Mads Barloese1,2, Bryan Haddock3, Nunu T Lund2, Anja Petersen2, Rigmor Jensen2. 1. 1 Department of Clinical Physiology and Nuclear Medicine, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark. 2. 2 Danish Headache Center, Department of Neurology, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark. 3. 3 Department of Clinical Physiology, Nuclear Medicine and PET, Rigshospitalet-Glostrup, University of Copenhagen, Copenhagen, Denmark.
Abstract
BACKGROUND: The mechanisms behind the severe pain of cluster headache remain enigmatic. A distinguishing feature of the attacks is the striking rhythms with which they occur. We investigated whether statistical modelling can be used to describe 24-hour attack distributions and identify differences between subgroups. METHODS: Common hours of attacks for 351 cluster headache patients were collected. Probability distributions of attacks throughout the day (chronorisk) was calculated. These 24-hour distributions were analysed with a multimodal Gaussian fit identifying periods of elevated attack risk and a spectral analysis identifying oscillations in risk. RESULTS: The Gaussian model fit for the chronorisk distribution for all patients reporting diurnal rhythmicity (n = 286) had a goodness of fit R2 value of 0.97 and identified three times of increased risk peaking at 21:41, 02:02 and 06:23 hours. In subgroups, three to five modes of increased risk were found and goodness of fit values ranged from 0.85-0.99. Spectral analysis revealed multiple distinct oscillation frequencies in chronorisk in subgroups including a dominant circadian oscillation in episodic patients and an ultradian in chronic. CONCLUSIONS: Chronorisk in cluster headache can be characterised as a sum of individual, timed events of increased risk, each having a Gaussian distribution. In episodic cluster headache, attacks follow a circadian rhythmicity whereas, in the chronic variant, ultradian oscillations are dominant reflecting a loss of association with sleep and perhaps explaining observed differences in the effects of specific treatments. The results demonstrate the ability to accurately model chronobiological patterns in a primary headache.
BACKGROUND: The mechanisms behind the severe pain of cluster headache remain enigmatic. A distinguishing feature of the attacks is the striking rhythms with which they occur. We investigated whether statistical modelling can be used to describe 24-hour attack distributions and identify differences between subgroups. METHODS: Common hours of attacks for 351 cluster headachepatients were collected. Probability distributions of attacks throughout the day (chronorisk) was calculated. These 24-hour distributions were analysed with a multimodal Gaussian fit identifying periods of elevated attack risk and a spectral analysis identifying oscillations in risk. RESULTS: The Gaussian model fit for the chronorisk distribution for all patients reporting diurnal rhythmicity (n = 286) had a goodness of fit R2 value of 0.97 and identified three times of increased risk peaking at 21:41, 02:02 and 06:23 hours. In subgroups, three to five modes of increased risk were found and goodness of fit values ranged from 0.85-0.99. Spectral analysis revealed multiple distinct oscillation frequencies in chronorisk in subgroups including a dominant circadian oscillation in episodicpatients and an ultradian in chronic. CONCLUSIONS: Chronorisk in cluster headache can be characterised as a sum of individual, timed events of increased risk, each having a Gaussian distribution. In episodic cluster headache, attacks follow a circadian rhythmicity whereas, in the chronic variant, ultradian oscillations are dominant reflecting a loss of association with sleep and perhaps explaining observed differences in the effects of specific treatments. The results demonstrate the ability to accurately model chronobiological patterns in a primary headache.
Authors: Ilse F de Coo; Willebrordus P J van Oosterhout; Leopoldine A Wilbrink; Erik W van Zwet; Michel D Ferrari; Rolf Fronczek Journal: Headache Date: 2019-05-31 Impact factor: 5.887
Authors: David W Dodick; Peter J Goadsby; Christian Lucas; Rigmor Jensen; Jennifer N Bardos; James M Martinez; Chunmei Zhou; Sheena K Aurora; Jyun Yan Yang; Robert R Conley; Tina Oakes Journal: Cephalalgia Date: 2020-02-12 Impact factor: 6.292
Authors: Henrik W Schytz; Faisal M Amin; Rigmor H Jensen; Louise Carlsen; Stine Maarbjerg; Nunu Lund; Karen Aegidius; Lise L Thomsen; Flemming W Bach; Dagmar Beier; Hanne Johansen; Jakob M Hansen; Helge Kasch; Signe B Munksgaard; Lars Poulsen; Per Schmidt Sørensen; Peter T Schmidt-Hansen; Vlasta V Cvetkovic; Messoud Ashina; Lars Bendtsen Journal: J Headache Pain Date: 2021-04-08 Impact factor: 7.277