BACKGROUND/AIM: Recently, a framework has been presented that links vigilance regulation, i.e. tonic brain arousal, with clinical symptoms of affective disorders. Against this background, the aim of this study was to deepen the knowledge of vigilance regulation by (1) identifying different patterns of vigilance regulation at rest in healthy subjects (n = 141) and (2) comparing the frequency distribution of these patterns between unmedicated patients with major depression (MD; n = 30) and healthy controls (HCs; n = 30). METHOD: Each 1-second segment of 15-min resting EEGs from 141 healthy subjects was classified as 1 of 7 different vigilance stages using the Vigilance Algorithm Leipzig. K-means clustering was used to distinguish different patterns of EEG vigilance regulation. The frequency distribution of these patterns was analyzed in independent data of 30 unmedicated MD patients and 30 matched HCs using a χ² test. RESULTS: The 3-cluster solution with a stable, a slowly declining and an unstable vigilance regulation pattern yielded the highest mathematical quality and performed best for separation of MD patients and HCs (χ² = 13.34; p < 0.001). Patterns with stable vigilance regulation were found significantly more often in patients with MD than in HCs. CONCLUSION: A stable vigilance regulation pattern, derived from a large sample of HCs, characterizes most patients with MD and separates them from matched HCs with a sensitivity between 67 and 73% and a specificity between 67 and 80%. The pattern of vigilance regulation might be a useful biomarker for delineating MD subgroups, e.g. for treatment prediction.
BACKGROUND/AIM: Recently, a framework has been presented that links vigilance regulation, i.e. tonic brain arousal, with clinical symptoms of affective disorders. Against this background, the aim of this study was to deepen the knowledge of vigilance regulation by (1) identifying different patterns of vigilance regulation at rest in healthy subjects (n = 141) and (2) comparing the frequency distribution of these patterns between unmedicated patients with major depression (MD; n = 30) and healthy controls (HCs; n = 30). METHOD: Each 1-second segment of 15-min resting EEGs from 141 healthy subjects was classified as 1 of 7 different vigilance stages using the Vigilance Algorithm Leipzig. K-means clustering was used to distinguish different patterns of EEG vigilance regulation. The frequency distribution of these patterns was analyzed in independent data of 30 unmedicated MD patients and 30 matched HCs using a χ² test. RESULTS: The 3-cluster solution with a stable, a slowly declining and an unstable vigilance regulation pattern yielded the highest mathematical quality and performed best for separation of MD patients and HCs (χ² = 13.34; p < 0.001). Patterns with stable vigilance regulation were found significantly more often in patients with MD than in HCs. CONCLUSION: A stable vigilance regulation pattern, derived from a large sample of HCs, characterizes most patients with MD and separates them from matched HCs with a sensitivity between 67 and 73% and a specificity between 67 and 80%. The pattern of vigilance regulation might be a useful biomarker for delineating MD subgroups, e.g. for treatment prediction.
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Authors: Benjamin Cowley; Édua Holmström; Kristiina Juurmaa; Levas Kovarskis; Christina M Krause Journal: Front Hum Neurosci Date: 2016-05-09 Impact factor: 3.169
Authors: Thomas Liebe; Meng Li; Lejla Colic; Matthias H J Munk; Catherine M Sweeney-Reed; Marie Woelfer; Moritz A Kretzschmar; Johann Steiner; Felicia von Düring; Gusalija Behnisch; Björn H Schott; Martin Walter Journal: Neuroimage Clin Date: 2018-09-04 Impact factor: 4.881
Authors: Salvatore Campanella; Kemal Arikan; Claudio Babiloni; Michela Balconi; Maurizio Bertollo; Viviana Betti; Luigi Bianchi; Martin Brunovsky; Carla Buttinelli; Silvia Comani; Giorgio Di Lorenzo; Daniel Dumalin; Carles Escera; Andreas Fallgatter; Derek Fisher; Giulia Maria Giordano; Bahar Guntekin; Claudio Imperatori; Ryouhei Ishii; Hendrik Kajosch; Michael Kiang; Eduardo López-Caneda; Pascal Missonnier; Armida Mucci; Sebastian Olbrich; Georges Otte; Andrea Perrottelli; Alessandra Pizzuti; Diego Pinal; Dean Salisbury; Yingying Tang; Paolo Tisei; Jijun Wang; Istvan Winkler; Jiajin Yuan; Oliver Pogarell Journal: Clin EEG Neurosci Date: 2020-09-25 Impact factor: 1.843