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Disease Markers
Volume 22, Issue 1-2, Pages 65-72

Central Neurochemical Ultradian Variability in Depression

Ronald M. Salomon,1 Benjamin W. Johnson,2 and Dennis E. Schmidt1

1Department of Psychiatry, Vanderbilt University School of Medicine, Nashville, TN, USA
2Department of Anesthesiology, Vanderbilt University School of Medicine, Nashville, TN, USA

Received 11 November 2005; Accepted 11 November 2005

Copyright © 2006 Hindawi Publishing Corporation. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Depression is characterized by blunted behavior and neuroendocrine function that generally improve with antidepressant treatment. This study examined intrinsic variability in brain neurotransmitter function, since it may be a source of blunted behavior and neuroendocrine function in depression and a marker for the illness, and has not previously been analyzed using wavelet decomposition. To measure variability in monoamine metabolites, lumbar cerebrospinal fluid (CSF) was collected in serial samples in depressed patients before and after treatment. We hypothesized that changes in variability would be observed after treatment. Mechanisms that control such variability may be critical to the pathophysiology of depression. Method: Time series data was obtained from serial ten-min sampling over a 24-hr period (N = 144) from thirteen depressed patients, with a repeat collection after 5 weeks of antidepressant (sertraline or bupropion) treatment. Concentrations of tryptophan (TRP), the monoamine metabolites 5-HIAA (metabolite of serotonin) and HVA (metabolite of dopamine), and the HVA:5HIAA ratio were transformed to examine power in slowly (160 min/cycle) to rapidly (20 min/cycle) occurring events. Power, the sum of the squares of the coefficients in each d (detail) wavelet, reflects variability within a limited frequency bandwidth for that wavelet. Pre-treatment to post-treatment comparisons were conducted with repeated measures ANOVA. Results: Antidepressant treatment was associated with increased power in the d2 wavelet from the HVA (p = 0.03) and the HVA:5-HIAA ratio (p = 0.03) series. The d1 and d3 wavelets showed increased power following antidepressant treatment for the ratio series (d1, p = 0.01; d3, p = 0.05). Significant changes in power were not observed for the 5-HIAA data series. Power differences among analytes suggest that the findings are specific to each system. Conclusion: The wavelet transform analysis shows changes in neurochemical signal variability following antidepressant treatment. Patterns or degrees of variability may be as important as, or possibly more important than, the mean levels of monoamine transmitters. Studies of variability observed in healthy individuals and a larger depressed sample will be needed to verify a relationship with mood and treatment response. Neurochemical measures of time-variability may be a pivotal marker in depression.