We watch you while you sleep – neuroimaging of dreams
Revised essay by Marian Sauter for the application to the Axol Science Scholarship 2015/16
In the last decade, technology has made it possible to combine fMRI and EEG in one experiment while minimizing the artifacts that the two methods produce on each other when combined. This paved the way for combined studies, in this example for the purpose of studying sleep. In these studies, fMRI is used as the main method of interest while simultaneous EEG recordings are mostly done to prove that the subject is in fact sleeping. According to its EEG activity, sleep can be divided in four stages, in specific, a REM stage (rapid eye movement) and four NREM stages (non REM). The NREM stages are characterized by high theta activity with of occasional bursts of activity (sleep spindles) or high amplitude delta activity. Thus, they differ in EEG activity from active wakefulness, which is characterized by high beta activity. REM sleep is quite similar to wakefulness in its EEG activity. However, it incorporates a descending muscle tone (sleep paralysis) which makes it possible to disentangle it from wakefulness by additionally measuring EMG.
Processing of auditory stimuli
Studying sleep conceptually decreases the range of possible experimental manipulations. However, prominent auditory stimuli are still being processed. In the first studies Czisch and colleagues (2002; 2004) read the sleeping subjects a passage of a Mark Twain narrative. In contrast to wakefulness, this stimulation led to decreased activation in the auditory cortex during the NREM sleep as well as a prominent negative signal in the visual cortex. It was also accompanied by an increase in EEG frequency components in the low delta frequency range. This implies that the auditory stimulation made the subjects sleep more deeply and is in line with the hypothesis that reduced responsiveness to external stimuli is vital for consolidation and maintenance of sleep, including the ignoring of non-alerting background noises. However, it is also important that we are responsive to loud and sudden noises in order to awake upon potential danger. For this reason, Czisch and colleagues (2009) studied the reaction towards rare noises with an acoustic oddball paradigm. In their study, a rare odd tone (high frequency) occurred with a 20% probability within a continuous stream of frequent tones every two seconds. The rare tones elicited activation in the auditory cortex but led to a deactivation in the motor cortex and the amygdala during light NREM sleep, implying a suppression of movements and emotional processing. The authors interpret that as a sleep protecting mechanism.
Considering their findings are limited to light sleep, it is not possible to generalize to all sleep stages. Especially In deep sleep, rare odd tones should lead to higher activation in auditory and motor areas and lighten the sleep in order to prepare for a potential fight-or-flight response. Additionally, their rare tones were not really rare as they occurred about every ten seconds. I think their interpretations could be supported more strongly if the rare tones varied in both amplitude and frequency.
Sleep and consciousness
Another interesting process to study is consciousness as it is most often absent during sleep. Additionally, it is a physiological state different to a pathological state like a coma, or pharmacological induced loss of consciousness as for anesthesia. Since consciousness is a broad term, we’ll define it as having two key properties: the awareness to discriminate between internal and external information and the capacity to integrate that information into a global context. One way to investigate consciousness is to look at the brain during rest, without experimental manipulations. The network of fluctuations in activation patterns seen in this approach can be divided in the default mode network (DMN) and its anticorrelated network (ACN; Fox et al, 2005). While the DMN is generally active towards anything that requires attention towards the internal world (e.g. mind wandering), the anticorrelated attention system is involved when information from the external world is incorporated (Sämann et al, 2011). PET studies showed that states of low brain activity are often associated with unconsciousness (Braun et al, 1997). To study consciousness during sleep, one can now start by looking at how activity in the DMN and ACN changes throughout the stages of sleep. This is a reasonable approach since it has been shown that default mode network connectivity reflects the level of consciousness in non-communicative brain-damaged patients (Vanhaudenhuyse, 2010). Exemplary, a key difference between normal sleep and brain-damaged patients is that the inferior parietal lobe (IPL) is active during sleep, while activity diminishes in damaged brains. Additionally, more recent fMRI studies have identified activity patterns correlating with slow neuronal EEG (Boly, 2008). Further, with increased sleep depth, contributions of the posterior cingulate cortex and retrosplenial cortex, among others, to the DMN decreased (Sämann et al, 2011). These results indicate a role of those areas in the regulation of consciousness. The authors also postulate that such corticocortical synchronization could be necessary to maintain internal and external awareness.
Functional imaging of dreaming
A fundamental problem with studying dreaming is that we seem to lack an objective measure of if, when and what a person is dreaming. Classic studies on dreaming relied on subsequent self-report measures. More recently, researches have started to recruit participants who are experienced lucid dreamers. A lucid dream is a dream in which the subject becomes fully aware of his dream state, has full access to memory and can voluntarily change the dream plot. This offers a great opportunity to study dream content with combined EEG-fMRI. Unfortunately, lucid dreaming often happens during REM sleep, thus is accompanied by paralysis of the exoskeleton. That means the only way to communicate with the lucid dreamer is through eye movements. Before laying into the scanner, the subject is instructed to move their eyes twice from left to right if a dream becomes lucid. This shows up as a great effect in the EEG signal (LaBerge, 1981).
It was found that in a lucid dream the bilateral precuneus, cuneus, parietal lobules, prefrontal and occipito-temporal cortices activated strongly as compared with non-lucid REM sleep (Dresler et al, 2012). This suggests a reactivation of areas which are usually deactivated during REM sleep. This kind of activity might explain how it is possible to recover higher cognitive capabilities which are the central property of lucid dreaming. These additional activations are also co-localized with brain regions maximally expanded during evolution which could indicate their importance for higher-order consciousness. However, such conclusions should be drawn carefully, because in an ideal experiment, lucid dreaming should be compared with non-lucid dreaming in REM sleep and not just with any occurrence of non-lucid REM sleep. But as stated above, we have no objective measure of non-lucid dreaming yet, thus the validity of the contrasts used to investigate lucid dreaming remains questionable.
In conclusion, the neuroimaging of sleep can give us great insight in how consciousness could be represented in the brain. Whereas many scientists and philosophers argue that the inherent question of human consciousness, the hard problem of consciousness, cannot be answered from the perspective of neuroscience, perhaps, further experiments on sleep might be able to prove them wrong. I see a great future for neuroscientists in this regard, because the combination of EEG and fMRI allows for high temporal and spatial resolution while the contrasting of lucid dreaming versus non-lucid dreaming grants us access to consciousness like never before.
Boly, M., Phillips, C., Tshibanda, L., Vanhaudenhuyse, A., Schabus, M., Dang‐Vu, T. T., … & Laureys, S. (2008). Intrinsic brain activity in altered states of consciousness. Annals of the New York Academy of Sciences, 1129(1), 119-129.
Braun, A. R., Balkin, T. J., Wesenten, N. J., Carson, R. E., Varga, M., Baldwin, P., … Herscovitch, P. (1997). Regional cerebral blood flow throughout the sleep-wake cycle. An H2(15)O PET study. Brain : A Journal of Neurology, 120 ( Pt 7, 1173–97.
Czisch, M., Wehrle, R., Kaufmann, C., Wetter, T. C., Holsboer, F., Pollmächer, T., & Auer, D. P. (2004). Functional MRI during sleep: BOLD signal decreases and their electrophysiological correlates. The European Journal of Neuroscience, 20(2), 566–74. doi:10.1111/j.1460-9568.2004.03518.x
Czisch, M., Wehrle, R., Stiegler, A., Peters, H., Andrade, K., Holsboer, F., & Sämann, P. G. (2009). Acoustic oddball during NREM sleep: a combined EEG/fMRI study. PloS One, 4(8), e6749. doi:10.1371/journal.pone.0006749
Dresler, M., Wehrle, R., Spoormaker, V. I., Koch, S. P., Holsboer, F., Steiger, A., … Czisch, M. (2012). Neural correlates of dream lucidity obtained from contrasting lucid versus non-lucid REM sleep: a combined EEG/fMRI case study. Sleep, 35(7), 1017–20. doi:10.5665/sleep.1974
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102(27), 9673-9678.
La Berge, S. P., Nagel, L. E., Dement, W. C., & Zarcone, V. P. (1981). Lucid dreaming verified by volitional communication during REM sleep. Perceptual and Motor Skills, 52(3), 727–32.
Sämann, P. G., Wehrle, R., Hoehn, D., Spoormaker, V. I., Peters, H., Tully, C., … Czisch, M. (2011). Development of the brain’s default mode network from wakefulness to slow wave sleep. Cerebral Cortex (New York, N.Y. : 1991), 21(9), 2082–93. doi:10.1093/cercor/bhq295
Vanhaudenhuyse, A., Noirhomme, Q., Tshibanda, L. J.-F., Bruno, M.-A., Boveroux, P.,
Schnakers, C., … Boly, M. (2010). Default network connectivity reflects the level of consciousness in non-communicative brain-damaged patients. Brain : A Journal of Neurology, 133(Pt 1), 161–71. doi:10.1093/brain/awp313