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. 2015;552:75-102.
doi: 10.1016/bs.mie.2014.11.001. Epub 2014 Dec 26.

Neurophysiological Analysis of the Suprachiasmatic Nucleus: A Challenge at Multiple Levels


Neurophysiological Analysis of the Suprachiasmatic Nucleus: A Challenge at Multiple Levels

Johanna H Meijer et al. Methods Enzymol. .


Understanding the neurophysiology of the circadian timing system requires investigation at multiple levels of organization. Neurons of the suprachiasmatic nucleus (SCN) function as autonomous single-cell oscillators, which warrant studies at the single-cell level. Combining patch-clamp recordings of ion channels with imaging techniques to measure clock gene expression and intracellular calcium has proven extremely valuable to study cellular properties. To achieve and maintain rhythmic activity, SCN neurons require sufficient stimulation (i.e., input) from surrounding cells. At the network level, SCN rhythms are robust and can be measured in vitro, for example, in brain slices that contain the SCN. These recordings revealed that the collective behavior of the SCN neuronal network is strongly determined by the phase dispersal of the neurons. This phase dispersal is plastic, with high synchronization in short photoperiod, desynchronization in long photoperiod, and antiphase oscillations in aging and/or continuous light. In vivo recordings are needed in order to study the SCN as part of a larger network (i.e., interacting with other brain centers) and to study the SCN's response to light. Interestingly, superimposed on the circadian waveform are higher frequency fluctuations that are present in vivo but not in vitro. These fluctuations are attributed to input from other brain centers and computational analyses suggest that these fluctuations are beneficial to the system. Hence, the SCN's properties arise from several organizational levels, and a combination of approaches is needed in order to fully understand the circadian system.

Keywords: Cellular; Electrophysiology; Emergent; Hierarchical; In vitro; In vivo; Ionic channels; Neuronal network; SCN.

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