Tracking recurrence of correlation structure in neuronal recordings

BACKGROUND Correlated neuronal activity in the brain is hypothesized to contribute to information representation, and is important for gauging brain dynamics in health and disease. Due to high dimensional neural datasets, it is difficult to study temporal variations in correlation structure. NEW METHOD We developed a multiscale method, Population Coordination (PCo), to assess neural population structure in multiunit single neuron ensemble and multi-site local field potential (LFP) recordings. PCo utilizes population correlation (PCorr) vectors, consisting of pair-wise correlations between neural elements. The PCo matrix contains the correlations between all PCorr vectors occurring at different times. RESULTS We used PCo to interpret dynamics of two electrophysiological datasets: multisite LFP and single unit ensemble. In the LFP dataset from an animal model of medial temporal lobe epilepsy, PCo isolated anomalous brain states, where particular brain regions broke off from the rest of the brain's activity. In a dataset of rat hippocampal single-unit recordings, PCo enabled visualizing neuronal ensemble correlation structure changes associated with changes of animal environment (place-cell remapping). COMPARISON WITH EXISTING METHOD(S) PCo allows directly visualizing high dimensional data. Dimensional reduction techniques could also be used to produce dynamical snippets that could be examined for recurrence. PCo allows intuitive, visual assessment of temporal recurrence in correlation structure directly in the high dimensionality dataset, allowing for immediate assessment of relevant dynamics at a single site. CONCLUSIONS PCo can be used to investigate how neural correlation structure occurring at multiple temporal and spatial scales reflect underlying dynamical recurrence without intermediate reduction of dimensionality.

[1]  Piet Van Mieghem,et al.  Disruption of Functional Brain Networks in Alzheimer's Disease: What Can We Learn from Graph Spectral Analysis of Resting-State Magnetoencephalography? , 2012, Brain Connect..

[2]  J. Kurths,et al.  Recurrence-plot-based measures of complexity and their application to heart-rate-variability data. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[3]  B. McNaughton,et al.  Reactivation of Hippocampal Cell Assemblies: Effects of Behavioral State, Experience, and EEG Dynamics , 1999, The Journal of Neuroscience.

[4]  G. Paxinos,et al.  The Rat Brain in Stereotaxic Coordinates , 1983 .

[5]  C L Webber,et al.  Dynamical assessment of physiological systems and states using recurrence plot strategies. , 1994, Journal of applied physiology.

[6]  O. Sporns,et al.  Mapping the Structural Core of Human Cerebral Cortex , 2008, PLoS biology.

[7]  Charles E. Schroeder,et al.  The Signs of Silence , 2012, Neuron.

[8]  Christoph von der Malsburg,et al.  The Correlation Theory of Brain Function , 1994 .

[9]  D. Ruelle,et al.  Recurrence Plots of Dynamical Systems , 1987 .

[10]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[11]  I D Wilkinson,et al.  Neural activity in speech-sensitive auditory cortex during silence. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Boris Gourévitch,et al.  Evaluating information transfer between auditory cortical neurons. , 2007, Journal of neurophysiology.

[13]  M. Kendall.  A NEW MEASURE OF RANK CORRELATION , 1938 .

[14]  M. Greicius,et al.  Resting-state functional connectivity reflects structural connectivity in the default mode network. , 2009, Cerebral cortex.

[15]  Theodore W. Berger,et al.  Identification of functional synaptic plasticity from spiking activities using nonlinear dynamical modeling , 2015, Journal of Neuroscience Methods.

[16]  M. Mishkin,et al.  Spontaneous High-Gamma Band Activity Reflects Functional Organization of Auditory Cortex in the Awake Macaque , 2012, Neuron.

[17]  R. Muller,et al.  Place cell discharge is extremely variable during individual passes of the rat through the firing field. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[18]  Heekyung Lee,et al.  Interictal EEG Discoordination in a Rat Seizure Model , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[19]  J. O’Keefe.  A review of the hippocampal place cells , 1979, Progress in Neurobiology.

[20]  R. Muller,et al.  The effects of changes in the environment on the spatial firing of hippocampal complex-spike cells , 1987, The Journal of neuroscience : the official journal of the Society for Neuroscience.

[21]  C. Gilbert,et al.  Brain States: Top-Down Influences in Sensory Processing , 2007, Neuron.

[22]  Karl J. Friston,et al.  A brain basis for musical hallucinations☆ , 2014, Cortex.

[23]  W. Press,et al.  Numerical Recipes: The Art of Scientific Computing , 1987 .

[24]  A. Fenton,et al.  Key Features of Human Episodic Recollection in the Cross-Episode Retrieval of Rat Hippocampus Representations of Space , 2013, PLoS biology.

[25]  Clara A. Scholl,et al.  Synchronized delta oscillations correlate with the resting-state functional MRI signal , 2007, Proceedings of the National Academy of Sciences.

[26]  Saskia Haegens,et al.  Laminar Profile and Physiology of the α Rhythm in Primary Visual, Auditory, and Somatosensory Regions of Neocortex , 2015, The Journal of Neuroscience.

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

[28]  Robert E. Hampson,et al.  Tracking the changes of hippocampal population nonlinear dynamics in rats learning a memory-dependent task , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[29]  E. Kandel,et al.  Increased Attention to Spatial Context Increases Both Place Field Stability and Spatial Memory , 2004, Neuron.

[30]  André A. Fenton,et al.  Early Cognitive Experience Prevents Adult Deficits in a Neurodevelopmental Schizophrenia Model , 2012, Neuron.

[31]  T. Sejnowski,et al.  Discovering Spike Patterns in Neuronal Responses , 2004, The Journal of Neuroscience.

[32]  Charles J. Lynch,et al.  Brain State Differentiation and Behavioral Inflexibility in Autism. , 2015, Cerebral cortex.

[33]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[34]  K M Gothard,et al.  Dynamics of Mismatch Correction in the Hippocampal Ensemble Code for Space: Interaction between Path Integration and Environmental Cues , 1996, The Journal of Neuroscience.

[35]  György Buzsáki,et al.  Neural Syntax: Cell Assemblies, Synapsembles, and Readers , 2010, Neuron.

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

[37]  F. Attneave,et al.  The Organization of Behavior: A Neuropsychological Theory , 1949 .

[38]  J. Palva,et al.  Functional Roles of Alpha-Band Phase Synchronization in Local and Large-Scale Cortical Networks , 2011, Front. Psychology.

[39]  A. Treisman.  The binding problem , 1996, Current Opinion in Neurobiology.

[40]  J. O’Keefe,et al.  Phase relationship between hippocampal place units and the EEG theta rhythm , 1993, Hippocampus.

[41]  Joanna Moncrieff,et al.  Do Antidepressants Cure or Create Abnormal Brain States? , 2006, PLoS medicine.

[42]  T. Hafting,et al.  Frequency of gamma oscillations routes flow of information in the hippocampus , 2009, Nature.

[43]  M. Wilson,et al.  Temporally Structured Replay of Awake Hippocampal Ensemble Activity during Rapid Eye Movement Sleep , 2001, Neuron.

[44]  Andrey V Olypher,et al.  Measuring the Quality of Neuronal Identification in Ensemble Recordings , 2011, The Journal of Neuroscience.

[45]  W. Knight.  A Computer Method for Calculating Kendall's Tau with Ungrouped Data , 1966 .

[46]  E. Bostock,et al.  Experience‐dependent modifications of hippocampal place cell firing , 1991, Hippocampus.

[47]  Vinod Menon,et al.  Functional connectivity in the resting brain: A network analysis of the default mode hypothesis , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[48]  Olaf Sporns,et al.  Mapping Information Flow in Sensorimotor Networks , 2006, PLoS Comput. Biol..

[49]  C. Malsburg.  Binding in models of perception and brain function , 1995, Current Opinion in Neurobiology.

[50]  J. O'Keefe,et al.  The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. , 1971, Brain research.

[51]  C. von der Malsburg.  Binding in models of perception and brain function. , 1995, Current opinion in neurobiology.

[52]  D. Weinberger,et al.  Postpubertal Emergence of Hyperresponsiveness to Stress and to Amphetamine after Neonatal Excitotoxic Hippocampal Damage: A Potential Animal Model of Schizophrenia , 1993, Neuropsychopharmacology.

[53]  A. Fenton,et al.  Dynamic Grouping of Hippocampal Neural Activity During Cognitive Control of Two Spatial Frames , 2010, PLoS biology.

[54]  D. Javitt,et al.  Global dynamics of selective attention and its lapses in primary auditory cortex , 2016, Nature Neuroscience.

[55]  William W Lytton,et al.  Unmasking the CA1 Ensemble Place Code by Exposures to Small and Large Environments: More Place Cells and Multiple, Irregularly Arranged, and Expanded Place Fields in the Larger Space , 2008, The Journal of Neuroscience.

[56]  B. McNaughton,et al.  Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex , 1995, Journal of Neuroscience Methods.

[57]  J. Kurths,et al.  Estimation of dynamical invariants without embedding by recurrence plots. , 2004, Chaos.

[58]  William W. Lytton,et al.  Synaptic information transfer in computer models of neocortical columns , 2011, Journal of Computational Neuroscience.

[59]  Jean-Lon Chen,et al.  Dynamic changes of ICA‐derived EEG functional connectivity in the resting state , 2013, Human brain mapping.

[60]  Christof Koch,et al.  Theta Phase Segregation of Input-Specific Gamma Patterns in Entorhinal-Hippocampal Networks , 2014, Neuron.

[61]  William Bialek,et al.  Spikes: Exploring the Neural Code , 1996 .

[62]  Simone Kühn,et al.  Resting-state brain activity in schizophrenia and major depression: a quantitative meta-analysis. , 2013, Schizophrenia bulletin.

[63]  Robert E. Hampson,et al.  Nonlinear modeling of neural population dynamics for hippocampal prostheses , 2009, Neural Networks.

[64]  H. Kantz,et al.  Analysing the information flow between financial time series , 2002 .