1. Slow and large Dynamics in migraine and opportunities to intervene Markus A. Dahlem MPI of the Physics of Complex Systems, Dresden B C activator, u immobile inhibitor, v long-range inhibitor, w A activator: front propagation activator-inhibitor: pulse propagation (includes re-entry patterns in 2D) activator: front propagation act.-2-inhibitors: localized structures (spots) MPI Leipzig for Human Cognitive and Brain Sciences, August 26, 2014

2. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 3. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 4. HH-type conductance-based C V t = INa IK Ileak +Iapp (1) INa = gNam3 h(V ENa) IK = gK n4 (V EK ) Ileak = gleak(V Vrest) n t = n(1 n) n, h t (2) (4) HH: Hodgkin-Huxley 5. From HH-type conductance-based to conductance- & ion-based models (2nd generation model) ion reservoirs isolated boundary system surroundings energy source extracellular intracellular C V t = INa IK IleakIpump+Iapp (1) INa = gNam3 h(V ENa) IK = gK n4 (V EK ) Ileak = gleak(V Vrest) n t = n(1 n) n, h t (2) (4) [ion]e t = A FVolo Iion [ion]i t = A FVoli Iion (5) HH: Hodgkin-Huxley 6. From HH-type conductance-based to conductance- & ion-based models (2nd generation model) ion reservoirs isolated boundary system surroundings energy source extracellular intracellular Modeling new (mainly slow) phenomena tissue energetics ATP comsumption Gibbs free engergy modeling oxygen glucose deprivation tissue properties (neuropil vs granular) neurovascular coupling osmotic pressure (water homeostasis) (temperature) clinical comorbitities seizure activity (epilepsy) cortical spreading depression (migraine) periinfarct depolarization (stroke) HH: Hodgkin-Huxley 7. Metabolically driven slow dynamics from sec to min Bursting, tonic ring, spreading depression (SD) in HH model with time-dependent ion concentrations. Hubel et al., PLOS Comp. Biology. 10, e1003551 (2014) Hubel & Dahlem, arXiv:1404.3031 (under review in PLOS Comp. Biology ) 8. SD: Wave of massive ionic imbalance -1 -2 -3 -4 -7 -8 1 min 20 mV log [cat] , M (mM) Ve Na + Na + K + Ve K + Ca++ Ca++ H + 0 10 20 30 s 150 60 50 3 1.5 0.08 unit act. Lauritzen, M., TINS 10,8 (1987) Xenon 133 method, radionuclide used to image brains blood ow. Olesen, J. , Larsen, B. and Lauritzen, M., Ann. Neurol. 9, 344 (1981) 9. What is cortical spreading depression on macro-scale? Homeostatic insult self-organzied as 2D patterns in gray matter. -1 -2 -3 -4 -7 -8 1 min 20 mV log [cat] , M (mM) Ve Na + Na + K + Ve K + Ca++ Ca++ H + 0 10 20 30 s 150 60 50 3 1.5 0.08 unit act. any textbook from ~2004 m anywebsites M. Lauritzen, Trends in Neurosciences 10,8 (1987). A controversial debate but ...essential view of a reaction-diusion process still holds ... Herreras (2005) J. Neurophysiol. 94:3656 Somjen & Strong (2005) J. Neurophysiol. 94:3656 10. Principal mechanisms of slow and largescale reactiondiusion patterns B C activator, u immobile inhibitor, v long-range inhibitor, w A activator: front propagation activator-inhibitor: pulse propagation (includes re-entry patterns in 2D) activator: front propagation act.-2-inhibitors: localized structures (spots) 11. Historical note: Macroscopic approach is non-reductionist, hence various analogies to animate and inanimate systems 12. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 13. Duvernoy et al., 1981 Architecture of the cerebral vasculature Blinder, Tsai, et al., Nature Neuro, 2013 14. Model of SD in gray matter Cortex SD 15. Model of SD in gray matter or in the brain? Cortex circulation with innervation bone SD Circulation outside the parenchyma M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013) M. A. Dahlem, Migraines and Cortical Spreading Depression, In: Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer-Verlag Berlin Heidelberg, 2014. 16. Model of SD in gray matter or in the brain? Cortex circulation with innervation bone SD release of noxious agents Circulation outside the parenchyma M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013) M. A. Dahlem, Migraines and Cortical Spreading Depression, In: Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer-Verlag Berlin Heidelberg, 2014. 17. Model of SD in gray matter or in the brain? SPG SSN TCC TG Cortex circulation with innervation bone SD release of noxious agents peripheralpathway Circulation outside the parenchyma parasympathetic control TG: trigeminal ganglion TCC: trigeminocervical complex SSN: superior salivatory nucleus SPG: sphenopalatine ganglion M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013) M. A. Dahlem, Migraines and Cortical Spreading Depression, In: Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer-Verlag Berlin Heidelberg, 2014. 18. Model of SD in gray matter or in the brain? o on HY,TH SPG SSN TCC PAG LC RVM TG Cortex circulation with innervation bone SD cortico- thalamic action release of noxious agents peripheralpathway Circulation outside the parenchyma parasympathetic control TG: trigeminal ganglion TCC: trigeminocervical complex SSN: superior salivatory nucleus SPG: sphenopalatine ganglion decending modulatory control and further subcortical structures RVM: rostral ventromedial medulla LC: locus coeruleus PAG: periaqueductal gray TH: thalamus HY: hypothalamus M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013) M. A. Dahlem, Migraines and Cortical Spreading Depression, In: Jaeger D., Jung R. (Ed.) Encyclopedia of Computational Neuroscience. Springer-Verlag Berlin Heidelberg, 2014. 19. Neurovascular coupling: Regulation of cerebrovascular tone by perivascular nerves E. Hamel, Perivascular nerves and the regulation of cerebrovascular tone, J. Appl. Physiol, 1001059-1064, 2006 20. Neurovascular coupling: Regulation of cerebrovascular tone by activity B Pia Z X Y Spherical treadmill PMT A Imaging cortical vascular dynamics during voluntary locomotion Figures courtesy to Yurong Gao and Patrick Drew (Penn State) 21. Neurovascular coupling: Regulation of cerebrovascular tone by activity Hypothesis: Blood is oversupplied because targeted, intracranial vessels cannot dilate adequately Vasodilation Brain tissue resistance CSF Brain Pial funnel Arteriole Baseline Active Mechanical restriction of dilation by brain tissue plays an important role in shaping the vasodilation pattern Surface arteries dilate 2-4x more than intracortical arteries order-0 order-1 -1 0 1 2 3 4 5 -1 0 1 2 3 4 5 5 0 5 10 15 20 25 Other regions Time, sec Time, sec Diameter Change(%) surface order 0 surface order 1 depth 50m depth 100m depth 150m depth 200m 5 0 5 10 15 20 25 F/H region Figures courtesy to Yurong Gao and Patrick Drew (Penn State) 22. Neurovascular coupling: Regulation of cerebrovascular tone by blood pressure A B D E The ratio of vertices to edges is a measure of redund network. The combined data from rats and mice is con a ratio of 3 to 2 (r2 = 0.99, P < 0.001) (Fig. 2B). This edg ratio coincides with that for a simple lattice in which have a coordination number of 3, such as a hexagonal gr comparison, the scaling is 1 for binary trees and buse The observed edge-to-vertex ratio of 3 to 2 implies that edges within backbone with N vertices, N/2 of the edge redundant connections, or anastomoses. These anasto stitute only 8.4 0.2% (mean SE) and 6.2 0.2% o number of edges in the network for rats and mice, resp A second issue is the size of the loops in the pial bac each network, we calculated the set that contained the s dependent loops, analogous to Kirchhoff loops in cir (Fig. 2C). The number of such loops is 3.4 times larger f mice. This nding is not inconsistent with the larger terr MCA network in rats versus mice, by a factor of 2.8 (Ta distribution of edges in loops that comprise the bac a mode at four edges and is the same for rats and mic (P > 0.1, KS-test). The predominance of four rather tha per loop, together with a coordination number of 3 for e implies that no obvious lattice captures all features networks. Lastly, our measures indicate that the proba sity of loops with a given area is observed to be ex distributed with mean values of 0.94 0.14 mm2 (me condence interval) and 0.72 0.06 mm2 for rat an A D E B C Fig. 1. Complete mapping of the middle cerebral artery. (A) A attened cortical hemisphere from a uorescent vascular ll, overlaid with a tracing of all vessels within the middle cerebral artery network. (B) In each tracing, the edges (green lines) connect vertices positioned at branches, with coordination number of 3 (red circles), and vertices positioned at the location of penetrating A B Fig. 3. Structural robustness to cumulative edge removal for measure networks and ideal lattices. (A) Average backbone robustness to ed moval for networks for rat, mice, and honeycomb lattices of differen Robustness is computed by progressively removing graph edges at ra while computing the fractions of vertices that become isolated fro A C D B The surface artery network is an interconnected mesh Blinder, Shih, et al., PNAS, 2010 23. Neurovascular coupling: Regulation of cerebrovascular tone by blood pressure Mass conservation, inlet conditions (i.e., pressure at middle cerebral artery (MCA)), and outlet conditions (i.e., capillary pressure) j=i cji = pi pj Rji + cii pi pc Rci = 0 cii Rci + j=i cji Rji pi j=i cji Rji pj = cii pc Rci Ap = b 24. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 25. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 26. Migraine = Headache 27. What is a migraine aura? based on: M. A. Dahlem & S. C. Muller Biol. Cybern. 88,419 (2003) M. A. Dahlem et. al. Eur. J. Neurosci. 12,767 (2000). 28. Mapped visual symptoms on cortex via fMRI retinotopy 1 cm 10 1 3 5 7 15 17 19 21 23 25 27 min Visual hemifield Primary visual cortex Dahlem & Hadjikhani (2009) PLoS ONE 4: e5007. 29. RD waves w/ global inhibitory coupling on curved media Macroscopic SD model on gyried cortex. u t = u 1 3 u3 v + Du 1 g i gij g u i v t = (u + + K H(u)di ) SD in the folded cortex posses critical properties. First approximation: localized SD follows shortest path. 30. Hot spots left visual eld 23 min 21 19 17 17 15 13 11 97 5 10 1 cm Visual hemifield Primary visual cortex Cooperation with Andrew Charles, UCLA. 31. Hot spots right visual eld 1 cm 10 1 3 5 7 15 17 19 21 23 25 27 min Visual hemifield Primary visual cortex 32. Labyrinth path in reverse direction 1 cm 10 1 3 5 7 15 17 19 21 23 25 27 min Visual hemifield Primary visual cortex 33. Observed wave forms of SD in gyrencephalic brain E. Santos, et al., Radial, spiral and reverberating waves of spreading depolarisation occur in the gyrencephalic brain, NeuroImage 99:244-255 (2014). 34. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 35. History of electrical & magnetic stimluation Non-drug treatment for headaches (AD 47) Scribonius Largus, court physician to the Roman emperor Claudius 47 AD used the black torpedo sh (electric rays) to treat migraine. 36. History of electrical & magnetic stimluation Non-drug treatment for headaches (1788) P. J. Koehler and C. J. Boes, A history of non-drug treatment in headache, particularly migraine. Brain 133:2489-500. 2010 37. History of electrical & magnetic stimluation Non-drug treatment for headaches (1887) P. J. Koehler and C. J. Boes, A history of non-drug treatment in headache, particularly migraine. Brain 133:2489-500. 2010 38. History of electrical & magnetic stimluation Non-drug treatment for headaches (1896) First steps in miniaturization 39. History of electrical & magnetic stimluation Non-drug treatment for headaches (1961) (1985) Zeitschrift EEG-EMG, Georg Thieme Verlag Stuttgart 40. History of electrical & magnetic stimluation Non-drug treatment for headaches (2013) Courtesy eNeura Inc. USA Disclosure: Conict of interest Consulting services in 2013 for Neuralieve Inc. (trading as eNeura Therapeutics) 41. History of electrical & magnetic stimluation Non-drug treatment for headaches (2014) Courtesy Cerbomed GmbH, Germany 42. Modern neuromodulation (invasive) 43. Modern neuromodulation (invasive) Courtesy Autonomic Technologies, Inc. 44. Modern neuromodulation (invasive) Courtesy Autonomic Technologies, Inc. 45. Modern neuromodulation (invasive) Courtesy St. Jude Medical Inc. 46. Modern neuromodulation (invasive) Courtesy St. Jude Medical Inc. 47. Past and modern neuromodulation in migraine 48. Neuromodulation in migraine hypothalamic deep brain stimulation (hDBS), sphenopalatine ganglion stimulation (SPGS) occipital nerve stimulation (ONS), cervical spinal cord stimulation (cSCS), hypothalamic deep brain stimulation (hDBS), vagus nerve stimulation (VNS), transcutaneous electrical nerve stimulation (TENS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternate current stimulation (tACS). 49. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 50. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura magnetic electrical HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). 51. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). 52. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). 53. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura HY,TH TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). 54. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura HY,TH TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). 55. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura HY,TH SPG SSN TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). 56. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura HY,TH SPG SSN TCC TG cortex cranial circulation bone cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). M. A. Dahlem, J. Kurths, M. D. Ferrari, K. Aihara, M. Scheer, and A. May, Understanding Migraine using Dynamical Network Biomarkers (accepted, Cephalalgia, 2 pages, 1 gure, arxiv pre-print before peer review) (2014). 57. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura magnetic electrical HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). M. A. Dahlem, J. Kurths, M. D. Ferrari, K. Aihara, M. Scheer, and A. May, Understanding Migraine using Dynamical Network Biomarkers (accepted, Cephalalgia, 2 pages, 1 gure, arxiv pre-print before peer review) (2014). 58. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura magnetic electrical HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). M. A. Dahlem, J. Kurths, M. D. Ferrari, K. Aihara, M. Scheer, and A. May, Understanding Migraine using Dynamical Network Biomarkers (accepted, Cephalalgia, 2 pages, 1 gure, arxiv pre-print before peer review) (2014). M. A. Dahlem, S. Rode, A. May, N. Fujiwara, Y. Hirata, K. Aihara, J. Kurths, Towards dynamical network biomarkers in neuromodulation of episodic migraine, Translational Neuroscience, 4,282-294 (2013). 59. Migraine Generator Network & Dynamical Network Biomarker attack-free headache prodrome p ostdrome p ostdrome aura magnetic electrical HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) M. A. Dahlem, Migraine generator network and spreading depression dynamics as neuromodulation targets in episodic migraine. Chaos, 23, 046101 (2013). Karatas H et al., Spreading depression triggers headache by activating neuronal Panx1 channels. Science, 339:1092-5 (2013). M. A. Dahlem, J. Kurths, M. D. Ferrari, K. Aihara, M. Scheer, and A. May, Understanding Migraine using Dynamical Network Biomarkers (accepted, Cephalalgia, 2 pages, 1 gure, arxiv pre-print before peer review) (2014). M. A. Dahlem, S. Rode, A. May, N. Fujiwara, Y. Hirata, K. Aihara, J. Kurths, Towards dynamical network biomarkers in neuromodulation of episodic migraine, Translational Neuroscience, 4,282-294 (2013). 60. Tipping point behavior in migraine magnetic electrical HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation attack-free interval prodromal phase pain phase time M. A. Dahlem et al. Understanding migraine using dynamical network biomarkers Cephalalgia in press (2014). 61. Pain comes from the meninges not the cortex 0 1 2 3 4 5 0 5 10 15 20 25 30 35 time depletedsurfacearea slow dynamics a b c d e f MIA x y 62. Pain comes from the meninges not the cortex 0 1 2 3 4 5 0 5 10 15 20 25 30 35 time depletedsurfacearea slow dynamics a b c d e f AP: acute phaseIP: ignition phase MIA x y 63. Pain comes from the meninges not the cortex 0 1 2 3 4 5 0 5 10 15 20 25 30 35 time depletedsurfacearea slow dynamics a b c d e f AP: acute phaseIP: ignition phase MIA x y arachnoid blood arachnoid blood bone sensory innervation dura small MIA large MIA SD SD dural sinuses cortex pia cortex pia For example, can a sulcal pial siphoning of K+ lead to focally raised concentration in the meninges? 64. Pain comes from the meninges not the cortex 0 1 2 3 4 5 0 5 10 15 20 25 30 35 time depletedsurfacearea slow dynamics a b c d e f AP: acute phaseIP: ignition phase MIA x y IP Stim. AP Stim. arachnoid blood arachnoid blood bone sensory innervation dura small MIA large MIA SD SD dural sinuses cortex pia cortex pia For example, can a sulcal pial siphoning of K+ lead to focally raised concentration in the meninges? 65. Outline Neuron: Electrophysiology with time-dependent ion concentrations Vasculature: Regulation of cerebrovascular tone by nerves & blood Application: Personalized and predictive medicine Individal activity patterns in migraine Non-drug treatment in migraine Controlling the migraine generator network Summary & Conclusion 66. Disability and burden in migraine [...] migraine alone is responsible of almost 3% of disability attributable to a specic disease worldwide [...]. This places migraine as the eighth most burdensome diseases, the seventh among non-communicable diseases and the rst among the neurological disorders ranked in the GBD report. GBD = Global Burden of Disease (WHO report) Leonardi, M. and Raggi, A., Burden of migraine: international perspectives, Neurol. Sci., 34, 2013. 67. Summary & Conclusions HH model with time-dependet ion concentrations 4 variables & time scales (AP/SD), complete bifurcation analysis, correct slow-fast analysis. 68. Summary & Conclusions HH model with time-dependet ion concentrations 4 variables & time scales (AP/SD), complete bifurcation analysis, correct slow-fast analysis. Thermodynamic foundation, e.g. appropriately dening a free energy function, cyclic processes (analogy: steam engine) and more. 60 40 20 0 20 40 0 10 20 30 40 50 60 A B [K ]gain + / mM [K]e +/mM clearance reuptakereuptake trans- membrane activator(u) inhibitor (v) innersolutioninnersolution outer solution 69. Summary & Conclusions HH model with time-dependet ion concentrations 4 variables & time scales (AP/SD), complete bifurcation analysis, correct slow-fast analysis. Thermodynamic foundation, e.g. appropriately dening a free energy function, cyclic processes (analogy: steam engine) and more. This leads to macroscopic phenomenological descriptionpattern formation principles than guide us to look for missing mechanism, such as long-range/global/fast diusion inhibition. B C activator, u immobile inhibitor, v long-range inhibitor, w A activator: front propagation activator-inhibitor: pulse propagation (includes re-entry patterns in 2D) activator: front propagation act.-2-inhibitors: localized structures (spots) 70. Summary & Conclusions HH model with time-dependet ion concentrations 4 variables & time scales (AP/SD), complete bifurcation analysis, correct slow-fast analysis. Thermodynamic foundation, e.g. appropriately dening a free energy function, cyclic processes (analogy: steam engine) and more. This leads to macroscopic phenomenological descriptionpattern formation principles than guide us to look for missing mechanism, such as long-range/global/fast diusion inhibition. Inhibition (likely) implementet by regulation of cerebrovascular tone by perivascular nervesextrinsic (TG, SPG) or intrinsic (conductive response of penetrating arterioles). 71. Summary & Conclusions HH model with time-dependet ion concentrations 4 variables & time scales (AP/SD), complete bifurcation analysis, correct slow-fast analysis. Thermodynamic foundation, e.g. appropriately dening a free energy function, cyclic processes (analogy: steam engine) and more. This leads to macroscopic phenomenological descriptionpattern formation principles than guide us to look for missing mechanism, such as long-range/global/fast diusion inhibition. Inhibition (likely) implementet by regulation of cerebrovascular tone by perivascular nervesextrinsic (TG, SPG) or intrinsic (conductive response of penetrating arterioles). Whole brain model of migraine dynamics opens up model-based control for predictive (hot spots & labyrinths) and personalized medicine. attack-free headache prodrome p ostdrome p ostdrome aura magnetic ele ctr ica l HY,TH SPG SSN TCC PAG LC RVM TG cortex cranial circulation bone ON OFF cranial innervation1 day 5-60min 4-72h day 1 daysto m onths(avg2weeks ) dynamical network biomarkers m igraine cycle (i) (ii) (iii) 72. Cooperation & Funding Niklas Hubel, Frederike Kneer, Thomas Isele, Julia Schumacher, Bernd Schmidt (TU Berlin, HU Berlin, Magdeburg) Nouchine Hadjikhani (Martinos Center for Biomedical Imaging, MGH) Steven Schi Patrick Drew, Yurong Gao (Penn State Center for Neural Engineering) Huaxiong Huang (York Uni, Toronto) Timothy David (Uni of Canterbury, NZ) Kazuyuki Aihara (Uni of Tokyo, Japan) Arne May (Uni Hamburg, Germany) Jens Dreier (Department of Neurology, Charite; Berlin, Germany) Edgar Santos, Oliver W. Sakowitz (Department of Neurosurgery, Heidelberg, Germany) berlin Migraine Aura Foundation 73. Additional slides 74. SD triggers trigeminal meningeal aerents, ie, headache see e.g.: Bolay et al. Nature Medicine 8, 2002 Review: Eikermann-Haerter & Moskowitz, Curr Opin Neurol. 21, 2008 Figure: Dodick & Gargus SciAm, August 2008 75. Including cell swelling Electrophysiology Thermodynamics break down of ion grandients cell swelling Iin Iin Iout Iout Normal neuron in healthy brain ECV 20% ECV 5% AMPA/ kainate 70 mV 10 mV SK Na + Na + K+ Na+ DR Ca 2+ Ca 2+ Ca 2+ Ca 2+ Ca 2+ Na +Na + Ca2+ K+ Na+ Ca2+ Ca 2+ K + Swollen neuron during spreading depolarization H2O Nonspecific cation channelsInsufficient sodium pump NMDAR J.P. Dreier Nature Medicine 17 2011 massive release of Gibbs free energy J.P. Dreier et al. Neuroscientist 19 2012 76. Modeling the migraine auraischemic stroke continuum 0 1 2 3 4 5 6 time (s) 100 50 0 50 voltage(mV) V EK ENa Iapp gate deactivation Hopf gate membrane voltage SNIC Hopf SNIC Recovery eletrogenic pump Fold Seizurelike activity in ischaemic stroke hypoxic tissue Spreading depression (ceiling level) n nV Ipump [K+]o [K+ ]o = 10mM 77. Modeling the migraine auraischemic stroke continuum Ischemia-induced migraine, Migrainous infarction, Persistent migraine w/o infarction (see below). Dahlem et al. Physica D 239, 889 (2010) 0 1 2 3 4 5 6 time (s) 100 50 0 50 voltage(mV) V EK ENa Iapp gate deactivation Hopf gate membrane voltage SNIC Hopf SNIC Recovery eletrogenic pump Fold Seizurelike activity in ischaemic stroke hypoxic tissue Spreading depression (ceiling level) n nV Ipump [K+]o [K+ ]o = 10mM 78. Common etiology or 2 mechanisms in MWoA and MWA? SD headacheprodrome aura trigger heightened susceptibility delayed trigger 1. Only one upstream trigger? 2. MWoA & MWA share same pain phase? 3. Silent aura? 4. Even prevalent? 5. Delayed headache link? 6. Missing the pain phase? SD: Spreading Depression, see next slide 79. SD does not curl-in in human cortex 1cm 10 min Only about 2-10% but not 50% cortical surface area is aected! right: modied from Hadjikhani et al. PNAS 98:4687 (2001). Dahlem & Hadjikhani, PLoS ONE, 4: e5007 (2009). 80. SD does not curl-in in human cortex 1cm 10 min 1cm SD curls in to form spirals with T=2.45min! spiral core Only about 2-10% but not 50% cortical surface area is aected! right: modied from Hadjikhani et al. PNAS 98:4687 (2001). Dahlem & Hadjikhani, PLoS ONE, 4: e5007 (2009). Dahlem & Muller, Exp. Brain Res. 115,319, (1997). 81. Re-entrant SD waves with functional block Z-type rotation causes a wave break in the spiral core. Dahlem & Muller (1997) Exp. Brain Res. 115:319 82. Re-entrant SD waves with anatomical block Reshodko, L. V. and Bures, J Biol. Cybern. 18,181 (1975) 83. Drugs adjust excitability:retracting & collapsing waves a b c d e f g h i j k l Dahlem et al. 2D wave patterns ... . (2010) Physcia D 84. Simulation of an engulng SD wave In cooperation with Bernd Schmidt, Magdeburg In cooperation with Jens Dreier & Denny Milakara, Charite 85. Migraine scotoma reveal functional properties Pattern matching 4 7 9 13 A B C Dahlem & Tusch, J. Math Neuroscie. 2,14 (2012) 86. Migraine scotoma reveal functional properties Pattern matching Curved retinotopic mapping 4 7 9 13 A B C a d b c e m m Dahlem & Tusch, J. Math Neuroscie. 2,14 (2012) 87. Migraine scotoma reveal functional properties Pattern matching Curved retinotopic mapping 4 7 9 13 A B C 2 4 6 8 10 12 14 0.1 0.2 0.3 2 4 6 8 10 12 14 20 40 60 80 100 120 140 2 4 6 8 10 12 14 0.2 0.4 0.6 0.8 1 Dahlem & Tusch, J. Math Neuroscie. 2,14 (2012)