Journal article
Joint analysis of spikes and local field potentials using copula
NeuroImage (Orlando, Fla.), v 133, pp 457-467
Jun 2016
PMID: 27012500
Abstract
Recent technological advances, which allow for simultaneous recording of spikes and local field potentials (LFPs) at multiple sites in a given cortical area or across different areas, have greatly increased our understanding of signal processing in brain circuits. Joint analysis of simultaneously collected spike and LFP signals is an important step to explicate how the brain orchestrates information processing. In this contribution, we present a novel statistical framework based on Gaussian copula to jointly model spikes and LFP. In our approach, we use copula to link separate, marginal regression models to construct a joint regression model, in which the binary-valued spike train data are modeled using generalized linear model (GLM) and the continuous-valued LFP data are modeled using linear regression. Model parameters can be efficiently estimated via maximum-likelihood. In particular, we show that our model offers a means to statistically detect directional influence between spikes and LFP, akin to Granger causality measure, and that we are able to assess its statistical significance by conducting a Wald test. Through extensive simulations, we also show that our method is able to reliably recover the true model used to generate the data. To demonstrate the effectiveness of our approach in real setting, we further apply the method to a mixed neural dataset, consisting of spikes and LFP simultaneously recorded from the visual cortex of a monkey performing a contour detection task.
•Present a novel statistical method based on copula to jointly model spikes and LFPs•Bridge the levels between single neuron and local network activity•Define a new Granger causality measure for analysis of mixed data•Contour-induced change in Granger causality only observed from spikes to LFP in V4
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Details
- Title
- Joint analysis of spikes and local field potentials using copula
- Creators
- Meng Hu - School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104Mingyao Li - Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania 19104Wu Li - State Key Laboratory of Cognitive Neuroscience and Learning and IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, ChinaHualou Liang - School of Biomedical Engineering, Drexel University, Philadelphia, PA 19104
- Publication Details
- NeuroImage (Orlando, Fla.), v 133, pp 457-467
- Publisher
- Elsevier
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- School of Biomedical Engineering, Science, and Health Systems
- Web of Science ID
- WOS:000377048600041
- Scopus ID
- 2-s2.0-84962407689
- Other Identifier
- 991014878304104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Neuroimaging
- Neurosciences
- Radiology, Nuclear Medicine & Medical Imaging