Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling
Standard
Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling. / Lüdtke, Niklas; Logothetis, Nikos K; Panzeri, Stefano.
in: MAGN RESON IMAGING, Jahrgang 28, Nr. 8, 10.2010, S. 1113-9.Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › SCORING: Zeitschriftenaufsatz › Forschung › Begutachtung
Harvard
APA
Vancouver
Bibtex
}
RIS
TY - JOUR
T1 - Testing methodologies for the nonlinear analysis of causal relationships in neurovascular coupling
AU - Lüdtke, Niklas
AU - Logothetis, Nikos K
AU - Panzeri, Stefano
N1 - Copyright © 2010 Elsevier Inc. All rights reserved.
PY - 2010/10
Y1 - 2010/10
N2 - We investigated the use and implementation of a nonlinear methodology for establishing which changes in neurophysiological signals cause changes in the blood oxygenation level-dependent (BOLD) contrast measured in functional magnetic resonance imaging. Unlike previous analytical approaches, which used linear correlation to establish covariations between neural activity and BOLD, we propose a directed information-theoretic measure, the transfer entropy, which can elucidate even highly nonlinear causal relationships between neural activity and BOLD signal. In this study we investigated the practicality of such an analysis given the limited data samples that can be collected experimentally due to the low temporal resolution of BOLD signals. We implemented several algorithms for the estimation of transfer entropy and we tested their effectiveness using simulated local field potentials (LFPs) and BOLD data constructed to match the main statistical properties of real LFP and BOLD signals measured simultaneously in monkey primary visual cortex. We found that using the advanced methods of entropy estimation implemented and described here, a transfer entropy analysis of neurovascular coupling based on experimentally attainable data sets is feasible.
AB - We investigated the use and implementation of a nonlinear methodology for establishing which changes in neurophysiological signals cause changes in the blood oxygenation level-dependent (BOLD) contrast measured in functional magnetic resonance imaging. Unlike previous analytical approaches, which used linear correlation to establish covariations between neural activity and BOLD, we propose a directed information-theoretic measure, the transfer entropy, which can elucidate even highly nonlinear causal relationships between neural activity and BOLD signal. In this study we investigated the practicality of such an analysis given the limited data samples that can be collected experimentally due to the low temporal resolution of BOLD signals. We implemented several algorithms for the estimation of transfer entropy and we tested their effectiveness using simulated local field potentials (LFPs) and BOLD data constructed to match the main statistical properties of real LFP and BOLD signals measured simultaneously in monkey primary visual cortex. We found that using the advanced methods of entropy estimation implemented and described here, a transfer entropy analysis of neurovascular coupling based on experimentally attainable data sets is feasible.
KW - Algorithms
KW - Animals
KW - Computer Simulation
KW - Electrophysiology/methods
KW - False Positive Reactions
KW - Humans
KW - Image Processing, Computer-Assisted/methods
KW - Models, Statistical
KW - Neurons/metabolism
KW - Oxygen/blood
KW - Probability
KW - Reproducibility of Results
KW - Time Factors
KW - Visual Cortex/pathology
U2 - 10.1016/j.mri.2010.03.028
DO - 10.1016/j.mri.2010.03.028
M3 - SCORING: Journal article
C2 - 20409664
VL - 28
SP - 1113
EP - 1119
JO - MAGN RESON IMAGING
JF - MAGN RESON IMAGING
SN - 0730-725X
IS - 8
ER -
