Influence of vigilance on resting state brain activity

Abstract

Aims: Despite task absence the brain remains functionally and metabolically active exhibiting coherent activity in characteristic sets of brain regions, called resting state networks (RSN). RSN manifest as correlation patterns in “spontaneous” BOLD signal fluctuations with 10–100 s period [1]. Their origins remain obscure. Typically, in a wake resting state experiment data stems from subjects resting with eyes closed in the MRI scanner for ~10 min [2]. Hypothesis: We hypothesized that in resting state experiments subjects do not maintain constant wakefulness but that (1) the temporal profiles of RSN are influenced by fluctuations in vigilance and (2) cortical activity fluctuations by subcortical brain activity [3]. Method: We studied 55 non sleep-deprived German subjects with EEG-fMRI under above-mentioned conditions. Vigilance throughout the experiment was scored [4]. Next, a support vector machine classifier was trained to perform sleep staging [5] based on RSN functional connectivity matrices and applied to an independent resting state fMRI data set of 76 Chinese subjects [6]. Results: 50% of the 55 subjects did not maintain steady wakefulness until minute 5, vigilance state switches occurred up to a rate of 1/min. Sleep stage classifier performance was 87%, and results could be reproduced in the independent data set (“Chinese”). Excluding the thalamus from the classification procedure resulted in poorer (<70%) sleep scoring performance. Conclusion: 1. changes in vigilance occur at a similar temporal scale as that of RSN fluctuations. 2. RSN configuration is sleep stage specific and highly dependent on thalamic activity. 4. In classical resting state experiments up to 50% of the subjects do not maintain steady wakefulness for beyond 5 minutes. When proposing RSN differences as biomarkers for diseases [7], vigilance bias between patient (e.g. resulting from drugs, encephalopathy, age) and control groups needs to be controlled for. Literatur: 1. Fox, M.D. and M.E. Raichle, Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging. Nat Rev Neurosci, 2007. 8(9): p. 700-11. 2. Van Dijk, K.R., et al., Intrinsic functional connectivity as a tool for human connectomics: theory, properties, and optimization. Journal of neurophysiology, 2010. 103(1): p. 297-321. 3. Friston, K., Functional and Effective Connectivity in Neuroimaging: A Synthesis. Hum Brain Mapp, 1996. 2: p. 56-68. 4. AASM, The AASM Manual for the Scoring of Sleep and Associated Events- Rules, Terminology and Technical Specifications. 2007, Chicago: American Academy of Sleep Medicine. 5. Crisler, S., et al., Sleep-stage scoring in the rat using a support vector machine. Journal of neuroscience methods, 2008. 168(2): p. 524-34. 6. Biswal, B.B., et al., Toward discovery science of human brain function. Proceedings of the National Academy of Sciences of the United States of America, 2010. 107(10): p. 4734-9. 7. Greicius, M., Resting-state functional connectivity in neuropsychiatric disorders. Curr Opin Neurol, 2008. 21(4): p. 424-30. Funding: Bundesministerium fur Bildung und Forschung (grant 01 EV 0703) and Landes-Offensive zur Entwicklung Wissenschaftlich-okonomischer Exzellenz (LOEWE, Neuronale Koordination Forschungsschwerpunkt Frankfurt).