global dynamics in large scale brain models , 2009, Journal of Neuroscience Methods.

[40]  Francesca Mastrogiuseppe,et al.  Linking Connectivity, Dynamics, and Computations in Low-Rank Recurrent Neural Networks , 2017, Neuron.

[41]  Norbert Wiener,et al.  Extrapolation, Interpolation, and Smoothing of Stationary Time Series, with Engineering Applications , 1949 .

[42]  Karl J. Friston,et al.  Population dynamics: Variance and the sigmoid activation function , 2008, NeuroImage.

[43]  Karl J. Friston,et al.  Large-scale DCMs for resting-state fMRI , 2017, Network Neuroscience.

[44]  Karlheinz Meier,et al.  Introducing the Human Brain Project , 2011, FET.

[45]  T. Robbins,et al.  Inhibition and the right inferior frontal cortex , 2004, Trends in Cognitive Sciences.

[46]  J J Hopfield,et al.  Neurons with graded response have collective computational properties like those of two-state neurons. , 1984, Proceedings of the National Academy of Sciences of the United States of America.

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

[48]  Evan M. Gordon,et al.  Precision Functional Mapping of Individual Human Brains , 2017, Neuron.

[49]  G. Deco,et al.  Inversion of a large-scale circuit model reveals a cortical hierarchy in the dynamic resting human brain , 2019, Science Advances.

[50]  Bruce Fischl,et al.  FreeSurfer , 2012, NeuroImage.

[51]  Olaf Sporns,et al.  Network structure of cerebral cortex shapes functional connectivity on multiple time scales , 2007, Proceedings of the National Academy of Sciences.

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

[53]  Gabriele Lohmann,et al.  Critical comments on dynamic causal modelling , 2012, NeuroImage.

[54]  Timothy Dozat,et al.  Incorporating Nesterov Momentum into Adam , 2016 .

[55]  Brian Caffo,et al.  Comparing test-retest reliability of dynamic functional connectivity methods , 2017, NeuroImage.

[56]  Michael W. Cole,et al.  Activity flow over resting-state networks shapes cognitive task activations , 2016, Nature Neuroscience.

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

[58]  Bruce M Psaty,et al.  Comparison of 2 Treatment Models: Precision Medicine and Preventive Medicine. , 2018, JAMA.

[59]  Angela D Friederici,et al.  The language network , 2012, Current Opinion in Neurobiology.

[60]  Danna Zhou,et al.  d. , 1934, Microbial pathogenesis.

[61]  Adeel Razi,et al.  Dynamic causal modelling revisited , 2017, NeuroImage.

[62]  Biyu J. He.  Spontaneous and Task-Evoked Brain Activity Negatively Interact , 2013, The Journal of Neuroscience.

[63]  Maurizio Corbetta,et al.  Warnings and caveats in brain controllability , 2017, NeuroImage.

[64]  Evan M. Gordon,et al.  Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRI , 2017, bioRxiv.

[65]  Viktor K. Jirsa,et al.  The Virtual Brain: a simulator of primate brain network dynamics , 2013, Front. Neuroinform..

[66]  ShiNung Ching,et al.  Dimensionality reduction impedes the extraction of dynamic functional connectivity states from fMRI recordings of resting wakefulness , 2018, Journal of Neuroscience Methods.

[67]  Bharat B. Biswal,et al.  Test-retest reliability of dynamic functional connectivity in resting state fMRI , 2018, NeuroImage.

[68]  Karl J. Friston,et al.  The Dynamic Brain: From Spiking Neurons to Neural Masses and Cortical Fields , 2008, PLoS Comput. Biol..

[69]  Matthew F. Singh,et al.  Scalable surrogate deconvolution for identification of partially-observable systems and brain modeling , 2020, bioRxiv.

[70]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[71]  Dimitri Van De Ville,et al.  The dynamic functional connectome: State-of-the-art and perspectives , 2017, NeuroImage.

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

[73]  Aline Seuwen,et al.  Specificity of stimulus-evoked fMRI responses in the mouse: The influence of systemic physiological changes associated with innocuous stimulation under four different anesthetics , 2014, NeuroImage.

[74]  Karl J. Friston,et al.  Dynamic causal modelling , 2003, NeuroImage.

[75]  Eswar Damaraju,et al.  Tracking whole-brain connectivity dynamics in the resting state. , 2014, Cerebral cortex.

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

[77]  Timothy O. Laumann,et al.  Data Quality Influences Observed Links Between Functional Connectivity and Behavior , 2017, Cerebral cortex.

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

[79]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

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

[81]  Karl J. Friston,et al.  Nonlinear Responses in fMRI: The Balloon Model, Volterra Kernels, and Other Hemodynamics , 2000, NeuroImage.

[82]  Tsuyoshi Murata,et al.  {m , 1934, ACML.

[83]  M. D’Esposito,et al.  The Inferential Impact of Global Signal Covariates in Functional Neuroimaging Analyses , 1998, NeuroImage.

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

[85]  Aaron Kucyi,et al.  Dynamic functional connectivity of the default mode network tracks daydreaming , 2014, NeuroImage.

[86]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[87]  Evan M. Gordon,et al.  On the Stability of BOLD fMRI Correlations , 2016, Cerebral cortex.

[88]  A. Turken,et al.  Left inferior frontal gyrus is critical for response inhibition , 2008, BMC Neuroscience.

[89]  Euan A Ashley,et al.  The precision medicine initiative: a new national effort. , 2015, JAMA.

[90]  D. Donoho.  For most large underdetermined systems of linear equations the minimal 𝓁1‐norm solution is also the sparsest solution , 2006 .

[91]  J. Cowan,et al.  Excitatory and inhibitory interactions in localized populations of model neurons. , 1972, Biophysical journal.

[92]  Steen Moeller,et al.  Evaluation of 2D multiband EPI imaging for high-resolution, whole-brain, task-based fMRI studies at 3T: Sensitivity and slice leakage artifacts , 2016, NeuroImage.