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β-Amyloid accumulation in the human brain after one
night of sleep deprivation
, Gene-Jack Wang
, Corinde E. Wiers
, Sukru B. Demiral
, Min Guo
, Sung Won Kim
, Veronica Ramirez
, Amna Zehra
, Clara Freeman
, Gregg Miller
, Peter Manza
, Tansha Srivastava
Susan De Santi
, Dardo Tomasi
, Helene Benveniste
, and Nora D. Volkow
Laboratory of Neuroimaging, National Institute on Alcohol Abuse and Alcoholism, National Institutes of Health, Bethesda, MD 20892;
Inc., Boston, MA 02108; and
Department of Anesthesiology, Yale School of Medicine, New Haven, CT 06510
Edited by Michael E. Phelps, University of California, Los Angeles, CA, and approved March 13, 2018 (received for review December 14, 2017)
The effects of acute sleep deprivation on β-amyloid (Aβ) clearance
in the human brain have not been documented. Here we used PET
F-florbetaben to measure brain Aβburden (ABB) in
20 healthy controls tested after a night of rested sleep (baseline)
and after a night of sleep deprivation. We show that one night of
sleep deprivation, relative to baseline, resulted in a significant in-
crease in Aβburden in the right hippocampus and thalamus. These
increases were associated with mood worsening following sleep
deprivation, but were not related to the genetic risk (APOE geno-
type) for Alzheimer’s disease. Additionally, baseline ABB in a
range of subcortical regions and the precuneus was inversely as-
sociated with reported night sleep hours. APOE genotyping was
also linked to subcortical ABB, suggesting that different Alz-
heimer’s disease risk factors might independently affect ABB in
nearby brain regions. In summary, our findings show adverse ef-
fects of one-night sleep deprivation on brain ABB and expand on
prior findings of higher Aβaccumulation with chronic less sleep.
Beta-amyloid (Aβ) is present in the brain’s interstitial fluid
(ISF) and is considered a metabolic “waste product”(1).
Mechanisms by which Aβis cleared from the brain are not
completely understood (2), although there is evidence that sleep
plays an important role in Aβclearance (3). In rodents, chronic
sleep restriction led to increases in ISF Aβlevels (4) and in a
Drosophila model of Alzheimer’s disease (AD), chronic sleep
deprivation (SD) resulted in higher Aβaccumulation (5). In
healthy humans, imaging studies have revealed associations be-
tween self-reports of less sleep duration or poor sleep quality and
higher Aβburden (ABB) in the brain (6–8), which is a risk factor
for AD. This association has been considered bidirectional be-
cause increased ABB could also lead to impairments in sleep (9,
10). Notably, increased ABB in the brain has been associated
with impairment of brain function (11, 12). Thus, strategies that
prevent Aβaccumulation in the brain could promote healthy
brain aging and be useful in preventing AD. In this respect, there
is increasing evidence that sleep disturbances might contribute to
AD, in part by facilitating accumulation of Aβin the brain (13).
To better characterize ABB dynamics, studies have focused on
the effects of sleep patterns on ABB in the CNS. In rodents, it
has been shown that Aβclearance from the brain’s ISF pre-
dominately occurred during sleep (4), which was ascribed to the
glymphatic pathway, operating most efficiently during sleep (3,
14, 15). Clinical studies have also shown that Aβlevels in the
cerebrospinal fluid (CSF) are the highest before sleep and the
lowest after wakening, while CSF Aβclearance was counteracted
by SD (16). However, there are some inconsistencies between
animal models and findings in humans (17), and Aβincreases in
human CSF could reflect factors other than ABB increases in the
brain itself (18–21). Notably, the effects of acute SD on Aβ
clearance in the human brain have not been documented. This
observation will be important for understanding the contribution
of sleep to Aβclearance from the brain and the regional speci-
ficity of such effects.
Here we evaluated the effects of one-night SD on ABB in
healthy controls to investigate whether sleep affects clearance of
Aβfrom the human brain. For this purpose, we used positron
emission tomography (PET) with which it is now possible to
measure ABB in the living human brain. There are several val-
idated PET radiotracers for this purpose, including
florbetaben (FBB) (22, 23). It is believed that such radio-
tracers predominantly bind to insoluble Aβ
but there is recent evidence that they also bind to soluble Aβ
forms (28). Thus, we reasoned that PET and FBB could be used
to detect increases in ABB because of acute SD, directly in the
human brain (3). First, we aimed to assess the effect of one-night
SD on brain ABB with PET-FBB in healthy controls (n=20, 22–
72 y old, 10 females) (Table S1), and compared the measures to
baseline brain ABB captured at the same time of the day but
following a night of rested sleep [referred to as rested-
wakefulness (RW)]. Second, we aimed to replicate in our sam-
ple the previously reported association between sleep history and
brain ABB (when measured after RW) (6–8). For our first aim,
we hypothesized that one night of SD would increase ABB in the
hippocampus, which shows some of the earliest structural and
functional changes in AD (29, 30). For our second aim, we hy-
pothesized that history of poor sleep would be associated with
There has been an emerging interest in sleep and its associa-
tion with β-amyloid burden as a risk factor for Alzheimer’s
disease. Despite the evidence that acute sleep deprivation el-
evates β-amyloid levels in mouse interstitial fluid and in human
cerebrospinal fluid, not much is known about the impact of
sleep deprivation on β-amyloid burden in the human brain.
Using positron emission tomography, here we show that acute
sleep deprivation impacts β-amyloid burden in brain regions
that have been implicated in Alzheimer’s disease. Our obser-
vations provide preliminary evidence for the negative effect of
sleep deprivation on β-amyloid burden in the human brain.
Author contributions: N.D.V. conceived study; E.S.-K., G.-J.W., C.E.W., S.B.D., S.W.K.,
S.D.S., D.T., H.B., an d N.D.V. desig ned research; E. S.-K., G.-J.W. , C.E.W., S.B.D ., M.G.,
S.W.K., E.L., V.R., A.Z., C.F., G.M., P.M., T.S., S.D.S., D.T., H.B., and N.D.V. performed re-
search; E.S.-K. analyzed data; and E.S.-K. and N.D.V. wrote the paper.
Conflict of interest statement: S.D.S. was an employee of Piramal Pharma Inc. , which
partly supported the radiotracer for this study.
This article is a PNAS Direct Submission.
This open access article is distributed under Creative Commons Attribution-NonCommercial-
NoDeriv atives L icense 4.0 ( CC BY-NC- ND).
To whom correspondence may be addressed. Email: firstname.lastname@example.org, gene-
email@example.com, or firstname.lastname@example.org.
This article contains supporting information online at www.pnas.org/lookup/suppl/doi:10.
www.pnas.org/cgi/doi/10.1073/pnas.1721694115 PNAS Latest Articles
higher ABB in the hippocampus, precuneus, and medial pre-
frontal cortex (6, 8, 31).
Acute SD Effects. To compare the differences in FBB binding
[quantified as relative standard uptake value (SUVr) and used as
a marker of ABB] (Methods) after acute SD versus that obtained
after RW, we used a voxelwise paired ttest in statistical para-
metric mapping (SPM) (Methods). This analysis showed that
images obtained after SD compared with those obtained after
RW had significantly higher FBB binding (ABB increases) in a
right lateralized cluster (Fig. 1A) that comprised hippocampal,
parahippocampal, and thalamic regions (Table S2). Of note, the
increases in FBB SUVr in this cluster were robust and observed
in 19 of 20 participants (Fig. 1B) from RW (mean =1.35,
standard deviation =0.06) to SD (mean =1.42, standard de-
viation =0.07; a 5% increase, P<0.0001). To further confirm
this finding, we quantified FBB SUVr in an a priori hippocampal
region of interest (ROI) (Methods and Fig. 1D) and compared
the measures after SD to those after RW. This ROI analysis also
showed a significant increase in FBB SUVr in the right hippo-
campus ROI from RW to SD (P=0.046, two-tailed, Cohen’sd=
0.48) but not in the left hippocampus (P=0.4). The magnitude
of the Aβchanges in the hippocampal cluster varied significantly
between subjects (Fig. 1B)(−0.58% to +16.1%). We found that
this variability was not associated with gender, age, or apolipo-
protein E (APOE)-based odds ratio for AD (ORAD) (Methods)
(P>0.3). Notably, changes in FBB SUVr in the subcortical
cluster were significant in both males (P=0.0008) and females
(P=0.003). In addition, reported sleep hours (SH) and total
score (TS) for sleep quality (Pittsburgh Sleep Questionnaire
Inventory, PSQI) (Methods) were not associated with these SD-
related increases. Thus, the mechanisms accounting for the ob-
served between-subject variability are still unclear. Subjective
behavioral assessment revealed that SD negatively impacted
mood compared with RW (Methods and Fig. S1). We assessed if
the effects of SD on mood were correlated with increases in ABB
in the right hippocampal cluster. This analysis showed that mood
worsening was negatively associated with changes in FBB SUVr
[r(16) =−0.50, P=0.03] (Fig. 1C) such that participants with
larger increases in ABB in the hippocampal cluster (Fig. 1A) had
more mood worsening after SD. Because the quantification of
ABB using FBB SUVr can be sensitive to blood perfusion ef-
fects, we quantified FBB accumulation using measures of bind-
ing potential (BPnd), which are less sensitive to blood perfusion
effects than SUVr measures. BPnd in the cluster where we ob-
served the SD effect (Methods and Fig. 1A) was also significantly
higher in SD relative to RW [t(19) =3.57, P=0.002] (Fig. S2A).
Moreover, SD-related changes in BPnd were significantly cor-
related with those observed with FBB SUVr [r(18) =0.53, P=
0.016] (Fig. S2B), further supporting that SD-related increases in
FBB SUVr are not primarily driven by perfusion effects.
Sleep, APOE, and ABB. Prior studies had reported an association
between reported SH and sleep quality and (cortical) ABB in
healthy middle-aged and older individuals (6–8). We tested
whether we would corroborate those observations in our sample
using the measures obtained during RW. We found that reported
average SH inversely correlated with FBB SUVr [r(18) =−0.5,
P=0.024] and with BPnd [r(18) =−0.57, p=0.009] at RW in the
subcortical cluster that showed increases in ABB with SD (Fig.
2A), thus supporting long-term susceptibility of these regions to
increased ABB with less SH. Voxelwise regression analysis of
FBB SUVr on SH showed that less SH was associated with
higher FBB SUVr in the bilateral putamen, parahippocampus,
and right precuneus (Fig. 2Band Table S3). Interestingly, the
SH-related brain areas were more extensive than the areas as-
sociated with (acute) SD-induced ABB increases. For regional
ABB, SH-related subcortical regions (Table S3) had minimal
overlap with subcortical areas related to ORAD (Fig. 2B),
which included the bilateral lentiform nucleus and pallidum
(Table S4). These observations suggested that different brain
R² = 0.2535
-6 -4 -2 0 2 4
Mood Change: SD-RW
*p = 0.046
Fig. 1. Effects of one-night SD on ABB. (A) Voxelwise paired ttest between RW and SD conditions highlighting the hippocampus as well as other subcortical
<0.05, cluster-size corrected) (Table S1). (B) Subject-level changes in FBB SUVr (in the red cluster identified in A) from RW to SD. There was no
significant effect of gender, or gender ×sleep interaction (P>0.15). (C) Association between changes in mood from RW to SD and changes in the FBB SUVr
for the cluster identified in A. Mood change was quantified using the principal component of the changes in self-report measures from RW to SD, which
accounted for 35.5% of the variance. Self-report measures of alert, friendly, happy, social, and energetic significantly decreased, and measures of tired and
difficulty staying awake significantly increased from RW to SD (P<0.001, two-tailed) (see also Fig. S1). (D) Average FBB SUVr in a priori hippocampus ROIs
across subjects. Error bars show standard deviation (Methods).
www.pnas.org/cgi/doi/10.1073/pnas.1721694115 Shokri-Kojori et al.
regions could be independently affected by different AD-risk
factors (i.e., sleep vs. APOE). This observation is consistent
with prior findings reporting that the APOE genotype did not
moderate the relationship between sleep measures and ABB in
the brain (7).
Our findings provide preliminary evidence for the role of SD on
Aβaccumulation in the human brain. The increases in ABB after
SD in the hippocampus, which is considered among the most-
sensitive brain regions to AD neuropathology (29), is consistent
with epidemiological data identifying impaired sleep as a risk
factor for AD (9, 10) and with recent evidence showing that
disruption of deep sleep increases Aβin human CSF (32). We
also showed Aβincreases in the thalamus after SD, which is a
brain region that shows increases in Aβin the early stages of AD
(33). The regional increases in ABB that we observed from RW
to SD might reflect decreased clearance of Aβ, presumably from
lack of sleep, thus supporting the role of glymphatic system in
clearing Aβfrom the brain during sleep. While this effect was
observed in rodents (3), no study as of now has been able to
directly measure Aβclearance reflecting glymphatic function in
the human brain. Other mechanisms have been shown to stim-
ulate Aβclearance during sleep, such as γ-oscillations during the
rapid eye-movement cycle (34). Alternatively, Aβincreases in the
hippocampus after SD could reflect increases in Aβsynthesis
associated with endogenous neuronal activity during SD (35), or
sleep-related changes in hippocampal neurogenesis (36, 37).
While we are interpreting our findings of increases in ABB after
SD to reflect Aβaccumulation due to lack of glymphatic clear-
ance (or other unknown mechanisms), we cannot rule out the
possibility that they reflect increases in the synthesis of Aβ(38)
from lack of sleep. Preclinical studies that monitor Aβclearance
during sleep using PET radiotracers alongside optical imaging of
ISF are needed to corroborate whether Aβligands and PET have
potential as markers of glymphatic function in the human brain.
Future work should also study the extent that elevated ABB in
the brain is related to increases in Aβlevels in the peripheral
tissue following SD (18, 20).
Detection of Aβplaques with PET is clinically relevant for the
diagnosis of AD, while elevated brain ABB in mild cognitive
impairment (MCI) has been suggested as a risk factor for pro-
gression to AD (22, 39). ABB increases as a function of aging
and AD severity, with an estimated 17% increase from younger
to older adults (40). Relative to healthy elderly, estimated in-
creases of 21% and 43% have been reported in individuals with
MCI and AD, respectively (40). In our study, the increases in
ABB due to one-night SD were smaller (5%) (Fig. 1 Aand B)
and were observed in a subcortical cluster that included the
hippocampus. At this stage, we are uncertain whether such SD-
related increases in ABB may subside following rested sleep. In
addition, the magnitude of the observed effects should be
interpreted with caution considering the methodological limita-
tions and potential confounds, such as the effects of blood-flow
changes and the limited sensitivity of current PET radiotracer to
soluble Aβ(28). While, prior studies have reported associations
with chronic poor sleep and higher average ABB across large a
priori-selected cortical areas (6–8), our results highlight the
relevance of studying subcortical regions for the associations
between ABB and sleep.
It has been shown in rodents that Aβplaques can form very
rapidly (41), while Aβplaques in their earliest stages show the
highest levels of neuritic dystrophy (42), thus suggesting that
increases in ABB due to one-night SD could adversely impact
the brain. Chronic poor sleep could then result in higher baseline
ABB levels, helping to explain the association with higher ABB
at RW (in the cluster showing SD effects) (Fig. 1A) and less
reported SH (Fig. 2A). In addition, SH negatively correlated with
ABB in the precuneus, putamen, and parahippocampus (Fig. 2B
and Table S3). These findings are consistent with studies show-
ing that chronic sleep restriction lead to elevated cortical Aβ
oligomers levels (that are neurotoxic) in the mouse cortex (43),
which might in part reflect up-regulation of β-secretase 1 (44).
However, a confounder is that increased ABB could in turn
exacerbate sleep problems (10).
It is noteworthy that ABB increases in the hippocampus are
less pronounced in MCI (45) or early-stage AD than in the
precuneus (22). Instead, changes in hippocampal volume, glu-
cose metabolism (30), and the blood–brain barrier (46) were
most evident in patients with early-stage AD. Thus, it is possible
R² = 0.2522
Sleep Hours (SH)
Fig. 2. Relationship between SH, APOE genotype, and ABB. (A) Regression
of FBB SUVr (indexing ABB) for the red cluster shown in Fig. 1Aat RW
against SH. (B) Three-dimensional rendering of the areas showing an asso-
ciation between higher FBB SUVr at RW and lower SH (red clusters, P
0.05, cluster-size corrected) (Table S3), as well as areas showing an associa-
tion between higher FBB SUVr and higher APOE-based genetic risk for AD
(quantified as log of ORAD) (green clusters, P
<0.05, cluster-size cor-
rected, except the right-sided subcortical cluster, which was cluster size-
corrected at q
<0.05) (Table S4). The subcortical clusters related to SH and
ORAD had minimal spatial overlap (18 voxels, <3%) (Tables S3 and S4).
Notably, FBB SUVr within the SH-related subcortical clusters (red, B) was not
associated with ORAD (P=0.25) and FBB SUVr within the ORAD-related
subcortical clusters (green, B) was not associated with SH (P=0.2).
Shokri-Kojori et al. PNAS Latest Articles
that the increases in ABB in the hippocampus with sleep dis-
ruptions might trigger local neurotoxicity without necessarily
resulting in marked plaque accumulation. The precuneus was not
significantly affected by SD; however, we found an association
between reported SH and ABB in this region (Fig. 2Band Table
S3), again suggesting that distinct processes might mediate the
effects of acute vs. chronic SD on regional ABB. Future work
should investigate the extent to which the effects of acute SD and
less SH might reflect distinct mechanisms (i.e., neuro-
inflammation triggered by chronic poor sleep) (47, 48) or sen-
sitivity of FBB to different forms of Aβ(soluble oligomers vs.
plaques) (28). One limitation of this work includes the inability
of PET-FBB to distinguish soluble from insoluble Aβ(27, 28,
49). We suggested that the interruption of glymphatic clearance
by SD would increase ABB in the brain, yet our findings do not
demonstrate the mechanisms that account for the Aβaccumu-
lation with SD. In our study, estimates of ABB were obtained
using FBB SUVr, which is sensitive to physiological factors such
as blood flow, which could have been impacted by SD (50, 51).
However, recent evidence suggests that blood-flow effects are
small on FBB SUVr estimated with later time points (52)
(Methods). We also corroborated the FBB SUVr findings with
BPnd measures that are less sensitive to blood-flow effects
In our study, we did not predict the laterality effect for the SD-
related increases in ABB, and while this could reflect the sen-
sitivity of the glymphatic system to orientation of the head during
sleep (14), we did not record head position. Consistent with the
recognized role of the hippocampus and thalamus in mood dis-
orders (53), the association between SD-related increases in
ABB and mood worsening (Fig. 1C) supported the functional
significance of elevated ABB. This association could reflect the
previously reported contribution of the hippocampus in modu-
lating mood changes that follow SD (54). The effects of SD on
the hippocampus have also been implicated in the memory im-
pairment associated with SD, although we did not measure ef-
fects of SD on memory in our study. Even though our sample was
small (n=20), we were able to identify a significant effect of SD
on brain ABB with no significant interaction with gender. Be-
cause of the small sample size of our study, future studies are
needed to assess the generalizability to a larger and more diverse
population and to more reliably characterize potential
In summary, this study documents an effect of one-night SD
on ABB in the hippocampus, thus providing preliminary evi-
dence that sleep, among other factors, could influence Aβ
clearance in the human brain. Our results highlight the relevance
of good sleep hygiene for proper brain function and as a po-
tential target for prevention of AD (31, 55).
Participants. Twenty-two healthy individuals were recruited at the National
Institutes of Health, of which 20 (10 females, age: 39.8 y ±10.4, range: 22–
72 y old) completed two PET scan sessions to measure ABB. All participants
provided informed consent to participate in the study that was approved by
the Institutional Review Board at the NIH (Combined Neurosciences White
Panel). Exclusion criteria were: (i) urine positive for psychotropic drugs; (ii )
history of alcohol or drug use disorders; (iii ) present or past history of
neurological or psychiatric disorder, including evidence of cognitive im-
pairment; (iv) use of psychoactive medications in the past month (i.e., opiate
analgesics, stimulants, sedatives); (v) currently taking prescription medica-
tions (i.e., antihistamines, antihypertensive, antibiotics); (vi ) medical condi-
tions that may alter cerebral function; (vii ) cardiovascular and metabolic
diseases; and (viii) history of head trauma with loss of consciousness longer
than 30 min. Table S1 summarizes physiological and neuropsychological
assessment to ensure participants were healthy and were not cognitively
Structural MRI. Participants also underwent MRI in a 3.0 T Magnetom Prisma
scanner (Siemens Medical Solutions) using a 32-channel head coil to collect
T1-weighted 3D MPRAGE (TR/TE =2,400/2.24 ms, 0.8-mm isotropic resolu-
tion) and T2-weighted spin-echo multislice (TR/TE =3,200/564 ms, 0.8-mm in-
plane resolution). MRI was processed using the minimal preprocessing
pipeline of the Human Connectome Project (56). Specifically, FreeSurfer v5.3
(Martinos Center for Biomedical Imaging; https://surfer.nmr.mgh.harvard.
edu/) was used for anatomical data segmentation. In addition, each MRI
image underwent gradient distortion correction, field map processing,
spatial normalization to the stereotactic space of the Montreal Neurological
Institute (MNI) with 2-mm isotropic resolution, and brain masking using
routines from University of Oxford’s Center for Functional Magnetic Reso-
nance Imaging of the Brain Software Library release 5.0 (https://fsl.fmrib.ox.
ac.uk/fsl/fslwiki). FreeSurfer segmentation (‘wmparc.nii’) in the MNI space
was used for generating a subject-specific mask of the hippocampus (label
numbers: 17 and 53 for left and right hippocampus, respectively).
PET Data Acquisition. The PET scans were performed using a high-resolution
research tomography Siemens scanner on two separate scan days with FBB.
FBB was injected through an intravenous catheter in about 1 min using a
Harvard pump. Dynamic scanning started immediately after FBB in-
jection, with 1.23-mm isotropic resolution using list-mode acquisition.
During the PET imaging procedures, the participants rested quietly under
dim illumination. To ensure that subjects did not fall asleep, they were
monitored throughout the procedure and asked to keep their eyes open
(no fixation cross). Information about head movement was collected
using a cap with small light reflectors and a Polaris Vicra (Northern Digital)
head-tracking system to minimize motion-related image blurring. Before
FBB injection, a transmission scan was obtained using cesium-137 to correct
FBB-PET Scans. Each participant underwent two FBB-PET scans to measure
ABB, one scan on a day following RW and another scan on a day following a
night of SD. For this purpose, participants stayed overnight at the Clinical
Center at the NIH before their scheduled SD or RW scans. For the SD condition,
participants were instructed to wake up at 8:00 AM on the morning before
the SD night. Upon arrival, participants were continuously accompanied by a
nurse to ensure that they stayed awake for the SD condition during their stay.
For the RW conditions, nurses observed whether patients were asleep every
hour from 10:00 PM to 7:00 AM. On the day of RW, the participants were
woken up at 7:00 AM. On both days, participants remained under supervision
of the nurse and were brought to the PET imaging center before the scan,
which started around 1:30 PM. Thus, participants remained awake for a total
of about 31 h (including the scan length) during the SD condition. No food
was given after midnight and caffeinated beverages were discontinued 24 h
before the study. Patients had a light breakfast and lunch on the scan day. The
order of RW and SD scans was counterbalanced across subjects and were, on
average, 15 d apart (standard deviation =19 d). In preparation for the scans,
a catheter was placed for radiotracer injection. Dynamic scanning started
right after intravenous injection of about 9.5 mCi (or less) of FBB, which
lasted for 120 min. During FBB scans, participants were encouraged to listen
to music to stay awake.
PET Data Analysis. FBB uptake in the brain was quantified using the SUVr
using the whole cerebellum as reference region for images obtained from
later time points (90–110 min) (57, 58). We chose the SUVr method given the
recent evidence (for a radiotracer from the same family as FBB) that the
SUVr approach of estimating tracer accumulation, relative to other non-
invasive kinetic modeling approaches, had one of the highest correlations,
with arterial input compartment modeling results (R
=0.95), and had
comparable accuracy, while maintaining much simpler modeling require-
ments and not suffering from voxel-level noise on model fitting (59). Despite
the concern that SUVr estimates of radiotracer accumulation are affected by
confounds such as blood-flow changes, recent work has suggested a limited
influence of blood-flow changes on FBB SUVr measures obtained from the
later time points, similar to the range used in our study (i.e., 90–110 min)
(52). SUVr images for FBB were coregistered with individual subjects’T1-
weighted images and resampled into 2-mm isotropic resolution before be-
ing transformed into the MNI space using the same normalization param-
eters that were generated for T1-weighted (and T2-weighted) images. For
statistical parametric mapping, images were smoothed with a 4-mm kernel
and masked at 0.5 SUVr to remove voxels outside the brain. We also per-
formed a follow-up BPnd analysis to address the concern that FBB SUVr
might have been affected by confounds, such as changes in blood flow and
tracer clearance properties (52, 60). Specifically, regional BPnd was
www.pnas.org/cgi/doi/10.1073/pnas.1721694115 Shokri-Kojori et al.
calculated using a noninvasive simplified reference tissue model (SRTM) (61,
62) that was fitted to the time activity curve (0–120 min) derived from the
subcortical cluster showing a significant effect of SD on FBB SUVr (Fig. 1A).
We also corroborated SRTM-based BPnd findings using the reference Logan
approach (Fig. S2) (59, 63). Kinetic modeling of dynamic PET data was per-
formed in the PMOD Kinetic Modeling Tool v3.605 (PMOD Technologies).
For consistency with the SUVr approach, the whole cerebellum was used as
the reference region for the BPnd analyses.
Pittsburgh Sleep Quality Index. The PSQI questionnaire (64) was administered
to all participants (n=20). For the analyses, we used the number of SH and
the TS quality score from the PSQI, because prior studies have linked these
sleep measures to brain ABB (6, 65, 66). While SH is a self-report of partici-
pants’SH at night (excluding times spent awake in bed), TS is a composite of
scores of sleep quality, latency, hours, efficiency, medication, disturbances
because of one’s health or sleep partners, and daytime life quality (lower TS
indicates better sleep quality).
APOE Genotyping. SNPs of the APOE gene (rs7412 and rs429358) have been
shown to influence brain glucose metabolism and ABB (67, 68). Accordingly,
in all participants we genotyped rs7412 and rs429358 SNPs of APOE and
computed a log of ORAD for each participant, following the methods of a
previous study (69), normalized relative to the population risk for AD. We
used ORAD to evaluate whether APOE influenced the association between
sleep and ABB.
Mood Questionnaires. Mood questionnaires were collected periodically (five
times) throughout the RW and SD scans in 18 of the 20 participants. Ques-
tionnaires were acquired at least an hour apart, prior (three measures),
during (one measure), and after (one measure) the PET scans. On a scale of 0–
10, subjects rated whether they felt alert, tired, hungry, friendly, happy, sad,
anxious, irritable, social, confused, bored, comfortable, energetic, caffeine
craving, and difficulty staying awake. The scores across the five time-points
were averaged for each mood measure for each scan day. The first principle
component of the standardized SD–RW difference scores for the 15 self-
report mood measures was computed to summarize the change in mood
from RW to SD into one component. This component accounted for 35.5%
of the variance of the mood change measures (SD–RW) and was positively
correlated with changes in ratings of feeling alert, friendly, happy, social,
and energetic (P<0.01, two-tailed), and negatively correlated with changes
in ratings of feeling tired, anxious, and irritable from RW to SD (P<0.01,
two-tailed). Higher scores along this component reflected positive changes
in mood from RW to SD.
Statistical Parametric Mapping. SPM8 (Wellcome Trust Centre for Neuro-
imaging) (70) was used for performing a voxelwise paired ttest between SD
and RW in FBB SUVr and voxelwise correlation between behavioral or
genotyping measures and PET data. All effects were thresholded at P≤
0.015 in SPM8 and a minimum cluster size of 300 voxels (2-mm isotropic). We
chose this threshold based on the low sensitivity of FBB to soluble Aβ(28),
but effects were corrected for multiple comparisons for cluster size using the
random field theory (71) (family-wise error, FWE), or unless indicated, with
false-discovery rate (FDR) in SPM8.
Data Availability. Subject-level measures used in the manuscript are available
in Dataset S1. Please contact corresponding authors for additional
ACKNOWLEDGMENTS. We thank Joanna Fowler for the insightful com-
ments; Christopher Wong, Lori Talagala, and Minoo McFarland for their
assistance with behavioral and imaging data collection; David Goldman, Hui
Sun, and Melanie Schwandt for assistance with APOE genotyping; Jeih-San
Liow and Santi Bullich for insights about PET data analysis; and Kimberly
Herman, Tom Lionetti, and Rosa Clark for assistance with monitoring
participants. This work was supported by NIH/National Institute on Alcohol
Abuse and Alcoholism intramural research program (Grant Y1AA3009).
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