pii: sp-00770-13
http://dx.doi.org/10.5665/sleep.4242
GRAY MATTER-SPECIFIC CHANGES IN BRAIN BIOENERGETICS AFTER ACUTE SLEEP DEPRIVATION
Gray Matter-Specific Changes in Brain Bioenergetics after Acute Sleep
Deprivation: A 31P Magnetic Resonance Spectroscopy Study at 4 Tesla
David T. Plante, MD1; George H. Trksak, PhD2,3,4,5; J. Eric Jensen, PhD3,5; David M. Penetar, PhD2,3,4,5; Caitlin Ravichandran, PhD5,6; Brady A. Riedner, PhD1;
Wendy L. Tartarini, MA4; Cynthia M. Dorsey, PhD3,4,5; Perry F. Renshaw, MD, PhD7; Scott E. Lukas, PhD2,3,4,5; David G. Harper, PhD5,8
1
Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI; 2Behavioral Psychopharmacology Research
Lab, McLean Hospital, Belmont, MA; 3Brain Imaging Center, McLean Hospital, Belmont, MA; 4Sleep Research Laboratory, McLean Hospital, Belmont,
MA; 5Harvard Medical School, Boston, MA; 6Laboratory for Psychiatric Biostatistics, McLean Hospital, Belmont, MA; 7The Brain Institute, University
of Utah School of Medicine, Salt Lake City, UT; 8Geriatric Psychiatry Program, McLean Hospital, Belmont, MA
Study Objectives: A principal function of sleep may be restoration of brain energy metabolism caused by the energetic demands of wakefulness.
Because energetic demands in the brain are greater in gray than white matter, this study used linear mixed-effects models to examine tissue-type
specific changes in high-energy phosphates derived using 31P magnetic resonance spectroscopy (MRS) after sleep deprivation and recovery sleep.
Design: Experimental laboratory study.
Setting: Outpatient neuroimaging center at a private psychiatric hospital.
Participants: A total of 32 MRS scans performed in eight healthy individuals (mean age 35 y; range 23-51 y).
Interventions: Phosphocreatine (PCr) and β-nucleoside triphosphate (NTP) were measured using 31P MRS three dimensional-chemical shift
imaging at high field (4 Tesla) after a baseline night of sleep, acute sleep deprivation, and 2 nights of recovery sleep. Novel linear mixed-effects
models were constructed using spectral and tissue segmentation data to examine changes in bioenergetics in gray and white matter.
Measurements and Results: PCr increased in gray matter after 2 nights of recovery sleep relative to sleep deprivation with no significant changes
in white matter. Exploratory analyses also demonstrated that increases in PCr were associated with increases in electroencephalographic slow
wave activity during recovery sleep. No significant changes in β-NTP were observed.
Conclusions: These results demonstrate that sleep deprivation and subsequent recovery-induced changes in high-energy phosphates primarily
occur in gray matter, and increases in phosphocreatine after recovery sleep may be related to sleep homeostasis.
Keywords: magnetic resonance spectroscopy, nucleoside triphosphate, phosphocreatine, sleep deprivation
Citation: Plante DT, Trksak GH, Jensen JE, Penetar DM, Ravichandran C, Riedner BA, Tartarini WL, Dorsey CM, Renshaw PF, Lukas SE, Harper
DG. Gray matter-specific changes in brain bioenergetics after acute sleep deprivation: a 31P magnetic resonance spectroscopy study at 4 Tesla.
SLEEP 2014;37(12):1919-1927.
INTRODUCTION
The energy requirements of the brain are quite high relative
to other organs, with the brain accounting for approximately
20% of the body’s resting metabolism despite only constituting
2% of body mass.1 Although energy consumption in the brain
is used for a myriad of biological processes, the greatest proportion of energy is harnessed for neuronal activity, with excitatory neurotransmission consuming the majority of energy
expended.2–4 Decreases in excitatory neurotransmission in multiple cortical regions during nonrapid eye movement (NREM)
sleep relative to wakefulness, are suggested by characteristic
electroencephalogram (EEG) slowing during sleep, as well as
positron emission tomography (PET) and single-photon emission computed tomography (SPECT) studies.5–7 This decrease
in metabolic expenditure during sleep relative to wakefulness
broadly supports the hypothesis that one of the principal functions of sleep is restoration of brain energy metabolites caused
by the energetic demands of wakefulness.8,9
A commentary on this article appears in this issue on page 1881.
Submitted for publication December, 2013
Submitted in final revised form May, 2014
Accepted for publication July, 2014
Address correspondence to: David T. Plante, MD, Wisconsin Sleep, 6001
Research Park Boulevard, Madison, WI 53719; Tel: (608) 262-0130; Fax:
(608)-263-0265; E-mail: [email protected].
SLEEP, Vol. 37, No. 12, 2014
1919
Adenosine triphosphate (ATP), the molecular unit of energy currency in the brain, is the primary source of chemical
potential energy used by cells. However, in tissues with high
and fluctuating energy requirements, such as the brain, simply
adjusting intracellular concentrations of ATP to meet energy
storage/utilization requirements would be problematic because
levels of ATP and its catabolic products are key regulators of
a number of fundamental metabolic processes.10 Thus, phosphocreatine (PCr) serves as a vital, rapidly mobilizable energy
reserve to maintain ATP concentration in the brain, as a source
of high-energy phosphoryl groups that can be donated to adenosine diphosphate (ADP) to form ATP, a reversible reaction
catalyzed by the enzyme creatine kinase.10
The role of ATP and PCr in the brain as related to sleep
and wakefulness is an important area of research given the
aforementioned theories of sleep in energy restoration and
converging lines of evidence suggesting a critical role for adenosine in sleep homeostasis.11 Animal models of sleep deprivation have demonstrated adenosine increases in the basal
forebrain and cortex during sleep deprivation and declines
during recovery sleep, and that exogenous adenosine applied
in the basal forebrain mimics the effects of prolonged wakefulness during recovery sleep (i.e., increased sleep time and
slow wave activity).12–15 Because intracellular adenosine is
primarily produced from adenosine monophosphate (AMP),
a catabolic by-product of ATP hydrolysis, sleep homeostatic
mechanisms and brain bioenergetics have been linked, albeit
indirectly, through adenosine.16 Using a rat model, Dworak and
Brain Bioenergetics in Sleep Deprivation—Plante et al.
colleagues provided more direct evidence of the role of sleep
in brain energy metabolism by demonstrating that (1) ATP increases above waking levels during sleep, (2) the ATP surge is
positively correlated with slow wave activity (a marker of sleep
homeostasis), and (3) ATP levels return to baseline by the end
of the sleep period. Additionally, ATP remained constant during
a sleep deprivation protocol, delaying the sleep related increase
in ATP; however, PCr decreased in the basal forebrain, as well
as frontal and cingulate cortices, suggesting PCr was depleted
to maintain ATP levels during extended wakefulness.17
Although tissue-dependent assays that measure brain highenergy phosphates in response to sleep deprivation in animal
models cannot be used in human studies, 31P magnetic resonance spectroscopy (MRS) allows for noninvasive in vivo
quantification of bioenergetic compounds including PCr and
nucleoside triphosphates (NTP; the majority of which is ATP,
given higher levels of adenosine relative to cytidine and guanosine triphosphates18). 31P MRS has been used in prior sleep
deprivation studies in healthy subjects, demonstrating no
change in brain bioenergetics after sleep deprivation relative
to baseline values.19,20 However, after recovery sleep, increases
in β-NTP (which is proportional to ATP21), as well as γ-NTP
(a nonspecific resonance that includes nucleoside diphosphates
and triphosphates), total NTP (a summation of α, β, and γ NTP
resonances), and PCr have been described, suggesting part of
the recovery process after sleep deprivation includes increases
in high-energy phosphates.20,22 Notably, a significant limitation
of 31P MRS studies of sleep deprivation is the large brain area
over which spectra are collected for adequate signal-to-noise
ratio, which results in poor spatial resolution. Although precise
localization of bioenergetic changes using MRS is currently
not possible, the use of linear mixed effects models that combine magnetic resonance tissue segmentation with spectral data
have been successfully used to examine tissue type (i.e., gray
matter and white matter) specific changes in brain metabolites
in physiologic and pathologic processes.2,23–32 These analytical
techniques are particularly pertinent for 31P MRS studies of
brain bioenergetics in response to sleep deprivation because the
metabolic rate is approximately 25–50% higher in gray than
white matter.1,2
Thus, the current study used a novel linear mixed-effects
model to examine changes in brain bioenergetics using 31P
MRS across a paradigm of total sleep deprivation and recovery
sleep. Based on the increased metabolic demand in gray relative to white matter, energetic costs of extended wakefulness,
and previous reports of increases in high-energy phosphates
after recovery sleep from sleep deprivation, we hypothesized
that there would be tissue type-specific alterations in high-energy phosphates with decreases in PCr and β-NTP from baseline after sleep deprivation and subsequent rise beyond baseline
in these metabolites after recovery sleep, predominantly occurring in gray matter.
MATERIALS AND METHODS
Participants
Individuals included in this study were eight (five male)
healthy adults (mean age 35 y; range 23-51 y) who were previously recruited from the greater Boston, MA area via print,
SLEEP, Vol. 37, No. 12, 2014
radio, and web-based advertisements as part of a larger study
of sleep deprivation in substance dependence.22 Participants
were included in the current post hoc analysis if they had intact three-dimensional chemical shift imaging (3D-CSI) MRS
scans and sleep recordings (see next paragraphs). After initial
telephone screening, individuals participated in an in-person
visit during which written informed consent was obtained.
This visit included evaluation for psychiatric disorders with
the Structured Clinical Interview for Diagnostic and Statistical
Manual of Mental Disorders, Fourth Edition (SCID)33 along
with detailed medical history and physical examination. Baseline laboratory values included urine drug screen and pregnancy tests for women of reproductive age. In addition, prior to
the sleep deprivation paradigm, participants had in-laboratory
screening polysomnography to rule out clinically significant
sleep disordered breathing (apnea-hypopnea index > 10/h) or
periodic limb movements of sleep (periodic limb movement
index > 10/h). Individuals were excluded if they had evidence
of current Axis I disorder, including drug or alcohol abuse/dependence, any significant medical, sleep, or neurologic illness,
or contraindications to magnetic resonance scanning.
The study was approved by the Institutional Review Board
of McLean Hospital. All individuals were compensated for
their participation in this study.
Sleep Deprivation Protocol
Individuals who met inclusion/exclusion criteria participated in a total sleep deprivation protocol. After completing
in-laboratory screening polysomnography, participants were
instructed to follow their usual sleep schedules, and to avoid
napping and caffeine consumption for the approximately 5-7
days between screening and the sleep deprivation protocol.
Participants returned to the laboratory for a baseline night of
sleep, followed by 40 h of constant wakefulness confirmed via
direct supervision by study staff. Following sleep deprivation,
participants had 2 consecutive nights of recovery sleep in the
laboratory. Participants were allowed to leave the laboratory
facility between their first and second nights of recovery sleep,
with instructions to avoid napping. Four separate magnetic resonance scans were performed between 07:00 and 08:00 h following baseline night of sleep (BSL), after sleep deprivation,
and following both nights of recovery sleep (REC1 and REC2).
Magnetic Resonance Imaging
Imaging and spectroscopy were performed on a whole body
4-Tesla MR scanner (Varian/UNITYInova, Palo Alto, CA) using
a dual tuned proton-phosphorus transverse electromagnetic
head coil (Bioengineering Inc., Minneapolis, MN) operating at
170.3 MHz for proton and 68.95 MHz for phosphorus. Manual
shimming on the unsuppressed global water signal yielded a
typical unsuppressed water line width of 20–30 Hz. A threeplane scout image set initially determined the individual’s position within the coil, followed by acquisition of high-contrast,
T1-weighted sagittal and axial image sets (echo time/repetition
time (TE/TR) = 6.2/11.4 ms, field of view (FOV) = 22 cm × 22
cm, readout-duration = 4 ms, receive bandwidth = ± 32 kHz,
in-plane matrix size = 128 × 256 mm (sagittal), 256 × 256 mm
(axial), in-plane resolution = 1.90 mm × 0.94 mm (sagittal), 0.94
mm × 0.94 mm (axial), axial-plane matrix size = 32 (sagittal),
1920
Brain Bioenergetics in Sleep Deprivation—Plante et al.
64 (axial) axial-plane resolution = 2.5 mm (sagittal and axial),
scan time = 2 min, 30 sec (sagittal), 5 minutes (axial)) of the
entire brain using a three-dimensional, magnetization-prepared
fast low angle shot imaging sequence (3D-mpFLASH).
P Magnetic Resonance Spectroscopy
31
P 3D-CSI used the phosphorus channel of the dual tuned
head coil. Acquisition parameters were: TR = 500 ms; tipangle = 32°; Rx bandwidth = ± 2 kHz; complex points = 1024;
readout duration = 256 ms; prepulses = 10; preacquisition
delay = 1.905 ms; FOV (x,y,z) = 330 mm; nominal volume = 13.1
cc; maximum phase-encode matrix dimension (x,y,z) 14 × 14 ×
14 (zero-filled out to 16 × 16 × 16 prior to reconstruction). The
3D-CSI sequence used a spherically bound, sparse-omission34,35
reduced-phase encoding scheme, with k-space points randomly
omitted from the 14 × 14 × 14 matrix in such a way that the
degree of k-space point omission gradually increases toward
outer k-space. The variable k-space sampling density preserves
the sensitivity of the measurement as well as the spatial localization, while greatly reducing scan time, which was approximately 46 min.
The 31P 3D-CSI raw datasets were first zero-padded within
a 16 × 16 × 16 matrix and each k-space free-induction decay
(FID) digitally corrected in amplitude, accounting for the discrepancy between theoretical and integer-weighted k-space
filter functions. Once spatially resolved, the 31P 3D-CSI grid
was coregistered with the axial T1-weighted images such that
the grid was centered midsagittally inside the brain according
to anatomical landmarks in both the sagittal and axial planes. A
4 × 7 × 3 matrix of voxels was centered within the brain as to
exclude voxels adjacent to the temporalis muscle, thus minimizing signal contamination from these muscles (Figure 1).
Additionally, voxels that were too close to the superior and inferior surfaces of the skull were omitted because of low signalto-noise ratio and susceptibility artifact. Automated software
then zero-order phase-corrected each spectrum using the PCr
resonance as a navigator and extracted the spatially resolved
spectral FIDs (time-domain) from each voxel in each scan for
separate fitting of each spectrum.
All offline image processing used commercial and customwritten software for the purpose of grid-shifting, partial volume
analysis, and tissue segmentation. For 31P 3D-CSI spectral fitting, we used a spectral time-domain fitting program, based on
the Marquardt-Levenberg nonlinear, least-squares algorithm,
incorporating prior knowledge of spectral peak assignments,
chemical shifts and J-coupling constants.35 Our spectral model
included 10 phosphorus-containing molecules: γ, α, β-NTP,
PEtn, PCho, GPEtn, GPCho, 2,3 diphospho-glyceride (DPG),
inorganic phosphate (Pi), membrane-bound phospholipid (MP)
and PCr. The model assumes lorentzian lineshape for the singlet PCr and Pi resonances, lorentzian doublets (1:1) for the
γ- and α-NTP resonances and a lorentzian-modeled triplet
structure (1:2:1) for the β-NTP resonance where the NTP Jcoupling constant was fixed to 16 Hz. Our spectral model is described in more detail in elsewhere.35 Phosphorus metabolites
are reported relative to total phosphorus signal and multiplied
by a factor of 102 for modeling purposes. Intracellular pH was
calculated using methods described previously by Petroff and
colleagues.36
31
SLEEP, Vol. 37, No. 12, 2014
Figure 1—(A) T1-weighted anatomical images depicting placement of
3D-CSI grid. (B) Representative 31P spectrum from a single voxel in the
parieto-occipital cortex displayed with (C) modeled fit. Spectrum is shown
with 10 Hz exponential filtering for display.
Tissue Segmentation and Image Post-processing
Image segmentation was performed using a PC running VMware for FSL version 4.1. For voxel tissue partial-volume estimation, the high-resolution T1-weighted axial images were first
segmented into cortical gray matter (cGM), white matter (WM),
subcortical gray matter (scGM) and cerebrospinal fluid (CSF)
compartments using FSL 4.1 (FMRIB Software Library; Analysis Group, FMRIB; Oxford, UK). This package allows for optimized and fully automated segmenting of both cortical tissue
(“FAST” - FMRIB Software Library; Analysis Group, FMRIB;
Oxford, UK) as well as subcortical tissue (“FIRST” - FMRIB
Software Library; Analysis Group, FMRIB; Oxford, UK).
When segmented, the images were then reformatted for input
into an in-house automated voxel coregistration and partial
volume analysis program written using C-code. In this process,
we convolved the mathematically modeled, three-dimensional
point-spread function (3D-PSF) from the sparse k-space sampling scheme, digitally sampled in a 256 × 256 × 64 matrix,
with the coregistered binary images (also digital matrices of
256 × 256 × 64) to obtain theoretically correct pixel-counts of
the contribution of each tissue type to each voxel based on the
3D-PSF weighted distribution.35 Subsequently, the volumetric
tissue contribution for each MRS voxel was determined and
volumetric contributions of total gray matter (GM = cGM +
scGM), WM, and CSF calculated.
1921
Polysomnography and EEG Spectral Analysis
During sleep episodes, participants were monitored with electrophysiologic monitoring including EEG, electrooculogram
(EOG), and electromyogram (EMG) using Alice Sleepware
(Philips Respironics, Murrysville, PA) according to standard
procedures.37 Because scoring standards have changed since
the initial recordings, a registered polysomnographic technologist rescored sleep stages in 30-sec epochs in accordance with
Brain Bioenergetics in Sleep Deprivation—Plante et al.
current staging criteria for analysis.38 EEG data from C3-A2
was used for spectral analysis. Spectral analysis was performed
in MATLAB (Mathworks, Natick, MA) in consecutive 6-sec
epochs (Welch’s averaged modified periodogram with a Hamming window) for all epochs of NREM sleep, consistent with
prior studies.39,40 Automated artifact rejection was conducted
to remove individual 6-sec epochs with high-frequency noise
or interrupted contact with the scalp. Specifically, individual
epochs were excluded if the high frequency power between
20–30 Hz was in the top 0.05% of power in that range for all
NREM epochs for a given polysomnographic recording. Slow
wave activity (SWA) was defined as the power density in the
1–4.5 Hz range.
Statistics
The primary energetic metabolites of interest in this study
were β-NTP and PCr. Other variables related to brain bioenergetics that were examined on an exploratory basis included the
metabolites γ-NTP and unbound Pi, the ratios of Pi to β-NTP
(Pi/β-NTP),41 Pi to PCr (Pi/PCr),42 and pH. Linear mixed effects models were constructed for each of the 31P metabolites of
interest in this study.28,29,43 A minimal model, using subjects as a
random effect and total phosphorus signal as a fixed effect, was
used to identify peaks that were questionable because of contamination by muscle or other artifact, with voxels rejected if
the studentized residual from the minimal model was > 3 or ≤ 3.
The full model used for analysis included random effects of
subject and individual subject scan nested within subject. Additionally, fixed effects included total phosphorous signal – metabolite of interest, condition (BSL, sleep deprivation, REC1,
REC2), partial volume (GM + WM) and tissue type (GM –
WM) with age and sex included as covariates of no interest.
Because an effect of position in the MRS slab (i.e., dorsal to
ventral position and inclusion of the basal ganglia) was observed in preliminary analyses, voxel region (superior, medial,
inferior, or basal ganglia) was also included as a covariate of no
interest in the model. Interaction terms were added for partial
volume by condition and tissue type by condition because we
hypothesized metabolite differences between tissue types (i.e.,
gray and white matter) across the sleep deprivation paradigm.
A two-way (time × tissue type) interaction, multiple degrees of freedom test was performed for each variable to examine tissue-type changes across the experimental condition.
Significant interactions were then examined to determine if
significant changes occurred in gray and/or white matter. Post
hoc tests that used the gray and white matter contribution
from voxels of mixed composition were then used to compare
differences between time points. If no significant tissue type
interaction was found for a given metabolite, effects of total
tissue (50% GM; 50% WM) were evaluated, in case there
were metabolic changes across the paradigm that were not related to tissue type. We applied Bonferroni multiple testing
corrections to primary and exploratory metabolite results
separately and thus all reported P values for primary metabolites are multiplied by a factor of 2; P values for exploratory
metabolites are multiplied by a factor of 5. Alpha was fixed
at 0.05 for significance. All metabolite levels are reported
as least squares mean ± standard error of the mean. Linear
mixed effects models were fitted with the restricted maximum
SLEEP, Vol. 37, No. 12, 2014
likelihood method implemented by JMP Pro Version 10 (SAS
Institute, Cary, NC).
We also explored associations between SWA and tissuespecific levels of metabolites of primary interest for which we
observed differences across sleep deprivation and recovery, to
examine whether changes observed were related to sleep homeostasis. For associations between deprivation and recovery
1 scans, SWA was parameterized as a percent change from the
baseline night of sleep to first night of recovery sleep. For associations between deprivation and REC2 scans (which had 2
nights of recovery sleep between scans), SWA was parameterized as the percent change in SWA from baseline to the first
night of recovery sleep plus the percent change from baseline
to the second night of recovery sleep.
RESULTS
Primary Metabolites
Changes in PCr across experimental conditions differed significantly by tissue type (F3,2475 = 4.54; P = 0.007) (Figure 2A).
Substantial changes across conditions were observed in GM
(F3,219 = 4.38; P = 0.010) but not WM (F3,62 = 0.76; P = 1.0).
Changes in GM during the protocol were characterized by a
nonsignificant reduction in PCr of 3.9% from BSL to sleep
deprivation (t2,284 = -1.19; P = 0.47) followed by increases in
GM PCr from sleep deprivation to REC1 and REC2 of 7.3%
(t2,247 = 2.25; P = 0.051) and 10.1% (t2,158 = 3.51; P = 0.001),
respectively.
Changes across experimental conditions did not differ significantly by tissue type for β-NTP (F3,2477 = 1.37; P = 0.50), nor
was there a significant change in β-NTP across experimental
conditions in total tissue (50% gray matter/50% white matter)
(F3,30 = 1.41; P = 0.52) (Figure 2B).
Secondary Metabolites
Similar to PCr, changes in both γ-NTP and Pi/β-NTP prominently occurred in gray matter. Changes in γ-NTP across experimental conditions differed significantly by tissue type
(F3,2489 = 4.71; P = 0.014) (Figure 3A). Substantial changes
were observed in GM (F3,224 = 8.24; P < 0.001), but not WM
(F3,63 = 0.55; P = 1.0). In GM, though there was no substantial
change from BSL to sleep deprivation (t2,293 = -0.33; P = 1.0),
after recovery nights 1 and 2 of sleep, γ-NTP rebounded to
values 9.2% and 11.8% greater than sleep deprivation (sleep deprivation to REC1: t2,254 = 2.96; P = 0.017; sleep deprivation to
REC2: t2,161 = 4.29; P < 0.001). Overall, there was a significant
net enhancement of γ-NTP in GM of 10.6% after 2 nights of recovery sleep relative to baseline levels (t2,233 = 3.55; P = 0.002).
Changes in Pi/β-NTP across experimental conditions also
differed significantly by tissue type (F3,2445 = 8.02; P < 0.001)
(Figure 3B). Substantial changes across conditions were observed in GM (F3,183 = 4.19; P = 0.034) but not WM (F3,56 = 3.15;
P = 0.16). Changes in GM during the protocol were characterized increases in GM Pi/β-NTP from BSL to REC2 (t2,190 = 3.35;
P = 0.005), without other significant changes in GM across
sleep deprivation and recovery.
Pi and pH demonstrated changes that predominantly occurred in white matter. Changes in Pi across conditions differed
significantly by tissue type (Pi; F3,2485 = 8.21; P < 0.001) with
1922
Brain Bioenergetics in Sleep Deprivation—Plante et al.
Figure 2—Model-derived least-squares means of (A) phosphocreatine (PCr) and (B) β-nucleoside triphosphate (β-NTP) area under the curve (error bars
represent standard error). We estimated metabolite contributions of gray matter and white matter in voxels of mixed composition to aid in the interpretations
of interactions. (A) PCr showed a significant difference in association with condition between gray and white matter, in accordance with our hypothesis
(F3,2475 = 4.54; P = 0.007). Post hoc analyses revealed a trend toward increases in PCr from sleep deprivation to REC1 (t2,247 = 2.25; P = 0.051) and significant
increases in PCr from sleep deprivation to REC2 (t2,158 = 3.51; P = 0.001) in GM. (B) β-NTP did not demonstrate a significant difference in association with
condition between gray and white matter. * P ≈ 0.05; *** Significant difference corresponding to P ≤ 0.001. The dashed line is total tissue (50% gray matter;
50% white matter), and is depicted for comparison purposes. BSL, baseline night of sleep; REC1, recovery sleep night 1; REC2, recovery sleep night 2.
substantial changes observed in WM (F3,49 = 6.58; P = 0.004),
but not GM (F3,146 = 2.19; P = 0.46) (Figure 3C). There was
a significant 9.4% decrease in WM from BSL to sleep deprivation (t2,57 = -3.00; P = 0.020). Levels of Pi then rebounded
significantly from sleep deprivation to REC1 (t2,49 = 2.91;
P = 0.027) back to near-baseline levels. Additionally, a significant 10.1% decrease of Pi in WM from BSL to REC2 was observed (t2,50 = -3.33; P = 0.008).
Changes in pH across experimental conditions differed significantly by tissue type (F3,2482 = 4.13; P = 0.031), with significant changes observed in WM (F3,96 = 6.30; P = 0.003), but not
GM (F3,392 = 2.57; P = 0.27) (Figure 3D). pH in WM increased
from sleep deprivation to REC1 (t2,100 = 3.63; P = 0.002) and
REC2 (t2,78 = 3.83; P = 0.001), respectively.
Changes across the experimental condition did not differ significantly by tissue type for Pi/PCr (F3,2457 = 3.55; P = 0.070), nor
was there a significant change in Pi/PCr across experimental
conditions for total tissue (F3,30 = 0.28; P = 1.0) (Figure 3E).
Associations of High-Energy phosphates and SWA
Changes in PCr in GM from sleep deprivation to REC1 were
significantly associated with SWA (t = 2.65; P = 0.009), such
that increased SWA was associated with increases in PCr. However, changes in PCr in GM from sleep deprivation to REC2
were not significantly associated with SWA (t = 1.36; P = 0.19).
Associations between SWA and β-NTP were not explored because there were no significant differences for this metabolite
observed across sleep deprivation and recovery.
DISCUSSION
This study confirms our hypothesis that high-energy phosphates increase in gray matter during recovery sleep after
sleep deprivation. Specifically, our data indicate that PCr in
gray matter increases after the initial night of recovery sleep
SLEEP, Vol. 37, No. 12, 2014
following acute sleep deprivation. Moreover, these increases in
PCr in GM are associated with increases in SWA during the initial night of recovery sleep, suggesting changes in PCr in GM
resulting from recovery sleep are related to sleep homeostasis.
Notably, PCr continues to rise in gray matter after a second night
of recovery sleep, suggesting this increase in PCr begins during
the first night after recovery from acute sleep deprivation and
continues during subsequent nights of recovery. Contrary to our
hypothesis, we did not find significant increases in β-NTP across
the sleep deprivation protocol, suggesting ATP levels remain
constant in gray matter during sleep deprivation and recovery.
Neurons have high and fluctuating energy requirements, and
as such, may increase hydrolysis of ATP significantly within
short periods of time to compensate for increases in metabolic
demand.44 However, many prior studies have demonstrated that
ATP levels remain relatively constant in cells with similar energy
demands, such as skeletal and cardiac muscle, a phenomenon
that has been termed the stability paradox.45 That ATP levels remain constant despite increased hydrolysis is thought to be permitted, in part, by efficient phosphoryl transfer networks, such
as the PCr/creatine (Cr) kinase system, which bridges energy
consumption with production and secure energetic homeostasis
under stress.46 The PCr/Cr kinase system thus plays a key role
in cellular energy buffering upon cell activation or sudden stress
conditions, preventing rapid declines in ATP through the catabolism of PCr to form ATP, as well as transport of high-energy
phosphoryls from the mitochondria to cytosolic sites of high
ATP demand.44 Notably, even under conditions of high ATP turnover, concentrations of PCr change at proportions significantly
below the rate of ATP hydrolysis in skeletal muscle, suggesting
that small perturbations of the overall PCr pool measured with
MRS may reflect larger changes in ATP turnover.47
In this context, it is not surprising that we found changes in
PCr in gray matter, which has higher mitochondrial creatine
1923
Brain Bioenergetics in Sleep Deprivation—Plante et al.
Figure 3—Model-derived least-squares means of (A) γ-NTP, (B) Pi/β-NTP, (C) Pi, (D) pH, and (E) Pi/PCr area under the curve (error bars represent standard
error). We estimated metabolite contributions of gray matter and white matter in voxels of mixed composition to aid in the interpretations of interactions.
(A) γ-NTP showed a significant difference in association with condition between gray and white matter (F3,2489 = 4.71; P = 0.014). Significant increases
from sleep deprivation to REC1 (t2,254 = 2.96; P = 0.017) and REC2 (t = 4.29; t2,161 = 4.29; P < 0.001) with overall increases from BSL to REC2 (t2,233 = 3.55;
P = 0.002) were observed in GM. (B) Pi/β-NTP showed a significant difference in association with condition between gray and white matter (F3,2445 = 8.02;
P < 0.001). Significant increase from BSL to REC2 (t2,190 = 3.35; P = 0.005) was observed in GM. (C) Pi showed a significant difference in association with
condition between gray and white matter (F3,2485 = 8.21; P < 0.001). Significant decrease from BSL to sleep deprivation (t2,57 = -3.00; P = 0.020) and significant
increase from sleep deprivation to REC1 (t2,49 = 2.91; P = 0.027) were observed in WM, with an overall decline of Pi in WM from BSL to REC2 (t2,50 = -3.33;
P = 0.008). (D) pH showed a significant difference in association with condition between GM and WM (F3,2482 = 4.13;P = 0.031). pH in WM increased from
sleep deprivation to REC1 (t2,100 = 3.63; P = 0.002) and REC2 (t2,78 = 3.83; P = 0.001), respectively. (E) Pi/PCr did not demonstrate a significant difference
in association with condition between GM and WM. The dashed line is total tissue (50% gray matter; 50% white matter), and is depicted for comparison
purposes. * Significant difference corresponding to P ≤ 0.05. ** Significant difference corresponding to P ≤ 0.01. *** Significant difference corresponding to
P ≤ 0.001. BSL, baseline night of sleep; GM, gray matter; NTP, nucleoside triphosphate; PCr, phosphocreatine; Pi, inorganic phosphate; REC1, recovery
sleep night 1; REC2, recovery sleep night 2; WM, white matter.
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Brain Bioenergetics in Sleep Deprivation—Plante et al.
kinase activity than white matter,48 but not β-NTP, across the
sleep deprivation paradigm. However, it is certainly possible
that this study was not adequately powered to detect subtle differences in β-NTP in gray matter, especially because prior 31P
MRS studies that have not used linear mixed effects models to
examine tissue type-specific changes, have reported increases
in β-NTP after recovery sleep.20,22 Additionally, it is noteworthy
that the non-specific γ-NTP resonance, which includes both ATP
and ADP, increased in GM after REC1 and REC2. Measures
of β-NTP, although highly specific for ATP, may be less accurate than measures of γ-NTP because of the triplet structure of
β-NTP and its correspondingly lower signal-to-noise ratio than
γ- and α-NTP. Thus, although statistically significant changes in
γ-NTP were observed, it is not possible to determine if changes
in γ-NTP reflect changes in ATP and/or ADP given inherent
limitations of 31P MRS methodology of this study. However,
the changes in γ-NTP do suggest that changes in high-energy
diphosphates and triphosphates are occurring over the course
of sleep deprivation and recovery, and that these changes, like
PCr, are isolated to gray matter.
Consistent with other 31P MRS studies,19,20,22 we found no
significant differences in brain high-energy phosphates after
sleep deprivation when compared to baseline. This finding is
also consistent with animal studies that have examined whole
brain changes in high-energy phosphates, as PCr, ADP, and ATP
have been reported to remain constant relative to waking baseline during sleep deprivation.49 However, it is certainly possible
that this study was not adequately powered or that the duration
of sleep deprivation was not sufficient to detect changes from
BSL to sleep deprivation in high-energy phosphates. Although
speculative, the fact that there were similar patterns of nonsignificant decrease in GM from BSL to sleep deprivation among
all high-energy phosphates examined (PCr, β-NTP, γ-NTP)
suggests that further testing using adequately powered designs
may demonstrate significant decreases of these metabolites
from BSL to sleep deprivation. Additionally, the timing of the
sleep deprivation MRS scan occurred 24 h after BSL, and thus,
it is possible that changes in high-energy phosphates relative
to sleep deprivation may have been more robust if the sleep
deprivation scan had been obtained after the full course of sleep
deprivation (i.e., just prior to the first night of recovery sleep).
Also, the finding that increases in PCr, γ-NTP, and Pi/β-NTP
(which reflects mitochondrial phosphorylation potential50) after
sleep deprivation occur after 2 nights of recovery sleep is consistent with PET studies that demonstrate metabolic changes
induced by sleep deprivation do not fully normalize after one
night of recovery sleep.51
It is important to note that 31P MRS was performed during
wakefulness, and thus our results cannot be directly applied
to the ongoing debate regarding changes in brain high-energy
phosphates that may occur during the sleep period.17,52 Furthermore, the methodology used in this study did not measure ATP
or PCr turnover, as 31P MRS used in this study can only quantify
the relative concentration of a metabolite during a fixed period
of time. To more adequately study metabolic rates and/or the
dynamics of the creatine kinase system in sleep deprivation
with 31P MRS would require techniques such as magnetization
transfer, which is emerging as a novel and powerful imaging
protocol.53
SLEEP, Vol. 37, No. 12, 2014
Although speculative, the net effect of an increase in PCr in
GM after recovery sleep may be an adaptive response to the
metabolic stress of sleep deprivation. As such, increases in PCr
would raise the energetic buffering capacity in GM to protect
against the metabolic stress of subsequent sleep loss. An indirect support of this hypothesis comes from demonstrations that
oral creatine supplementation mitigates the detrimental effects
of sleep deprivation on executive tasks54,55 and increases PCr
in the brain.56 An alternative interpretation is that increases in
PCr represent a phenomenon that is not adaptive but related to
prolonged renormalization of brain bioenergetics after sleep deprivation that could be secondary to shifting of the equilibrium
constant of the creatine kinase reaction, increased oxidative
phosphorylation, or some other unspecified mechanism.
Our results of alterations in pH and Pi occurring in white
(rather than gray) matter are intriguing. Although pH, which
influences the equilibrium constant of the creatine kinase reaction,57 and Pi, which is an important signaling molecule in
oxidative phosphorylation,58,59 play important roles in brain
bioenergetics, it is possible that alterations in these factors
during sleep deprivation and recovery reflect other processes
such as glial maintenance of myelin that are indirectly linked
to brain bioenergetics. The overall energy expenditure of white
matter is significantly less than gray matter, with the majority of
energy used for housekeeping rather than neuronal signaling.60
Notably, the chemical reactions required for cholesterol and
phospholipid synthesis results in significant increases in inorganic phosphate through the consumption of ATP.60 Moreover,
recent evidence suggests that proteolipid protein, which plays
a key role in stability of the myelin sheath, may also regulate
extracellular pH and ATP.61 Given the results of this study, future research that examines changes in membrane regulation
in white matter induced by sleep deprivation and recovery are
indicated.
Our results must be interpreted carefully in light of important
technical and statistical limitations of the study design, which
may affect generalizability of the results. First, the 3D-CSI slab
encompassed a relatively large region (6 cm × 8 cm × 14 cm),
which included cortical (cingulate gyrus, as well as portions of
the frontal, temporal, parietal, and occipital lobes) and subcortical structures. However, the sampling region did not encompass
the entire brain and excluded some regions frequently examined
in single-voxel MRS, such as the hippocampus. Second, slab
placement was biased toward the center of the brain, resulting
in an oversampling of white relative to gray matter. As a result,
changes in GM are less precise than changes in WM and differences in statistical significance between tissue types should not
be equated with differences in the magnitude of change within
the different tissue compartments. Third, the modest number of
participants limits the representativeness of our sample and renders our results more sensitive to the small-sample behavior of
our statistical methods. Fourth, GM and WM voxels are mathematical extensions of the tissue-type regression that examine
the gray and white matter contribution of voxels of mixed composition and do not represent actual voxels observed in the dataset. Therefore, these calculated voxels are meant to quantify
relative contributions of tissue types across the entire region
sampled. Additionally, this study used a within-subjects design
and did not use a comparison group to verify that changes in 31P
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Brain Bioenergetics in Sleep Deprivation—Plante et al.
MRS metabolites were not caused by some other factor in addition to sleep deprivation and recovery (e.g., accommodation to
multiple scans). However, it is unlikely that repeated scans are
responsible for our results because previous 31P MRS studies
conducted in our laboratory have demonstrated stability of 31P
MRS metabolites on test-retest protocols.62 Finally, our analysis
divides brain tissue into two tissue types, gray and white matter,
and is not able to discern metabolic differences that may occur
in specific nuclei or areas within the brain over the course of
sleep deprivation and recovery. Thus future studies that use advances in MRS technology (e.g., ultra-high field 7 Tesla magnetic resonance scanners) and/or the use of regression modeling
to examine metabolic changes in specific brain regions may be
fruitful areas of further investigation. However, in lieu of the
development and dissemination of such technologies, adapting
the modeling techniques used in this study to widely available
magnetic resonance scanners (e.g., 3 Tesla) may be a more
pragmatic means of translating these findings to the study of
brain bioenergetics in healthy and pathologic conditions.
To our knowledge, this is the first study to apply linear
mixed-effects modeling in 31P MRS in a sleep deprivation and
recovery paradigm in humans. The finding of increased PCr in
gray matter after two nights of recovery sleep suggests brain
bioenergetics are modified by sleep in specific tissue types differentially during recovery after sleep deprivation. These findings and the novel modeling techniques used in this research
may ultimately be further leveraged to enhance the study of
sleep function in healthy individuals, as well as examine the
connections between sleep homeostasis and brain bioenergetics
in patients with neuropsychiatric disorders.
DISCLOSURE STATEMENT
This was not an industry supported study. This work was supported by an American Sleep Medicine Foundation Physician
Scientist Training Award (DTP), the National Institute on Drug
Abuse (DA016542 to CMD, DA00343 to SEL, K01DA025125
to GHT, and DA09448 to PFR), and the National Institute of
Mental Health (MH58681 to PFR). No funding source played
a role in the study design, data collection, analysis, and interpretation of the data, and the decision to submit the paper for
publication. Dr. Plante has received unrelated honoraria from
Cambridge University Press, the American Academy of Sleep
Medicine, and Oakstone Medical Publishing. Dr. Renshaw is a
consultant to Kyowa Hakko Kirin and Ridge Diagnostics. Dr.
Ravichandran contributed to manuscript development while
affiliated with Harvard Medical School and the Laboratory
for Psychiatric Biostatistics at McLean Hospital. She has received prior direct or indirect funding support through NIH, the
Rogers Family Foundation, and NARSAD. Dr. Riedner is financially supported in part by a grant from Philips Respironics
and is involved in several patent applications resulting from
research supported by Philips Respironics. The other authors
have indicated no financial conflicts of interest.
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