Nuclear calcium signalling in the regulation of brain function

2013 ◽  
Vol 14 (9) ◽  
pp. 593-608 ◽  
Author(s):  
Hilmar Bading
2000 ◽  
Vol 57 (3) ◽  
pp. 371-378 ◽  
Author(s):  
M. D. Bootman* ◽  
D. Thomas ◽  
S. C. Tovey ◽  
M. J. Berridge ◽  
P. Lipp

2009 ◽  
Vol 122 (14) ◽  
pp. 2337-2350 ◽  
Author(s):  
M. D. Bootman ◽  
C. Fearnley ◽  
I. Smyrnias ◽  
F. MacDonald ◽  
H. L. Roderick

2011 ◽  
Vol 7 ◽  
pp. S581-S581
Author(s):  
Ana Jose Moreno-Ortega ◽  
Antonio Garcia ◽  
Ana Ruiz-Nuño ◽  
Maria Cano-Abad

2004 ◽  
Vol 117 (15) ◽  
pp. 3087-3094 ◽  
Author(s):  
O. Gerasimenko

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.


Author(s):  
C. S. Potter ◽  
C. D. Gregory ◽  
H. D. Morris ◽  
Z.-P. Liang ◽  
P. C. Lauterbur

Over the past few years, several laboratories have demonstrated that changes in local neuronal activity associated with human brain function can be detected by magnetic resonance imaging and spectroscopy. Using these methods, the effects of sensory and motor stimulation have been observed and cognitive studies have begun. These new methods promise to make possible even more rapid and extensive studies of brain organization and responses than those now in use, such as positron emission tomography.Human brain studies are enormously complex. Signal changes on the order of a few percent must be detected against the background of the complex 3D anatomy of the human brain. Today, most functional MR experiments are performed using several 2D slice images acquired at each time step or stimulation condition of the experimental protocol. It is generally believed that true 3D experiments must be performed for many cognitive experiments. To provide adequate resolution, this requires that data must be acquired faster and/or more efficiently to support 3D functional analysis.


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