Our group investigates the network of cells that comprise the mammalian clock. The suprachiasmatic nuclei (SCN) contain about 103 neurons, of which a large number is endogenously rhythmic. We have shown that the network configuration of these neurons is dominantly involved in generating a coherent rhythm in ensemble activity, coding for different seasons (Rohling et al, 2006, J. Biol. Rhythms; Rohling et al, 2006, J. Physiol. Paris; VanderLeest et al, Curr. Biol., 2007) and coping with sudden changes of the light schedule associated with jet lag and shiftwork (VanderLeest et al, 2009, PLoS ONE; Rohling et al, 2011, PLoS ONE). For example, the coupling between the clock cells establishes a narrow phase relationship in short winter days and a broad phase relationship in long summer days (figure, taken from Rohling et al, 2006, J. Physiol. Paris).
In aging, we have shown that the coupling between the cells of the SCN becomes weakened (Farajnia et al, 2012, J. Neurosci.), which results in a desynchronized phase relationship among the cells, leading to a low-amplitude rhythm. A low-amplitude rhythm is also associated with neurodegenerative diseases, depression and sleep disorders (Meijer et al, 2012, Progress in Brain Research).
A destabilized SCN network might also be prone to migraine. Our group has shown that R192Q knock-in mice, a mouse-model for migraine, show large phase resetting properties. This indicates that the network within the SCN is less resilient for changes in the environment. It has been shown that the SCN not only drives rhythms throughout the CNS, but also receives inputs from the CNS that stabilize the SCN network. Either the network of the SCN itself, or the inputs from the CNS to the SCN may be retarded in migraine, leading to a low threshold for which migraine may be triggered.
Our research focuses on the complexity of the neuronal configurations: how do the neurons establish a stable configuration; how is it possible that stable neuronal configurations remain flexible and are able to change to other stable configurations, e.g., long summer days to short winter days, or jet lag; how is the network affected by aging and how can deficiencies be restored?
We use modeling techniques to create computational and mathematical models that can assist in finding mechanisms of how the SCN deals with these complexity issues. We incorporate experimentally obtained data as much as possible in our models and constantly update our models or create new models to account for newly found phenomena in our experimental data. For our models we use a diversity of techniques derived from the fields of nonlinear dynamics, oscillator theory, complexity and computer science.
Our current research is embedded in the NWO program ‘Complexity’ (www.nwo.nl/nwohome.nsf/pages/NWOA_7BUJ6J). In this grant one PhD student (Ashna Ramkisoensing) started Dec 2011, and a post doc (Changgui Gu) started April 2012.
- Rohling, J.H.T., Ramkisoensing, A., VanderLeest, H.T., Michel, S., Deboer, T., Meijer, J.H., Is the SCN clock a limit cycle oscillator?, 2012, in prep.
- Changgui Gu, Ashna Ramkisoensing, J.H.Meijer and Jos H.T. Rohling, Coupling strength and the portion of external stimuli receiving neurons play a key role in the entrainment range of SCN, 2012, in prep.
- Sahar Farajnia, Stephan Michel, Tom Deboer, Henk VanderLeest, Thijs Houben, Jos H.T. Rohling, Ashna Ramkisoensing, Roman Yasenkov, and Johanna H. Meijer, Evidence for neuronal desynchrony in the aged SCN clock, J. Neuroscience 32, 2012, 5891-5899.
- Johanna H. Meijer, Christopher S. Colwell, Jos H.T. Rohling, Thijs Houben and Stephan Michel, Dynamic neuronal network organization of the circadian clock, and possible deterioration in disease, Progress in Brain research, 2012, in press.
- Rohling J.H.T., vanderLeest, H.T., Michel, S., Vansteensel, M.J., Meijer, J.H., Phase resetting of the mammalian circadian clock relies on a rapid shift of a small population of pacemaker neurons. PLoS ONE 6, 2011, e25437.
- Meijer, J.H., Michel, S., vanderLeest, H.T., Rohling, J.H.T., Daily and seasonal adaptation of the circadian clock requires plasticity of the SCN neuronal network. EJN 32, 2010, 2143-2151.
- VanderLeest, H.T., Rohling, J.H.T., Michel, S., and Meijer, J.H., Phase shifting capacity of the circadian pacemaker determined by the SCN neuronal network organization. PLoS ONE. 4, 2009, e4976.
- Jos Rohling, Lex Wolters, and Johanna H. Meijer, Simulation of Day-Length Encoding in the SCN, in Proceedings of the 13th Annual Conference of the Advanced School for Computing and Imaging (ASCI), June 2007, Heijen, The Netherlands, pp 414-421.
- Rohling, J., Meijer, J.H., VanderLeest, H.T., Admiraal, J., Phase differences between SCN neurons and their role in photoperiodic encoding; a simulation of ensemble patterns using recorded single unit electrical activity patterns. Journal of Physiology - Paris 100, 2006, 261–270.
- Rohling, J., Wolters, L., and Meijer, J.H., Simulation of day-length encoding in the SCN: from single-cell to tissue-level organization. J. Biol. Rhythms 21, 2006, 301-313.