Investigating static nonlinearities in neurovascular coupling
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Investigating static nonlinearities in neurovascular coupling. / Magri, Cesare; Logothetis, Nikos K; Panzeri, Stefano.
In: MAGN RESON IMAGING, Vol. 29, No. 10, 12.2011, p. 1358-64.Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review
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TY - JOUR
T1 - Investigating static nonlinearities in neurovascular coupling
AU - Magri, Cesare
AU - Logothetis, Nikos K
AU - Panzeri, Stefano
N1 - Copyright © 2011 Elsevier Inc. All rights reserved.
PY - 2011/12
Y1 - 2011/12
N2 - Many statistical models of coupling between time changes of the band-limited power of neural signals and functional magnetic resonance imaging Blood Oxygenation Level Dependent (BOLD) signal time changes rely on linear convolution. The effect of nonlinear behaviors in single-trial relationships between neural signals and BOLD responses is rarely tested and included in models. Here we investigate whether using a static nonlinearity improves the prediction of single-trial BOLD responses from neural signals. A static nonlinearity is a nonlinear transformation of the convolution of neural responses which is implemented by the same nonlinear function for all time points. We evaluated this approach by applying it to simultaneous recordings of functional magnetic resonance imaging BOLD and band-limited neural signals (Local Field Potentials and Multi Unit Activity) from primary visual cortex of anaesthetized macaques. We found that using a simple polynomial static nonlinearity was sufficient to obtain highly significant improvements of the accuracy of single-trial BOLD prediction over the accuracy obtained with linear convolution. This suggests that static nonlinearities may be a useful tool for a compact and accurate statistical description of neurovascular coupling.
AB - Many statistical models of coupling between time changes of the band-limited power of neural signals and functional magnetic resonance imaging Blood Oxygenation Level Dependent (BOLD) signal time changes rely on linear convolution. The effect of nonlinear behaviors in single-trial relationships between neural signals and BOLD responses is rarely tested and included in models. Here we investigate whether using a static nonlinearity improves the prediction of single-trial BOLD responses from neural signals. A static nonlinearity is a nonlinear transformation of the convolution of neural responses which is implemented by the same nonlinear function for all time points. We evaluated this approach by applying it to simultaneous recordings of functional magnetic resonance imaging BOLD and band-limited neural signals (Local Field Potentials and Multi Unit Activity) from primary visual cortex of anaesthetized macaques. We found that using a simple polynomial static nonlinearity was sufficient to obtain highly significant improvements of the accuracy of single-trial BOLD prediction over the accuracy obtained with linear convolution. This suggests that static nonlinearities may be a useful tool for a compact and accurate statistical description of neurovascular coupling.
KW - Animals
KW - Cerebrovascular Circulation/physiology
KW - Computer Simulation
KW - Evoked Potentials, Visual/physiology
KW - Macaca mulatta
KW - Models, Neurological
KW - Nonlinear Dynamics
KW - Oxygen/metabolism
KW - Visual Cortex/physiology
KW - Visual Perception/physiology
U2 - 10.1016/j.mri.2011.04.017
DO - 10.1016/j.mri.2011.04.017
M3 - SCORING: Journal article
C2 - 21641741
VL - 29
SP - 1358
EP - 1364
JO - MAGN RESON IMAGING
JF - MAGN RESON IMAGING
SN - 0730-725X
IS - 10
ER -