EEG Vigilance Regulation Patterns and Their Discriminative Power to Separate Patients with Major Depression from Healthy Controls

Abstract

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.

Impact of Serum Cytokine Levels on EEG-Measured Arousal Regulation in Patients with Major Depressive Disorder and Healthy Controls

“…Our finding of more stable EEG vigilance regulation in MDD is in agreement with previous studies [2,3] and extends past findings using a refined version of the VIGALL algorithm in a larger sample of unmedicated depressed patients. The pattern across the 15-min EEG sampling frame of fewer declines into lower EEG vigilance stages, and a greater percentage of high EEG vigilance stages in the unmedicated depressed group, supports the theory of hyperstable arousal regulation in MDD [1].…”

“…Percentages of EEG vigilance stages were computed by dividing the number of segments of a given stage by the number of nonartifact segments [for a detailed description, see [3]]. Furthermore, according to the respective vigilance stage, each segment was allocated to a measure ranging from the value 7 (highest vigilance stage W) to 1 (sleep onset stage C).…”

“…Within this model, symptoms of sensation avoidance and social withdrawal are interpreted as an autoregulatory reaction of the organism to this state of high brain arousal: the organism aims to avoid any further arousal-increasing sensory or social stimulation. Using resting-state EEG (electroencephalography) recordings, a replicated finding is that patients with MDD compared to healthy controls show hyperstable EEG vigilance regulation characterized by fewer and later transitions to EEG signs of drowsiness or sleep onset [2,3]. …”

Personalized Medicine: Review and Perspectives of Promising Baseline EEG Biomarkers in Major Depressive Disorder and Attention Deficit Hyperactivity Disorder

“…Hegerl et al [38] and Olbrich et al [39] reported that MDD is associated with an increased EEG vigilance during rest with fewer and slower declines to lower vigilance stages during a 15-min resting condition. The appeal of this marker reflecting a high tone of CNS arousal can be found in the linkage between clinical symptoms and EEG parameters of wakefulness regulation.…”

Assessment of Wakefulness and Brain Arousal Regulation in Psychiatric Research

“…Regarding VIGALL's application in clinical groups and for treatment response prediction, several studies have been published recently: in major depression earlier EEG studies [58,59] as well as more recent VIGALLbased studies [60,61] revealed that depressed patients are characterized by a hyperstable arousal regulation, i.e. they show delayed or no decline to lower EEG vigilance stages under resting conditions.…”