Dynamical neuronal networks of the biological clock

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1 Dynamical neuronal networks of the biological clock Lorentz meeting Random structures on the brain December 6, 2017 Jos H T Rohling Laboratory for Neurophysiology LEIDEN UNIVERSITY MEDICAL CENTER

2 Brain networks NRC, May 24,

3 Brain networks 3 Bullmore and Sporns, 2009, Nat. Rev. Neurosci..

4 Network characteristics for the brain Small-world network: - Yap et al, 2011, PLoS ONE, - Wu et al, 2013, Front. Hum. Neurosci Scale-free network, containing hubs: - Hagmann et al, 2008, PLoS Biol Diseases preferentially impact hubs: - De Haan et al, 2012, PLoS Comput Biol 4

5 From the brain to cells Dance A, 2015, Nature 5

6 The SCN: A network of cells 6

8 24 hour rhythms Source: Wikipedia 8

9 De Mairan: circadian clock in plants Jean Jacques d'ortous de Mairan, a French astronomer, in 1729 described circadian rhythmicity in the mimosa. He exposed the plants to constant darkness and recorded the behavior. He observed an alteration in the folding and unfolding of the mimosa s leaves, which pertained in even complete darkness. Mimosa pudica De Mairan's key conclusion was that the daily rhythmic opening and closing of the leaves persisted even in the absence of sunlight. 9

Erratic sleep-wake pattern at first, then averaged just over 25 hours

10 Michel Siffre (1962) French cave explorer Six months living in a cave His biological clock was allowed to free-run He was wired and monitored Erratic sleep-wake pattern at first, then averaged just over 25 hours When he emerged it was the 179 th day but in his days it was only the 151 st. 10

11 Cave experiments Aschoff & Wever,

12 Behavioral experiments in the lab Nocturnal Diurnal Source: SFL ORG. Educational News Network Source: The Free Dictionary 12

13 Activity rhythm 13 Tobler et al., 1996, Nature 380(6575):

14 Suprachiasmatic nucleus (SCN) is master clock Anna A. Kondratova & Roman V. Kondratov, 2012, Nature Reviews Neuroscience 14

Cyclic environment Satellite data provided by The Living Earth Inc.

16 Seasonal changes in day length Geographic Names Database, US Naval Observatory 16

17 Seasonality depends on photoperiod Siberian Hamsters raised under short photoperiods and long photoperiods. Under short photoperiods the animals change their fur to white. Courtesy of David Hazlerigg, School of Biological Sciences, Aberdeen 17

18 long day short day 18 VanderLeest et al., Current Biology 17:

19 Multi-unit data (MUA) VanderLeest et al., Current Biology 17:

20 Examples of single cells 20 VanderLeest et al. Curr Biol (2007)

21 Network regulates seasonal patterns 8 h light 12 h light 16 h light Adjusted from Schaap et al., 2003 PNAS 100:

22 Recording techniques 22

23 Time traces 23

24 Different levels of organization: techniques Living organism Behaviour Integrated Network EEG Metabolic markers In vivo MUA Neuronal Network In vivo MUA In vitro MUA Cellular Network Gene expression Cell properties Calcium imaging Per2 protein concentration Time since start of recording (h) 24

25 Network properties of the SCN 25

26 Network measures Modularity of the network: (functional) clustering in the network Self-organization of the network: scale-free network / hubs Scale-free properties of the network / presence of hubs Efficiency of the network: Information spreading through the network: small-world 26

27 Bioluminescence Period2 protein recording 200 μm Luminescence (A.U.) Recording time (h) 27 Buijink et al, PLoS ONE, 2016

28 Single cell peak time distribution in LP and SP Buijink et al, PLoS ONE,

29 Single cell period variation in LP and SP Buijink et al, PLoS ONE,

30 Network detection 30

31 Threshold methods only find one cluster 31

32 Modules in the SCN Antle and Silver, TRENDS in Neuroscience,

33 Community detection based on random matrix theory Special thanks to Assaf Almog and Diego Garlaschelli 33

34 Communities of cells in PER2::LUC in LP and SP Buijink et al, PLoS ONE,

35 Regional influence on seasonal encoding LP SP LP SP Buijink et al, PLoS ONE,

36 Cluster correlation in SP higher than in LP LP SP 36

37 Cluster stability 37

38 Cluster stability on a longer time Days 38

39 Dynamic clusters Stable VM cluster (41%) Stable DL cluster (41%) Switching cells (18%) 3V OC 39

40 Summary We are able to distinguish different functional modules in the SCN These modules show regional differences in photoperiodic encoding In different photoperiods the modules have different cohesion The modules are not static over time: plasticity in the SCN? 40

41 Self-Organized Criticality 41

42 Detrended Fluctuation Analysis (DFA) Peng et al, 1994, Phys. Rev. E 42

43 Circadian rhythm is scale free Hu et al, 2012, PLoS ONE 43

44 Scale-freeness changes with age 44 Gu et al, 2015, PNAS

45 Exercise is good for you! Gu et al, 2015, PNAS 45

46 DFA: Network within a network is scale free Electrical activity (Hz) Time (h) Hu et al, 2012, PLoS ONE 46

47 Challenges 47

48 Circadian clock (Suprachiasmatic Nucleus: SCN) 1. Hierarchical network 2. Plasticity in the network 3. Timescales Anna A. Kondratova & Roman V. Kondratov, 2012, Nature Reviews Neuroscience 13,

49 1. Different levels of organization Living organism?? Integrated Network?? Neuronal Network?? Cellular Network?? 49 Genetic Network

50 2. Structural differences between day and night Bosler et al., 2015, Front. Neuroendocrinol. 50

51 3. Different timescales Behavioral activity Brain ensemble activity Cell-to-cell communication Gene expression activity Timescale 24 hours 24 hours milliseconds 24 hours 51

52 Conclusion Rhythm of the SCN can be measured at multiple organizational levels: multi-scale network models can be compiled based on SCN data! Multi-scale (hierarchical) network models should include: Functional network reorganisation at different times of the cycle Network plasticity working at different timescales 52

53 Overview Method Scale-free networks Detrended Fluctuation Analysis SCN regional analysis Hierarchical networks Community detection of single cell gene expression traces Simulation studies using Poincaré or Goodwin models Granger causality Data 1. Behavior 2. In vivo 3. In vitro PER2::LUC gene expression 1. In vivo 2. In vitro Behavior and in vivo Timescales Hindmarsh-Rose model In vitro 53

54 Acknowledgements Neurophysiology Group LUMC: Johanna H Meijer Stephan Michel Tom Deboer Erno Vreugdenhil Mayke Tersteeg Renate Buijink Anneke Olde Engberink Claudia Coomans Karim Fifel Maria Panagiotou Collaborations: Changgui Gu (University of Shanghai for Science and Technology) Diego Garlaschelli (Leiden University) Assaf Almog (Leiden University) Frank den Hollander (Leiden University) Evgeni Verbitsky (Leiden University) Henk Nijmeijer (Eindhoven University) Erik Steur (Eindhoven University) Rutger van Santen (Eindhoven University) This work was made possible by: NWO Complexity Grant NWO STW grant 54

55 Thank you 55

Make sure you remember the Key Concepts

A2 Psychology Term 1 Module 4 Physiological Psychology Biological Rhythms, Sleep and Dreaming Area of Study: Biological Rhythms. Lesson 7 Getting you Thinking pg 403 Make sure you remember the Key Concepts