Blood Flow: Supplying the sleeping brain

During sleep, the brain experiences large fluctuations in blood volume and altered coupling between neural and vascular signals.
  1. Stephanie D Williams
  2. Laura D Lewis  Is a corresponding author
  1. Department of Psychological and Brain Sciences, Boston University, United States
  2. Department of Biomedical Engineering, Boston University, United States

The amount of blood the brain receives is tightly regulated to respond to the metabolic needs of neurons. This ‘neurovascular coupling’ allows flexible delivery of energy, with blood rushing in when neurons are active (Iadecola, 2017). Many brain imaging techniques rely on this process. Functional magnetic resonance imaging (or fMRI for short), in particular, uses blood oxygenation signals to pinpoint changes in neural activity. Understanding neurovascular dynamics is therefore essential for neuroscience research and the correct interpretation of fMRI measurements.

Sleep profoundly transforms most aspects of brain physiology, including blood oxygenation signals (Horovitz et al., 2008), but its effects on neurovascular dynamics are not well understood. Now, in eLife, Patrick J. Drew and colleagues from Pennsylvania State University – with Kevin Turner as first author – report an in-depth characterization of how blood volume and neurovascular coupling change across arousal states in mice (Turner et al., 2020; Figure 1).

Large fluctuations in blood volume and altered neurovascular coupling occur during sleep in mice.

Changes in neurovascular dynamics were examined in head-fixed mice (left) which were awake (grey), in non-rapid eye movement sleep (NREM; blue), and rapid eye movement sleep (REM; red). (A) As inferred from changes in cortical hemoglobin levels, total blood volume increases drastically during sleep, especially during REM sleep. (B) Average arteriole dilation during sleep increases above the level found during the awake state. (C) Sleep occurs during eyes-open behavioral rest in head-fixed mice. NREM sleep (38% of rest periods) commonly occurred during behavioral rest. REM sleep was less common but still occurred (5% of rest periods). (D) The strength of coupling between neural signals (multi-unit activity, or MUA) and levels of cortical hemoglobin (HbT) increased during NREM sleep, and the timing of coupling shifted across sleep stages.

Image credit: Mouse illustrations adapted from Luigi Petrucco via SciDraw (CCBY 4.0); Blood cells illustrations adapted from John Chilton via SciDraw (CCBY 4.0); Panel C adapted from Turner et al., 2020 (CCBY 4.0).

To investigate this question, Turner et al. monitored behavioral, neural, and vascular signals while mice were awake or in non-rapid and rapid eye movement sleep (respectively NREM and REM). The heads of the animals were immobilized to allow imaging. However, mice can still sleep with their eyes open under these conditions, prompting Turner et al. to collect detailed behavioral and physiological measurements to determine sleep stages.

First, intrinsic optical signal imaging was used to infer the amount of hemoglobin, which in turn is correlated with the volume of blood in the brain. This method relies on the light reflection changing with the hemoglobin content, and it revealed large variations in the volume of blood in the brain during sleep. When mice entered NREM sleep, the amplitude of oscillations in hemoglobin levels more than doubled compared to the awake state; during REM sleep, they showed a prolonged increase in total hemoglobin that lasted more than 30 seconds. The amount of total hemoglobin changed drastically as mice woke up or fell asleep, highlighting that blood volume dynamics are coupled with arousal state. Together, these results show that blood volume in the brain fluctuates greatly during sleep.

In addition to these large-scale measurements of hemoglobin, Turner et al. tracked the diameter of tiny vessels known as arterioles. During NREM sleep, arteriole diameters fluctuated greatly, and they increased even further (over tens of seconds) when mice entered REM sleep. These measurements helped to unravel how arterioles, capillaries and veins each contribute to the changes in global blood volume measured by intrinsic optical signal imaging.

Next, Turner et al. examined whether the changes in blood volume during sleep were linked to variations in neural activity, which normally drive fluctuations in the diameter of arterioles (Mateo et al., 2017). This revealed that the strength of the neurovascular coupling changed depending on arousal states, increasing substantially during NREM sleep compared to wakefulness. The timing of the coupling was also altered, with the blood volume response being slower during REM sleep than in the awake state.

Many studies that use head-fixed mice do not systemically assess whether the animals are awake, raising the question of how much time they actually spend asleep. Analyzing all 15 seconds rest periods in which mice had their eyes open showed that they were actually sleeping in nearly half of these intervals. In addition, only measuring heart rate or whisker movements could not reliably identify sleeping periods. These findings suggest that head-fixed imaging studies should include measurements of arousal state to detect if the mice are asleep.

Overall, Turner et al. identify a striking transformation of neurovascular dynamics during sleep: blood volume increases and exhibits large oscillations, and coupling to neural activity is altered. Intriguingly, vascular dynamics during sleep, including variations in blood volume, are much larger than those driven by the well-studied stimuli and behaviors found in the awake state. Why is this the case? In addition to changes in neural activity and cognition, sleep also involves increased waste clearance from the brain, which has been linked to vascular oscillations (Xie et al., 2013; Iliff et al., 2013; Mestre et al., 2018; van Veluw et al., 2020). Thus, these results also help identify the consequences of sleep for the brain, and how blood dynamics may contribute to brain health.

References

Article and author information

Author details

  1. Stephanie D Williams

    Stephanie D Williams is in the Department of Psychological and Brain Sciences, Boston University, Boston, United States

    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1904-4874
  2. Laura D Lewis

    Laura D Lewis is in the Department of Biomedical Engineering, Boston University, Boston, United States

    For correspondence
    ldlewis@bu.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4003-0277

Publication history

  1. Version of Record published:

Copyright

© 2020, Williams and Lewis

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

Metrics

  • 4,592
    views
  • 284
    downloads
  • 4
    citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.

Download links

A two-part list of links to download the article, or parts of the article, in various formats.

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

  1. Stephanie D Williams
  2. Laura D Lewis
(2020)
Blood Flow: Supplying the sleeping brain
eLife 9:e64597.
https://doi.org/10.7554/eLife.64597
  1. Further reading

Further reading

    1. Neuroscience
    Simonas Griesius, Amy Richardson, Dimitri Michael Kullmann
    Research Article

    Non-linear summation of synaptic inputs to the dendrites of pyramidal neurons has been proposed to increase the computation capacity of neurons through coincidence detection, signal amplification, and additional logic operations such as XOR. Supralinear dendritic integration has been documented extensively in principal neurons, mediated by several voltage-dependent conductances. It has also been reported in parvalbumin-positive hippocampal basket cells, in dendrites innervated by feedback excitatory synapses. Whether other interneurons, which support feed-forward or feedback inhibition of principal neuron dendrites, also exhibit local non-linear integration of synaptic excitation is not known. Here, we use patch-clamp electrophysiology, and two-photon calcium imaging and glutamate uncaging, to show that supralinear dendritic integration of near-synchronous spatially clustered glutamate-receptor mediated depolarization occurs in NDNF-positive neurogliaform cells and oriens-lacunosum moleculare interneurons in the mouse hippocampus. Supralinear summation was detected via recordings of somatic depolarizations elicited by uncaging of glutamate on dendritic fragments, and, in neurogliaform cells, by concurrent imaging of dendritic calcium transients. Supralinearity was abolished by blocking NMDA receptors (NMDARs) but resisted blockade of voltage-gated sodium channels. Blocking L-type calcium channels abolished supralinear calcium signalling but only had a minor effect on voltage supralinearity. Dendritic boosting of spatially clustered synaptic signals argues for previously unappreciated computational complexity in dendrite-projecting inhibitory cells of the hippocampus.

    1. Neuroscience
    Christine Ahrends, Mark W Woolrich, Diego Vidaurre
    Tools and Resources

    Predicting an individual’s cognitive traits or clinical condition using brain signals is a central goal in modern neuroscience. This is commonly done using either structural aspects, such as structural connectivity or cortical thickness, or aggregated measures of brain activity that average over time. But these approaches are missing a central aspect of brain function: the unique ways in which an individual’s brain activity unfolds over time. One reason why these dynamic patterns are not usually considered is that they have to be described by complex, high-dimensional models; and it is unclear how best to use these models for prediction. We here propose an approach that describes dynamic functional connectivity and amplitude patterns using a Hidden Markov model (HMM) and combines it with the Fisher kernel, which can be used to predict individual traits. The Fisher kernel is constructed from the HMM in a mathematically principled manner, thereby preserving the structure of the underlying model. We show here, in fMRI data, that the HMM-Fisher kernel approach is accurate and reliable. We compare the Fisher kernel to other prediction methods, both time-varying and time-averaged functional connectivity-based models. Our approach leverages information about an individual’s time-varying amplitude and functional connectivity for prediction and has broad applications in cognitive neuroscience and personalised medicine.