title: Empirical Bayesian significance measure of neuronal spike response.
abstract: Functional connectivity analyses of multiple neurons provide a powerful bottom-up approach to reveal functions of local neuronal circuits by using simultaneous recording of neuronal activity. A statistical methodology, generalized linear modeling (GLM) of the spike response function, is one of the most promising methodologies to reduce false link discoveries arising from pseudo-correlation based on common inputs. Although recent advancement of fluorescent imaging techniques has increased the number of simultaneously recoded neurons up to the hundreds or thousands, the amount of information per pair of neurons has not correspondingly increased, partly because of the instruments' limitations, and partly because the number of neuron pairs increase in a quadratic manner. Consequently, the estimation of GLM suffers from large statistical uncertainty caused by the shortage in effective information.
  
  
  
  In this study, we propose a new combination of GLM and empirical Bayesian testing for the estimation of spike response functions that enables both conservative false discovery control and powerful functional connectivity detection. We compared our proposed method's performance with those of sparse estimation of GLM and classical Granger causality testing. Our method achieved high detection performance of functional connectivity with conservative estimation of false discovery rate and q values in case of information shortage due to short observation time. We also showed that empirical Bayesian testing on arbitrary statistics in place of likelihood-ratio statistics reduce the computational cost without decreasing the detection performance. When our proposed method was applied to a functional multi-neuron calcium imaging dataset from the rat hippocampal region, we found significant functional connections that are possibly mediated by AMPA and NMDA receptors.
  
  
  
  The proposed empirical Bayesian testing framework with GLM is promising especially when the amount of information per a neuron pair is small because of growing size of observed network.
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Kyoto, Japan. oba@i.kyoto-u.ac.jp.
authors.forename: Shigeyuki
authors.initials: S
authors.lastname: Oba
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Kyoto, Japan.
authors.forename: Ken
authors.initials: K
authors.lastname: Nakae
authors.affiliation: Graduate School of Pharmaceutical Sciences, University of Tokyo, Tokyo, Japan.
authors.forename: Yuji
authors.initials: Y
authors.lastname: Ikegaya
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Kyoto, Japan.
authors.forename: Shunsuke
authors.initials: S
authors.lastname: Aki
authors.affiliation: Graduate School of Information Science, Nara Institute of Science and Technology, Ikoma, Japan.
authors.forename: Junichiro
authors.initials: J
authors.lastname: Yoshimoto
authors.affiliation: Graduate School of Informatics, Kyoto University, Yoshida-Honmachi, Kyoto, Japan.
authors.forename: Shin
authors.initials: S
authors.lastname: Ishii
mesh_terms: D000200:Action Potentials; D000465:Algorithms; D000818:Animals; D019540:Area Under Curve; D001499:Bayes Theorem; D056654:CA3 Region, Hippocampal / Q000502:physiology; D020013:Calcium Signaling / Q000502:physiology; D003198:Computer Simulation; D016014:Linear Models; D008959:Models, Neurological; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology*; D012372:ROC Curve; D051381:Rats; D046509:Tissue Culture Techniques; D056969:Voltage-Sensitive Dye Imaging
keywords: Empirical Bayesian testing; Functional connectivity of neurons; Generalized linear model; Spike statistics