Sleep-physiological correlates of brachycephaly in dogs

Abstract

The shape of the cranium is one of the most notable physical changes induced in domestic dogs through selective breeding and is measured using the cephalic index (CI). High CI (a ratio of skull width to skull length > 60) is characterized by a short muzzle and flat face and is referred to as brachycephaly. Brachycephalic dogs display some potentially harmful changes in neuroanatomy, and there are implications for differences in behavior, as well. The path from anatomy to cognition, however, has not been charted in its entirety. Here, we report that sleep-physiological markers of white-matter loss (high delta power, low frontal spindle frequency, i.e., spindle waves/s), along with a spectral profile for REM (low beta, high delta) associated with low intelligence in humans, are each linked to higher CI values in the dog. Additionally, brachycephalic subjects spent more time sleeping, suggesting that the sleep apnea these breeds usually suffer from increases daytime sleepiness. Within sleep, more time was spent in the REM sleep stage than in non-REM, while REM duration was correlated positively with the number of REM episodes across dogs. It is currently not clear if the patterns of sleep and sleep-stage duration are mainly caused by sleep-impairing troubles in breathing and thermoregulation, present a juvenile-like sleeping profile, or are caused by neuro-psychological conditions secondary to the effects of brachycephaly, e.g., frequent REM episodes are known to appear in human patients with depression. While future studies should more directly address the interplay of anatomy, physiology, and behavior within a single experiment, this represents the first description of how the dynamics of the canine brain covary with CI, as measured in resting companion dogs using a non-invasive sleep EEG methodology. The observations suggest that the neuroanatomical changes accompanying brachycephaly alter neural systems in a way that can be captured in the sleep EEG, thus supporting the utility of the latter in the study of canine brain health and function.

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Brain Structure and Function (2023) 228:2125–2136
https://doi.org/10.1007/s00429-023-02706-y
ORIGINAL ARTICLE
Sleep‑physiological correlates ofbrachycephaly indogs
IvayloBorislavovIotchev1· ZsóaBognár1,2· KatinkaTóth3· VivienReicher2,3,5· AnnaKis3,4· EnikőKubinyi1,6,7
Received: 6 April 2023 / Accepted: 31 August 2023 / Published online: 24 September 2023
© The Author(s) 2023
Abstract
The shape of the cranium is one of the most notable physical changes induced in domestic dogs through selective breeding
and is measured using the cephalic index (CI). High CI (a ratio of skull width to skull length > 60) is characterized by a short
muzzle and flat face and is referred to as brachycephaly. Brachycephalic dogs display some potentially harmful changes in
neuroanatomy, and there are implications for differences in behavior, as well. The path from anatomy to cognition, however,
has not been charted in its entirety. Here, we report that sleep-physiological markers of white-matter loss (high delta power,
low frontal spindle frequency, i.e., spindle waves/s), along with a spectral profile for REM (low beta, high delta) associated
with low intelligence in humans, are each linked to higher CI values in the dog. Additionally, brachycephalic subjects spent
more time sleeping, suggesting that the sleep apnea these breeds usually suffer from increases daytime sleepiness. Within
sleep, more time was spent in the REM sleep stage than in non-REM, while REM duration was correlated positively with
the number of REM episodes across dogs. It is currently not clear if the patterns of sleep and sleep-stage duration are mainly
caused by sleep-impairing troubles in breathing and thermoregulation, present a juvenile-like sleeping profile, or are caused
by neuro-psychological conditions secondary to the effects of brachycephaly, e.g., frequent REM episodes are known to
appear in human patients with depression. While future studies should more directly address the interplay of anatomy, physi-
ology, and behavior within a single experiment, this represents the first description of how the dynamics of the canine brain
covary with CI, as measured in resting companion dogs using a non-invasive sleep EEG methodology. The observations
suggest that the neuroanatomical changes accompanying brachycephaly alter neural systems in a way that can be captured
in the sleep EEG, thus supporting the utility of the latter in the study of canine brain health and function.
Keywords Animal models· Neuroanatomy· Spectral power· Sleep spindles· REM· Non-REM
Introduction
Brachycephaly, characterized by a relatively short head and
flat face, is one of the most salient morphological changes
imposed upon dogs by selective breeding. The extent to
which the skull was shortened and the face flattened in
some modern breeds is unmatched among wild canines and
an accelerating trend in breeding practices [see, e.g., (Teng
etal. 2016)]. The degree to which a dog's head shape is
brachycephalic is measured with the cephalic index (CI),
which is the ratio of skull width to length, thus higher in
more brachycephalic animals. Some authors specifically
define brachycephaly as CI exceeding a value of 60 [(Stone
etal. 2016) skull width/length*100].
CI is associated with a wide variety of changes, some
more predictable than others, observed across behavior, per-
ception, and health. Brachycephalic dogs are more vulner-
able to respiratory and cardiovascular disorders [reviewed
* Ivaylo Borislavov Iotchev
ivaylo.iotchev@gmail.com
1 Department ofEthology, Eötvös Loránd University,
Budapest, Hungary
2 Doctoral School ofBiology, Eötvös Loránd University,
Budapest, Hungary
3 Institute ofCognitive Neuroscience andPsychology,
Research Centre forNatural Sciences, Budapest, Hungary
4 ELTE-ELKH NAP Comparative Ethology Research Group,
Budapest, Hungary
5 Developmental andTranslational Neuroscience Research
Group, Institute ofCognitive Neuroscience andPsychology,
Research Centre forNatural Sciences, Budapest, Hungary
6 MTA-ELTE Lendület “Momentum” Companion Animal
Research Group, Budapest, Hungary
7 ELTE NAP Canine Brain Research Group, Budapest,
Hungary
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2126 Brain Structure and Function (2023) 228:2125–2136
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in Packer and O'Neill (2021)], but perhaps more surpris-
ingly, there is also physiological (McGreevy etal. 2004)
and behavioral (Gácsi etal. 2009; Bognár etal. 2021) evi-
dence for better visual capacity than most canines concern-
ing acuity and binocular processing. One cluster of differ-
ences, in particular, is pushing for a thorough neuroscientific
assessment of breed differences defined by CI. First, evi-
dence is growing that the shortening of the canine skull is
accompanied by anatomical changes (Schmidt etal. 2015;
Czeibert etal. 2020; Rusbridge and Knowler 2021). Loss
of white matter and cortical surface, unusually large ventri-
cles, hydrocephaly, as well as hypoxia in the brain, which
affect brain health and function. Second, there is currently a
growing catalog of behavioral changes observed as a func-
tion of CI (Gácsi etal. 2009; Horschler etal. 2019; Bognár
etal. 2021). At least some of the findings suggest that cog-
nitive performance might be worse in brachycephalic dogs
(Horschler etal. 2019).
There is currently no complete sketch of the path from
anatomy to behavior. A step that is specifically missing is
measuring activity in the living dog's brain as a function of
CI. This is crucial for two reasons. For properly assessing
the welfare implications of breeding for high CI, we need to
understand how the causal chain from changes in appearance
to changes in behavior unfolds on every level. A broader,
but more theoretical concern is the fruitful ground offered
by selective breeding for studying evolutionary principles.
This was famously demonstrated in the farm fox experiment
(Trut 1999), which helped sketch a scenario for the emer-
gence of domestication. In the case of breeds characterized
by differences in brain anatomy, selective breeding can be
specifically applied to the study of brain evolution.
Recent advances in the field of canine neuro-cognition
(Bunford etal. 2017) have resulted in measurement tech-
niques suitable for recording dogs' brain activity in a fully
non-invasive and, thus, ecologically valid manner. The
perhaps most accessible of those methods is canine poly-
somnography (EEG measurement during sleep), since the
relative absence of motor activity during sleep accounts for
a low incidence of artifacts even in untrained animals. Over
the last few years, research in dogs (Kis etal. 2017c; Iotchev
etal. 2017, 2020a, b) has corroborated the notion that brain
activity during sleep correlates with awake cognitive perfor-
mance (Genzel etal. 2014), behavior (Carreiro etal. 2023),
as well as affective and mood states (Kis etal. 2017b; Kiss
etal. 2020). This either reflects sleep-specific contributions
to information processing, e.g., sleep-dependent memory
consolidation (Genzel etal. 2014), or the general state of
mechanisms that manifest in both sleep and waking EEG
(Chen etal. 2016). The present study will likewise employ
sleep EEG recordings to see if parameters previously shown
to relate to dog behavior and cognition are associated with
canine brachycephaly.
So far, most human brain pathologies are reported to
leave marks in the sleep-recorded EEG. They affect the spec-
tral properties of the signal (Castelnovo etal. 2020; Stern
2020), the latency and duration of sleep stages, e.g., REM
(Palagini etal. 2013), and the expression of transients like
sleep spindles (Lopez and Hoffmann 2010; Merikanto etal.
2019) and K-complexes (Rodríguez-Labrada etal. 2019).
However, a few studies have investigated how sleep EEG
changes in direct response to anatomical and structural brain
changes. The vast majority of work in humans either directly
compares sleep quality (i.e., duration, efficiency, and subjec-
tive reports) to lesions (Babu Henry Samuel etal. 2022) and
loss of gray matter (Grau-Rivera etal. 2020) or white matter
(Bai etal. 2022), thus circumventing EEG. In other works,
sleep EEG parameters are compared to psychiatric diag-
nosis (Keshavan etal. 1998; Palagini etal. 2013), thereby,
in most cases, leaving out a direct assessment of anatomy.
Of the few more deeply examined anatomical conditions,
white-matter loss is of particular interest, since it is one of
the reported anatomical correlates of brachycephaly in dogs
(Schmidt etal. 2015). Sanchez etal. (2019, 2020) offer a few
observations on how the sleep EEG signal in traumatic brain
injury (TBI) patients changes in response to white-matter
loss. They found sleep spindles to be relatively resilient,
with only the intrinsic frequency of frontal spindles being
negatively correlated with white-matter loss. White matter
loss was also associated with an increase in power and peak-
to-peak amplitude for the delta frequency band [0.5–4 Hz
(Sanchez etal. 2019)]. Both effects were observed within the
TBI populations, while there was no difference found in the
comparison between TBI and healthy controls.
Sleep macrostructure, i.e., the duration of the REM and
non-REM phases of sleep, can be specifically helpful regard-
ing the earlier mentioned goal to model brain evolution in
dog breeds. Macrostructure varies strongly between species
(Zepelin etal. 2005), and some preliminary findings sug-
gest that it may also differ between dogs and the closely
related wolf (Reicher etal. 2022). However, results relating
to macrostructure may not be easy to interpret in the absence
of behavioral measures, since early development (Zepelin
etal. 2005) and mood disorders (Palagini etal. 2013) might
also account for a prolonged duration (and early onset) of
the REM sleep stage.
In the present study, we investigate a range of sleep
parameters (macrostructure, spectral power, and sleep spin-
dles) as a function of CI. Two different, but not mutually
exclusive, global effects are expected to result from high
CI. First and straightforward, the reduction of the cortical
surface and white matter around ventricles in high CI dogs
may be an anatomical indicator of neuropathology. It can be
thus expected to correlate with EEG markers of worse cogni-
tive performance, i.e., low REM beta power, high REM delta
power (Kis etal. 2017c), low sleep spindle density, and/
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2127Brain Structure and Function (2023) 228:2125–2136
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or amplitude. Second, breeding for high CI may be driven
by aiming for dogs with cute appearances and thus further
enforces some of the juvenile features that increased during
initial domestication (Trut 1999). It has been shown in vari-
ous works [reviewed in Pörtl and Jung (2019)] that this juve-
nilization also expresses in physiology and may thus reflect
in sleep measures, as well. One possibility is to find pro-
longed REM phases in the brachycephalic dog, since REM is
abundant in the early development of some species and may,
in fact, be a carry-over from fetal life (Zepelin etal. 2005).
Under the latter hypothesis, we would also expect spectral
properties of the non-REM sleep stage to be affected, as they
rapidly change during early development [decrease in delta
and increase in higher frequencies like beta, sigma, and theta
until the dogs reach 14 months of age (Reicher etal. 2021)].
Methods
Ethical statement
According to the Hungarian regulations of animal experi-
mentation, our non-invasive polysomnography research does
not qualify as an animal experiment (‘1998. évi XXVIII.
Törvény’ 3.§/9.—the Animal Protection Act).The Hungar-
ian Scientific Ethical Committee of Animal Experiments
has also issued a specific permission (under the number PE/
EA/853–2/2016) for our non-invasive protocol. All owners
volunteered to participate in the study and were informed
about the procedure before the start of the recordings.
Subjects
Polysomnographic EEG and CI from 92 dogs (48 ♀, mean
age ± SD: 8.2 ± 3.4 years, age range: 1–14 years) were avail-
able for analysis in this study. Of these dogs, 38 (41.3%) are
mixed breeds, while the remaining purebred animals belong
to 27 different breeds. For all dogs, the mean CI ± SD was
53.3 ± 5.8. Only 21 dogs were reproductively intact (22.8%)
and 4 dogs (4.3%) were of unknown reproductive status.
The EEG data were taken from a constantly growing data-
base, and therefore, there is an overlap in subjects with the
other studies from our group (Iotchev etal. 2019, 2020a).
CI definition andmeasurement
The CI was calculated as the ratio of the maximum width of
the skull (from one zygomatic arch to the other) multiplied
by 100 and divided by the skull's maximum length (from the
nose to the occipital protuberance, see also Figs.1 and 2).
The CI of each dog was measured from photographs with
the GIMP image editing program 2.2.13. (http:// www. gimp.
org/). The photographs were taken either when the dogs
visited our laboratory for behavioral testing (Bognár etal.
2021) or at home by the owner (based on specific instruc-
tions). Each photograph was taken from the same angle (per-
pendicular to the top of the skull; see examples in Bognár
etal. (2021). Although the distance of the camera to the
top of the dogs' skull was not uniform, this did not affect
the measurement, as the cephalic index is a ratio. The reli-
ability of measuring the cephalic index from photographs
was previously checked by comparison with a second, naïve
coder (ICC: 0.91, p < 0.001) and using a caliper [ICC: 0.98,
p < 0.001, originally reported in Bognár etal. (2021)].
EEG implementation andanalysis
The method for measuring polysomnography in dogs was
first described by Kis etal. (2014); subsequent variations
are discussed by Iotchev etal. (2019, 2020a). In all varia-
tions of the setup, there is an active frontal electrode, which
is identically placed (Fz). In 82.6% of all dogs (76 animals),
however, there was a second active electrode (Cz), placed
centrally on the skull, between Fz and the occipital bone.
The exact position of the Fz and Cz electrodes relative to
the brain and skull is depicted in Fig.2A for dolichoce-
phalic dogs and Fig.2B for brachycephalic dogs. Electrodes
(both Fz and Cz if active) were referenced against the occipi-
tal bone. Due to the reference type, the setup is unipolar,
but in 17.4% of the sample (16 dogs), only Fz was active.
Other electrodes were placed on the left musculus tempo-
ralis (ground) and on the zygomiotica, for measuring eye
movements (electrodes F7 and F8). Cardiac and respiratory
Fig. 1 Calculation of the cephalic index (CI). CI is the ratio of the
maximum width of the head (A) multiplied by 100, then divided by
the head’s maximum length (B). CI is higher for brachycephalic dogs
(common threshold value > 60)
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2128 Brain Structure and Function (2023) 228:2125–2136
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frequencies, as well as muscle tone, were monitored to aid
subsequent sleep-stage identification. In all set-ups, the type
of electrode used was gold-coated Ag/AgCl fixed at the scalp
surface with EC2 Grass Electrode Cream (Grass Technolo-
gies, USA). Impedance was kept below 15 kΩ. The electrode
signals were collected and preprocessed with a 30-channel
Flat Style SLEEP La Mont Headbox and an HBX32-SLP
32-channel preamplifier (La Mont Medical Inc., USA).
All recordings were first-time measurements, mean-
ing that the animals were new to the sleeping laboratory,
although the dogs were given a brief (5–10 min) free explo-
ration of the room prior to electrode attachment. The record-
ings were exclusively afternoon measurements (starting
time: from 12 to 6 pm). The intended recording duration
based on the protocol was 3 h (mean ± SD: 168.6 ± 32.8
min). During a recording, the dog was alone in a darkened
room with their owner. The owner was positioned on a mat-
tress, and the dog could freely choose to settle down on the
same mattress or on an adjacent rug; no restrictions were
applied to the animals’ movement. Experimenters were only
present in the sleeping area during electrode placement and
detachment.
Sleep-stage identification followed the criteria outlined
by Kis etal. (2014) and was further validated by Gergely
etal. (2020). In short, polysomnographic monitoring of
the hindleg muscles, eye muscles, heartbeat, and the EEG
were used to categorize the signal into wakefulness, drowsi-
ness, REM, and non-REM. Categorization of the signal was
performed separately for each epoch of 20 s length, while
artifacts were identified within 4-s-long epochs. Wakeful-
ness was defined by the presence of high-frequency and
amplitude eye movements, elevated muscle tone, and a fast
activity EEG signal. Drowsiness was scored when the ampli-
tude and frequency of the eye movements decreased, and the
muscle tone was attenuated compared to wakefulness, but
EEG activity remained of predominantly high frequency. For
non-REM classification, we required delta (1–4 Hz) to be
at ≥ 15 μV, i.e., presenting a markedly slowed down activity
compared to the other stages; eye movements to be absent or
of very low amplitude, and muscle tone to be likewise low.
In both drowsiness and non-REM, respiration was expected
to be regular, while irregular respiration and heartbeat,
complete muscle atonia, combined with fast, irregular EEG
activity and rapid eye movements were required to catego-
rize the signal as REM. In Fig.3, we demonstrate an exam-
ple for the polysomnography of each sleep stage in each of
two dogs—one low CI, dolichocephalic animal (Barka, 2A)
and a high CI, brachycephalic subject (Olivér, 2B).
A method for automatic sleep spindle detection was first
introduced by Iotchev etal. (2017). It remained constant in
subsequent studies, with the exception of the filter repre-
sentation, which was changed in 2019 [(Iotchev etal. 2019)
from discrete time zero-pole-gain to a second-order section]
to account for the effects of different recording devices on
the filter response of the EEG signal.
In the context of the present study, we now also introduce
a new method for spectral density analysis. Since our previ-
ous work in the dog (Kis etal. 2017c; Reicher etal. 2022)
made use of a locally distributed software for spectral data
extraction (Fercio’s EEG Plus software, 2009–2022, devel-
oped by Ferenc Gombos), we argue that future replication
efforts across research groups may benefit from implement-
ing a more widely used software like Matlab. To this end,
we devised a Matlab-based script for relative power extrac-
tion based on the spectrogram function therein. Indices for
detecting artifact-free segments and identifying the sleep
stage to which a segment corresponds were incorporated
into the data prior to uploading it in Matlab. Following
sleep-stage selection and artifact rejection, the signal was
first filtered with a Butterworth (second-order section rep-
resentation) filter (passband boundaries: 0.1–30 Hz, stop-
band boundaries: 0.05–35 Hz). The passband boundaries
Fig. 2 Electrode placement Fz and Cz (active electrodes, in red) and Ref (reference, in purple) in dolichocephalic dogs (A) and brachycephalic
dogs (B). Possible implications for our measurements are elaborated on in the Discussion. Images are courtesy of Dr. Kálmán Czeibert
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2129Brain Structure and Function (2023) 228:2125–2136
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of the filter were chosen to match parameters common in
both awake (Batterink and Paller 2017; Moser etal. 2021;
Batterink and Zhang 2022) and sleep (Waterman etal. 1993;
Lyamin etal. 2008; Batterink and Zhang 2022) EEG studies
across human and non-human animals. Next, absolute power
values were extracted with the spectrogram function in Mat-
lab, specifying 4-s-long time-windows with 50% (2 s) over-
lap for a time–frequency analysis. These parameters match
our earlier settings for calculating band-specific power in
the dog (Kis etal. 2017c). As in our automated sleep spin-
dle detection, zero-padding was applied to achieve a 0.1
Hz resolution. Thus, obtained power values were averaged
across time-windows within each sleep-stage (REM versus
non-REM), recording and dog and separately for the power
bands alpha, beta, theta, and delta. As previously in the dog
(Kis etal. 2017c) and wolf (Reicher etal. 2022), alpha was
defined as 8–12 Hz, beta as 12–30 Hz, theta as 4–8 Hz,
and delta as 1–4 Hz. Sigma [12–16 Hz (Kis etal. 2014) or
9–16 Hz (Iotchev etal. 2017)] was not analyzed, because
we instead quantified sleep spindles as discrete events. After
the power spectrum for each band had been averaged across
time-windows, a second averaging across frequencies within
the band of interest ensured a single final value for that band,
sleep stage, and recording. Relative power in, e.g., REM
alpha was the percent of absolute alpha power from the sum
of REM alpha, REM beta, REM theta, and REM delta. Rela-
tive power values for the four bands of interest and from
each sleep stage were subsequently used in our statistical
analyses.
Statistical analysis
Pearson correlations were used to compare CI with any of
the sleep variables: duration of REM, non-REM, drowsiness,
and wakefulness in minutes; relative power for alpha, beta,
theta, and delta in each REM and non-REM; density, fre-
quency, and amplitude of fast (≥ 13 Hz), slow (≤ 13 Hz), and
generic (9–16 Hz) spindles. Correlations of CI with relative
power in REM and non-REM were corrected for the duration
of the respective sleep stage by adding the latter as a control
variable in partial correlations. Possible confounds from age,
sex, and reproductive status were tested in a series of control
analyses inquiring if CI was correlated with age or different
for male and female; intact and neutered dogs. The last two
comparisons were conducted as independent samples t tests.
All analyses were conducted in SPSS v25.
Results
Control analyses
Dogs of different ages were uniformly distributed among
different head shapes, as evidenced by the lack of corre-
lation between CI and age (p = 0.968). There was also no
difference in average CI between sub-samples defined by
sex (p = 0.241) or reproductive status (p = 0.602). The dura-
tion of the recordings was not correlated with CI (p = 0.438)
which suggests that variations in this parameter cannot
explain below results.
Sleep‑stage durations
CI was significantly positively correlated with time spent
in REM (r = 0.307, P = 0.003, Fig.4A) and negatively with
time spent in wakefulness (r = −0.233, P = 0.025, Fig.4B).
Ten recordings were substantially shorter than intended
by protocol (< 2 h). Removing these data points did not
Fig. 3 Polysomnographs of sleep stages in a dolichocephalic, low CI
dog (A) and a brachycephalic, high CI dog (B). Channel order and
color-coding: EEG trace (Fz exemplifies both active channels) - dark
blue, eye-movements - dark turquoise, muscle tone - magenta, respi-
ration - blue, heartbeat - red
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2130 Brain Structure and Function (2023) 228:2125–2136
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change the results: CI was again correlated with time spent
in REM (r = 0.383, P < 0.001) and with time spent awake
(r = −0.275, P = 0.012).
CI was not linked to the number of awakenings
(P = 0.570).
CI was positively correlated with a higher REM to non-
REM ratio (r = 0.248, P = 0.020).
A positive link was observed between CI and relative time
spent asleep when only REM and non-REM were included
in the definition of sleep (r = 0.220, P = 0.035). The associa-
tion was not significant when drowsiness was also counted as
part of sleep (P = 0.099). Other correlations between CI and
sleep macrostructure variables (non-REM and drowsiness
duration) were not significant (P > 0.3).
REM episodes
We also calculated the average duration of an REM epi-
sode by dividing the total time spent in REM by the num-
ber of transitions into REM. Average REM episode dura-
tion was not correlated with CI (r = 0.108, P = 0.378), nor
was CI linked to the number of REM episodes (r = 0.152,
P = 0.188). For the sample as a whole, however, total REM
duration and the number of REM episodes were correlated
(r = 0.770, P < 0.001).
Relative power
On Fz, during REM, relative beta (12–30 Hz) power was
negatively correlated with CI (r = −0.261, P = 0.025) and
positively with delta (1–4 Hz) power (r = 0.238, P = 0.041).
These results are summarized in Fig.5. Correcting for REM
duration with partial correlations, the effect remained sig-
nificant for beta power (r = −0.232, P = 0.049), but not delta
power (P = 0.139). No other correlations were significant
with CI on Fz (theta, 4–8 Hz and alpha, 8–12 Hz in REM;
all bands in non-REM; P > 0.1).
On Cz, during REM, relative delta (1–4 Hz) power was
positively correlated with CI (r = 0.257, P = 0.046). The
effect was not significant after correcting for REM duration
(P = 0.083). No other correlations were significant with CI
Fig. 4 CI and sleep stage durations (in minutes). Correlations were significant with REM duration (A) and time spent awake (B)
Fig. 5 CI and relative power on Fz during REM. Correlations were significant for the beta (A) and delta (B) frequency bands
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2131Brain Structure and Function (2023) 228:2125–2136
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on Cz (theta, 4–8 Hz; alpha, 8–12 Hz; beta, 12–30 Hz; in
REM and all bands in non-REM; P > 0.05).
Sleep spindles
On Fz, fast sleep spindle frequency was found to correlate
negatively with CI (r = −0.287, P = 0.013, Fig.6). No other
sleep spindle variables were found to correlate with CI for
fast spindles (P > 0.1), slow spindles (P > 0.3), nor across all
spindles (P > 0.2).
On Cz, no sleep spindle variables were found to correlate
with CI for fast spindles (P > 0.2), slow spindles (P > 0.6),
nor across all spindles (P > 0.5).
Discussion
A high cephalic index (CI) indicates a short skull and flat
face (brachycephaly) in the dog. We found that CI is associ-
ated with several sleep-physiological variables. Primarily,
effects were observed on the REM sleep phase, with both
macrostructure and spectral profile being affected. Shorter-
headed dogs spent more time sleeping, and within sleep,
more time was spent in REM than non-REM, which is sur-
prising, because usually the opposite is true, with the first
two stages of non-REM dominating in, e.g., adult human
sleep (Carscadon and Dement 2000). Macrostructure find-
ings were confirmed for both absolute and relative measures
of duration; the latter was a control for minor variations in
the duration of the recordings. The REM sleep phase of
brachycephalic dogs exhibited less relative beta and more
relative delta power compared to dogs of lower CI. The
effect on beta did not seem to be explained by the over-
all longer lasting REM phase but was only detectable over
the frontal electrode. In non-REM sleep, only the intrinsic
frequency of fast frontal spindles was found lower with
increasing CI.
Research on the sleep of brachycephalic dogs has so far
mainly focused on the propensity of these breeds for sleep
apnea (Pratschke 2014). In humans, this condition is asso-
ciated with increased daytime sleepiness (Gabryelska and
Białasiewicz 2020), which may explain the here observed
longer sleeping times for brachycephalic dogs. Moreover, the
present findings are the first results to show that the sleep of
more brachycephalic breeds is also characterized by func-
tionally relevant brain activity differences.
The literature offers two, not mutually exclusive, expla-
nations for why we should expect sleep physiology to be
altered by breeding for brachycephaly. First, and most
straightforward, anatomical studies have revealed that brach-
ycephalic dogs display anatomical distortions in the brain on
different levels of organization (Schmidt etal. 2015; Czeib-
ert etal. 2020; Rusbridge and Knowler 2021), which we can
expect to also be expressed in sleep-dependent brain activity
as the result of more general differences in brain function
and health, but also due to effects on breathing (Barker etal.
2021; Gleason etal. 2022; Mitze etal. 2022; Niinikoski etal.
2023) and (respiratory) thermoregulation (Davis etal. 2017;
Gallman etal. 2023) that affect sleep quality. The role of
these conditions finds no direct support here, however, since
the number of awakenings did not correlate with CI. The
propensity for sleep apnea associated with canine brachy-
cephaly (Pratschke 2014) may contribute to some anatomi-
cal changes or add to their effect on the brain. This pos-
sibility is discussed with regard to how sleep apnea may
affect humans [see, e.g., Ahuja etal. (2018)] and is apparent
from the memory impairments reported for this condition
(Wallace and Bucks 2013; Lee etal. 2016). Second, at least
some brachycephalic breeds likely acquired their traits due
to breeding for more paedomorphic features, which elicit a
caring response in humans (Hecht and Horowitz 2015). This
could work via the same selection mechanisms which played
a role during initial domestication and are associated with
more juvenile features across appearance, physiology, and
behavior (Leach etal. 2003; Pörtl and Jung 2019). Under this
second hypothesis, we specifically expect patterns associated
with juvenile (sleep) physiology.
The combined observation of higher delta power and
lower sleep spindle intrinsic frequency in more brachyce-
phalic breeds matches with the literature on sleep EEG cor-
relates of white-matter loss in humans (Sanchez etal. 2019,
2020). Specifically, this pattern could reflect the white-mat-
ter loss for which brachycephalic dogs are reported to be
at higher risk (Schmidt etal. 2015). Still, some important
differences need to be taken into account between our results
and the human findings before an analogy is embraced pre-
maturely. First, in humans, both delta power and spindle
frequency are correlated with white-matter loss only within
Fig. 6 Fast sleep spindle frequency on Fz as a function of CI
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2132 Brain Structure and Function (2023) 228:2125–2136
1 3
a patient population. We instead find the effect to emerge
across breeds of presumably healthy dogs. Since in dogs,
genetic variation is stronger between breeds than within
breeds (Bannasch etal. 2020), this may affect the visibility
of the effect compared to human samples. Second, we cannot
exclude that the increased REM delta in our sample is linked
to increased total REM duration, while in humans, it is also
non-REM delta which was compared with white-matter loss
(Sanchez etal. 2019) and showed no effect here. Why delta
activity increases with more pronounced white-matter dam-
age is not conclusively established and contradicts expecta-
tions based on results from young and aging humans (Carrier
etal. 2011; Piantoni etal. 2013). Among the explanations
offered by Sanchez etal. (2019) is the proposition that delta
synchrony is a cortical default state (Sanchez-Vives etal.
2017) enhanced when the cortex suffers de-afferentiation
as a result of injury. The decrease in spindling frequency is
more comparable with the human findings. Both are signifi-
cant for frontally recorded sleep spindles. However, Sanchez
etal. (2020) do not differentiate sleep spindles into slow and
fast, while we report an effect specific to the fast sub-type.
EEG-related observations with potentially functional
significance derive from the spectral profile of the REM
sleep phase. High beta and low delta power during REM
are associated with higher intelligence in human females and
better learning performance in dogs (Ujma etal. 2017; Kis
etal. 2017c). In this sample, higher CI was associated with
the reverse pattern, thus corroborating the anatomical find-
ings (Schmidt etal. 2015; Czeibert etal. 2020; Rusbridge
and Knowler 2021), which suggests that we should expect
a weaker cognitive performance in high CI, brachycephalic
dogs. Notably, the association between CI and REM beta
power seems independent of total REM duration, while aver-
age REM episode length was not linked with CI. A possible
relationship between REM duration and REM delta power
needs to be further examined, as the latter may not be inde-
pendent observations. Correlations with delta are also just
below the significance level. The meaning of decreased spin-
dling frequency in higher CI dogs is more difficult to inter-
pret. Most spindle–cognition associations in humans, rats,
and mice (Eschenko etal. 2006; Cox etal. 2012; Latchou-
mane etal. 2017) and all so far observed in the dog (Iotchev
etal. 2017, 2020a) concern spindle density and post-sleep
recall. Intrinsic frequency (the waves/second of an average
spindle) is more ambivalent. It is reported more seldomly to
correlate with learning performance than density. When an
association was observed, it was positive for young subjects
(Kuula etal. 2019), but negative in older humans (Guad-
agni etal. 2020) and older dogs (Iotchev etal. 2020b), in
which a higher intrinsic frequency is either a compensation
for emergent pathology or by itself reflects the shortening
of thalamo-cortical connections (Gaudreault etal. 2017).
Importantly, white-matter deterioration does not generally
affect spindle properties equally in young and old subjects
(Gaudreault etal. 2018). The lack of an association with
other spindle variables strengthens earlier findings in the dog
(Iotchev etal. 2017, 2019, 2020a, b), which were potentially
limited by the breed variability of the samples. The present
finding suggests that this is not a concern for breeds dis-
tinguished by head shape with regard to key variables like
spindle density.
Our results concerning (relative) sleep duration and the
ratio of REM to non-REM sleep in turn lend some support
to the hypothesis of brachycephalic dogs having more juve-
nile brains. Not only do young animals sleep longer, but
in many altricial species (animals that are born relatively
immature), the percentage of time spent in REM is high-
est during the first postnatal days and hypothesized to be
a carry-over from fetal life [see Zepelin etal. (2005)]. As
the newborns of humans (Kurth etal. 2015), dogs (Reicher
etal. 2021), and rats (Jouvet‐Mounier etal. 1969) progress
in their development, REM durations decrease in favor of a
more pronounced non-REM sleep stage. The current study
alone cannot prove beyond doubt, however, that the higher
percentage of REM sleep observed in brachycephalic dogs is
a juvenile trait. Relative differences in REM between wolves
and dogs, albeit preliminary, suggest a higher proportion of
REM in the captive, hand-raised wolf (Reicher etal. 2022)
and thus, REM duration as a potential marker of juvenile
sleeping patterns needs to be taken with caution. Crucially,
early development and maturation in dogs (Reicher etal.
2021) and humans (Kurth etal. 2015) is characterized by
spectral changes in the non-REM sleep stage. We did not
observe CI-dependent differences in non-REM power for
the tested frequency bands. Looking at the number and
average length of REM episodes did not conclusively link
either to the correlation of CI with total REM length, but
for the sample as a whole total REM length and number
of REM episodes were correlated positively. This suggests
that across dogs, a higher density of REM episodes, also
observed in, e.g., human depression (Palagini etal. 2013),
underlies longer total time spent in REM. The most plausible
explanation for prolonged REM in brachycephalic dogs will
need to eventually integrate behavioral findings with the here
observed EEG differences between breeds.
The EEG data used here come from a database containing
single (first) polysomnography measurements for each dog
and without any behavioral manipulation prior to sleep. This
was done to avoid experimental manipulations that cause
an alteration in sleep characteristics (including macrostruc-
ture, EEG spectrum, and spindle parameters) and could thus
potentially confound the relationship between CI and default
brain activity, which we wanted to examine first. This, how-
ever, is a trade-off which poses two limitations. First, we do
not control for the "firstnight" effect that dogs experience in
novel sleeping places (Reicher etal. 2020); thus, the results
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2133Brain Structure and Function (2023) 228:2125–2136
1 3
may be specific to a setting in which the sleeping place is
unfamiliar. Second, there is no direct measure of cognitive
performance related to these recordings. Our interpretation
of how these dynamics may relate to cognition is based on
previous findings in the human and dog literature. Specifi-
cally, a high beta, low delta REM profile was found to cor-
relate with post-sleep recall in a smaller sample of dogs (Kis
etal. 2017c), but it is not clear how this test-specific out-
come relates to general intelligence, which was the correlate
of this spectral profile in human females (Ujma etal. 2017).
CI, test performances, and polysomnographic data need to
be more directly related to each other in future efforts.
A more serious concern is that CI can be expected to cor-
relate with electrode distance to brain surface (see Fig.3),
with electrodes being closer to the brain in more brachy-
cephalic dogs. This may affect the absolute amplitude and
power of the signal but cannot explain why correlations
with beta and delta on Fz are of opposite directions. As a
precaution against the effects of different skull thicknesses,
only relative measures of power were compared. Likewise,
our sleep spindle detection uses a relative threshold for the
amplitude criterion (Iotchev etal. 2017). One particularly
pressing concern related to skull thickness is the filtering
effect of the skull bone on higher frequencies like beta (but
also alpha and mu). This is implied by observations related
to the breach rhythm response of the EEG signal in patients
with surgically altered skull surfaces (Cobb etal. 1979). The
breaching response suggests, however, that rhythms like beta
should be attenuated by a thicker skull. We instead observed
a higher frontal beta power in dogs with lower CI, whose
skull bone under Fz is thicker (Fig.3), and thus, beta power
differences linked to CI do not seem to be explained by the
bone’s filtering properties. We should also note, however,
that head size and skull thickness can vary greatly among
breeds within both brachycephalic and dolichocephalic dogs
as well. In the current study, we could not account for such
variation (as no MR scans were available for the subjects).
Future attempts to compare CI and sleep physiology
could incorporate health and cognitive assessments and
(f)MRI scans to address another set of limitations inher-
ent to the present study. Specifically, a direct link from
anatomy to sleep physiology can be demonstrated more
conclusively, if we can rule out the intermediate effects
of mood, which was shown to affect dogs’ sleep mac-
rostructure (Kis etal. 2017a). Neuropathology is often
comorbid with depression in humans [see, e.g., discussed
in Ross and Rush (1981), Moldover etal. (2004)] and it is
currently not known which neural activity patterns in the
dog are direct consequences of anatomical changes ver-
sus those preceded by comorbid alterations in mood and
emotional systems. The simultaneous application of EEG
and (f)MRI could be used in the future to specifically
test the white-matter hypothesis more directly. Here, the
argument, which was presented above, is more indirect,
integrating the present findings with the literature.
Overall, the present findings support the notion that
artificial selection changes neural substrates of cogni-
tion in the dog. Previous work pointing at the anatomical
(Schmidt etal. 2015; Czeibert etal. 2020; Rusbridge and
Knowler 2021) and behavioral (Horschler etal. 2019) indi-
cations for this process is now complemented with activity
from the living dog brain, measured during periods of rest
and sleep. The evidence jointly points to neuro-cognitive
limitations for more brachycephalic dogs. During sleep,
these reflect in both structural and spectral changes of the
REM sleep stage. The EEG profile suggests that corre-
lations with CI most likely reflect the white-matter loss
reported for brachycephalic breeds.
Acknowledgements The authors would like to thank Dr. Kálmán
Czeibert for the images in Fig.3, used and altered with his explicit
permission, and Eda Köşeli for help during the coding of CI. All own-
ers are participating with their dogs in the EEG measurements.
Author contributions AK and EK conceived the study. IBI developed
hypotheses, wrote analysis algorithms for relative power and sleep
spindles, and wrote the initial manuscript draft. AK, VR, KT, ZB, and
IBI were involved in data collection and sleep-stage scoring. AK and
KT were involved in data management. ZB conducted CI measure-
ments. AK, EK, IBI, VR, ZB, and KT reviewed and co-wrote the final
manuscript.
Funding Open access funding provided by Eötvös Loránd University.
The study was supported by the Hungarian Academy of Sciences via
a grant to the MTA-ELTE ’Lendület/Momentum’ Companion Ani-
mal Research Group (Grant No. PH1404/21) and the National Brain
Programme 3.0 (NAP2022-I-3/2022). ZB was supported by the
ÚNKP-22–3 New National Excellence Program of the Ministry for
Innovation and Technology from the source of the National Research,
Development and Innovation Fund (ÚNKP-22–3-II-ELTE-577). IBI
was employed under a grant by the European Research Council (ERC)
under the European Union’s Horizon 2020 research and innovation
program (Grant Agreement No. 950159), while working on this study.
AK was supported by the Ministry of Innovation and Technology of
Hungary from the National Research, Development and Innovation
Fund (FK 128242), ÚNKP, and the János Bolyai Scholarship.
Data availability The dataset used and/or analyzed during the current
study will be made available by the corresponding author upon reason-
able request.
Declarations
Conflict of interest The authors declare no conflict of interest.
Ethical statement According to the Hungarian regulations of animal
experimentation, our non-invasive polysomnography research does not
qualify as an animal experiment (‘1998. évi XXVIII. Törvény’ 3.§/9.—
the Animal Protection Act).The Hungarian Scientific Ethical Com-
mittee of Animal Experiments has also issued a specific permission
(under the number PE/EA/853–2/2016) for our non-invasive protocol.
All owners volunteered to participate in the study and were informed
about the procedure before the start of the recordings.
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2134 Brain Structure and Function (2023) 228:2125–2136
1 3
Open Access This article is licensed under a Creative Commons Attri-
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