Connectal coding: discovering the structures linking cognitive phenotypes to individual histories

Cognitive phenotypes characterize our memories, beliefs, skills, and preferences, and arise from our ancestral, developmental, and experiential histories. These histories are written into our brain structure through the building and modification of various brain circuits. Connectal coding, by way of analogy with neural coding, is the art, study, and practice of identifying the network structures that link cognitive phenomena to individual histories. We propose a formal statistical framework for connectal coding and demonstrate its utility in several applications spanning experimental modalities and phylogeny.

[1]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[2]  Danielle S. Bassett,et al.  Multi-scale brain networks , 2016, NeuroImage.

[3]  Ulrike von Luxburg,et al.  Clustering Stability: An Overview , 2010, Found. Trends Mach. Learn..

[4]  S. V. N. Vishwanathan,et al.  Graph kernels , 2007 .

[5]  L. Onsager.  Crystal statistics. I. A two-dimensional model with an order-disorder transition , 1944 .

[6]  Peter Norvig,et al.  Artificial Intelligence: A Modern Approach , 1995 .

[7]  Jean-Philippe Thiran,et al.  The Connectome Viewer Toolkit: An Open Source Framework to Manage, Analyze, and Visualize Connectomes , 2011, Front. Neuroinform..

[8]  Danielle S. Bassett,et al.  From Maps to Multi-dimensional Network Mechanisms of Mental Disorders , 2018, Neuron.

[9]  Joshua T. Vogelstein,et al.  A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data , 2011, 1107.4228.

[10]  W. Little.  The existence of persistent states in the brain , 1974 .

[11]  D. O. Hebb,et al.  The organization of behavior , 1988 .

[12]  N. Renier,et al.  iDISCO: A Simple, Rapid Method to Immunolabel Large Tissue Samples for Volume Imaging , 2014, Cell.

[13]  Edward R. Scheinerman,et al.  Random Dot Product Graph Models for Social Networks , 2007, WAW.

[14]  W Y Zhang,et al.  Discussion on `Sure independence screening for ultra-high dimensional feature space' by Fan, J and Lv, J. , 2008 .

[15]  S. Brenner,et al.  The structure of the ventral nerve cord of Caenorhabditis elegans. , 1976, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

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

[17]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[18]  Edward T. Bullmore,et al.  Neuroinformatics Original Research Article , 2022 .

[19]  Carey E. Priebe,et al.  Universally Consistent Latent Position Estimation and Vertex Classification for Random Dot Product Graphs , 2012, 1207.6745.

[20]  P. Erdös,et al.  Theory of the locomotion of nematodes: control of the somatic motor neurons by interneurons. , 1993, Mathematical biosciences.

[21]  Stefan Theil.  Trouble in Mind , 2015 .

[22]  Carey E. Priebe,et al.  Statistical Inference on Random Dot Product Graphs: a Survey , 2017, J. Mach. Learn. Res..

[23]  L. Paninski,et al.  Common-input models for multiple neural spike-train data , 2007, Network.

[24]  W. Denk,et al.  Serial Block-Face Scanning Electron Microscopy to Reconstruct Three-Dimensional Tissue Nanostructure , 2004, PLoS biology.

[25]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[26]  Jianqing Fan,et al.  Sure independence screening for ultrahigh dimensional feature space , 2006, math/0612857.

[27]  Philipp J. Keller,et al.  Reconstruction of Zebrafish Early Embryonic Development by Scanned Light Sheet Microscopy , 2008, Science.

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

[29]  Rex E. Jung,et al.  Multimodal Neuroimaging in Schizophrenia: Description and Dissemination , 2017, Neuroinformatics.

[30]  Emily Underwood,et al.  Neuroscience. Barcoding the brain. , 2016, Science.

[31]  E. Marder,et al.  Central pattern generators and the control of rhythmic movements , 2001, Current Biology.

[32]  E. Marder,et al.  Similar network activity from disparate circuit parameters , 2004, Nature Neuroscience.

[33]  Lav R. Varshney,et al.  Structural Properties of the Caenorhabditis elegans Neuronal Network , 2009, PLoS Comput. Biol..

[34]  Jeff W Lichtman,et al.  Why not connectomics? , 2013, Nature Methods.

[35]  G. Allan Johnson,et al.  Waxholm Space: An image-based reference for coordinating mouse brain research , 2010, NeuroImage.

[36]  Thomas E. Nichols,et al.  Thresholding of Statistical Maps in Functional Neuroimaging Using the False Discovery Rate , 2002, NeuroImage.

[37]  Carey E. Priebe,et al.  A semiparametric two-sample hypothesis testing problem for random graphs , 2017 .

[38]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[39]  Timothy O. Laumann,et al.  Functional Network Organization of the Human Brain , 2011, Neuron.

[40]  Stefan R. Pulver,et al.  Ultra-sensitive fluorescent proteins for imaging neuronal activity , 2013, Nature.

[41]  Carey E. Priebe,et al.  Community Detection and Classification in Hierarchical Stochastic Blockmodels , 2015, IEEE Transactions on Network Science and Engineering.

[42]  Gaël Varoquaux,et al.  Learning and comparing functional connectomes across subjects , 2013, NeuroImage.

[43]  Olaf Sporns,et al.  The Human Connectome: A Structural Description of the Human Brain , 2005, PLoS Comput. Biol..

[44]  Paolo Arena,et al.  An insect brain computational model inspired by Drosophila melanogaster: Simulation results , 2010, The 2010 International Joint Conference on Neural Networks (IJCNN).

[45]  Jun Li,et al.  Hypothesis Testing For Network Data in Functional Neuroimaging , 2014, 1407.5525.

[46]  S. Brenner.  The genetics of Caenorhabditis elegans. , 1974, Genetics.

[47]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[48]  George W. Fitzmaurice,et al.  Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing , 2017, CHI.

[49]  J. Mann,et al.  A Review of the Functional and Anatomical Default Mode Network in Schizophrenia , 2017, Neuroscience Bulletin.

[50]  Edward R. Scheinerman,et al.  Modeling graphs using dot product representations , 2010, Comput. Stat..

[51]  Daniele Durante,et al.  Nonparametric Bayes Modeling of Populations of Networks , 2014, 1406.7851.

[52]  Carey E. Priebe,et al.  Limit theorems for eigenvectors of the normalized Laplacian for random graphs , 2016, The Annals of Statistics.

[53]  P. Hagmann.  From diffusion MRI to brain connectomics , 2005 .

[54]  K. Deisseroth,et al.  CLARITY for mapping the nervous system , 2013, Nature Methods.

[55]  Elizabeth M C Hillman,et al.  Optical brain imaging in vivo: techniques and applications from animal to man. , 2007, Journal of biomedical optics.

[56]  J. Marchini,et al.  Genome-wide association studies of brain imaging phenotypes in UK Biobank , 2018, Nature.

[57]  Peter D. Hoff,et al.  Latent Space Approaches to Social Network Analysis , 2002 .

[58]  Ann K. Shinn,et al.  Default mode network abnormalities in bipolar disorder and schizophrenia , 2010, Psychiatry Research: Neuroimaging.

[59]  Vince D. Calhoun,et al.  A High-Throughput Pipeline Identifies Robust Connectomes But Troublesome Variability , 2017, bioRxiv.

[60]  J J Hopfield,et al.  Neural networks and physical systems with emergent collective computational abilities. , 1982, Proceedings of the National Academy of Sciences of the United States of America.

[61]  Edward T. Bullmore,et al.  The Multilayer Connectome of Caenorhabditis elegans , 2016, PLoS Comput. Biol..

[62]  R. Simes,et al.  An improved Bonferroni procedure for multiple tests of significance , 1986 .

[63]  R. C. Eaton,et al.  The Mauthner cell and other identified neurons of the brainstem escape network of fish , 2001, Progress in Neurobiology.

[64]  J. White,et al.  Neuronal connectivity in Caenorhabditis elegans , 1985, Trends in Neurosciences.

[65]  Jörn Diedrichsen,et al.  In search of the engram, 2017 , 2018, Current Opinion in Behavioral Sciences.

[66]  Keith Heberlein,et al.  Imaging human connectomes at the macroscale , 2013, Nature Methods.

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

[68]  P. Osten,et al.  Mapping brain circuitry with a light microscope , 2013, Nature Methods.

[69]  Bing Chen,et al.  An open science resource for establishing reliability and reproducibility in functional connectomics , 2014, Scientific Data.

[70]  Feng Li,et al.  The complete connectome of a learning and memory centre in an insect brain , 2017, Nature.

[71]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[72]  Alexander S. Ecker,et al.  Improved Estimation and Interpretation of Correlations in Neural Circuits , 2015, PLoS Comput. Biol..

[73]  D. Hassabis,et al.  Neuroscience-Inspired Artificial Intelligence , 2017, Neuron.

[74]  Carey E. Priebe,et al.  A statistical interpretation of spectral embedding: The generalised random dot product graph , 2017, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

[75]  Uri T Eden,et al.  A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. , 2005, Journal of neurophysiology.

[76]  C. Priebe,et al.  A central limit theorem for an omnibus embedding of random dot product graphs , 2017, 1705.09355.

[77]  W. Pitts,et al.  A Logical Calculus of the Ideas Immanent in Nervous Activity (1943) , 2021, Ideas That Created the Future.

[78]  Lu Wang,et al.  Symmetric Bilinear Regression for Signal Subgraph Estimation , 2018, IEEE Transactions on Signal Processing.

[79]  J. Harrow,et al.  Multiple evidence strands suggest that there may be as few as 19 000 human protein-coding genes , 2014, Human molecular genetics.

[80]  Edward T. Bullmore,et al.  Network-based statistic: Identifying differences in brain networks , 2010, NeuroImage.

[81]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[82]  James G. King,et al.  Reconstruction and Simulation of Neocortical Microcircuitry , 2015, Cell.

[83]  Michael W. Cole,et al.  A whole-brain and cross-diagnostic perspective on functional brain network dysfunction , 2018, bioRxiv.

[84]  J. Rilling,et al.  Comparative Primate Connectomics , 2018, Brain, Behavior and Evolution.

[85]  Hawoong Jeong,et al.  Statistical properties of sampled networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[86]  G. Johnson,et al.  A Diffusion MRI Tractography Connectome of the Mouse Brain and Comparison with Neuronal Tracer Data , 2015, Cerebral cortex.

[87]  Peter F. Neher,et al.  The challenge of mapping the human connectome based on diffusion tractography , 2017, Nature Communications.

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

[89]  Michael W. Cole,et al.  From connectome to cognition: The search for mechanism in human functional brain networks , 2017, NeuroImage.

[90]  Rex E. Jung,et al.  Computing scalable multivariate glocal invariants of large (brain-) graphs , 2013, 2013 IEEE Global Conference on Signal and Information Processing.

[91]  M. Helmstaedter.  Cellular-resolution connectomics: challenges of dense neural circuit reconstruction , 2013, Nature Methods.

[92]  M. Konishi,et al.  A circuit for detection of interaural time differences in the brain stem of the barn owl , 1990, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[93]  E. G. Gray,et al.  Electron Microscopy of Synaptic Contacts on Dendrite Spines of the Cerebral Cortex , 1959, Nature.

[94]  Linda Douw,et al.  The Connectome Visualization Utility: Software for Visualization of Human Brain Networks , 2014, PloS one.

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

[96]  Kathryn B. Laskey,et al.  Stochastic blockmodels: First steps , 1983 .

[97]  Joshua T. Vogelstein,et al.  Covariate-assisted spectral clustering , 2014, Biometrika.

[98]  Zhengwu Zhang,et al.  Tensor network factorizations: Relationships between brain structural connectomes and traits , 2018, NeuroImage.

[99]  Tiago P. Peixoto.  Hierarchical block structures and high-resolution model selection in large networks , 2013, ArXiv.

[100]  S. Wasserman,et al.  Stochastic a posteriori blockmodels: Construction and assessment , 1987 .

[101]  Bin Yu,et al.  Spectral clustering and the high-dimensional stochastic blockmodel , 2010, 1007.1684.

[102]  Romain Brette.  Is coding a relevant metaphor for the brain? , 2019, The Behavioral and brain sciences.

[103]  Oliver Griesbeck,et al.  Fluorescent proteins as sensors for cellular functions , 2004, Current Opinion in Neurobiology.

[104]  Danielle S Bassett,et al.  Brain graphs: graphical models of the human brain connectome. , 2011, Annual review of clinical psychology.

[105]  Carey E. Priebe,et al.  A Consistent Adjacency Spectral Embedding for Stochastic Blockmodel Graphs , 2011, 1108.2228.

[106]  Mingzhou Ding,et al.  Evaluating causal relations in neural systems: Granger causality, directed transfer function and statistical assessment of significance , 2001, Biological Cybernetics.

[107]  Carey E. Priebe,et al.  Graph Classification Using Signal-Subgraphs: Applications in Statistical Connectomics , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[108]  S. W. Emmons,et al.  Neural Circuits of Sexual Behavior in Caenorhabditis elegans. , 2018, Annual review of neuroscience.

[109]  Travis A. Jarrell,et al.  The Connectome of a Decision-Making Neural Network , 2012, Science.

[110]  Fang-Cheng Yeh,et al.  Local connectome phenotypes predict social, health, and cognitive factors , 2017, bioRxiv.

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

[112]  Disa Mhembere,et al.  A Comprehensive Cloud Framework for Accurate and Reliable Human Connectome Estimation and Meganalysis , 2017 .

[113]  Eric D. Kolaczyk,et al.  Statistical Analysis of Network Data , 2009 .

[114]  B. Efron.  SIMULTANEOUS INFERENCE : WHEN SHOULD HYPOTHESIS TESTING PROBLEMS BE COMBINED? , 2008, 0803.3863.

[115]  Yufeng Zang,et al.  Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes , 2013, NeuroImage.

[116]  Yong He,et al.  BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics , 2013, PloS one.

[117]  Jay N. Giedd,et al.  High resolution whole brain imaging of anatomical variation in XO, XX, and XY mice , 2013, NeuroImage.

[118]  S. Herculano‐Houzel.  The Human Brain in Numbers: A Linearly Scaled-up Primate Brain , 2009, Front. Hum. Neurosci..

[119]  R. Mark Henkelman,et al.  Sexual dimorphism revealed in the structure of the mouse brain using three-dimensional magnetic resonance imaging , 2007, NeuroImage.

[120]  Edoardo M. Airoldi,et al.  A Survey of Statistical Network Models , 2009, Found. Trends Mach. Learn..